PEPTIDE SELF-ASSEMBLY FROM THE · Chapter Two: Conformational Changes and Molecular Mobility in...

PEPTIDE SELF-ASSEMBLY FROM THE

MOLECULAR TO THE MACROSCOPIC SCALE AT

STANDARD CONDITIONS

Ahmad I. Athamneh

Dissertation submitted to the faculty of the Virginia Polytechnic Institute and State University in

partial fulfillment of the requirements for the degree of

Doctor of Philosophy In

Biological Systems Engineering

Justin R. Barone, Chair Chenming (Mike) Zhang

Ryan S. Senger Abby W. Morgan

November 16, 2010 Blacksburg, VA

Keywords: peptide materials, plasticized biopolymers, nanomaterials, multiscale self-assembly

Peptide Self-Assembly from the Molecular to the Macroscopic

Scale at Standard Conditions

ABSTRACT

This dissertation attempts to address the problem of how to prepare protein-based materials with

the same level of order and precision at the molecular level similar to the structures we find in

nature. It is divided into two parts focusing on feedstock and processing. Part one is devoted to

discussing the use of agricultural proteins as a feedstock for material production. Particularly, it

focuses on the effect of hydrogen bonding, or lack thereof, between proteins as mediated by

hydration or plasticization. The effect of varying plasticizer (glycerol) levels on mechanical

properties of a series of proteins is discussed in the context of primary and secondary structure of

these proteins. We have found that the extent to which a protein can be plasticized is dependent

on its molecular and higher order structure and not simply molecular weight, as it was often

assumed in previous studies.

The second part of the dissertation focuses on the study of self-assembly as a way to make useful

peptide-based materials. There are major efforts underway to study protein self-assembly for

various medical and industrial reasons. Despite huge progress, most studies have focused on

nanoscale self-assembly but the crossover to the macroscopic scale remains a challenge. We

show that peptide self-assembly into macroscopic fibers is possible in vitro under physiological

conditions. We characterize the fibers and propose a mechanism by which they form. The

macroscopic fibers self-assemble from a combination of β- and α-peptides and are similar to

other naturally-occurring systems in which templated self-assembly is used to create functional

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peptide materials. Finally, the ability to control macroscopic properties of the fiber by varying

the ratio of constituent peptides is demonstrated.

Owing to the richness of the amino acid building blocks, peptides are highly versatile structural

and functional building blocks. The ability to extend and control peptide self-assembly over

multiple length scales is a significant leap toward incorporating peptide materials into dynamic

systems of higher complexity and functionality.

iv

Acknowledgments

I would like to thank my advisor Dr. Justin R. Barone and PhD committee members Dr. Chenming

(Mike) Zhang, Dr. Ryan Senger and Dr. Abby Morgan for their guidance and support through out

the course of this research.

.

v

Dedication

My father, Ibrahim M. Athamneh, and mother, Ameena M. Al-Yaseen

My beloved wife Ruba

My brothers Safwan, Khaled, and Mohammad

My sister Hana

… To whom I owe everything I have achieved in my life.

.

vi

TABLE OF CONTENT

Abstract...................................................................................................................................................ii

Table of Content.................................................................................................................................... vi

List of figures....................................................................................................................................... viii

List of tables ........................................................................................................................................xiii

CHAPTER ONE: Introduction ............................................................................................................. 1

Motivation..................................................................................................................................................................1

The Cross-β Structural Motif: A Model For Studying Self-Assembly .....................................................................3

Green Feedstock For a Green Process .......................................................................................................................6

Self-Assembly From the Nanometer to the Micrometer Scale..................................................................................7

Hypotheses.................................................................................................................................................................8

Dissertation outline ....................................................................................................................................................8

References..................................................................................................................................................................9

CHAPTER TWO: Conformational Changes and Molecular Mobility in Plasticized Proteins ...........16

Abstract....................................................................................................................................................................16

Introduction..............................................................................................................................................................16

Experimental section ...............................................................................................................................................20

Results and Discussion ............................................................................................................................................22

Conclusion ...............................................................................................................................................................36

References................................................................................................................................................................37

CHAPTER THREE: Self-Assembling Peptide From Non-Structural Proteins................................. 43

Abstract....................................................................................................................................................................43

Introduction..............................................................................................................................................................44

Materials and Methods ............................................................................................................................................46


Conclusion ...............................................................................................................................................................58

vii

References................................................................................................................................................................58

CHAPTER FOUR: Peptide Self-Assembly into Macroscopic Fibers at Physiological Conditions ... 64

Abstract....................................................................................................................................................................64

Introduction..............................................................................................................................................................65



Conclusion ...............................................................................................................................................................86

References................................................................................................................................................................88

CHAPTER FIVE: Templated Self-Assembly of Large Fibers Using a Model System....................... 96

Abstract....................................................................................................................................................................96

Introduction..............................................................................................................................................................97


Results and Discussion ..........................................................................................................................................101

Conclusion .............................................................................................................................................................116

References..............................................................................................................................................................117

CHAPTER SIX: Conclusions .............................................................................................................123

Appendix..............................................................................................................................................125

A.1 Model fitting and optimization .......................................................................................................................126

A.2 Simulation: formation of fibrillar aggregates as a function of Gliadin level. .................................................132

viii

LIST OF FIGURES

Chapter One: Introduction

Figure 1. Schematic drawing of a cross-β fibril............................................................................. 5

Figure 2. a, Transmission electron micrograph of negatively stained TWG nanofibrils. Scale bar:

100 nm. b, Scanning electron micrograph of TWG fiber. Scale bar: 5 µm. c, Optical image

showing a single TWG fiber glued to a glass fiber. Scale bar: 0.1 mm. ................................ 7

Chapter Two: Conformational Changes and Molecular Mobility in Plasticized Proteins

Figure 1. Elastic modulus of plasticized egg albumin (EA) (¡), lactalbumin (LA) (o), feather

keratin (FK) (¯), and wheat gluten (WG) (Í) films as a function of glycerol content. 22

Figure 2. Amide I peak position in the FT-IR spectra of plasticized egg albumin (¡),

lactalbumin (o), feather keratin (¯), wheat gluten (Í), and corn zein (+) films as a

function of glycerol content. ............................................................................................. 23

Figure 3. Amide II peak position in the FT-IR spectra of plasticized egg albumin (¡),

lactalbumin (o), feather keratin (¯), wheat gluten (Í), and corn zein (+) films as a

function of glycerol content. ............................................................................................. 24

Figure 4. Critical plasticization concentration, c*, obtained from modulus (¡), FT-IR amide I

(o), and amide II (¯) data as function of the number of polar amino acids per protein. 26

Figure 5. Absolute change in the FT-IR amide I peak position for each protein as a function of

the number of polar amino acids per protein (o), and critical plasticization concentration,

c*, as determined form FT-IR amide I data (¡)............................................................... 27

ix

Figure 6. Absolute change in the amide I peak position (¡), and modulus, E, (o) at c* for each

protein as a function of the percent cysteine in egg albumin (EA), lactalbumin (LA),

feather keratin (FK), wheat gluten (WG), and corn zein (CZ). ........................................ 28

Figure 7. XRD patterns for WG made with variable glycerol concentrations (a) full pattern and

(b) peak around 0.46 nm (∼23 °2θ). ................................................................................. 29

Figure 8. XRD patterns for FK made with variable glycerol concentrations (a) full pattern and

(b) peak around 0.46 nm (∼23 °2θ). ................................................................................. 30

Figure 9. Molecular weight between intermolecular interactions, Mc, at c* as function of the

average distance between inter-molecular cross-links in the proteins.............................. 33

Figure 10. Molecular distance between peptide bonds and side groups on two different

molecules as a function of glycerol content. * denotes concentrations where new Bragg

peak was observed on WG................................................................................................ 35

Chapter Three: Self-Assembling Peptide From Non-Structural Proteins

Figure 1. Schematic drawing of a cross-β fibril........................................................................... 45

Figure 2. SDS-page profile of wheat gluten and hydrolysis products: (1) native wheat gluten, (2)

molecular weight marker, (3) Tryp-pH5.7, and (4) Tryp-pH8. ........................................ 49

Figure 3. SEM micrographs of (a) Control and (b-d) Tryp-pH8. ................................................ 50

Figure 4. FT-IR spectra of (1) Control, (2) Tryp-pH5.7, and (3) Tryp-pH8. .............................. 51

Figure 5. X-ray powder diffraction patterns of of (1) native (Control) and trypsin-hydrolyzed

wheat gluten at (2) pH 5.7 and (3) pH 8 ........................................................................... 52

x

Figure 6. Effect of trypsin treatment on gliadin (top) and glutenin (bottom) with regard to

formation of cross-β structures as indicated by (a) FT-IR amide I band, and (b) XRD.

Control samples are shown in blue. .................................................................................. 56

Figure 7. Schematic illustration of cross-β fibril formation. ....................................................... 57

Chapter Four: Peptide Self-Assembly into Macroscopic Fibers at Physiological Conditions

Figure 1. a, Overlaid FPLC chromatograms. b, SDS-PAGE profile of native (lane 2) and

hydrolyzed (lane 3) wheat gluten showing reduction of most components to <10 kDa

fragments. The band at the line between stacking and running gels in lane 3 was due to

aggregates. Only two bands <10 kDa were present in the supernatant of partially

dissolved fibers (lane 4). ................................................................................................... 71


100 nm. b, Scanning electron micrograph of TWG fiber. Scale bar: 5 µm. c, Optical

image showing a single TWG fiber glued to a glass fiber. Scale bar: 0.1 mm................. 72

Figure 3. Amide I and II of the Fourier transform-infrared spectra of native (dashed) and tryptic

wheat gluten showing decreased α content and increased β content. .............................. 73

Figure 4. Raman spectra of native (bottom) and tryptic wheat gluten (top) showing α to β

transformation. .................................................................................................................. 74

Figure 5. X-ray diffraction patterns of native (dotted) and tryptic wheat gluten......................... 75

Figure 6. Fluorescent microscopy images of the Th-T stained tryptic wheat gluten fiber. ......... 76

xi

Figure 7. Tendency for β-aggregation and secondary structure prediction at each amino acid

position for Gd20, Gd46 and GtL75. Aggregation scores, percentage per residue, were

estimated using the TANGO algorithm. Secondary structure predicted using the GOR4

algorithm. .......................................................................................................................... 79

Figure 8. Amide I and II regions of the Raman spectra of TWG fibers oriented perpendicular

(top), 45° (middle) and perpendicular (bottom) to the laser’s plane of polarization. ....... 80

Figure 9. a, Shift to lower wavenumber of νs(CH3) and νs(CH2) bands with time indicated an

increasing hydrophobic environment. b, Estimated secondary structure content as a

function of time, determined by fitting of the FT-IR amide I band. c, The intensity ratios

νs(CH3) to νas(CH3) and νs(CH3) to δas(CH3) indicated amount of hydrophobic packing.82

Figure 10. a, TWG Fibers embedded in epoxy resin matrix. b, Nanoindentation image of

longitudinal and transverse sections showing indentation imprints. c, Comparison of

nanoindentation results in longitudinal and transverse sections. Error bars represent 95%

confidence interval; n=9. .................................................................................................. 83

Figure 11. Schematic illustration of the templated self-assembly process leading to formation of

macroscopic cross-β fibers................................................................................................ 84

Chapter Five: Templated Self-Assembly of Large Fibers Using a Model System

Figure 1. Size-exclusion chromatograms of native and tryptic myoglobin and gliadin. ........... 102

Figure 2. FE-SEM micrographs of macroscopic fibers formed after adding myoglobin to trypsin-

hydrolyzed gliadin. ......................................................................................................... 102

xii

Figure 3. Dependence of the number of self-assembled macroscopic fibers found in the solution

on the ratio of gliadin to myoglobin. .............................................................................. 104

Figure 4. Time-dependent change in secondary structure in 50% Trypsin-gliadin-myoglobin

solution calculated from FT-IR spectroscopy data. ........................................................ 104

Figure 5. a) Time dependent α-helix and (b) β-sheet content in trypsin-myoglobin-gliadin

solutions estimated using FT-IR spectroscopy. c) Dependence of the rate of α-helix

unraveling and β-sheet increase on gliadin content. d) Dependence of the number of

fibers found in the solution on the rate of β-sheet increase. ........................................... 107

Figure 6. The intensity ratios (b) νs(CH3) to δas(CH3) indicated amount of hydrophobic packing.

........................................................................................................................................ 108

Figure 7. a, Scanning electron micrographs of macroscopic fibers formed in trypsin-myoglobin-

gliadin solution (TMG) using different gliadin to myoglobin ratios. Scale bars: 100 µm.

b, Comparison of macroscopic properties of different TMG fibers. Aspect ratio values are

averages of up to 16 independent measurements. Reduced modulus values are averages

of up to 24 independent measurements. Error bars represent standard error of the mean.

........................................................................................................................................ 110


macroscopic cross-β fibers.............................................................................................. 111

Figure 10. Comparison of experimental and simulated data. .................................................... 116

xiii

LIST OF TABLES

Chapter Two: Conformational Changes and Molecular Mobility in Plasticized Proteins

Table 1. Amino acid composition of feather keratin (FK), egg albumin (EA), lactalbumin (LA),

wheat gluten (WG) and corn zein (CZ). ........................................................................... 18

Table 2. Summary of FT-IR amide I deconvolution. .................................................................. 25

Table 3. The critical plasticization point, c*, for each protein as defined from amide I, amide II,

and modulus data. ............................................................................................................ 27

Chapter Three: Self-Assembling Peptide From Non-Structural Proteins

Table 1. Amide I band deconvolution results. ............................................................................ 52

Table 2. Possible tryptic gliadin fragments with glutamine (Q), asparagine (N), proline (P) and

glycine (G) composition. .................................................................................................. 55

Chapter Four: Peptide Self-Assembly into Macroscopic Fibers at Physiological Conditions

Table 1. Possible tryptic wheat gluten fragments with tendency for β-aggregation predicted

using the TANGO algorithm and glutamine (Q), asparagine (N), proline (P) and glycine

(G) composition. ............................................................................................................... 77

Table 2. Properties of templating peptides: Gd20 and the templating peptides in curli and

barnacle cement proteins. ................................................................................................. 85

xiv

Table 3. Properties of templated peptides: GtL75 and templated peptides in curli and barnacle

cement proteins. Conformational switches in these peptides are triggered upon

interaction with hydrophobic templating peptides, thus promoting β-aggregation.......... 86

Chapter Five: Templated Self-Assembly of Large Fibers Using a Model System

Table 1. Properties of templating peptides from gliadin and myoglobin................................... 102

Table 2. Net percent change in secondary structure content calculated from FT-IR spectroscopy

data for trypsin-gliadin-myoglobin solutions with variable gliadin content. Statistically

insignificant (α=0.05) values are not shown. ................................................................. 105

1

CHAPTER ONE

INTRODUCTION

MOTIVATION

The problem this dissertation attempted to address was how to prepare protein-based materials

with the same level of order and precision at the molecular scale similar to the structures we find

in nature.

Nature relies on self-assembly to create sophisticated functional materials from simple

nanostructures. Natural proteinaceous materials are tough and lightweight. Silk, collagen,

cartilage and feather keratin are but a few examples of natural materials self-assembled from

protein molecules with physical properties hard to match in the synthetic world. Self-assembly

is the thermodynamically favored association of molecules. It is, therefore, inherently precise

and requires minimum energy input. Additionally, nature synthesizes proteins using the

‘greenest’ chemistry, i.e., at room to human body temperature, one atmosphere pressure and

close to neutral pH. If the mechanism of protein self-assembly could be understood, materials of

superior mechanical properties could be produced in a simple, green process using a biological,

green feedstock. The implications of self-assembly of proteins are actually wider than just

structural materials. It is know that a variety of serious, incurable disorders such as Alzheimer’s

disease, Type II diabetes, and Creutzfeldt–Jakob disease are caused by insoluble, self-assembled

protein fibrils generally known as amyloids [1].

2

Green processing and green feedstocks are keys to mitigating the environmental impact of

petroleum usage. It is also an effective way to deal with the troubled economics of an unstable

and increasingly politicized petroleum supply. The polymer industry has been particularly

vulnerable to increasing petroleum prices because, unlike in the case of fuels, rising cost cannot

be easily transferred to consumers. Accordingly, there has been increased interest in the

development of products from renewable resources [2, 3] including biological feedstocks that are

the by-products of the food and agricultural industries. Insoluble proteins like corn zein (CZ),

wheat gluten (WG), feather keratin (FK), egg albumin (EA), and lactalbumin (LA) are some

examples of biological feedstocks that are normally disposed, cannot be converted to fuel very

easily, have limited nutritional value, but have been generally shown to be easily processed into

useful biodegradable polymers [4-8].

To be utilized, some of the protein structure must first be broken down into smaller units so that

a new product can be formed. Unfortunately, most protein materials lose their useful properties

when removed from their native assembly and reprocessed for use in other applications; often

showing increased density and brittle behavior [4]. Two factors with major impact on the

properties of proteinaceous materials are hydration and native hierarchical organization from the

atomic to the macroscopic scale. Hydration prevents excess intermolecular hydrogen bonding

thus retaining structural flexibility [9]. Dehydrated proteins form an extensive hydrogen-bonding

network leading to high modulus, stress to break, and density and low strain to break. In

addition, reprocessed proteins lose their native hierarchical organization, which impart shape and

properties to the material [10]. Individual protein molecules separated from their natural

assembly do not usually possess the properties of the assemblage of components. It is the

3

interaction of protein molecules at different length scales that come together to form a

macroscopic material of high properties.

Past efforts to develop processing techniques to reprocess proteins into materials have essentially

addressed the dehydration problem and gave little to no attention to how much the native

structure was compromised [10]. Understanding how proteins interact with themselves and with

water is important as it determines the final structure and properties of the protein material. The

first goal of this research was to understand protein-protein and protein-solvent interactions as a

function of solvent concentration. We are interested in the case where the protein is not fully

dissolved in solvent but the solvent is the minority phase, similar to bound water in naturally

occurring structural materials. Studying this case in vitro is sometimes referred to as

“plasticization”. Plasticization is a popular method to utilize proteins for structural materials [10-

16]. It involves the use of small polar molecules such as water, glycerol, ethylene glycol, or

propylene glycol. Plasticizers decrease protein-protein hydrogen bonding interactions and

increase free volume and molecular mobility resulting in greater structural flexibility. This

mimics natural hydration and, therefore, is a step toward restoring the pre-processed, original

protein structure. So, while there will be a significant loss of properties due to disruption of the

native structure, plasticization can still be used to prepare useful polymers from proteins but

more importantly understand the role of protein-protein and protein-plasticizer interactions [13-

16]. Unfortunately, protein plasticization is poorly understood and assumed to be dependent on

the protein’s molecular weight without considering the diversity of structure and composition.

THE CROSS-β STRUCTURAL MOTIF: A MODEL FOR STUDYING SELF-ASSEMBLY

4

In the first part of the research we focused simply on the amount of protein-protein interactions

as plasticizer molecules were added without concern for the type of protein-protein interaction.

Next we focused on a specific type of protein interaction that leads to self-assembly of well-

defined larger structures. Self-assembly of cross-β structure is a process driven by backbone

(main chain) interactions. It is independent of the amino acid sequence and occurs in unfolded

and/or partially folded protein molecules [17]. This type of interaction proceeds to form fibrils

with an exceptionally stable core structure known as the cross-β structure. It was termed cross-β

because extended β-strands run perpendicular to the fibril axis as illustrated in Figure 1 [18].

