125:583125:583Biointerfacial Biointerfacial

CharacterizationCharacterizationMolecular Models 2Molecular Models 2

November 6, 2006November 6, 2006

Focal AreasSurrogate Molecular Modeling to Accelerate Polymer Design and

Optimization

• Virtual Combinatorial Chemistry: Compressing Large Polymer Libraries into Representative Subsets

• Quantitative Structure-Performance Relationship (QSPR) Models: Predicting Cell-Material Interactions from the Polymer’s Chemical Structure

Atomistic Molecular Modeling to Explore Polymer Properties and Polymer-Protein Interactions

• Molecular Simulations of Water Transport Through Polymers

• Scoring Functions for Study of Polymer-Protein Interactions

F = MA

exp(-E/kT)

domain

quantumchemistry

moleculardynamics

Monte Carlo

mesoscale continuum

Scales of Time & Dimension in Molecular Simulations

Length Scale

Tim

e S

cale

10-10 M 10-8 M 10-6 M 10-4 M

10-12 S

10-8 S

10-6 S

Motivation: Why atomistic MD simulations?

Mechanisms

MD simulations provide a molecular level picture of structure and dynamic property/structure relationships

“What If” experiments

MD simulations provide a bridge between modelers and experimentalists, leading to synergies and new insights into materials properties

Property PredictionMD simulations allow prediction of properties

for

Existing materials whose properties are difficult to measure or poorly understood

Novel materials which have not been synthesized

Example: PEG in WaterPEG is a water soluble polymer used in wide variety of biomedical applications

poly(ethylene glycol)

WaterWaterPOLYMER CHAINS

r (Å)

1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0

pair distribution functions

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

DMEDMP

Oether-Hw

Oether-Ow

static properties (structure, energy, pressure, molecule packing)

dynamic and transport properties (diffusion coefficient, atom mobility, thermal conductivity, shear viscosity, infrared absorption coefficient )

Extracting Properties from Simulations

polymer-water solutions

radial (pair) distribution function

Skin interaction model:

Layers from the top: (1) Vacuum; (2) Polymer; (3) skin model.

Polymer, Small Moleculewater

Research StudyWater uptake and flux in polymers

• Experimental data from Michniak Laboratory

Polymer Batch NoEquilibration

Water Content%

Flux(µl/cm2/day)

n=2

p(DTM suberate) AR1_033004_5 46 12.19±0.03

p(DTO succinate) AR1_040504_7 3.