Cross-β fibrils are essentially made of stacked β-sheets that grow in the lateral direction into

structures of high aspect ratio. It is the same structural motif implicated in amyloidosis. Recently,

there has been growing interest in cross-β fibrils in materials science because of their mechanical

properties combined with their flexibility, versatility and ability to self-assemble [19]. It has been

suggested that cross-β fibrils could serve as a structural motif for nanomaterials applications or

in the fabrication of nanodevices [19-23]. Cross-β fibrils combine the versatility of protein

sequences with self-assembly, adjustable properties and topographies, outstanding strength, and

an ability to withstand varied and extreme environmental conditions. These properties made

cross-β structures an attractive structural motif for medical, bionanotechnology, and

nanotechnology applications. Cross-β fibrils can potentially be used as a platform for

development of biomaterials, advanced catalysis systems, biosensors and diagnostic devices.

One of the most interesting features of an amyloid fibril is that the formation of its basic cross-β

structure does not require the presence of specific side chain interactions or sequence patterns

and is primarily the result of main chain interactions [17]. Thus, it is a step above simply the

amount of hydrogen bonding but not at the level of highly specific amino acid interactions.

5

Accumulating evidence suggests that virtually any polypeptide can fibrillate provided that the

right conditions are met [24, 25]. These are usually extreme denaturing conditions such as low

pH and high temperature that disrupt the protein’s ability to fold based on side chain interactions

allowing main chain interactions to dominate. Cross-β fibrils are a generic class of material

despite the diversity of peptide building blocks, which is a very interesting feature from a

material science point of view.

Although the amino acid sequence does not dictate the fundamental organization of the cross-β

structure, the nature of, and interactions between, the side chains have significant influence on

the rate and conditions under which it can form [17]. Certain features of the polypeptide,

stemming from the peculiarity of its amino acid sequence such as hydrophobicity, charge, and

secondary structure propensity, have strong influence on the kinetics of self-assembly [26]. This

perhaps explains the occurrence of cross-β structures in nature under physiological conditions, as

is the case in amyloidosis. Some living organisms, such as green Lacewing, bacteria and

barnacles, utilize the inherent ability of proteins to form cross-β structures to generate novel and

diverse structures that can endure harsh environments [27-31]. Although most of the functional

cross-β structures in nature are on the nanometer scale, micron-sized structures have also been

fibril axis

Figure 1. Schematic drawing of a cross-β fibril.

6

identified [29]. For this and other reasons mentioned earlier, the cross-β motif represents a good

model for studying protein self-assembly.

GREEN FEEDSTOCK FOR A GREEN PROCESS

There are major efforts underway to develop cross-β-based materials for various applications

[32]. The current state of the art is to start with de novo peptides that are known, or designed, to

self-assemble into cross-β structures [33-35]. However, the high cost of de novo synthesis, as it

exists today, represents a major hurdle to large-scale production of cross-β-based materials [20].

We propose an alternative method for production of self-assembling peptides through distilling

peptide behavior to generic self-assembly features. A great example is the self-assembling “Q-

block” of polyglutamine [26]. Glutamine (Q) rich short peptides or Q-blocks can be obtained

from the proteolysis of plant proteins such as wheat glutens. This suggests that peptide self-

assembling materials can be produced through proteolysis of existing proteins that can be

obtained cheaply. Glutens contain repeated sequences of Q with frequent proline (P) at the end of

the repeat sequence [36]. This mimics the cross-β forming Q-blocks. Wheat gluten is

increasingly being removed from bread and cereal products because of its connection to Celiac

disease [37]. It contains 58% glutenin and 42% gliadin [36]. Gliadin has a molecular weight of

about 36,000 g/mol. Glutenin contains 28% high molecular weight fraction of about 68,000 -

100,000 g/mol and 72% low molecular weight fraction of about 36,000 - 44,000 g/mol. Glutens

are examples of plant storage proteins that have no structural function. They are not known to

fibrillate unless subjected to severe denaturing conditions [38] and/or external mechanical stress

through processes like extrusion [39] and spinning [40].

7

To prove the concept of utilizing agricultural proteins to derive self-assembling peptides, we

chose to work with wheat gluten and trypsin as a model system. The choice to hydrolyze gluten

with trypsin was not fortuitous. Alzheimer’s disease is believed to occur following the

proteolysis of beta amyloid precursor protein (βAPP) to Aβ1-40 peptides that can then self-

assemble [41]. Serine proteases like trypsin are implicated in this proteolysis [42]. Additionally,

trypsin’s high specificity enables better-controlled experiments and informed analysis.

SELF-ASSEMBLY FROM THE NANOMETER TO THE MICROMETER SCALE

Upon incubating wheat gluten with trypsin, we observed the formation of macroscopic fibers.

Macroscopic fibers assembled in tryptic wheat gluten and were hierarchically structured showing

organization from the nanometer to the micrometer scale. With a diameter raging from 10 - 15

µm, the large fibers were bundles of 10 - 20 nm diameter cross-b fibrils (Figure 2). Cross-β

structural units formed in other enzyme-protein systems, including tryptic gliadin, but did not



showing a single TWG fiber glued to a glass fiber. Scale bar: 0.1 mm.

8

assemble into large fibers. Individually, neither tryptic glutenin nor tryptic gliadin formed fibers

at physiological conditions. The structure of tryptic glutenin showed little change and remained

rich in disordered and α-helix conformations. By contrast, tryptic gliadin formed elementary

cross-β structural units, but these did not assemble into large fibers. These observations

suggested a cooperative process involving more than one peptide required to form macroscopic

fibers.

Our results clearly demonstrated that peptide self-assembly beyond the nanoscale was possible in

vitro under physiological conditions. The question then was: what is the correct combination of

properties that makes it possible?

HYPOTHESIS

Macroscopic protein fibers self-assemble when α-helix-rich peptides undergo α to β transition in

the presence of a hydrophobic peptide with high β-aggregation potential.

DISSERTATION OUTLINE

The first part of this dissertation, chapter two, is devoted to discussing the effect of hydrogen

bonding, or lack thereof, between proteins as mediated by hydration or plasticization. The effect

of varying plasticizer (glycerol) levels on mechanical properties of a series of proteins (WG, FK,

LA, EA, CZ) is discussed in the context of primary and secondary structure of these proteins. We

found that the extent to which a protein can be plasticized was dependent on its primary structure

and not simply molecular weight, as it was often assumed in previous studies. The effect of

plasticizer is complex. It increased order (relative β-sheet content) at low concentration by

allowing chains to align but the addition of low molecular weight solvent counteracts the

9

ordering to reduce modulus. At higher concentrations, an overall drop in modulus is observed

because hydrogen bonding between proteins is progressively taken away in favor of protein-

solvent interactions. The effect is mitigated by having less polar groups or more cysteine, i.e.,

protein-protein interactions that are not hydrogen bonding.

The second part, chapters three, four and five, focus on peptide self-assembly. Chapter three

demonstrates a new concept of using a complex protein such as wheat gluten to derive self-

assembling peptides for advanced nanomaterials applications. Chapter four concentrates on

characterizing self-assembled WG fibers and the process leading to their formation. The chapter

illustrates a hierarchical self-organization process form the nanometer to the macroscopic scale.

The chapter also discusses a possible templated self-assembly process leading to the formation of

micrometer scale fibers. Building on the conclusions of chapter four, chapter five is devoted to

discussing the kinetics of the process of fiber self-assembly.

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[20] Gazit, E., 2007, "Self-assembled peptide nanostructures: the design of molecular building

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13

Human Disease," Annu. Rev. Biochem., 75(1), pp. 333-366.

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[33] Rubin, N., Perugia, E., Goldschmidt, M., Fridkin, M., and Addadi, L., 2008, "Chirality of

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[34] Oppenheim, T., Knowles, T. P. J., Lacour, S. P., and Welland, M. E., 2010, "Fabrication

and characterisation of protein fibril-elastomer composites," Acta Biomaterialia, 6(4), pp.

14

1337-1341.

[35] Kopecek, J., and Yang, J., 2009, "Peptide-directed self-assembly of hydrogels," Acta

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[36] Fox, P. F., and Condon, J. J., 1982, Food Proteins, Applied Science Publishers, London,

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[37] Kasarda, D. D., and D'Ovidio, R., 1999, "Deduced Amino Acid Sequence of an α-Gliadin

Gene from Spelt Wheat (Spelta) Includes Sequences Active in Celiac Disease," Cereal

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[38] Kasarda, D. D., Bernardin, J. E., and Thomas, R. S., 1967, "Reversible Aggregation of α-

Gliadin to Fibrils," Science, 155(3759), pp. 203-205.

[39] Reddy, N., and Yang, Y., 2007, "Novel Protein Fibers from Wheat Gluten,"

Biomacromolecules, 8(2), pp. 638-643.

[40] Woerdeman, D. L., Ye, P., Shenoy, S., Parnas, R. S., Wnek, G. E., and Trofimova, O.,

2005, "Electrospun Fibers from Wheat Protein: Investigation of the Interplay between

Molecular Structure and the Fluid Dynamics of the Electrospinning Process,"


[41] Benjannet, S., Elagoz, A., Wickham, L., Mamarbachi, M., Munzer, J. S., Basak, A.,

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Its Ectodomain Shedding.," J. Biol. Chem., 276(14), pp. 10879-10887.

15

[42] Katona, G., Berglund, G. I., Hajdu, J., Gráf, L., and Szilágyi, L., 2002, "Crystal structure

reveals basis for the inhibitor resistance of human brain trypsin," J. Mol. Biol., 315(5),

pp. 1209-1218.

CHAPTER TWO

CONFORMATIONAL

CHANGES AND MOLECULAR

MOBILITY IN PLASTICIZED

PROTEINS

Reprinted with permission from Ahmad I. Athamneh, et al., Conformational Changes and

Molecular Mobility in Plasticized Proteins. Biomacromolecules, 2008. 9(11): p. 3181-3187.

Copyright 2009 American Chemical Society.

ABSTRACT

Most biopolymers exist in a plasticized state whether it is naturally with water or unnaturally

with glycerol or other suitable polyol to make a flexible material. We have found that the extent

to which a biopolymer can be plasticized is dependent on its molecular and higher order

structures outside of simply molecular weight. Lactalbumin, ovalbumin, corn zein, wheat gluten,

and feather keratin were plasticized with glycerol from very low to very high amounts. The

conformation of the proteins was monitored with Fourier transform-infrared (FT-IR)

spectroscopy and x-ray powder diffraction (XRD) and correlated with the tensile modulus.

17

Protein conformational changes were pronounced for polar proteins with a low amount of

cysteine. FT-IR showed that the conformational changes resulted in ordering of the protein at

low to moderate plasticization levels. For proteins with little resistance to conformational

changes, additional small-scale ordering occurred around the glass transition as observed in

XRD. Accurate comparison of plasticized proteins was dependent on knowing whether or not the

protein was glassy or rubbery at room temperature as no differences arose in the glassy state. The

transition from glassy to rubbery behavior with plasticization level can be found from modulus,

FT-IR, and XRD data.

INTRODUCTION

Growing concern with the long-term availability, political implications, and environmental

impact of petroleum usage has led to increased interest in the development of products from

renewable resources [1, 2]. The petroleum dilemma is not only important for the production of

fuels but for the production of pharmaceuticals, chemicals, and plastics. Approximately 200

billion pounds of plastic are produced worldwide each year, of which over 60 billion tons are

thrown away in the United States alone [1]. The production of this plastic requires between 6 and

8 percent of the world’s total oil production [2].

One way to improve this situation is through the production of polymers from renewable

resources [3, 4]. However, attempts to make fuels, chemicals, and plastics from plant materials

such as corn starch or soybean oil have had the perverse effect of driving up agricultural

commodities and food prices to all-time highs as well as reducing exportation of needed food to

other countries. While the use of renewable resources may be noble in producing fuels,

chemicals, and plastics, it does not appear as if there is enough for all of the industries that would

18

rely on them. One alternative may be to use biological feedstocks that are the by-products of the

food and agricultural industries such as insoluble proteins. Some examples include corn zein

(CZ), wheat gluten (WG), feather keratin (FK), egg albumin (EA), and lactalbumin (LA). The

primary structure information of these proteins is listed in Table 1. These proteins cannot be

converted to fuel very easily, have limited nutritional value in the food or feed markets, but have

been generally shown to be easily processed into useful biodegradable polymers [5-9].

Table 1. Amino acid composition of feather keratin (FK), egg albumin (EA), lactalbumin (LA),

wheat gluten (WG) and corn zein (CZ).

FKa EAb LAc WGd CZe Ala 4 34 3 11 29 Arg 5 15 1 13 2 Asn 3 17 12 17 10 Asp 2 14 9 16 Cys 7 6 8 12 2 Glu 2 33 9 1 Gln 5 16 4 147 41 Gly 11 19 6 14 5 His 7 3 8 2 Ile 5 25 8 13 9 Leu 6 32 13 28 43 Lys 20 12 8 Met 16 1 6 Phe 4 20 4 20 13 Pro 12 14 2 50 23 Ser 16 38 7 19 15 Thr 4 15 7 10 5 Try 3 4 6 Tyr 1 10 4 12 8 Val 9 31 6 14 5 total 96 385 123 424 213 molecular weight, g/mol 11455 49670 16364 57519 27114 number of polar residues 38 185 68 250 84

a Arai et al [10]. b Nisbet et al [11]. c Whitaker and Tannenbaum [12]. d Kinsella [13]; data

calculated based on amino acid composition of glutenin and gliadin. Wheat gluten is 58%

glutenin and 42% gliadin. e Di Gioia et al [14].

19

Proteins perform structural (i.e., skin, hair, muscle) and biological (i.e., enzymes for digestion,

nitrogen storage) functions in nature. While CZ, FK, WG, LA, and EA comprise both classes,

the focus here is on using them as starting materials for structural polymers. To that end, the

primary, secondary, tertiary, and quarternary structures of the proteins determine physical

properties such as strength, stiffness, and flexibility [15]. A protein such as WG appears to serve

only as a means for the plant to store nitrogen and amino acids. The lack of secondary structure

in wheat gluten is not surprising because that would be an obstacle to the plant accessing the

needed nitrogen and amino acids for biosynthesis. Conversely, FK serves an important structural

function for the bird. Feather keratin has a high amount of β-sheet content and inter-molecular

cystine bonding to achieve its structural function [16].

Upon isolation from the native state, all the proteins show increased density and brittle behavior.

Solubilized and regenerated proteins will hydrogen bond strongly with one another. Modulus,

stress to break, and density for these proteins are high and strain to break is low. One popular

method to utilize these proteins for structural polymers is to plasticize them with small polar

molecules such as water, glycerol, ethylene glycol, or propylene glycol [17-23]. While the

intrinsic protein molecular arrangement is not regained, some level of hydration mimicking

natural hydration is so plasticization is one small step toward the protein behaving more

naturally. Glycerol is the most preferred because of its low price, bio-based nature, ability to

survive typical thermal processing because of a high boiling point, and ability to be retained by

the protein in the solid state. It is believed that plasticizers decrease protein-protein hydrogen

bonding interactions and increase free volume and molecular mobility resulting in greater

polymer flexibility. While there is a cost to modulus and stress at break, useful polymers are still

20

possible because many applications do not require the very high values found in unplasticized

proteins [20-23].

There are many reports in the literature comparing the properties of plasticized proteins [24-27].

However, it is dubious that valid comparisons can be made based on equal weight or molar

amounts of plasticizer alone. Each protein not only has a different molecular weight, but varying

polar group amounts, secondary structure, and cysteine contents. If the proteins were in a mostly

glassy state, it would be difficult to find large differences in their physical properties. It is the

intent of this study to examine the effects of varying plasticizer amount on a series of proteins of

varying primary and secondary structure and separate out the effects of each on polymer physical

properties. Then a consistent model to compare the physical properties of different plasticized

proteins can emerge.

EXPERIMENTAL SECTION

Materials. Feather keratin was obtained from Featherfiber Corporation (Nixa, MO). The feathers

were ground according to a previously used procedure [28]. Wheat gluten and corn zein were

obtained from MP Biomedicals LLC (Solon, OH). Type II egg albumin and lactalbumin were

obtained from Sigma-Aldrich (Saint Louis, MO). Glycerol was obtained from VWR

International (West Chester, PA).

Preparation of Films. Samples of 40 g total were prepared at 0-50 wt% glycerol. The samples

were mixed on a Waring blender on high speed for approximately 4 minutes. After mixing the

temperature of the samples was 35-40 °C. Films were prepared by pressing 5 g of each sample

between Teflon coated aluminum on a Carver Press Autofour/30 Model 4394 for two minutes at

21

160 °C and pressing stress ranging from 8.6 to 46.0 MPa depending on the protein and

plasticization level. It has been shown that this type of processing can change all cystine bonding

in the protein to inter-molecular and this was the intent [29].

Fourier Transform Infrared (FT-IR) Spectroscopy. FT-IR spectra were obtained using a

Thermo Electron Avatar 370DTGS Spectrometer with a Pike Miracle ATR diamond cell. The

spectra were collected using 64 scans at 4 cm-1 resolution from 4000-525 cm-1. A blank was run

between each sample to ensure that the cell was completely clean and a background was

collected prior to each run. Deconvolution of the amide I band into individual components was

accomplished with OMNIC v 7.3 software. The spectral region 1725-1575 cm-1 of the original

spectra was fitted with Gaussian/Lorentzian peaks. The number of peaks and their positions were

determined by the automatic peak finding feature of the program at low sensitivity and full width

at half height of 1.928. All spectra were fitted using no baseline and a target noise of 10.0. The

same procedure was followed for all samples.

X-Ray Diffraction (XRD). Film samples were scanned on a PANalytical X'Pert PRO X-ray

diffractometer using Co radiation (wavelength = 1.78901 Å) generated at 40 kV and 40 mA.

Scanning proceeded with a Theta/Theta goniometer from 2-70 °2θ with a step size of 0.0668545

°2θ at a time of 600 s.

Mechanical Properties. Test samples (2.54 cm wide x 5.08 cm gage length) were prepared

according to ASTM D882-91. The average thickness of each sample was obtained from three

measurements using a micrometer. Force and elongation data were collected using a TA-XT2i

mechanical tester at 1.2 mm/s and converted into stress-strain data based on original sample

dimensions using a spreadsheet program. Modulus was determined from the initial low strain

22

linear range of the stress-strain curve. Eight films were tested for each glycerol concentration of

every protein and the results are presented as averages with 95% confidence intervals.

RESULTS AND DISCUSSION

Physical Properties. Figure 1 shows the modulus, E, behavior for each protein as a function of

plasticization. CZ data was not obtained because of the difficulty in molding large enough

samples to tensile test. Plotting the data as a function of the weight percentage of glycerol

showed a split between all of the curves. Plotting the data as a function of the number of glycerol

molecules per protein molecule (mol glycerol/mol protein) showed that the modulus values were

very similar at low levels of plasticization. As more glycerol was added to the proteins, WG and

EA showed a marked decrease in E while LA and FK showed much smaller deviations. This

behavior was reminiscent of the glassy to rubbery transition in polymers as a function of

Figure 1. Elastic modulus of plasticized egg albumin (EA) (¡), lactalbumin (LA) (o), feather

keratin (FK) (¯), and wheat gluten (WG) (Í) films as a function of glycerol content.

23

temperature. “At temperatures below the glass transition, polymers have approximately the same

value of modulus”[30] and the observed behavior suggested that at 298 K all of the proteins were

in a glassy state at low levels of glycerol plasticization [31]. Mechanical studies of whole

feathers have produced modulus values of about 1.5-2.5 GPa [32, 33]. Extrapolation of the

modulus data back to 0 wt% glycerol yielded a similar value indicating that modulus was

maintained upon reconstitution of the proteins from their native state.

Figure 2. Amide I peak position in the FT-IR spectra of plasticized egg albumin (¡),

lactalbumin (o), feather keratin (¯), wheat gluten (Í), and corn zein (+) films as a function of

glycerol content.

Conformational Changes. FT-IR analysis can be used to assess secondary structure changes in

proteins [34, 35]. Specifically, the amide I and II bands at ca. 1625 cm-1 and 1550 cm-1,

respectively, can be used to note changes in protein structure [20, 36]. In this case, changes in the

protein originating from plasticization were observed. Amide I originated from C=O stretching

24

vibrations and has been shown to be a sensitive indicator of different types of protein secondary

structure [34]. Figure 2 shows the change in the amide I peak position as a function of added

glycerol. In general, the amide I peak shifted to lower wavenumber until reaching an inflection

and then saturating. Shifts in amide I to lower values were indicative of more ordering,

specifically β-sheet formation and the extent of the ordering was protein-dependent [20, 34].

This method of analyzing the amide I data was more straightforward than the method used by

Gao et al [20] that relied on plotting the ratio of the peak intensity at 1625 cm-1 (β-sheet) to those

at 1650 cm-1 and 1660 cm-1 (other structures and disordered portions). The inflection in the

amide I vs. glycerol curve defined a critical point in the plasticization. Table 2 summarizes

results of the deconvolution of the amide I band into individual components representing various

structural conformations. Peaks were assigned according to Goormaghtigh et al [37] and

Figure 3. Amide II peak position in the FT-IR spectra of plasticized egg albumin (¡),

lactalbumin (o), feather keratin (¯), wheat gluten (Í), and corn zein (+) films as a function of

glycerol content.

25

references therein. Plotting the ratio of β-sheet and α-helix (ordered) to random and β-turn

(disordered), as determined from the deconvolution, at each glycerol concentration (not shown)

was consistent with Figure 2.

Table 2. Summary of FT-IR amide I deconvolution.

% Glycerol

0 10 20 30 40 50 FK

β-sheet 42 47 42 44 39 36

Random 17 12 14 21 25 22 α-helix 16 18 22 18 21 19

β-turn 25 23 23 17 15 23

EA β-sheet 41 42 44 53 47 41

Random 23 12 12 13 17 21 α-helix 20 21 19 18 19 17

β-turn 16 25 25 16 17 21

LA β-sheet 48 46 55 62 61 53

Random 11 8 10 10 12 12 α-helix 17 22 18 14 12 12

β-turn 24 24 17 15 14 22

WG β-sheet 34 37 43 45 33 34

Random 30 17 15 9 13 11 α-helix 20 22 24 23 36 29

β-turn 16 25 19 23 18 26

The amide II band originated in C-N stretching and N-H deformation vibrations [34]. As

glycerol was added, it was observed that the amide II peak position shifted to a higher frequency

for all of the proteins as shown in Figure 3. Eventually, the shifting of the amide II peak

26

saturated. The saturation point was defined as a critical point in amide II behavior with

plasticization.

Figure 4. Critical plasticization concentration, c*, obtained from modulus (¡), FT-IR amide I

(o), and amide II (¯) data as function of the number of polar amino acids per protein.

Shown in Figure 1 is an arrow pointing to the critical point found for each protein from the

amide I data in Figure 2. For WG and EA, the rapid decrease in modulus occurred right after the

critical point while the modulus of LA and FK had a much less severe drop, or deviation from

the solid line, after the critical point. A critical point was also defined in the modulus as the point

at which the modulus deviated from the line in Figure 1. The critical point from amide I, amide

II, and modulus data was defined as the critical plasticization, c*. These values are listed in

Table 3. Figure 4 shows a plot of c* obtained from each data set versus the number of polar

amino acids per protein. Moles of glycerol/moles of protein was a number indicating how many

27

glycerol molecules were associated with the protein and the amide I results showed a nearly 1:1

correlation. This indicated that all polar groups were associated with a glycerol molecule at c*.

Table 3. The critical plasticization point, c*, for each protein as defined from amide I, amide II,

and modulus data.

c*, (mol glycerol/mol protein) Amide I Amide II Modulus Wheat gluten 133 69 119 Lactalbumin 75 129 76 Egg Albumin 232 254 190 Feather Keratin 13 124 56 Corn zein 33 69

100

101

102

101

102

103

! A

mid

e I

Number of polar amino acids/protein or c*

0.74, r2=0.95

0.42, r2=0.71

WG

FK

EA

LA

CZ

WG

EA

FKCZ

LA

Figure 5. Absolute change in the FT-IR amide I peak position for each protein as a function of

the number of polar amino acids per protein (o), and critical plasticization concentration, c*, as

determined form FT-IR amide I data (¡).

28

The absolute change in the amide I peak position from no glycerol to c* for each protein is

plotted in Figure 5. A strong correlation with c* and the number of polar amino acids per protein

was again found. Plotting the change in the amide II peak position again revealed a constant

number around 14 cm-1. Although both amide I and amide II changed as a function of

plasticization, amide I was strongly correlated with the level of plasticization and the protein

polarity whereas amide II was not.

Each protein also had different levels of cysteine. Figure 6 plots the change in the amide I peak

as a function of the percent cysteine in the protein. The change was less pronounced for more

cysteine indicating that disulfide bonds resisted conformational change with plasticization.

Figure 6. Absolute change in the amide I peak position (¡), and modulus, E, (o) at c* for each

protein as a function of the percent cysteine in egg albumin (EA), lactalbumin (LA), feather

keratin (FK), wheat gluten (WG), and corn zein (CZ).

29

Figures 7 and 8 show the XRD spectra as plasticization level increased. For brevity, only WG

and FK are shown, which showed the largest and smallest change in the amide I peak position in

Figure 5. As more glycerol was added to WG, the peak around 1.00 nm decreased until it was

nearly flat at 50 wt% glycerol, indicative of amorphous behavior, as shown in Figure 7a. This

was a concentration effect from more glycerol and less ordered protein per unit volume. Also

with an increase in plasticization a new Bragg peak appeared on top of the original peak around

0.46 nm beginning at 20 wt% glycerol and disappearing at 50 wt% glycerol as shown in Figure

7b. The new Bragg peak was maximum at 20 wt% glycerol, which was consistent with Figure 2

and the ratio of ordered to disordered secondary structure obtained from the deconvolution of

amide I data. However, the new Bragg peak persisted after c*. Interestingly, the intensity of the

peak around 1.00 nm did not steadily decrease and remained steady over this plasticization

range.

1 104

2 104

3 104

4 104

5 104

5 10 15 20 25 30 35 40

0 wt%10 wt%20 wt%30 wt%40 wt%50 wt%

Inte

nsity

2!

2.5 104

3 104

3.5 104

4 104

4.5 104

18 20 22 24 26 28

0 wt%10 wt%20 wt%30 wt%40 wt%50 wt%

Inte

nsity

2!

(a) (b)

Figure 7. XRD patterns for WG made with variable glycerol concentrations (a) full pattern and

(b) peak around 0.46 nm (∼23 °2θ).

30

The same did not happen for FK as shown in Figure 8a. The peak around 1.00 nm decreased in

intensity but saturated at 40-50 wt% glycerol and did not become flat like in WG. The original

peak around 0.46 nm did not show evidence of a new superimposed peak indicating that the

original β-sheet structure was intact as shown in Figure 8b. FT-IR amide I deconvolution also

indicated more resistance to conformational changes in FK than in WG as evidenced by the less

severe change in the order/disorder ratio for FK as a function of plasticization. In both FK and

WG, the peak around 1.00 nm showed a more marked intensity decrease than did the peak

around 0.46 nm.

1 104

2 104

3 104

4 104

5 104

5 10 15 20 25 30 35 40

10 wt%20 wt%30 wt%40 wt%50 wt%

Inte

nsity

2!

2.5 104

3 104

3.5 104

4 104

4.5 104

18 20 22 24 26 28

0 wt%10 wt%20 wt%30 wt%40 wt%50 wt%

Inte

nsity

2!

(a) (b)

Figure 8. XRD patterns for FK made with variable glycerol concentrations (a) full pattern and

(b) peak around 0.46 nm (∼23 °2θ).

Molecular Mobility and Effect on Modulus. At 298 K, c* was more than likely the

plasticization concentration where the glass transition temperature, Tg was equal to 298 K. The

31

Couchman-Karasz relationship (eq 1) has been shown to be an effective way to predict the Tg of

biopolymers as a function of plasticization level [30, 38, 39].

!

lnTgTg,gly

"

# $ $

%

& ' ' =

wp lnTg,pTg,gly

"

# $ $

%

& ' '

wgly

Tg,pTg,gly

"

# $ $

%

& ' ' + wp

(1)

where the glass transition temperature of glycerol, Tg,gly was 180 K, wgly and wp were the mass

fraction of glycerol and protein, respectively, and Tg,p was the glass transition temperature of

protein.[40] The weak glass transition temperatures of the individual proteins have been shown to

be difficult to find experimentally using traditional techniques such as differential scanning

calorimetry [41]. Indeed, we have been able to find only the Tg of WG to be 453 K using DSC,

which was in good agreement with literature values [42, 43]. Using Tg,p values for the WG, CZ,

and EA proteins found in Katayama et al [41] at 448 K, 437 K, and 481 K, respectively, and

setting Tg=298 K, it was possible to find values for wgly through wgly+wp=1 and the Couchman-

Karasz relationship. Transforming wgly to c* values yields 198, 190, and 91 mol glycerol/mol

protein for WG, EA, and CZ, respectively. These were values that occurred near the inflection in

the amide I data in Figure 2 indeed suggesting that the conformational changes at the inflection

point in FT-IR occurred because of Tg=298 K being reached at c*. Although the proteins

possessed differences in secondary structure and cystine bonding, these differences did not

manifest in the glassy state. In comparing the properties of the different proteins, differences did

not occur until after reaching c*.

In the glassy state, addition of plasticizer increased order and the increase in ordering was more

pronounced for proteins with more polar groups and less cysteine. However, this ordering was

32

not enough to differentiate the modulus of the proteins because the glassy behavior continued to

dominate. Proteins were able to hydrogen bond multiple times per amino acid and this was the

origin of the glassy behavior. Small amounts of plasticization did not allow the protein to

become rubbery. The large drop in modulus at c* was a transition from a glassy to a rubbery

state at high levels of plasticization. This transition was pronounced for proteins with a large

number of polar groups and little cysteine. However, for less polar proteins with large amounts

of cysteine, there was little deviation from the original glassy behavior, even after a transition in

the amide I peak in FT-IR. As plasticization level was increased, the Tg=298 K of the proteins

was met at c*. WG and EA showed more typical polymeric behavior of a drop in modulus before

reaching the rubbery plateau. FK and LA showed no transition from the glassy state to the

rubbery plateau because of the large cysteine content forming a highly cross-linked network. For

the rubbery plateau, the modulus was described by

!

E ="RTMc (2)

where Mc was the molecular weight between inter-molecular interactions, ρ was density, R was

the ideal gas constant, and T was absolute temperature so the modulus was directly proportional

to the number of molecules per unit volume [31]. The density of each protein was found from the

method of Fischer et al [44]. Adding glycerol decreased the density of the material slightly, by

about 3 % at the highest plasticization levels. The molecular weight between intermolecular

interactions, Mc, was calculated from Equation 2 using the experimental data for E. It was found

that Mc increased with plasticization, which was indicative of protein molecules being pushed

apart by glycerol molecules so that protein-glycerol interactions replaced protein-protein

intermolecular interactions. Figure 9 plots Mc at c* as a function of the distance between inter-

33

molecular cross-links in the proteins, obtained by dividing the protein molecular weight by the

number of cysteine amino acids. A very strong correlation was found and the dashed line in

Figure 9 shows a slope of 1. Plotting E at c* versus the cysteine percentage in each protein as

shown in Figure 6 yielded a good correlation but not as strong as that in Figure 9. The transition

from glassy to rubbery behavior can be found using FT-IR by observing changes in the amide I

peak. However, for highly cross-linked networks, modulus will not show traditional changes

associated with a glass to rubber transition. Cystine bonding not only resisted conformational

changes induced by plasticization but maintained modulus at temperatures higher than Tg. Many

reports of plasticized biopolymers, especially proteins, exist to show the potential of these

materials as replacements for synthetic polymers [17-27]. To properly compare proteins to note

effects of secondary structure and cystine bonding on properties, the proteins needed to be in a

state of differentiation, which was not the glassy state. At room temperature, 298 K, the most

Figure 9. Molecular weight between intermolecular interactions, Mc, at c* as function of the

average distance between inter-molecular cross-links in the proteins.

34

likely use temperature for plasticized proteins, comparing proteins at equal weight or mole

fraction of plasticizer did not ensure that all proteins were outside the glassy state and any

modulus differences may occur simply because one protein was in the glassy state and one was

not. At Tg=298 K, proteins above c* were in a state where modulus was differentiated by

differences in secondary structure (for instance, β-sheets) or high amounts of cystine bonding as

shown in Figure 9.

Primary Sequence Features Sensitive to Plasticization. Carbonyls and amines originated in

the peptide bond as well as the amino acid side groups in the protein. As glycerol entered the

protein structure, the hydrogen on the glycerol hydroxyl hydrogen-bonded with at least the

oxygen on a protein carbonyl group as suggested by the FT-IR data. In some cases the oxygen

on the glycerol hydroxyl hydrogen-bonded with the hydrogen on a protein amine. Clearly, the

number of polar side groups plays a role in the extent of plasticization of the molecule. The more

polar side groups, the more the protein can be plasticized. Figure 10 shows the changes in the

XRD peak positions at 0.46 and 1.00 nm, which corresponded to two distances between protein

molecules: a main chain carbonyl on one molecule hydrogen bonding to a main chain amine on

another (0.46 nm) and the total distance between two amino acid side groups interacting (1.00

nm), which was normal to the 0.46 nm distance [45]. As glycerol was added to FK and WG, the

peptide bonds on two different protein molecules were brought slightly closer together up to c*

and the saturation point more closely resembled that obtained from amide II data. For WG, the

hydrogen-bonding distance between two adjacent main chains stayed roughly constant over the

range of glycerol concentration where the new Bragg peak was observed. The side groups on two

different molecules were also brought closer together for FK up to c*. For WG, the side groups

were pushed away from each other up to c* then showed little change until the highest

35

plasticization. This region of plasticization where there is little change in molecular distances but

the appearance of a new Bragg peak may indicate that an increase in possible conformational

states upon reaching a rubbery state resulted in small-scale ordering of some of the amino acids

in WG, which was only possible because of its large polarity and lack of cross-linking. In a

highly plasticized state, both proteins ended up with approximately the same molecular

dimensions in both directions but with different amounts of order.

0.44

0.445

0.45

0.455

0.46

0.465

0.95

0.96

0.97

0.98

0.99

1

1.01

0 100 200 300 400 500 600 700

WG

FK

WG

FK

Mole

cula

r D

ista

nce (

C=

O--

H-N

), n

m

Mole

cula

r D

ista

nce (

C-R

1--

R2-C

), n

m

mol glycerol/mol protein

* *

*

* *

*

Figure 10. Molecular distance between peptide bonds and side groups on two different

molecules as a function of glycerol content. * denotes concentrations where new Bragg peak was

observed on WG.

Taking the FT-IR and XRD data together, the amide II region was sensitive to changes in the

distance between two adjacent main chains and this did not change much while the amide I

36

region was sensitive to overall changes with plasticization especially changes in interactions

between side groups. Glycerol ordered the carbonyl side groups found in asparagine, glutamine,

aspartic acid, and glutamic acid. Since these amino acids make up 37% of WG and only 18% of

FK, WG showed the largest change in conformation as evidenced by amide I and in fact showed

the creation of an entirely new ordered phase upon reaching a rubbery state as evidenced in XRD

data (Figure 7b). Alternately, the presence of cysteine made the protein less sensitive to

plasticization. Cystine bonding increased the resistance of proteins to conformational changes

induced by glycerol and allowed proteins of high cysteine content to maintain modulus even at

high levels of plasticization above Tg.

CONCLUSION

To properly compare proteins to note effects of primary and secondary structural features on

mechanical properties, the proteins need to be in a state of differentiation, which is not the glassy

state, i.e., Tg greater than 298 K. Plasticization did not differentiate the proteins in the glassy

state. In the glassy state, addition of plasticizer increased order in the protein. The extent of the

ordering was protein-dependent and was more pronounced for proteins with more polar groups

and less cysteine.

At low levels of glycerol plasticization all of the proteins remained in a glassy state at 298 K. As

more glycerol was added a critical plasticization point, c*, was reached when all polar groups in

the protein were associated with glycerol molecules. At c*, Tg dropped to 298 K, and differences

in secondary structure and cystine bonding were reflected in the modulus. Sensitivity to

plasticization increased with increased amount of polar side groups in the primary structure,

especially those with carbonyl groups, i.e, glutamine, asparagine, glutamic acid, and aspartic

37

acid. Conversely, the presence of cysteine made the protein less sensitive to plasticization due to

formation of a robust cystine cross-linked network.

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York.

CHAPTER THREE

SELF-ASSEMBLING PEPTIDE

FROM NON-STRUCTURAL

PROTEINS

ABSTRACT

Wheat gluten is an amorphous storage protein. Trypsin hydrolysis of wheat gluten produced

glutamine-rich peptides. Some peptides were able to self-assemble into fibrous structures

extrinsic to native wheat gluten. The final material was an in situ formed peptide composite of

highly ordered nanometer-sized fibrils and micron-sized fibers embedded in an unassembled

peptide matrix. Fourier transform infrared spectroscopic and X-ray diffraction data suggested

that the new structures resembled cross-β fibrils found in some insect silk and implicated in prion

diseases. The largest self-assembled fibers were about 10 µm in diameter with right-handed

helicity and appeared to be bundles of smaller nanometer-sized fibrils. Results demonstrated the

potential for utilizing natural mechanisms of protein self-assembly to design advanced materials

that can provide a wide range of structural and chemical functionality.

44

INTRODUCTION

Naturally occurring structural components, such as the materials comprising bone, muscle, joint,

skin, feather, hoof, horn, nail and hair, are structures of self-assembled protein molecules.

Proteins removed from structural materials do not retain the properties of the original material

demonstrating that it is the native assembly of proteins responsible for the properties [1]. Past

research has focused on developing processing techniques to reform proteins into materials for

various applications without regard to how much the native structure was compromised [1].

Nature routinely arranges proteins into larger structures via one of two mechanisms. First,

through folding into a highly selected and specific conformation based on side chain interactions.

Hence, the amino acid sequence of the protein determines the secondary and tertiary structures,

which in turn determine the arrangement of multiple folded protein molecules in the quaternary

structure. Folding is the most common mechanism of building proteinaceous structures in nature.

Second, unfolded and/or partially folded protein molecules can assemble through main chain

interactions independent of the amino acid sequence [2]. The resulting fibrous structures have a

highly stable, robust common core structure known as the cross-β in which peptide strands run

perpendicular to the fibril axis as illustrated in Figure 1 [3]. Cross-β fibrils are made of stacked

β-sheets that grow in the lateral direction into structures of high aspect ratio. Understanding

mechanisms of protein self-assembly would be key to designing and constructing useful

materials for various applications. The implications of self-assembly of proteins are actually

wider than just structural materials. It is known that prion diseases such as Alzheimer’s disease,

Type II Diabetes, and Creutzfeldt-Jakob disease are caused by self-assembled cross-β fibrils

generally known as amyloids [4, 5]. Accumulating evidence suggests that cross-β fibrils form a

45

generic class of material despite the diversity of peptide building blocks [6, 7]. Although the

amino acid sequence does not dictate the fundamental organization of the cross-β structure [2],

studies have shown that certain features such as enrichment of glutamine residues facilitate self-

assembly into cross-β structures [5].

fibril axis

Figure 1. Schematic drawing of a cross-β fibril.

The superior mechanical properties, flexibility, versatility and ability to self-assemble have made

cross-β fibrils an attractive structural motif for nanomaterials applications and for the fabrication

of nanodevices [8]. The current state of the art for cross-β-based materials is to start with

peptides that are known cross-β formers such as polyglutamine, Aβ1-40, hen lysozyme, and

SAA1-12 [5, 9]. Large molecular weight peptides like the known cross-β formers are best

produced through de novo methods [10]. However, the high cost of de novo materials, as it exists

today, represents a major hurdle to large-scale production of peptide-based materials [10].

Here, we show that peptide-starting materials for cross-β structures can be produced through

proteolysis of an abundant plant protein that can be obtained cheaply. Proteolysis captures the

simplicity and potential large-scale production of short chain peptides. Wheat gluten (WG) is the

term for a nonstructural, highly disordered mixture of nitrogen storage proteins with an

abundance of glutamine (Q) in the amino acid composition. It is easily hydrated and gives dough

46

its elasticity [11]. Wheat gluten contains repeated sequences of Q with proline (P) at the end of

the repeat sequence [12]. This mimics the cross-β forming so-called “Q-blocks” studied by

Perutz and others [5]. Gluten is increasingly being removed from bread and cereal products

because of its connection to Celiac disease [13]. It contains 58% glutenin (Gt) and 42% gliadin

(Gd) [12]. Gd has a molecular weight of ~30 kDa and a compact globular conformation with a

low content of β-structures [14]. Gt contains 28% high molecular weight (HMW) fraction of

~68-100 kDa and 72% low molecular weight (LMW) fraction of ~36-44 kDa [14]. The structure

of LMW Gt is similar to Gd [14]. HMW Gt subunits are disordered with little regular structure

but their mobility increases and β-sheets form at intermediate levels of hydration [11, 15]. Gluten

proteins are not known to fibrillate unless subjected to severe denaturing conditions [16-18]

and/or external mechanical stress through processes like extrusion [19] and spinning [20]. Here,

tryptic treatment of WG produced glutamine-rich peptides or “Q-blocks” that self-assembled into

the cross-β structure forming building blocks for highly ordered, fibrous structures from the

nanometer to the micrometer scale. Findings demonstrated the potential for utilizing natural

mechanisms of self-assembly and cheap protein sources to develop advanced high-performance

materials.

MATERIALS AND METHODS

Proteolysis. Twenty g WG (protein 75-80%; starch 9-19%; moisture 5-8%; fat (ether extract 0.5-

1.5%; ash .8-1.2%) (VWR International, West Chester, PA) was suspended in 800 ml de-ionized

H2O at 37°C. The reaction was initiated by the addition of trypsin (Type I from bovine pancreas,

Sigma-Aldrich, Saint Louis, MO) to give a final enzyme-to-substrate ratio of 1:1000 (w/w). In

the case of Gd and Gt (TCI America, Portland, OR), 5 g were suspended in 200 ml de-ionized

47

H2O and the same enzyme-to-substrate ratio was used. Two enzymatic reactions were carried

out. The first with pH maintained in the range 8.0–8.3 by manual addition of 1.0 M sodium

hydroxide (Tryp-pH8). The second reaction was conducted with no pH adjustment (Tryp-pH5.7),

which started at pH 5.7 and ended at pH 5.2. Control samples were prepared with no enzyme

addition at pH 5.7 and pH 8 and showed the same results so the pH 5.7 data is presented as

“Control”. Mixtures were incubated at 37°C with continuous gentle stirring for 48 h. At the end

of the reaction, mixtures were heated to 95°C for 30 min to deactivate the enzyme, poured into

dishes made of Teflon-coated aluminum sheets, and dried under the fume hood at room

temperature.

SDS-polyacrylamide gel electrophoresis (SDS-PAGE). A Mini-PROTEAN Tetra

Electrophoresis System with a precast Tris-HCl gel consisting of 4% stacking gel and 15%

separating gel was used for SDS-PAGE. All materials and equipment for SDS-PAGE were

purchased from Bio-Rad Laboratories (Hercules, CA) unless otherwise indicated. Reducing

sample buffer was prepared by adding 2-mercaptoethanol (Sigma-Aldrich, Saint Louis, MO) to

Laemmli sample buffer (62.5 mM Tris-HCl, pH 6.8, 25% glycerol, 2% SDS, 0.01 Bromophenol

Blue) for a final concentration of 5%. Dried protein samples (1 mg) were suspended in 300 µl

sample buffer, centrifuged at 4000 rpm for 5 min, and the supernatants (10 µl) were used to load

the gel. Electrophoresis was done at a constant current of 20 mA for 100 min. The gel was

stained by Coomassie Brilliant Blue R-250.

Fourier transform infrared (FT-IR) spectroscopy. FT-IR spectra of dried material were

obtained using a Thermo Electron Nicolet 6700 Spectrometer with a Smart Orbit ATR diamond

cell. The spectra were collected using 64 scans at 4 cm-1 resolution from 4000-525 cm-1. A blank

48

was run between each sample to ensure that the cell was completely clean and a background was

collected prior to each run. Deconvolution of the amide I band into individual components was

performed using OMNIC v 7.3 software. The range 1725−1575 cm-1 of the original spectra was

fitted with Gaussian/Lorentzian peaks [21]. The number and position of peaks were determined

by the automatic peak finding feature of the OMNIC program using low sensitivity and full

width at half-height of 3.857. All spectra were fitted using no baseline and a target noise of 10.0.

Scanning electron microscopy (SEM). SEM micrographs were obtained using a FEI Quanta

600 FEG environmental SEM instrument in high vacuum mode with a 10-14 mm working

distance, accelerating voltage of 5 kV, and Everhardt-Thornley SE-detector.

X-ray diffraction (XRD). XRD patterns were recorded on a PANalytical X'Pert PRO X-ray

diffractometer using Co radiation (wavelength = 1.78901 Å) generated at 40 kV and 40 mA, and

scanned with a Theta/Theta goniometer from 2-70 °2θ with a step size of 0.0668545 °2θ at a

time of 600 s.


The SDS-PAGE profile of native and hydrolyzed WG indicated that the peptides resulting from

hydrolysis at pH 8, which was the optimum for trypsin activity, were smaller and more

uniformly sized (Figure 2). Only three bands were observed in the Tryp-pH8 hydrolysate: two

sharp around 37 and 29 kDa and one broad below 10 kDa. In comparison, seven bands were

observed in Tryp-pH5.7 hydrolysate: six sharp around 37, 29, 25, 18, 15 and 10 kDa and one

broad below 10 kDa. Tryp-pH5.7 hydrolysate was completely water-soluble, whereas Tryp-pH8

hydrolysate was only partially soluble. No fibrils or fibers were observed in the Control or the

49

hydrolysate at pH 5.7 (Figure 3a). However, fibrous structures were evident in the SEM

micrographs of trypsin-hydrolyzed WG at pH 8 (Figures 3b-d). Small fibrils with diameter,

D~100-500 nm and length, L~2.5-10 mm were observed throughout the sample as shown in

Figure 3b. Larger fibers had more consistent diameters and lengths of Df~10 µm and Lf~100 mm

and appeared to be bundles of smaller fibrils with a right-handed helical twist (Figure 3c and d).

The large fibers were sporadic throughout the sample. Material between fibrils and fibers was

unassembled peptides.

Figure 2. SDS-PAGE profile of wheat gluten and hydrolysis products: (1) native wheat gluten,

(2) molecular weight marker, (3) Tryp-pH5.7, and (4) Tryp-pH8.

The shape and maxima of the amide I peak in the FT-IR spectra of Tryp-pH8 (Figure 4)

suggested that the new structure resembled that of cross-β fibrils with a sharp maximum at 1624

cm-1 [22]. Cleavage of WG at bulky side groups arginine and lysine produced shorter glutamine-

rich peptides that favored assembly into cross-β structures. Hydrolysis at pH 5.7 did not produce

as sharp an amide I maximum but the data indicated some cross-β formation albeit weaker than

50

at pH 8. The modicum of cross-β structures produced in Tryp-pH5.7 was not sufficient to

assemble into mature fibrils that could be detected in SEM. Compared with Tryp-pH5.7, the

sharper peak at 1624 cm-1 in Tryp-pH8 indicated a higher amount of cross-β structure and less

native β-sheets [22]. Within the 48 hour time frame of the experiment, the smaller glutamine rich

Q-blocks of Tryp-pH8 could self-assemble into fibrils and fibers whereas the larger peptides of

Tryp-pH5.7 could not. Allowing the experiments to proceed for longer time showed the

emergence of fibrils in Tryp-pH5.7. Hydrolyzing wheat gluten at the optimal pH of 8 and then

dropping pH to 5.7 did not interfere with assembly kinetics. This suggested a conformational

dependence on self-assembly as shorter peptides could extend their backbone faster and allow

them to hydrogen bond together independently of pH. Given literature reports of cross-β

formation under high or low pH, it is highly likely that a severe decrease or increase in the pH of

Tryp-pH5.7 would allow it to self-assemble [6]. Tryp-pH5.7 peptides were longer and would

require stronger denaturing conditions to uncoil the molecules and allow them to self-assemble

side-by-side.

Figure 3. SEM micrographs of (a) Control and (b-d) Tryp-pH8.

51

1500 1550 1600 1650 1700

1624

Ab

so

rba

nc

e

Wavenumber, cm-1

2

3

1

Figure 4. FT-IR spectra of (1) Control, (2) Tryp-pH5.7, and (3) Tryp-pH8.

The deconvolution of the amide I band (1600–1700 cm-1) in the spectra of Control, Tryp-pH5.7

and Tryp-pH8 revealed additional peaks listed in Table 1. The characteristic peak at 1684 cm-1 in

Tryp-pH8 indicated anti-parallel β-sheet formation likely originating from the non-fibril peptides

surrounding cross-β fibrils [23]. The peaks at 1675 cm-1 in Tryp-pH5.7 and 1671 cm-1 in Tryp-

pH8 were contributions from the asymmetrical CN3H5 stretching vibration of arginine side

groups [24]. Arginine was abundant on peptide ends in the trypsin-hydrolyzed protein unlike the

Control where a similar peak was not detected. The arginine values were similar for Tryp-pH5.7

and Tryp-pH8 but the SDS-PAGE showed a much more thorough hydrolysis in Tryp-pH8.

Therefore, the deconvolution may reveal that hydrolysis at arginine was not pH dependent within

the pH range studied but hydrolysis at lysine was, with lysine being inaccessible to trypsin at pH

5.7. Contributions from glutamine residues were evident in the spectra of both native and

hydrolyzed WG. The characteristic peaks at 1615 cm-1 in Control, 1608 cm-1 in Tryp-pH5.7, and

52

1615 cm-1 in Tryp-pH8 originated from a glutamine side chain NH2 bending vibration [24]. The

glutamine contribution to the amide I band was reduced after enzymatic hydrolysis from 21% in

Control to 15% in Tryp-pH5.7 and 2% in Tryp-pH8. This reduction correlated positively with the

amount of cross-β structures in the hydrolyzed protein and was likely due to the involvement of

glutamine side chains in rigorous hydrogen bonding regimes ascribed to cross-β structures [25].

It was more difficult to infer structural information from the amide II band (1500–1600 cm-1)

over the amide I band despite the clear shift in amide II from lower to higher wavenumber.

Generally, amide II has been shown to be less sensitive to variation in secondary structure [21].

Additionally, this spectral region was sensitive to individual amino acid side chains such as those

of arginine and lysine [26]. Significant contributions from individual amino acids would mask

any secondary structure information especially for shorter peptides.

Table 1. Amide I band deconvolution results.

Control Tryp-pH5.7 Tryp-pH8 assignment frequency, cm-1 % frequency, cm-1 % frequency, cm-1 % β-sheet 1629 21 1623 27 1626 44 Random coil 1642 18 1640 19 1644 2 31-helix 1653 13 1652 14 1656 28 β-turn 1666 16 1664 14 1697 5 1690 1694 Antiparallel β-sheet 1680 8 1684 5 1684 10 Glutamine side group 1615 21 1608 15 1615 2 Arginine side group - - 1675 7 1671 8

XRD patterns confirmed the formation of cross-β structures in the trypsin-hydrolyzed WG

(Figure 5). The characteristic reflection of cross-β structures at ~0.48 nm was evident in Tryp-

pH5.7 and Tryp-pH8 [3]. The Control displayed a diffraction pattern indicative of a

predominantly amorphous material with very little order. The 0.46 nm length represented the

distance between peptide main chains in a native β-sheet but was very broad [27]. Upon

53

treatment with trypsin, a sharp peak appeared superimposed on the Tryp-pH5.7 at 0.46 nm. The

sharp peak at 0.46 nm in Tryp-pH5.7 indicated an increase in traditional β-sheet content. Tryp-

pH5.7 had more unhydrolyzed, long peptides that formed traditional β-sheets between certain

peptide segments but those segments would not further self-assemble into distinct fibrils and

fibers. Tryp-pH5.7 did show a very small peak at 0.48 nm indicative of some cross-β formation.

The size of the peak at 0.48 nm correlated with the sharpness of the FT-IR peak at 1624 cm-1.

Although a small amount of cross-β building blocks were available in Tryp-pH5.7 these building

blocks were not enough to initiate fibril and fiber formation because the longer peptide segments

could not extend enough to continue assembly. Conversely, with more efficient hydrolysis at pH

8, there were enough short peptides to extend and form mature cross-β fibrils and fibers. The

cross-β structure has been shown to be insoluble in many solvents and this may explain why

2

3

1

0.5 1 1.5 2 2.5 3 3.5

0.48 nm

0.46 nm

1.85 nm

1.00 nm

1.54 nm

1/d , nm-1

Inte

nsit

y

Figure 5. X-ray powder diffraction patterns of of (1) native (Control) and trypsin-hydrolyzed

wheat gluten at (2) pH 5.7 and (3) pH 8

54

Tryp-pH5.7 was more soluble than Tryp-pH8 [28]. In fact, the insoluble Tryp-pH8 fraction gave

a strong FT-IR peak at 1624 cm-1 similar to that in Figure 4 and was rich with fibers when

examined under SEM.

The larger lengths around and greater than 1.00 nm represented the distances between β-sheets

and were a function of peptide side groups [25, 27]. As shown in Figure 5, the peak at 1.00 nm

shifted and sharpened after trypsin treatment. The inter-sheet distance shifted from 1.0 nm in the

Control to 1.54 nm in Tryp-pH5.7. For Tryp-pH8, two peaks were superimposed on one another,

one at 1.54 nm and one at 1.85 nm. The reported inter-sheet distance of the cross-β structure of

de novo synthesized glutamine-rich short peptides was 1.6 nm [25], and the 1.85 nm length in

Tryp-pH8 likely originated from arginine or lysine [29]. So it appeared that at pH 5.7 the

majority of β-sheets were of the traditional type with glutamine mediating the inter-sheet

distance, which was shown in reference 14. In contrast, at pH 8, β-sheets of the cross-type

formed most likely at peptide ends where arginine and lysine were more abundant due to a

higher degree of hydrolysis.

Wheat gluten was a crude protein mixture so to discern the role of each component, glutenin and

gliadin were hydrolyzed separately with trypsin at pH 8. FT-IR and XRD data showed that the

trypsin-hydrolyzed gliadin peptides formed cross-β structures and trypsin-hydrolyzed glutenin

did not (Figure 6). Interestingly, FT-IR and XRD data for glutenin control indicated that the

sample may have already contained some cross-β structures using the peak maxima in both data

sets as an indicator. This may have been a result of the separation procedures used because

glutenin has been shown to form the cross-β structure under strong denaturing conditions [17].

However, no significant cross-β formation was observed in trypsin-hydrolyzed glutenin in 48

55

hours. Concentrating on the observed stronger cross-β former gliadin, possible tryptic fragments

were evaluated in terms of their ability to form cross-β structures based on literature information.

Cross-β forming ability has been linked to high Q and asparagine (N) and low P and glycine (G)

content [30, 31]. Table 2 shows possible Q-rich tryptic gliadin fragments along with Q, N, P and

G composition. Among the Q-rich fragments, the 5.4 kDa fragment had less P and G and more

N. Therefore, it was most likely the 5.4 kDa tryptic gliadin fragment that self-assembled into

cross-β fibrils. Although very strong cross-β formers, no large fibers were observed in trypsin-

hydrolyzed gliadin peptides. This suggested a cooperative assembly process to build large cross-

β fibers where a larger molecular weight component was required.

Table 2. Possible tryptic gliadin fragments with glutamine (Q), asparagine (N), proline (P) and

glycine (G) composition.

sequence [32] molecular weight, KDa Q, % N, % P, % G, %

VPVPQLQPQNPSQQQPQEQVPLVQ

QQQFLGQQQPFPPQQPYPQPQPF

PSQQPYLQLQPFLQPQLPYSQPQP

FRPQQPYPQPQPQYSQPQQPISQQ

QQQQQQQQQQQQQQQQQIIQQIL

QQQLIPCMDVVLQQHNIVHGK

16.4 43.9 1.4 20.1 1.4

SQVLQQSTYQLLQELCCQHLWQIPE

QSQCQAIHNVVHAIILHQQQK

5.4 28.3 2.2 2.2 -

QQQQPSSQVSFQQPLQQYPLGQG

SFRPSQQNPQAQGSVQPQQLPQFE

EIR

5.7 36.0 2.0 14.0 6.0

56

Figure 6. Effect of trypsin treatment on gliadin (top) and glutenin (bottom) with regard to

formation of cross-β structures as indicated by (a) FT-IR amide I band, and (b) XRD. Control

samples are shown in blue.

The observed cross-β fibrils suggested the following self-assembly mechanism, depicted

schematically in Figure 7: proteolysis followed by a heating step produced 1) short, extended

peptides with the ability to 2) self-assemble into ordered building blocks (cross-β sheets) through

main–chain hydrogen bonding. 3) An “elementary unit” of two cross-β sheets stacked face-to-

face was formed with inter-sheet distance mediated by the size of the glutamine, arginine, and

lysine side chains, ~1.6-1.9 nm, and inter-peptide spacing of 0.48 nm (Figure 5.3). When enough

self-assembling peptides were present in the solution, more elementary units formed and stacked

57

into 4) cross-β superstructures. These superstructures further assembled into 5) fibrils of D~100-

500 nm or 67-333 stacked cross-β sheets and L~2.5-10 µm or 5,000-20,000 peptides side-by-side

along the fiber length with extended peptides oriented perpendicular to the fibril axis (Figure 3b).

Control and trypsin-hydrolyzed Gt never reached stage 1. Tryp-pH5.7 made it to stage 3.

Trypsin-hydrolyzed Gd reached stage 4. Combined Gt and Gd peptides formed fibrils that

twisted together into a right-handed conformation to form fibers of ultimate Df~10 µm and

Lf~100 µm (Figure 3c and 3d). Tryp-pH8 assembled beyond stage 5 to micron-sized fibers. The

fibrils were pitched 60o to the fiber axis with pitch distance of hf~1 µm. Fiber pitch was

determined by measuring the angle of the fibril twist relative to the fiber axis on SEM

micrographs (Figure 3). Eventually, the fiber cannot accommodate any more fibrils because the

energetic cost to keep twisting is too high so the final fiber size is thermodynamically limited [7,

33-35]. The chirality of naturally-occurring amino acids predicts a left-handed helical

conformation [36]. The observed fibrils and fibers were much larger than previously seen self-

assembled fibrous peptides and had right-handed helicity.

proteolysis,

95˚C

long chain,

disordered proteinshorter, extended

peptides

ordered building block

(!-sheet)cross-!

“elementary unit”

cross-! superstructure

(stable template)

cross-! fibril

main-chain

interaction

stacking

further assembly

(vertical and lateral)

more stacking

fibril axis,

direction of growth

~1.5 nm

! " #

$

%

Figure 7. Schematic illustration of cross-β fibril formation.

58

CONCLUSION

Proteolysis of wheat gluten followed by brief thermal treatment initiated a self-assembly process

through aggregation of shorter extended peptides into cross-β sheets, which in turn provided the

basic building blocks for further assembly. Stacking of cross-β sheets, at an inter-sheet distance

of ~1.6-1.9 nm, then provided a stable template for further peptide assembly in the lateral

direction leading to the formation of a mature cross-β fibril of high aspect ratio. Concentration of

the solution allowed cross-β fibrils to further assemble into ~10 µm diameter fibers. The fact that

these highly ordered cross-β structures originated from wheat gluten demonstrated the potential

for large-scale production of amyloid-forming peptides through proteolysis of abundant proteins.

These can then be utilized to construct advanced nanomaterials that can provide a wide range of

structural and chemical functionality.

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[15] Anjum, F. M., Khan, M. R., Din, A., Saeed, M., Pasha, I., and Arshad, M. U., 2007,

"Wheat Gluten: High Molecular Weight Glutenin Subunits: Structure, Genetics, and

Relation to Dough Elasticity," J. Food Sci., 72(3), pp. R56-R63.

[16] Kasarda, D. D., Bernardin, J. E., and Thomas, R. S., 1967, "Reversible Aggregation of α-

Gliadin to Fibrils," Science, 155(3759), pp. 203-205.

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M., and Gerrard, J. A., 2009, "Wheat glutenin proteins assemble into a nanostructure with

unusual structural features," J. Cereal Sci., 49(1), pp. 157-162.

[18] McMaster, T. J., Miles, M. J., Kasarda, D. D., Shewry, P. R., and Tatham, A. S., 2000,

"Atomic Force Microscopy of A-Gliadin Fibrils and in situ Degradation," J. Cereal Sci.,

31(3), pp. 281-286.

[19] Reddy, N., and Yang, Y., 2007, "Novel Protein Fibers from Wheat Gluten,"


[20] Woerdeman, D. L., Ye, P., Shenoy, S., Parnas, R. S., Wnek, G. E., and Trofimova, O.,

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[21] Vedantham, G., Sparks, H. G., Sane, S. U., Tzannis, S., and Przybycien, T. M., 2000, "A

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Protein Sci, 13(12), pp. 3314-3321.

[23] Zurdo, J., Guijarro, J. I., and Dobson, C. M., 2001, "Preparation and Characterization of

Purified Amyloid Fibrils," J. Am. Chem. Soc., 123(33), pp. 8141-8142.

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peptide compounds in water (H2O) solutions. I. Spectral parameters of amino acid residue

absorption bands," Biopolymers, 30(13-14), pp. 1243-1257.

[25] Sikorski, P., and Atkins, E., 2005, "New Model for Crystalline Polyglutamine

Assemblies and Their Connection with Amyloid Fibrils," Biomacromolecules, 6(1), pp.

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[28] Ippel, J. H., Olofsson, A., Schleucher, J. r., Lundgren, E., and Wijmenga, S. S., 2002,

"Probing solvent accessibility of amyloid fibrils by solution NMR spectroscopy," Proc.

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[29] Fraser, R. D. B., and Mac Rae, T. P., 1973, Conformation in Fibrous Proteins and Related

Synthetic Polypeptides, Academic Press, New York.

[30] DePace, A. H., Santoso, A., Hillner, P., and Weissman, J. S., 1998, "A critical role for

amino-terminal glutamine/asparagine repeats in the formation and propagation of a yeast

prion," Cell, 93, pp. 1241-1252.

[31] Rauscher, S., Baud, S., Miao, M., Keeley, F. W., and PomËs, R., 2006, "Proline and

Glycine Control Protein Self-Organization into Elastomeric or Amyloid Fibrils,"

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[32] Okita, T. W., Cheesbrough, V., and Reeves, C. D., 1985, "Evolution and heterogeneity of

the alpha-/beta-type and gamma-type gliadin DNA sequences," J. Biol. Chem., 260(13),

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[33] Davies, R. P. W., Aggeli, A., Beevers, A. J., Boden, N., Carrick, L. M., Fishwick, C. W.

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Tape Forming Peptides," Supramol. Chem., 18(5), pp. 435-443.

[34] Nyrkova, I. A., Semenov, A. N., Aggeli, A., and Boden, N., 2000, "Fibril stability in

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63

[35] Nyrkova, I. A., Semenov, A. N., Aggeli, A., Bell, M., Boden, N., and McLeish, T. C. B.,

2000, "Self-assembly and structure transformations in living polymers forming fibrils,"

Eur. Phys. J. B, 17(3), pp. 499-513.

[36] Aggeli, A., Nyrkova, I. A., Bell, M., Harding, R., Carrick, L., McLeish, T. C. B.,

Semenov, A. N., and Boden, N., 2001, "Hierarchical self-assembly of chiral rod-like

molecules as a model for peptide β-sheet tapes, ribbons, fibrils, and fibers," Proc. Nat.

Acad. Sci., 98(21), pp. 11857-11862.

CHAPTER FOUR

PEPTIDE SELF-ASSEMBLY

INTO MACROSCOPIC FIBERS

AT PHYSIOLOGICAL

CONDITIONS

ABSTRACT

There are major efforts underway to study protein self-assembly for various medical and

industrial reasons. Despite huge progress, most studies have focused on nanoscale self-assembly

but the crossover to the macroscopic scale remains a challenge. In this chapter, self-assembly of

macroscopic fibers from a combination of peptides is reported. The hierarchical multiscale self-

assembly was a cooperative process involving hydrophobic β- and α-peptides. Characterization

of the fibers is discussed and a mechanism by which they form is proposed. Finally, a

comparison with other natural systems is presented to show that the proposed self-assembly

mechanism may be a route utilized by several very different natural systems to create

sophisticated functional peptide materials. The ability to extend peptide self-assembly beyond

the nanoscale will have significant implications on design and fabrication of new functional

materials.

65

INTRODUCTION

Nature relies on self-assembly from the molecular to the macroscopic scale to create

sophisticated functional materials. Naturally occurring protein-based structures have become the

subject of growing interest in materials science because of their superior mechanical properties,

versatility, and ability to self-assemble [1-3]. Of particular interest are cross-β fibrils which are

highly organized, self-assembled protein aggregates featuring an extensive β-sheet structure

stabilized by a dense network of backbone hydrogen bonds [4, 5]. Recent studies highlighted the

fact that cross-β fibrils are among the most robust biological materials and are comparable in

strength to steel [6, 7]. It has been suggested that cross-β structure could serve as a structural

motif for nanomaterials applications or in the fabrication of nanodevices [1-3, 8-10].

Furthermore, cross-β fibrils have been the subject of an explosive number of studies published in

recent years primarily because of their connection to human pathological conditions ranging

from neurodegenerative disorders to systemic amyloidoses [11]. There are major efforts

underway to understand peptide self-assembly in general and to develop cross-β-based materials

for various applications. Despite huge progress, studies have focused on nanoscale self-assembly

but the crossover to the macroscopic scale remains a challenge. Furthermore, studies have

mainly relied on self-assembly under non-natural conditions, i.e., low pH, high temperature or

high ionic strength, even though in nature most structures assemble under physiological or near

physiological conditions.

The formation of the cross-β structure does not require the presence of specific amino acid

sequence and is primarily the result of main chain interactions [12]. Accumulating evidence

suggests that virtually any polypeptide can fibrillate provided that the right conditions are met

66

[13, 14]. These are usually extreme denaturing conditions that enable the conversion from a

soluble functional state into highly ordered fibrillar aggregates. In effect, cross-β fibrils are a

generic class of material despite the diversity of peptide building blocks, which is a very

interesting feature from a material science point of view. Although the amino acid sequence does

not dictate the fundamental organization of the cross-β structure, the nature of, and interactions

between, the side chains have significant influence on the rate and conditions under which it can

form [12]. Certain features of the polypeptide, such as hydrophobicity, charge, and secondary

structure propensity, have strong influence on the kinetics of self-assembly and these arise from

the sequence [15]. This perhaps explains the occurrence of cross-β structures in nature under

physiological conditions, as is the case in amyloidosis. Additionally, some living organisms

utilize the inherent ability of proteins to form cross-β structures to generate structures that can

endure harsh environments [16-20]. Although most of the functional cross-β structures in nature

are on the nanometer scale, macroscopic structures have also been identified [18].

It was possible to form macroscopic fibers in tryptic wheat gluten (TWG) [21]. In the previous

chapter, we presented preliminary data that suggested a cooperative assembly process was

required to form the fibers, but the process was not clearly understood. Here, we analyze the

hierarchical self-assembly of TWG peptides more in depth. Unlike in the previous study, TWG

fiber described here self-assembled under physiological conditions (pH 8 and 37°C) without

post-hydrolysis thermal treatment. The goal was to eliminate temperature as a variable, but

observations could yield insight into biological peptide aggregation as it relates to disease.

Results clearly demonstrated that peptide self-assembly beyond the nanoscale was possible in

vitro under physiological conditions. Our observations suggest that the macroscopic fibers self-

assembled as a result of the interaction between hydrophobic β-sheet-forming peptides and α-

67

helix rich peptides. Interestingly, this kind of assembly, which involved templating and an α to β

transition, might be more prevalent in nature than what we previously thought. For example,

curli and chaplin proteins in bacteria and barnacle cement proteins self-assemble into cross-β

fibrils designed to perform various biological functions in adverse environments [17, 19, 20].

These proteins have similarities to the TWG self-assembling peptides in terms of

hydrophobicity, secondary structure and β-aggregation tendency. Barnacle cement proteins are

particularly interesting because they offer an example of self-assembly from the nanometer to the

micrometer scale.


Enzymatic Hydrolysis. Wheat gluten (VWR International, West Chester, PA) was incubated in

a 2.5% w/w aqueous suspension with trypsin (Sigma-Aldrich, Saint Louis, MO) at 1:1000

enzyme-to-substrate ratio by weight. The solution was maintained at 37 °C and pH 8 by manual

addition of 1.0 M sodium hydroxide with continuous gentle stirring. Control reactions were

carried out under identical conditions without adding trypsin. Aliquots were frequently taken and

dried on Teflon-coated aluminum dishes under the fume hood at room temperature for further

analysis.

Size-exclusion chromatography (SEC). SEC was performed using the Superdex 200 HR

column on the ÄKTApurifier 10 FPLC system (GE Healthcare Biosciences, Pittsburgh, PA).

Samples were prepared by dissolving the protein in 0.1 M acetic acid, vortexing, centrifuging at

4000 rpm for 15 min, and injecting 0.5 ml of the supernatant onto the column. The column was

eluted with 0.1 acetic acid at 0.5 ml/min. Fraction were collected in 1 ml eppendorf tubes using

Frac-920 Fraction Collector (GE Healthcare Biosciences, Pittsburgh, PA).

68

SDS-PAGE characterization of fibers. Fibers were manually isolated and washed 10 times

with pure H2O to remove any unassembled proteins. The washing step involved suspending in

H2O, vortexing for 2 min, then centrifuging and discarding the supernatant. Fibers were then

incubated overnight under vigorous agitation in SDS-PAGE sample buffer containing 2% SDS.

The mixture was then centrifuged and the supernatant run on a 4-15% Tris-HCl gradient gel.

SDS-PAGE was performed using a Mini-PROTEAN Tetra Electrophoresis System with a

precast Tris-HCl gel (Bio-Rad Laboratories, Hercules, CA). Reducing sample buffer was

prepared by adding 2-mercaptoethanol (Sigma-Aldrich, Saint Louis, MO) to Laemmli sample

buffer (62.5 mM Tris-HCl, pH 6.8, 25% glycerol, 2% SDS, 0.01 Bromophenol Blue) for a final

concentration of 5%. Electrophoresis was done at a constant current of 20 mA for 100 min. The

gel was stained with Biosafe Coomassie Stain (Bio-Rad Laboratories, Hercules, CA).

Thioflavin-T (Th-T) Binding Assay. Fluorophore Th-T (Sigma-Aldrich, Saint Louis, MO) was

used to confirm the core cross-β structure of the self-assembled TWG fibers. Twenty µl TWG

solution was dried on a glass slide, stained with 1% Th-T solution for 10 min and gently washed

with de-ionized H2O. Spatially resolved fluorescence images of the Th-T stained TWG fibers

were taken using a Zeiss Axio Imager M1 microscope. Th-T dye was excited at ~480 nm and

emission was collected at ~510 nm through a 10x objective.

Fourier Transform Infrared (FT-IR) Spectroscopy. Native and dried TWG (5 mg) were

mixed with 200 mg KBr and pressed into a disc using a die press. Transmission FT-IR spectra

were recorded on a Thermo Nicolet 6700 FT-IR Spectrometer (Thermo Fisher Scientific Inc.,

Waltham, MA) using 256 scans at 4 cm-1 resolution from 400-4000 cm-1. To study the reaction

kinetics, spectra of dried samples were obtained using the Smart Orbit ATR diamond cell. The

69

spectra were collected using 256 scans at 4 cm-1 resolution from 4000-525 cm-1. A blank was run

between each sample to ensure that the cell was clean and a background was collected prior to

each run. Deconvolution and fitting of the amide I band was performed using OMNIC v 7.3

software. The spectral range 1700−1600 cm-1 was fitted with Gaussian/Lorentzian peaks. The

number and position of peaks were determined by the automatic peak finding feature of the

OMNIC program using low sensitivity and full width at half-height of 3.857. All spectra were

fitted using constant baseline and a target noise of 10.0.

Raman Spectroscopy. Spectra were obtained using a Bruker Senterra dispersive Raman

spectrometer with confocal microscope (Bruker, Billerica, MA). Samples were excited with a

785 nm laser. Analyses were carried out with a spatial resolution of 9-15 cm-1, output laser

power of 100 mW, and 10 s acquisition time. Scanning sites were examined following each

measurement to make sure the laser beam did not cause any damage. Spectra were baseline

corrected in the OPUS software using the Concave Rubberband Method with 5 iterations and 64

baseline points.

Scanning Electron Microscopy (SEM). Twenty µl of protein-enzyme solutions were deposited

on a SEM stub and allowed to dry under the fume hood. SEM micrographs were obtained using a

FEI Quanta 600 FEG environmental SEM (FEI Company, Hillsboro, OR) in high vacuum mode

with a 10-14 mm working distance, accelerating voltage of 5 kV, and Everhardt-Thornley SE-

detector.

Transmission Electron Microscopy (TEM). Samples were prepared by suspending 5 mg

protein in 10 ml de-ionized H2O followed by sonication for 5 min. The solution was 10-fold

diluted before a 5 µl drop was applied to a 200 mesh carbon-coated support film on a copper grid

70

and allowed to dry at room temperature. The samples were then negatively stained by applying a

5 µl drop of 2% uranyl acetate (Electron Microscopy Sciences, Hatfield, PA) solution to the grid

for 5 min and finally dried under the fume hood. All specimens were examined on a Philips EM

420 Scanning Transmission Electron Microscope at an accelerating voltage of 120 kV.

X-Ray Diffraction (XRD). XRD patterns were recorded on a PANalytical X'Pert PRO X-ray

diffractometer (Westborough, MA) using Co radiation (wavelength = 1.78901 Å) generated at 40

kV and 40 mA. Scanning was done with a Theta/Theta goniometer from 2-70 °2θ with a step

size of 0.0668545 °2θ at a time of 600 s.

Nanoindentation. Nanoindentation measurements were carried out on microtomed surfaces of

transverse and longitudinal cross-sections of fibers embedded in EMbed 812 resin (Electron

Microscopy Sciences, Hatfield, PA) using a Hysitron TriboIndenter (Minneapolis, MN) with a

Berkovich diamond 142.3 degree, 3-sided pyramidal tip. Indentations were carried out at

ambient conditions under displacement rate control of 100 nm s-1 and 1000 nm maximum

displacement. The reduced modulus, Er, was determined according to Oliver-Pharr [22], as

!

Er

="S

2 Ac

(1)

where Ac was the contact area and S was the unloading stiffness, determined as the initial slope

of a polynomial function fit over 95-20% of the unloading curve. The fiber elastic modulus, Ef,

was related to the reduced modulus by

!

1Er

=1"# f

2

E f

+1"# t

2

Et (2)

where νf and νt were Poisson’s ratio of the fiber and indenter tip, respectively.

71


Macroscopic fibers assembled from TWG peptides under physiological conditions

SEC and SDS-PAGE analyses of native and hydrolyzed WG confirmed that trypsin had reduced

WG into smaller fragments (Figure 1). There were several distinct bands in native WG between

25 and 100 kDa that disappeared 24 hrs following tryptic treatment. Only three bands remained

in THWG: two sharp around 37 and 25 kDa, and one very broad below 10 kDa. The apparent

band at around 250 kDa in THWG8 was most likely protein aggregates and occurred at the line

between stacking and running gels. Conservation of bands around 37 and 25 kDa indicated

protease-resistant peptides known to WG [23].

Figure 1. a, Overlaid FPLC chromatograms. b, SDS-PAGE profile of native (lane 2) and

hydrolyzed (lane 3) wheat gluten showing reduction of most components to <10 kDa fragments.

The band at the line between stacking and running gels in lane 3 was due to aggregates. Only

two bands <10 kDa were present in the supernatant of partially dissolved fibers (lane 4).

72

Macroscopic fibers extrinsic to native WG were observed in TWG solution after 24 hrs. Dried

TWG solution contained numerous fibrils and fibers that appeared to be consistent in size and

morphology as evidenced in TEM and SEM micrographs (Figure 2). Larger fibers were helical,

about 10 - 15 µm in diameter, and appeared to be bundles of smaller fibrils of about 10 - 20 nm

in diameter. SDS-PAGE analysis of deconstructed fibers, showed the presence of two peptides of

molecular weight <10 kDa. This was a key finding as it provided an important clue into what

peptides were forming the fibers.

TWG fibers possessed cross-β core structure

FT-IR, Raman, and XRD data suggested significant structural rearrangement involving α to β

transformation and the formation of cross-β structures as requisites for large fiber formation.

Dried WG and TWG samples were examined by FT-IR and Raman spectroscopy and XRD to

assess the underlying secondary structure of the resulting fibrous material. Overall change in the



showing a single TWG fiber glued to a glass fiber. Scale bar: 0.1 mm.

73

shape and position of the FT-IR amide I and amide II bands suggested a significant structural

reorganization involving unfolding of a native helical structure and increasing β-aggregation

(Figure 3). Relative decrease in the absorbance of amide I 1659 cm-1 and amide II 1538 cm-1

peaks was consistent with reduction in α-helical content [24]. In general, the range of cross-β

structures extends roughly from 1611 cm–1 to 1630 cm–1, while native β-sheet proteins produce

peaks around 1630 cm–1 to 1643 cm–1 [25]. The spectrum of TWG had a maximum at 1628 cm-1

indicating predominantly cross-β conformation. The increase in β content was also indicated by

the shoulder at 1520 cm-1 [24, 26].

Raman spectroscopy of native and hydrolyzed WG corroborated FT-IR data and showed

significant α to β transformation. As shown in Figure 4, most of the peaks assignable to α-helix

1500 1550 1600 1650 1700

Wavenumber, cm-1

1628cross-

side chainCOOH

-sheet

-helix

-helix

TWG

WG

Figure 3. Amide I and II of the Fourier transform-infrared spectra of native (dashed) and tryptic

wheat gluten showing decreased α content and increased β content.

74

in WG, such as 1656, 1375, 1340, 1300, 1107, and 935 cm-1, shifted, weakened or disappeared in

TWG. At the same time, peaks assignable to β-sheets, such as 1680, 1670, 1028 and 980 cm-1

were more evident in TWG [26-28]. The presence of cross-β structures in THWG was indicated

by the characteristic peaks at 1558 cm-1 and 1400 cm-1 [28].

The XRD pattern of WG (Figure 5) was typical of a protein with a “fringed micelle”

microstructure with two diffuse halos occurring at Bragg spacings of about 0.46 and 1.00 nm

[29, 30]. The 0.46 nm spacing represented the distance between two β-strands in a native

(globular) β-sheet independent of the protein’s primary structure [29]. The 1.0 nm spacing

represented the inter-sheet distance between two stacked β-sheets and was a function of the

shape and size of the amino acid side chains [30]. The 0.46 nm peak shifted to 0.47 nm in TWG

wavenumber cm-1

ram

an

in

ten

sity

cro

ss-!

glo

bula

r-!

cro

ss-!

!-s

heet

!-s

heet

!-s

heet

CH3 p

ackin

g

"-h

elix

"-h

elix

"-h

elix

"-h

elix

"-h

elix

"-h

elix

"-h

elix

900 1000 1100 1200 1300 1400 1500 1600 1700

Figure 4. Raman spectra of native (bottom) and tryptic wheat gluten (top) showing α to β

transformation. Most of the peaks associated with α-helix in wheat gluten shifted, weakened or

disappeared in in tryptic wheat gluten.

75

consistent with the inter-peptide distance in cross-β structures [4, 30]. Reflections with spacings

of 0.28 to 0.29 nm occur frequently in fibrils with the cross-β structure [4]. The inter-sheet

distance at 1.00 nm in WG shifted and sharpened (a strong indicator of ordering in powder

diffraction) after trypsin treatment suggesting a new distinct structural arrangement. The position

of superimposed peaks in TWG was closer together with peak maxima appearing at about 1.86

nm and 1.43 nm, which suggested more uniformity to the structure. It has been well established

that the inter-sheet stacking distance in cross-β structures can be closely related to the van der

Waals volumes of the amino acid residues, suggesting the amino acid side groups maintain the

inter-sheet distance [12]. Inter-sheet spacings ranged from 0.35 to 2.09 nm and were greatest for

peptides rich in amino acids with bulky side groups like arginine (R), lysine (K), glutamine (Q),

leucine (L) and isoleucine (I) [12, 15, 30, 31]. It appeared that these amino acids, which were

abundant at chain ends and in bulk of chain, were key determinants of inter-sheet stacking

distance in the TWG cross-β fibrils.

1 104

1.5 104

2 104

2.5 104

3 104

3.5 104

4 104

0.5 1 1.5 2 2.5 3 3.5 4

Inte

nsity

1/d , nm-1

0.47 nm

0.29 nm

0.28 nm

1.86 nm

1.43 nm

1.0 nm 0.46 nm

Figure 5. X-ray diffraction patterns of native (dotted) and tryptic wheat gluten.

76

The cross-β core structure of the self-assembled THWG fibers was further confirmed with

thioflavin-T (Th-T) binding assay. Th-T molecules bind specifically to cross-β proteins by

inserting themselves into channels made by the regular repeat of residues and run along the

length of the β-sheet [32]. Since native β-sheets are unlikely to have these binding channels,

Thioflavin-T has been extensively used to detect the presence of cross-β structures and their rates

of formation [32]. Figure 6 shows a representative fluorescence microscopy snapshots of the Th-

T stained THWG fibers. Strong fluorescence indicated cross β-sheet structural arrangement of

the self-assembled THWG fibers.

Figure 6. Fluorescent microscopy images of the Th-T stained tryptic wheat gluten fiber.

TWG Fiber assembly was a cooperative process involving a hydrophobic β-strand and α-

helix forming peptides.

Neither glutenin nor gliadin, the two components of WG, formed fibers when hydrolyzed at the

same conditions as TWG. Tryptic glutenin (TGt) remained rich in disordered and α-helix

conformations while tryptic gliadin (TGd) formed cross-β structural units but did not assemble

into large fibers [21]. This suggested a cooperative assembly process involving peptides from

both gliadin and glutenin to build large cross-β fibers. Accordingly, individual Gt and Gd

77

peptides (Table 1) were examined with regard to their role in the fiber assembly process. Peptide

names are listed in Table 1 and will be used henceforth.

Table 1. Possible tryptic wheat gluten fragments with tendency for β-aggregation predicted

using the TANGO algorithm and glutamine (Q), asparagine (N), proline (P) and glycine (G)

composition.

possible tryptic peptide sequence name

MW, KDa TANGO score

P (%) G (%) Q (%) N (%)

gliadin [43] MK Gd2 0.3 0 0.0 0.0 0.0 0.0 TFLILALLAIVATTATTAVR Gd20 2.1 1477 0.0 0.0 0.0 0.0 VPVPQLQPQNPSQQQPQEQVPLVQQQQFLGQQQPFPPQQPYPQPQPFPSQQPYLQLQPFLQPQLPYSQPQPFRPQQPYPQPQPQYSQPQQPISQQQQQQQQQQQQQQQQQQQIIQQILQQQLIPCMDVVLQQHNIVHGK

Gd139 16.4 2 0.201 0.014 0.439 0.014

SQVLQQSTYQLLQELCCQHLWQIPEQSQCQAIHNVVHAIILHQQQK

Gd46 5.4 207 0.022 0.0 0.283 0.022

QQQQPSSQVSFQQPLQQYPLGQGSFRPSQQNPQAQGSVQPQQLPQFEEIR

Gd50 5.7 0 0.14 0.06 0.36 0.02

NLARK Gd5 0.6 0

glutenin HMW [44]

QWQQSGQGQQGHYPTSLQQPGQGQQGHYLASQQQPGQGQQGHYPASQQQPGQGQQGHYPASQQQPGQGQQGHYPASQQEPGQGQQGQIPASQQQPGQGQQGHYPASLQQPGQGQQGHYPTSLQQLGQGQQTGQPGQK

GtH137 14.6 0 0.109 0.204 0.387 0.0

QQPGQGQQTGQGQQPEQEQQPGQGQQGYYPTSLQQPGQGQQQGQGQQGYYPTSLQQPGQGQQGHYPASLQQPGQGQPGQR

GtH80 8.6 0 0.125 0.225 0.413 0.0

QPGQGQQGYYPTSPQQPGQGQQLGQGQQGYYPTSPQQPGQGQQPGQGQQGHCPTSPQQSGQAQQPGQGQQIGQVQQPGQGQQGYYPTSVQQPGQGQQSGQGQQSGQGHQPGQGQQSGQEQQGYDSPYHVSAEQQAASPMVAK

GtH142 14.8 0 0.12 0.211 0.366 0.0

glutenin LMW [45]

PWQQQPLPPQQTFPQQPLFSQQQQQQLFPQQPSFSQQQPPFWQQQPPFSQQQPILPQQPPFSQQQQLVLPQQPPFSQQQQPVLPPQQSPFPQQQQQHQQLVQQQIPVVQPSILQQLNPCK

GtL120 14.1 0 0.225 0.0 0.425 0.8

AIIYSIILQEQQQVQGSIQSQQQQPQQLGQCVSQPQQQSQQQLGQQPQQQQLAQGTFLQPHQIAQLEVMTSIALR

GtL75 8.5 685 0.053 0.053 0.400 0.0

78

Cross-β fibril forming ability has also been linked, among other factors, to high Q and asparagine

(N) and low proline (P) and glycine (G) content [33, 34]. Qd46 had relatively low P and G and

high Q and N, and GtL75 had low P and G and high Q (Table 1). The β-aggregating propensity

of individual Gt and Gd peptides was evaluated using The TANGO algorithm [35]. The accuracy

of the TANGO algorithm has been established in the literature and was found to be more than

90% for a set of 176 experimentally validated peptides [36]. TANGO scores showed that only

Gd20, Gd46 and GtL75 had β-aggregation propensity at 37°C and pH 8. The whole length of the

Gd20 sequence had a strong β-aggregation tendency compared to weaker tendency of only small

portions of the Gd46 and GtL75 sequences (Figure 7). Other algorithms focusing on different

aspects of the amino acid sequence further corroborated TANGO predictions [37-41]. Gd20 was

mostly hydrophobic and as such had a strong tendency to form nonfibrillar β-aggregates,

consistent with XRD and FT-IR data on trypsin hydrolyzed Gd [21]; whereas fibrillar aggregates

generally involve peptides with polar sequences like that of Gd46 and GtL75 [42]. Gd46 and

Gd20 alone, without GtL, could not assemble into fibrillar structures as was evident from the

separate TGd and TGt experiments. These observations suggested that TWG fibers formed as a

result of the interaction between Gd20 and GtL75. This conclusion was confirmed with SDS-

PAGE analysis of deconstructed fibers, which showed the presence of 2 peptides of molecular

weight <10 kDa (Figure 1) consistent with the molecular weights of Gd20 and GtL75.

Ding et al [46] suggested a generic scenario of templated cross-β aggregation in which a

preformed β-sheet with a hydrophobic surface catalyzes the transition to β-conformation in α-

rich peptides. The interaction between the hydrophobic β-sheet surface and the hydrophobic

portions of the α-helix reduces the free energy barrier thus enabling an α to β transformation. α

to β transitions appear to be promoted on hydrophobic surfaces. Gd20 was mostly hydrophobic,

79

rich with β-stands and had the highest β-aggregation score. Secondary structure prediction,

performed using the GOR4 algorithm [47] showed that 35% of the Gd20 sequence was likely to

form β-strands compared with 4.4% for Gd46 and 6.7% for GtL75 (Figure 7). About half of the

GtL75 sequence adopted the α-helical conformation. It was likely that Gd20 peptides formed the

template that enabled α to β transformation in the α-rich GtL75 peptides and the subsequent

formation of large fibers. The α to β transformation was evident in FT-IR and Raman data as

discussed earlier.

Figure 7. Tendency for β-aggregation and secondary structure prediction at each amino acid

position for Gd20, Gd46 and GtL75. Aggregation scores, percentage per residue, were estimated

using the TANGO algorithm. Secondary structure predicted using the GOR4 algorithm.

80

Raman spectroscopy provided additional experimental evidence of the significant contribution of

Gd20 to TWG fiber assembly. Scanning of the TWG fibers while noting orientation revealed a

mutually reciprocating phenomenon of Raman intensity generated from polypeptide backbones

and side chains (Figure 8). In general, cross-β structures showed a characteristic Raman peak at

~1672 cm-1 originating from polypeptide backbones [28]. This peak was most defined and

intense with the TWG fiber axis perpendicular to the laser’s plane of polarization, which was

expected because in this arrangement polypeptide chains were parallel to the laser’s plane of

polarization. The same peak shifted and weakened when the fiber axis was parallel to the plane

of polarization, i.e., peptide chains were perpendicular to the plane of polarization. The opposite

Figure 8. Amide I and II regions of the Raman spectra of TWG fibers oriented perpendicular

(top), 45° (middle) and perpendicular (bottom) to the laser’s plane of polarization.

81

was true for the side chain CH2 and CH3 peaks in the 1400-1500 cm-1 region. This observation

indicated that the side chains, which were arranged approximately perpendicular to main chains,

contained an abundance of CH2 and CH3 groups. Eighteen out of the 20 amino acids in Gd20 had

CH3 groups in the side chains so Gd20 peptides appeared to be an important part of the fiber

assembly.

Hydrophobic interactions promoted nano- and micro-scale self-assembly

The reaction was followed using FT-IR over 120 hours time period. Deconvolution and fitting of

the amide I band in the FT-IR spectra of TWG displayed a loss of α-helix with a concurrent gain

of cross-β structure as a function of time (Figure 9a). The change in secondary structure content

was concurrent with a change in the environment of aliphatic moieties on the peptide (Figure

9b,c). The aliphatic moieties of amino acid side chains gave rise to characteristic absorbance

bands around 2960 cm-1, 2872 cm-1, 2855 cm-1 and 1445 cm-1 representing asymmetrical CH3

stretching, νas(CH3), symmetrical CH3 stretching, νs(CH3), symmetrical CH2 stretching, νs(CH2),

and asymmetrical CH3 deformation, δas(CH3), respectively. Shifts of ν(CH2) and ν(CH3) to

lower wavenumber have been shown to correlate with an increasingly dehydrated environment

for these chemical groups [48]. The findings showed hydrophobic groups tightly packing

together and α-helices unraveling to form β-sheets.

Gd20 was 95% hydrophobic and adopted β-strand secondary structure (Figure 7). GtL75 adopted

an α-helix-rich secondary structure and contained ‘conformational switches’, which were

sequence regions that have both the propensity for α-helix and β-sheet [38]. FT-IR analysis of

the self-assembly kinetics showed how the two peptides cooperatively built large fibers. During

the first 12 hrs, α to β transition was paralleled by an increasingly hydrophobic environment as

82

evidenced by shifting of νs(CH2). Eighteen out of the 20 amino acid side groups on Gd20

contained CH3 but 24/75 side groups on GtL75 had CH3. The absorbance ratios νs(CH3)/νas(CH3)

and νs(CH3)/δas(CH3) indicated CH3 packing by comparing symmetrical stretching (which was

enhanced by the constraint of packing) with asymmetrical and deformation vibration (Figure 9c).

The continuously increasing ratio after 30 hours showed tight packing of Gd20 amino acid side

groups and was a delayed process occurring after the α to β transition. Additionally, νs(CH3)

displayed substantial shifting to lower wavenumber after this time period (Figure 9b) suggesting

Gd20 side groups were not exposed to a hydrophobic environment until tightly packed.

Hydrophobic Gd20 peptides acted as nucleators for GtL75 α to β transitions, consistent with the

template-assisted model for cross-β fibril formation [46]. After about 30 hours, a sufficient

hydrophobic environment existed and all of the α to β transition had occurred. Further

Figure 9. a, Shift to lower wavenumber of νs(CH3) and νs(CH2) bands with time indicated an

increasing hydrophobic environment. b, Estimated secondary structure content as a function of

time, determined by fitting of the FT-IR amide I band. c, The intensity ratios νs(CH3) to νas(CH3)

and νs(CH3) to δas(CH3) indicated amount of hydrophobic packing.

83

aggregation involved tight packing of CH3 on Gd20 amino acid side groups over many length

scales.

Figure 10. a, TWG Fibers embedded in epoxy resin matrix. b, Nanoindentation image of

longitudinal and transverse sections showing indentation imprints. c, Comparison of

nanoindentation results in longitudinal and transverse sections. Error bars represent 95%

confidence interval; n=9.

Nanometer- and micrometer-scale ordering imparted anisotropic mechanical properties to the

macroscopic fibers. Nanoindentation was used to measure mechanical properties in the directions

transverse and longitudinal to the fiber axis (Figure 10). Fibers were embedded in epoxy resin

and microtome sectioned to reveal either transverse or longitudinal sections (Figure 10a,b). The

test yielded reduced moduli of 10.6 ± 1.2 GPa in transverse sections and 6.8 ± 1.0 GPa in

longitudinal sections demonstrating mechanical anisotropy in the fiber. In general, cross-β

nanofibrils have Young’s moduli in the range 2-20 GPa [6, 7, 49]. The remarkable transfer of

nanoscale mechanical properties to the macroscopic scale indicated efficient contact between

adjacent structures over multiple length scales through hydrophobic packing.

84


macroscopic cross-β fibers.

Is the templated assembly of α-helix rich peptides a natural motif?

Our results demonstrated that peptide self-assembly beyond the nanoscale was possible in vitro

under physiological conditions. Macroscopic protein fibers form when α-helix-rich peptides

undergo an α to β transition in the presence of a hydrophobic β-sheet template. A review of the

literature suggested that our identified self-assembly mechanism may be a route utilized by

several very different natural systems. Curli proteins in bacteria rely on templated self-assembly

85

processes involving hydrophobic and long α-helix-rich peptides to assemble cross-β fibrils for

various biological functions[19]. Curli are cross-β fibers assembled by enteric bacteria such as

Escherichia coli and Salmonella spp. to be the major proteinaceous component of a complex

extracellular matrix and critical determinants of biofilm formation. Polymerization of the major

curli subunit protein, csgA is dependent on the csgB nucleator [50]. csgB has two chains with a

total of 151 amino acids. The 21 amino acid chain, csgB-21, has characteristics similar to Gd20

(Table 2).

The protein Mrcp-100k is believed to form the cross-β fibrils observed in barnacle cement [17,

18]. Mrcp-100k has two chains of 18 and 975 amino acids with [51] characteristics similar to

Gd20 and GtL75, respectively (Table 2,3). Interestingly, micrometer-scale cross-β ‘rod-like’

Table 2. Properties of templating peptides: Gd20 and the templating peptides in curli and

barnacle cement proteins.

Peptide Sequence length hydrophobicity1 (red indicates hydrophobic)

β-sheet propensity?2

β-aggregation propensity?3

Gd20 20

T F L I L A L L A I V A T T A T T A V R

3

-3

Y Y

CsgB(1-21) [50] 21

M K N K L L F M M L T I L G A P G I A A A

3

-3

Y Y

Mrcp-100k(1-18) [51]

18

M M R L S L V A V L L V T V S V T G

3

-3

Y Y

1 based on the work of Hopp and Woods [52]

2 predicted using the SSpro v 4.5 [53]and GORIV [47]secondary struture prediction methods.

3 evaluated using multiple prediction algorithms [37-41]

86

features have been observed in barnacle cement8. The templating peptides in bacterial curli and

barnacle cement have characteristics similar to Gd20 in terms of hydrophobicity, secondary

structure and β-aggregation tendency (Table 2). The templated (polymerizable) peptides contain

conformational switches and have β-aggregation tendency similar to GtL75 (Table 3).

CONCLUSION

Our analysis suggested the following self-assembly mechanism, depicted schematically in Figure

11: In general, the self-assembly process was driven by hydrophobic interactions and the

structure stabilized through backbone hydrogen bonding and hydrophobic packing. Short

hydrophobic peptides self-assembled into (1) β-sheets. (2) An “elementary unit” of two β-sheets

stacked face-to-face was formed with inter-sheet distance mediated by the size of the of side

Table 3. Properties of templated peptides: GtL75 and templated peptides in curli and barnacle

cement proteins. Conformational switches in these peptides are triggered upon interaction with

hydrophobic templating peptides, thus promoting β-aggregation.

Peptide Sequence length

Secondary structure1

(blue: α-helix, purple: β-strand, orange: random coil )

α to β conformational switches?2

β-aggregation propensity? 3

GtL75

75 Y Y

CsgA [50] 151 Y Y

Mrcp-100k (19-993) [51]

975 Y Y

1 predicted using the SSpro v 4.5 [53]

2 based on the work by Hamodrakas et al [38]


87

chains, ~1.48 nm, and inter-peptide spacing of 0.47 nm. Our previous study with trypsin

hydrolyzed Gt and Gd [21] showed that control and TGt lacked the aggregating hydrophobic

sequence necessary to initiate the process, and TGd, while having the right sequence, did not go

beyond stage (2). The stacking distance in the elementary cross-β unit formed in TGd was 1.48

nm [21]. By contrast, in the presence of Gt peptides, the hydrophobic Gd20 β-sheets catalyzed

(3) an α to β transformation in long α-rich peptides, which then (4) joined the assembly through

backbone β-aggregation. The stacking distance changed with inclusion of the new peptides to ~

1.43 to 1.86 nm (Figure 5). (5) Aggregation proceeded in the vertical (stacking) and lateral (sheet

elongation) directions to eventually form nanoscale fibrils of diameter equal to multiples of β-

sheet stacking distance as seen in the EM micrograph. The fibrils then (6) interact and bundle

together to form large fiber with 10 - 15 µm in diameter and few hundred micrometers in length.

This latter process has origins in the packing of hydrophobic amino acid side chains.

The finding discussed in this chapter showed that multiscale hierarchical peptide self-assembly

was possible in vitro under physiological conditions. The self-assembled macroscopic fibers

from tryptic peptides were characterized and a mechanism by which they form was proposed.

We highlighted similarities between the TWG system and other naturally-occurring systems in

which templated self-assembly is used to create functional peptide materials. In the next chapter,

we show that the concept of multiscale self-assembly from a combination of β- and α- peptides

can be extended to other peptide systems. We also demonstrate the ability to control macroscopic

properties of the fiber by varying the ratio of constituent peptides. The ability to extend peptide

self-assembly beyond the nanoscale will have significant implications on design and fabrication

of new functional materials.

88

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and Rousseau, F., 2010, "Exploring the sequence determinants of amyloid structure using

position-specific scoring matrices," Nature Methods, 7(3), pp. 237-242.

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the alpha-/beta-type and gamma-type gliadin DNA sequences," J. Biol. Chem., 260(13),

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Romero, J. M., 1989, "Nucleotide sequences of the two high-molecular-weight glutenin

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95

profiles," Proteins, 47(2), pp. 228-235.

CHAPTER FIVE

TEMPLATED SELF-

ASSEMBLY OF LARGE

FIBERS USING A MODEL

SYSTEM

ABSTRACT

In previous chapters we examined the self-assembly of macroscopic fibers from peptide mixtures

and hypothesized that the minimum requirements were a long α-helix-rich peptide and a template

formed by hydrophobic β-peptides rich with aliphatic amino acids. Here we present a model

system showing that this was indeed a system capable of spontaneously forming large peptide

fibers. Macroscopic fibers self-assemble when α-helix-rich peptides undergo α to β transition in

the presence of a hydrophobic β-sheet template. A kinetic model was developed to describe the

process of fiber formation and was fitted to experimental data. The model successfully predicted

the rate of formation of fibrillar species as a function of the ratio of constituent peptides.

97

INTRODUCTION

Most studies of peptide aggregation are in the context of trying to understand amyloid formation

as it relates to neurological disorders such as Alzheimer’s disease or bovine spongiform

encephalopathy (BSE) [1]. Amyloid formation is generic to peptides as long as peptide chains

can be straightened to allow backbone hydrogen-bonding [2]. Amyloid typically appears in the

form of a nanofibril ~101-102 nm in diameter and >103 nm long [3-5]. The nanofibril has a

characteristic cross-β structure, where β-strands are arranged orthogonally to the fibril length [6,

7]. Traditional fibers made of β-sheets, such as silk or keratin, have the β-strands parallel to the

fiber axis [8]. X-ray diffraction shows that the cross-β structure has strands separated by 0.48

nm, compared to 0.46 nm in native or globular β-sheets [9]. Also characteristic of the cross-β

structure is the appearance of a FT-IR absorbance at about 1615-1621 cm-1, compared to 1630-

1643 cm–1 in traditional β-sheets [6]. The cross-β nanofibril is robust with excellent solvent

resistance and a modulus up to 10 GPa [10-12].

Cross-β structures are rare in nature. The most common occurrence is in the misfolded

pathogenic amyloid form. However, “functional” amyloids have been identified that are

beneficial cross-β structures. The egg stalk of the green lacewing fly suspends the eggs to protect

them [13, 14]. Curli proteins reside on the surface of bacteria to bind matrix and plasma proteins

[15, 16]. Chaplin proteins self-assemble into amyloid-like fibrils that enable microbial aerial

hyphae to grow into the air by lowering surface tension and covering the structure [17].

Barnacle adhesive is a composite of amyloid fibrils in a less ordered protein matrix [18, 19].

Amyloids as a motif in materials science overwhelmingly refer to the nanometer sized fibril form

98

and sometimes flat tapes [20, 21]. However, we have shown that fibers ~10 µm in diameter and

many millimeters long can self-assemble from the right combination of peptides [22].

In a previous study, we reported self-assembly of macroscopic fibers from tryptic wheat gluten

peptides. We hypothesized that the minimum requirements for the formation of fibers were a

long α-helix-rich peptide and a template formed by β-peptides rich with aliphatic amino acids.

The first 20 N-terminal amino acid sequence of gliadin aggregated to form the template, while

the long α-helix-rich peptide came from glutenin. In this paper, we show that the concept of

multiscale self-assembly from a combination of β-sheet and α-helix peptides can be extended to

other peptide systems. The experiment was repeated using myoglobin, which had most of its

sequence arranged in well-defined α-helices [23], and gliadin. Macroscopic fibers formed in the

trypsin-myoglobin-gliadin (TMG) solution. To form large fibers, a solution of myoglobin and

gliadin was maintained at 37oC and pH 8. Trypsin was added to release the N-terminal 3-22

peptide of gliadin (Gd20), which formed the template needed to recruit α-helical myoglobin

peptides into the fiber assembly. Macroscopic fibers formed in the trypsin-myoglobin-gliadin

(TGM) solution and their morphology and number influenced by the gliadin to myoglobin ratio.

Studying the kinetics of the reaction revealed a concentration-dependent process that involved a

competition between formation of non-fibrillar and fibrillar species.


Growing the fibers. Myoglobin (Sigma Aldrich) and gliadin (TCI America) were incubated

together, at variable gliadin-to-myoglobin ratio w/w, in a 2.5% w/w aqueous suspension with

trypsin (Sigma Aldrich) at 1:100 enzyme-to-substrate ratio by weight. The solution was

maintained at 37 °C and pH 8 by manual addition of 1.0 M sodium hydroxide with continuous

99

gentle stirring. Control reactions were carried out under identical conditions without adding

trypsin. Aliquots were frequently taken and dried under the fume hood at room temperature for

further analysis.

Size-exclusion chromatography (SEC). SEC was performed using the Superdex 200 HR

column on the ÄKTApurifier 10 FPLC system (GE Healthcare Biosciences, Pittsburgh, PA).

Myoglobin was dissolved in 50 mM potassium phosphate/ 0.15 NaCl, pH 7. Gliadin was

dissolved in in 0.1 M acetic acid, vortexed, centrifuged at 4000 rpm for 15 min, and 0.5 ml of the

supernatants injected onto the column. The column was conditions and eluted using the sample

buffer at 0.5 ml/min.

SDS-PAGE characterization of fibers. SDS-PAGE was performed using a Mini-PROTEAN

Tetra Electrophoresis System with a precast Tris-HCl gel (Bio-Rad Laboratories, Hercules, CA).

Reducing sample buffer was prepared by adding 2-mercaptoethanol (Sigma-Aldrich, Saint Louis,

MO) to Laemmli sample buffer (62.5 mM Tris-HCl, pH 6.8, 25% glycerol, 2% SDS, 0.01

Bromophenol Blue) for a final concentration of 5%. Electrophoresis was done at a constant

current of 20 mA for 100 min. The gel was stained with Biosfae Coomassie Stain (Bio-Rad

Laboratories, Hercules, CA).

Fourier Transform Infrared (FT-IR) Spectroscopy. Dried samples (5 mg) were mixed with

200 mg KBr and pressed into a disc using a die press. Transmission FT-IR spectra were recorded

on a Thermo Nicolet 6700 FT-IR Spectrometer (Thermo Fisher Scientific Inc., Waltham, MA)

using 256 scans at 4 cm-1 resolution from 525-4000 cm-1. Deconvolution of the amide I band into

individual components was performed using OMNIC v 7.3 software. The range 1725−1575 cm-1

of the original spectra was fitted with 13 Gaussian/Lorentzian peaks. The peaks were assigned to

100

secondary structural components as described in the literature [24-26]. Initial peak positions

were determined by the automatic peak finding feature of the OMNIC program using low

sensitivity and full width at half-height of 3.857. All spectra were fitted using constant baseline

and a target noise of 10.0.

Raman Spectroscopy. Spectra were obtained using a Bruker Senterra dispersive Raman

spectrometer with confocal microscope (Bruker, Billerica, MA). Samples were excited with a

785 nm laser. Analyses were carried out with a spatial resolution of 9-15 cm-1, output laser

power of 100 mW, and 50 s acquisition time. Scanning sites were examined following each

measurement to make sure the laser beam did not cause any damage. Spectra were baseline

corrected in the OPUS software using the Concave Rubberband Method with 10 iterations and

64 baseline points.

Scanning Electron Microscopy (SEM). Twenty µl TGM solutions were deposited on a SEM

stub and allowed to dry under the fume hood. SEM micrographs were obtained using a LEO

Zeiss 1550 FESEM (Carl Zeiss SMT, Oberkochen, Germany) with a 7 mm working distance and

5 kV accelerating voltage.

Nanoindentation. Nanoindentation experiments were performed at room temperature using a

Hysitron Triboindenter (Minneapolis, MN) with a Berkovich diamond 142.3 degree 3-sided

pyramidal tip Indentations were carried out at ambient conditions under displacement rate

control of 100 nm s-1 and 1000 nm maximum displacement. The reduced modulus, Er, was

determined according to Oliver-Pharr [27], as

!

Er

="S

2 Ac

(1)

101

where Ac was the contact area and S was the unloading stiffness, determined as the initial slope

of a polynomial function fit over 95-20% of the unloading curve. The fiber elastic modulus, Ef,

was related to the reduced modulus by

!

1Er

=1"# f

2

E f

+1"# t

2

Et (2)

where νf and νt were Poisson’s ratio of the fiber and indenter tip, respectively.


Myoglobin α-helical peptides key to formation of macroscopic fibers

SEC and SDS-PAGE analyses showed that trypsin treatment had limited effect on myoglobin

(Figure 1). Some cleavage occurred but the bulk of the protein remained intact. Therefore, some

smaller peptides were released while the majority of myoglobin maintained its native α-helix

conformation. By contrast, gliadin was largely reduced to smaller fragments. Gliadin was not

soluble unless hydrolyzed to shorter peptides, and none of the soluble gliadin fragments formed

large fibers. Myoglobin was soluble but remained in the α-helix conformation and did not form

large fibers unless subjected to tryptic hydrolysis. However, adding unhydrolyzed myoglobin to

hydrolyzed gliadin, i.e. in the presence of the Gd20 template, did allow the formation of

macroscopic fibers (Figure 2).

Myoglobin had two possible tryptic fragments with properties similar to Gd20 (Table 1), which

explained why macroscopic fibers formed in tryptic myoglobin, 0% TMG. These fragments have

properties similar to Gd20 in terms of hydrophobicity profile, secondary structure and beta-

aggregation tendency as predicted multiple algorithms covering different aspects of the amino

102

acid sequence (Table 1). Although limited, tryptic treatment was sufficient to release templating

peptides that enabled large fiber growth in hydrolyzed myoglobin when Gd20 peptides were not

available.

Figure 2. FE-SEM micrographs of macroscopic fibers formed after adding myoglobin to trypsin-

hydrolyzed gliadin.

17 kDa

> 100 kDa

14 kDa

10 kDa

37 kDa45 kDa

30 kDa

< 10 kDa

trypsin-hydrolyzed myoglobin

Myoglobin

trypsin-hydrolyzed gliadin

gliadin

Figure 1. Size-exclusion chromatograms of native and tryptic myoglobin and gliadin.

103

Table 1. Properties of templating peptides from gliadin and myoglobin

Peptide Sequence length hydrophobicity1 (red indicates hydrophobic)

β-sheet propensity?2

β-aggregation propensity?3

Gd20 20

T F L I L A L L A I V A T T A T T A V R

3

-3

Y Y

Myoglobin (65-78) [28]

14 3

-3H G N T V L T A L G G I L K

Y Y

Myoglobin (104-119) [28]

16 3

-3Y L E F I S D A I I H V L H A K

Y Y

1 based on the work of Hopp and Woods [29]

2 predicted using the SSpro v 4.5 [30]and GORIV [31]secondary struture prediction methods.


Kinetics of self-assembly was influenced by Gd20 peptide concentration

The spontaneous aggregation of Gd20 peptides into templating β-structures was a key driver of

the formation of the fibers. Accordingly, studying the kinetics of the reaction while varying the

concentration of Gd peptides provided important insight into the process. It was possible to vary

the concentration of Gd20 peptides by varying the ratio of gliadin-to-myoglobin. The experiment

was repeated with 0, 25, 50, 75 and 100% gliadin-to-myoglobin ratios and the reaction was

monitored over time using FT-IR spectroscopy. The number of fibers per 1 ml solution was

noted for each experiment and was found to be greatest in 50% TGM (Figure 3).

104

0

5

10

15

0 20 40 60 80 100

num

ber o

f fib

ers

per m

l

gliadin to myglobin ratio, % w/w

more Gd20 template

Figure 3. Dependence of the number of self-assembled macroscopic fibers found in the solution

on the ratio of gliadin to myoglobin.

6

8

10

30

50

1 10 100

random coil!-helix"-sheet"-turn

y = 14 + 0.0051x R2= 0.002

y = 28 - 0.19x R2= 0.54

y = 10 + 0.13x R2= 0.58

y = 34 + 0.044x R2= 0.046

seco

ndar

y st

ruct

ure,

%

time, hr

Figure 4. Time-dependent change in secondary structure in 50% Trypsin-gliadin-myoglobin

solution calculated from FT-IR spectroscopy data.

105

Figure 4 shows various secondary structure components as a function of time in 50% TGM

solution calculated by deconvolution and fitting of the FT-IR amide I band (see Materials and

Methods). There was no significant change (α=0.05) in the amount of random coil or β-turn

secondary structure in the solution over time. By contrast, there were significant changes

(α=0.05) in the amount of α and β secondary structure and the change was consistent with α to

β transformation. This suggested that, at 50% gliadin, any loss in α-helix was translated into gain

in β-sheets. The same was not true for other treatments with different gliadin levels. Although

significant (α=0.05) α to β transformation was observed at all gliadin levels, the amount of

random coil and/or β-turn also increased significantly (α=0.05) (Table 2). These results

suggested that when the optimum concentration of Gd20 peptide did not exist, the probability for

α-helical peptide to be recruited into the β-fiber assembly was reduced allowing peptides to fold

into random coils and β-turns instead. At 100% Gd, no fibers were found and the 5.1% net

increase in β-structure was due to non-fibrillar aggregates. At 100% Gd, β-content increased

Table 2. Net percent change in secondary structure content calculated from FT-IR spectroscopy

data for trypsin-gliadin-myoglobin solutions with variable gliadin content. Statistically

insignificant (α=0.05) values are not shown.

% change

gliadin content, % β-sheet α-helix random coil β-turn

0 47.6 -52.6 5.5 61.7

25 22.0 -24.5 55.5 -

50 95.2 -46.5 - -

75 109.3 -38.3 - 41.7

100 5.1 * -22.6 24.7 40.6

* No increase after 24 hours.

106

33.4% during the first 8 hrs, but the right α-helical peptides were not available to collaborate and

form fibrillar aggregates. Instead, some non-fibrillar aggregation occurred and the rest of the

unstable β-peptides folded into random coils and β-turns.

The number of fibers in the solution was also found to be proportional to the rate of formation of

β-structures, dβ/dt (Figure 5d). The rates of change in α and β content, dα/dt and dβ/dt, were

determined by fitting the time-dependent secondary structure date for each gliadin level data to a

power function (Figure 5a,b). It was not clear whether a relationship between the rate of α-helix

unraveling and gliadin level existed or not (Figure 5c); as only weak association, R2 = 0.3, was

observed. The highest rate of decrease in α-helix content was observed in 100% Myoglobin and

probably had to do with the fact that myoglobin was more than 78% helix (starting from a high

baseline). By contrast, the rate of increase in β-content was influenced by the amount of gliadin

in the solution (Figure 5c). Interestingly, the maximum rate of increase in β-sheet content

coincided with the maximum number of fibers found per 1 ml of solution. Overall the number of

fibers in the solution increased linearly with the rate of increase in β-sheet content.

The fact that the number of self-assembled fibers was influenced by the ratio of gliadin to

myoglobin demonstrated the dependence of aggregation kinetics on the concentration of the

Gd20 template. There was an optimum gliadin level for the formation of fibers, which suggested

an inhibitory role played by Gd20 peptides in addition to their critical roles of recruiting α-

peptides into the fiber assembly. Initially, the number of fibers increased with more templates

becoming available. But at higher concentrations, the probability of Gd20 peptides interacting

with each other, and thus forming non-fibrillar β-aggregates, increased.

107

10

100

1 10 100

0255075100

y = 39 * x^(-0.17) R= 0.77

y = 35 * x^(-0.1) R= 0.69

y = 33 * x^(-0.15) R= 0.81

y = 38 * x^(-0.14) R= 0.74

y = 26 * x^(-0.13) R= 0.69

!-h

elix

con

tent

, %

Time, hr

% Gliadin

a

1

10

100

1 10 100

0255075100

y = 7.1 * x^(0.092) R= 0.56

y = 6.9 * x^(0.096) R= 0.41

y = 7.7 * x^(0.2) R= 0.86

y = 5.9 * x^(0.22) R= 0.74

y = 20 * x^(0.019) R= 0.16

low

freq

uenc

y !-

shee

t con

tent

, %

time, hr

% Gliadin

b

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0

2

4

6

8

10

0 20 40 60 80 100

d!/dt d"/dt

rate

of i

ncre

ase

in !

-she

et c

onte

ct, d!/

dt

rate of decrease in "-sheet contect, d"

/dt

gliadin level, %

Y = 0.5 + .03*x - 0.0003*x2

R2 = 0.7

c

0

5

10

15

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

y = -2 + 9x R2= 0.7

num

ber o

f fib

ers

per m

l

rate of increase in !-sheet contect

d

Figure 5. a) Time dependent α-helix and (b) β-sheet content in trypsin-myoglobin-gliadin

solutions estimated using FT-IR spectroscopy. c) Dependence of the rate of α-helix unraveling

and β-sheet increase on gliadin content. d) Dependence of the number of fibers found in the

solution on the rate of β-sheet increase.

108

Hydrophobic packing was a key driver of the multiscale self-assembly process

Hydrophobic packing seemed to play a critical role in the formation of macroscopic fibers in

TGM solutions. Following the reaction using FT-IR showed a change in the environment of

hydrophobic moieties on the peptide in addition to the change in secondary structure content.

The aliphatic moieties of amino acid side chains gave rise to characteristic FT-IR absorbance

bands around 2960 cm-1 and 1445 cm-1 representing asymmetrical CH3 stretching, νas(CH3) and

asymmetrical CH3 deformation, δas(CH3), respectively. Eighteen out of the 20 amino acid side

groups on Gd20 contained CH3. The absorbance ratios νs(CH3)/δas(CH3) indicated CH3 packing

by comparing symmetrical stretching (which was enhanced by the constraint of packing) with

deformation vibration (Figure 6). The ratio of the two intensities can thus indicate ‘hydrophobic

packing density’ which correlated with molecular organization and assembly. It was interesting

0

0.5

1

1.5

2

2.5

0 10 20 30 40 50 60 70

255075100

y = 1 - 0.0013x R2= 0.0025

y = 0.59 + 0.0084x R2= 0.71

y = 0.54 + 0.0069x R2= 0.59

y = 0.55 + 0.00045x R2= 0.0088

! s(CH

3)/"as

(CH

3)

time, hr

Figure 6. The intensity ratios (b) νs(CH3) to δas(CH3) indicated amount of hydrophobic packing.

109

to note that sustained increase in the packing density in 50 and 75 % gliadin coincided with the

highest number of fibers in the solution. By contrast, at 100% gliadin, where no fibers formed,

there was essentially no change in hydrophobic packing density. At 25% gliadin, where some

fibers were noted, the initial increase in hydrophobic packing was not sustained after 24 hours,

indicating a critical template concentration needed to sustain the process of fiber self-assembly.

At 100% gliadin, although non-fibrillar aggregates occurred, there was no change in hydrophobic

packing density. These results suggested that the formation of fibrillar aggregates was associated

with a sustained change in hydrophobic packing, highlighting its role in micrometer scale self-

assembly.

Macroscopic properties can be controlled by varying the ratio of constituent peptides

SEM and nanoindentation analyses showed that the morphology and mechanical properties of

the fibers were influenced by the ratio of gliadin to myoglobin (Figure 7). It is known that,

despite a common core structure, cross-β nanofibrils often have heterogeneous morphology, size,

and mechanical strength depending on the constituent peptides [37]. With increased gliadin

content, fibers became shorter and more rigid. These observations were consistent with earlier

observations that shorter peptides assemble into nanofibrils with higher modulus [38]. It is also

interesting to note that the elastic modulus of micrometer scale “rod-like” objects observed in

barnacle cement ranged form 20 to 90 MPa similar to the values reported here for TGM fibers

[18]. Our observations demonstrated the utility of controlling the macroscopic properties by

varying the ratio of constituent peptides.

110

Figure 7. a, Scanning electron micrographs of macroscopic fibers formed in trypsin-myoglobin-

gliadin solution (TMG) using different gliadin to myoglobin ratios. Scale bars: 100 µm. b,

Comparison of macroscopic properties of different TMG fibers. Aspect ratio values are averages

of up to 16 independent measurements. Reduced modulus values are averages of up to 24

independent measurements. Error bars represent standard error of the mean.

Kinetic model

Based on our understanding of the multiscale self-assembly of TGM fibers, a kinetic model

utilizing time-dependant secondary structure data was developed to describe the process. In this

model, a template (βt) was formed as a result of β-aggregation of short peptides rich with

aliphatic amino acids (Gd20) (Figure 8-1). The hydrophobicity of βt catalyzed an α to β

transformation in long α-rich peptides leading to formation of an intermediate structure,

αβ (Figure 8-3). From this point, two scenarios were possible. First, βt interacts with another βt

to form non-fibrillar aggregates, βnf, and together leave the solution. Second, βt interacts with αβ

111

to form fibrillar aggregates, βf, which in turn form fibers. As such, βt performed an inhibitory

role with regard to the formation of fibers.


macroscopic cross-β fibers.

The proposed templated self-assembly process can be schematically represented as follows:

!

Gd20 +Gd20 k1" # " $t (3)

112

!

"t +# k2 , k3$ % & & #" (4)

!

"t + "t kd# $ # "nf (5)

!

"t +#" k f$ % $ "f (6)

where βt represents the template resulting from aggregation of Gd20 peptides, αβ is an

intermediate complex comprising the partially unfolded α-helix peptide and the template, and βf

and βnf represent fibrillar and non-fibrillar aggregates, respectively. Assuming a first-order

reaction-rate expression for each step, the balances for each species lead to the following set of

ordinary differential equations (ODE)s:

!

d [Gd20]dt

= - k1 [Gd20][Gd20] (7)

!

d ["t ]dt

= k1 [Gd20][Gd20] # k2["t ][$] + k3[$"] # k f ["t ][$"] # kd["t ]["t ] (8)

!

d ["]dt

= - k2 [#t ]["] + k3["#] (9)

!

d ["#]dt

= k2 [#t ]["] $ k3["#] $ k f [#t ]["#] (10)

!

d ["nf ]dt

= kd["t ]["t ] (11)

!

d ["f ]dt

= k f ["t ][#"] (12)

113

Equations 5–10 were solved numerically using the Runge-Kutta method. Kinetic constants were

estimated by performing a multidimensional parameter optimization using the steepest ascent

method [39]. The optimization procedure was automated using MATLAB (Appendix A.1). The

quality of fit of the model was assessed by calculating the mean square error, mse, between

experimental data points and simulation predictions,

!

mse =(xi " yi )

2

ni=1

n

# (12)

where xi was the simulation value at time = t, yi the raw data point value at time = t and n the

number of experimental data points.

The goal of the optimization procedure was to find the design vector x

!

x =

k1

k2

k3

kdk f

"

#

$ $ $

%

$ $ $

&

'

$ $ $

(

$ $ $

which minimizes the objective function, f (x) ) mse

To calculate mse, [αexp] and [βexp] measured using FT-IR spectroscopy for 50% TGM (Figure 9)

were used. Values of [αexp] were compared with those of [α] calculated from the model. Since βt

αβ, βf and βnf were all β structures, their sum, [βsim] was proportional to the amount of β-

structures measured with FT-IR, [βexp], i.e.,

!

["t ]+ [#"t ]+ ["nf ]+ ["f ] = ["sim ]$ ["ext ] (13)

114

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 10 100

y = 0.88 * x^(-0.15) R= 0.81

!-h

elix

con

tent

, [!

exp],

%

Time, hr

a

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 10 100

y = 0.4 * x^(0.2) R= 0.86

!-sh

eet c

onte

nt, [! ex

p], %

time, hr

b

Figure 9. Time-dependent α-helix (a) and β-sheet (b) content in 50% TGM estimated from FT-

IR data.

To start the optimization procedure, initial estimates of the kinetic constants (k1, k2, k3, kd, kf)

were determined from the experimental data. The rate of decrease in α-helix content, calculated

by fitting the 50% TGM data to a power function was

!

d ["exp]dt

= - 0.132 t#1.15

(14)

where the quantity 0.132 represented the rate constant for the unraveling of α-helix, k2. Similarly

k1 was estimated using the normalized time-dependent β−content for 50% gliadin (Figure 1b).

The rate of increase in β−content was

!

d ["exp]dt

= 0.08 t0.8

(15)

115

where the quantity 0.08 represented the rate constant for total β-aggregation. The initial design

vector was generated accordingly,

!

x =

0.080.1320.1320.1320.132

"

#

$ $ $

%

$ $ $

&

'

$ $ $

(

$ $ $

, and the initial objective function, f (x1) was calculated.

!

f (x1) = 0.1391

The following initial conditions were used:

[Gd200] =1, [βt0] = 0, [α0] = 1, [αβ0] = 0, [βnf0] = 0, [βf0] = 0

and after 8712 iterations, the following values were obtained:

!

x8712 =

0.26400.17100.13260.00630.0788

"

#

$ $ $

%

$ $ $

&

'

$ $ $

(

$ $ $

, f (x8712) = 0.04434

The model was then used to simulate the formation of fibril species as a function of gliadin level.

Results are summarized in Figure 9. The model was in good agreement with experimental data in

determining optimum gliadin level for fiber formation.

116

0 20 40 60 80 100

simulated fibrillar aggregates, [!f]

experimental d!/dt

number of fibers found in solution

0

0.2

0.4

0.6

0.8

1

norm

aliz

ed u

nits

gliadin level, %

Figure 10. Comparison of experimental and simulated data.

CONCLUSION

The previous chapter of this dissertation discussed multiscale self-assembly of macroscopic

fibers from a combination of β- and α- tryptic wheat gluten peptides. Results discussed in this

chapter showed that the concept of multiscale self-assembly from a combination of β- and α-

peptides could be extended to other peptide systems. The TGM system presented in this chapter

provided the opportunity to further understand the multiscale self-assembly process by enabling

control over the concentration of templating peptide. Results suggested that nano- and micro-

scale self-assembly were both associated with hydrophobic interactions of the aliphatic amino

acids. The remarkable transfer of properties from the nanostructures to the macroscopic fibers

also indicated intimate molecular interactions such as hydrophobic packing. Using the TGM

117

system, it was also possible to demonstrate the ability to control the macroscopic mechanical

properties by varying the ratio of constituent peptides.


and functional building blocks. The ability to extend, and control, peptide self-assembly over


systems of higher complexity and functionality.

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Applications, Springer, New york.

CHAPTER SIX

CONCLUSIONS

The overall goal of this work was to enhance our understanding of protein-based structural

materials and the way they assemble in nature. The work was divided into two parts focusing on

protein hydration and self-assembly. Part one was devoted to discussing the use of agricultural

proteins as a feedstock for bio-based polymers. Particularly, it focused on the effect of hydrogen

bonding, or lack thereof, between proteins as mediated by hydration or plasticization. The effect

of varying plasticizer (glycerol) levels on mechanical properties of a series of proteins was

discussed in the context of primary and secondary structure of proteins. We found that the extent

to which a protein can be plasticized was dependent on its primary structure and not simply

molecular weight, as it was often assumed in previous studies. In fact, the effect of plasticizer

was complex. It increased order (relative β-sheet content) at low concentration by allowing

protein chains to align, but the addition of low molecular weight plasticizer counteracted the

ordering to reduce elastic modulus. At higher plasticizer concentrations, an overall drop in elastic

modulus was observed because protein-protein hydrogen bonding was progressively taken away

in favor of protein-solvent interactions. The sensitivity to plasticization was higher for proteins

with more polar amino acids, especially those with carbonyl groups, i.e, glutamine, asparagine,

glutamic acid, and aspartic acid. Conversely, the presence of cysteine made the protein less

sensitive to plasticization due to formation of a robust cystine cross-linked network.

124

The second part of the dissertation focused on the study of protein self-assembly as a way to

make useful macroscopic materials. There are major efforts to study protein self-assembly for

various medical and industrial reasons. Despite huge progress, most studies have focused on

nanoscale self-assembly but the crossover to the macroscopic scale remains a challenge. In this

work, we have shown that multiscale peptide self-assembly was possible in vitro under

physiological conditions. We were able to assemble macroscopic fibers from a combination of α-

and β- peptides. Much of the work was focused on characterizing the fibers and trying to

understand the mechanism by which they form. The fibers had cross-β structural arrangement

and their formation was a cooperative process involving α-helix-rich peptide and a template

formed by hydrophobic β-peptides. A review of the literature suggested that our identified self-

assembly mechanism might be a route utilized by several very different natural systems. Finally,

We demonstrated the ability to control macroscopic properties by varying the ratio of constituent

peptides and to conserve properties from the molecular to the macroscopic scale.


and functional building blocks. The ability to extend and control peptide self-assembly over


systems of higher complexity and functionality. Future work may address the problem of

function

APPENDIX

126

A.1 MODEL FITTING AND OPTIMIZATION

New MATLAB program: Optimization_withODE.m

% Optimization.m % Adopted and modified by Ahmad Athamneh, 7/5/2010, form Bioprocess % Engineering course by Dr. Ryan S. Senger % This program performs a steepest ascent algorithm on % Reaction_ODE_solver.m % This program runs the optimization algorithm for a specified % number of iterations % The design vector is [k1; k2; ; kd ; kf ; kd] % The objective function is the mse between simulations and raw data % points clear; %Initiating data matrix used to calculate MSE % Experimental data. % Time column data(:,1)=[1;3;4;8;21;25;26;50;70]; % Normalized alpha helix content data estimated from FTIR data(:,2)=[1;0.851023657;0.966342115;0.777099518;0.637667709;0.660813006;0.712423896;0.673429712;0.496456602]; % Normalized beta sheet content data estimated from FTIR data(:,3)=[0.560689588;0.443489868;0.541068341;0.635500829;0.946547648;0.990779616;0.878435593;1.041674017;1]; %LFB 50% % initiate the design vector and kinetic constants % the design vector is [k1; k2; kd; kf; k3] design_vector=[.08 ; 0.132 ; 0.132 ; 0.132 ; 0.132 ]; delta=0.1; %constant used to approximate partial derivatives omega=0.0001; %step-size in the optimization program number_of_iterations=10000; %number of times to recalculate design vector %Initial conditions Gd0=1; %initial Gd20 concentration Tm0=0; %initial template concentration a0=1; %initial alpha species concentration aTm0=0; %initial alpha-beta complex (intermediate) concentration bd0=0; %initial nonfibrillar aggregates concentration f0=0; %initial fibrillar aggregates concentration

127

%initiate the gradient vector delf=zeros(size(design_vector,1),1); for z=1:number_of_iterations k10=design_vector(1,z); k20=design_vector(2,z); kd0=design_vector(3,z); kf0=design_vector(4,z); k30=design_vector(5,z); %running simulations to calculate finite differences k1=k10; k2=k20; kd=kd0; kf=kf0; k3=k30; Reaction_ODE_solver % this program solves ODEs for each set of

% kinetic constants and returns MSE mse1=mean_square_error; k1=k10+delta*k10; k2=k20; kd=kd0; kf=kf0; k3=k30; Reaction_ODE_solver mse2=mean_square_error; k1=k10-delta*k10; k2=k20; kd=kd0; kf=kf0; k3=k30; Reaction_ODE_solver mse3=mean_square_error; k1=k10; k2=k20+delta*k20; kd=kd0; kf=kf0; k3=k30; Reaction_ODE_solver mse4=mean_square_error; k1=k10; k2=k20-delta*k20; kd=kd0; kf=kf0; k3=k30;

128

Reaction_ODE_solver mse5=mean_square_error; k1=k10; k2=k20; kd=kd0+delta*kd0; kf=kf0; k3=k30; Reaction_ODE_solver mse6=mean_square_error; k1=k10; k2=k20; kd=kd0-delta*kd0; kf=kf0; k3=k30; Reaction_ODE_solver mse7=mean_square_error; k1=k10; k2=k20; kd=kd0; kf=kf0+delta*kf0; k3=k30; Reaction_ODE_solver mse8=mean_square_error; k1=k10; k2=k20; kd=kd0; kf=kf0-delta*kf0; k3=k30; Reaction_ODE_solver mse9=mean_square_error; k1=k10; k2=k20; kd=kd0; kf=kf0; k3=k30+delta*k30; Reaction_ODE_solver mse10=mean_square_error; k1=k10; k2=k20; kd=kd0; kf=kf0; k3=k30-delta*k30; Reaction_ODE_solver mse11=mean_square_error; %calculating del f delf(1,z)=(mse2-mse3)./(2.*delta.*k10); delf(2,z)=(mse4-mse5)./(2.*delta.*k20); delf(3,z)=(mse6-mse7)./(2.*delta.*kd0);

129

delf(4,z)=(mse8-mse9)./(2.*delta.*kf0); delf(5,z)=(mse10-mse11)./(2.*delta.*k30); error_total(1,z)=mse1; %printing results to the command window fprintf(['iteration ' num2str(z) ' of ' num2str(number_of_iterations) '.The mse value is: ' num2str(error_total(1,z)) '.' '\n']); %determining if the mse value is increasing (program is stopped if it %increases) if z>1 if error_total(1,z)>=error_total(1,z-1) fprintf(['The mse is increasing or identical to the last iteration. Optimization is finished.' '\n']); break end end %calculating the new design vector design_vector(:,z+1)=design_vector(:,z)-omega*delf(:,z); end %print the final results fprintf(['The final value of k1 is: ' num2str(design_vector(1,size(design_vector,2))) '\n']); fprintf(['The final value of K2 is: ' num2str(design_vector(2,size(design_vector,2))) '\n']); fprintf(['The final value of Kd is: ' num2str(design_vector(3,size(design_vector,2))) '\n']); fprintf(['The final value of Kf is: ' num2str(design_vector(4,size(design_vector,2))) '\n']); fprintf(['The final value of K3 is: ' num2str(design_vector(5,size(design_vector,2))) '\n']); %plotting the raw data points as circles subplot(2,1,1) plot(data(:,1),data(:,2),'o'); xlabel(['time']); ylabel(['[alpha-helix]']); hold on subplot(2,1,2) plot(data(:,1),data(:,3),'o'); xlabel(['time']); ylabel(['Beta Indicator']); hold on %plotting final simulation results (blue line) subplot(2,1,1) plot(t,a); subplot(2,1,2) plot(t,betaindicator) %re-calculate and plot the simulation results of the initial guess k1=design_vector(1,1); k2=design_vector(2,1); kd=design_vector(3,1); kf=design_vector(4,1);

130

k3=design_vector(5,1); Reaction_ODE_solver subplot(2,1,1) plot(t,a,'r'); legend('experimental','Optimized simulation','initial simulation'); subplot(2,1,2) plot(t,betaindicator, 'r'); legend('experimental','Optimized simulation','initial simulation'); fprintf(['done!' '\n']); end;

New MATLAB program: Reaction_ODE_solver.m

% Reaction_ODE_solver.m % The built-in MATLAB 'ODE45' function was used to solve the % reaction ODEs. % The M file equations.m evaluates the right-hand-side expresion of & the ODE. [t,y]=ode45(@(t,y) equations(t,y,k1,k2,kd,kf,k3),[0 75],[Gd0 Tm0 a0 aTm0 bd0 f0]); Gd=y(:,1); Tm=y(:,2); a=y(:,3); aTm=y(:,4); bd=y(:,5); f=y(:,6); beta_indicator=Tm+f+bd+aTm+0.560689588; %calculating the mse data(:,4)=0; %this column will be the simulation value of a data(:,5)=0; %this column will be the simulation value of b data(:,6)=0; %this column will be the delta a data(:,7)=0; %this column will be the delta b data(:,8)=0; %this column will be delta a + delta b for i=1:size(data,1) target=data(i,1); id=0; for j=1:size(t,1) if target>=t(j,1) id=j; end end if id>0

131

data(i,4)=a(id,1); data(i,5)=beta_indicator(id,1); else fprintf(['simulation data points do not exist for all raw data points' '\n']); Return end end data(:,6)=(data(:,2)-data(:,4)).^2; data(:,7)=(data(:,3)-data(:,5)).^2; data(:,8)=data(:,6)+data(:,7); mean_square_error=sum(data(:,8)./size(data,1)); end;

New MATLAB program: Equations.m

% equations.m % This function evaluates the right-hand-side expression of ODEs 7- % 12 on page 112 on dissertation. function dydt=equations(t,y,k1,k2,kd,kf,k3) dydt=zeros(size(y)); Gd20=y(1); Tm=y(2); a=y(3); aTm=y(4); bd=y(5); f=y(6); dydt(1)=-k1*Gd20*Gd20; dydt(2)=k1*Gd20*Gd20-k2*Tm*a+k3*aTm-kf*aTm*Tm-kd*Tm*Tm; dydt(3)=-k2*a*Tm+k3*aTm; dydt(4)=k2*Tm*a-k3*aTm-kf*aTm*Tm; dydt(5)=kd*Tm*Tm; dydt(6)=kf*Tm*aTm; end;

132

A.2 SIMULATION: FORMATION OF FIBRILLAR AGGREGATES AS A FUNCTION OF

GLIADIN LEVEL.

New MATLAB program: simulation.m

% simulation.m % written by Ahmad Athamneh, 7/28/2010 % This program simulates the formation of fibrillar aggregates as a % function of Gliadin level clear; %Initial conditions Tm0=0; %initial template concentration aTm0=0; %initial alpha-beta complex (intermediate) concentration bd0=0; %initial nonfibrillar aggregates concentration f0=0; %initial fibrillar aggregates concentration %Rate constant from Model fitting k1=0.2665; k2=0.17219; k3=.074138; kd=0.13044; kf=0.0010165; %time span, hrs time=75; for z=0:100; Gd0=z*2/100; % gliadin level varies from 0 to 2 (0 to 100%) a0=2-Gd0; % alpha species varies from 2 to 0 (100 to 0%) simulation_ODE_solver % This program runs the simulation for each % gliadin level %calculating the new design vector and store results. result(z+1,1)=Gd0; %Bf after 75 hours result(z+1,2)=final_f; %bd after 75 hours result(z+1,3)=final_bd; %bd after 75 hours result(z+1,4)=final_Tm; end

133

subplot(3,1,1); plot (result(:,1),result(:,2)); legend(' fibrillar aggregates '); subplot(3,1,2); plot (result(:,1),result(:,3)); legend(' nonfibrillar aggregates '); subplot(3,1,3); plot (result(:,1),result(:,4)); legend(' template '); end;

New MATLAB program: simulation_ODE_solver.m

% simulation_ODE_solver.m % The built-in MATLAB 'ODE45' function was used to solve the % reaction ODEs. % The M file equations.m evaluates the right-hand-side expresion of & the ODE. [t,y]=ode45(@(t,y) equations(t,y,k1,k2,kd,kf,k3),[0 time],[Gd0 Tm0 a0 aTm0 bd0 f0]); Gd=y(:,1); Tm=y(:,2); a=y(:,3); aTm=y(:,4); bd=y(:,5); f=y(:,6); final_f=f(size(f,1),1); final_Tm=Tm(size(Tm,1),1); final_bd=bd(size(bd,1),1);

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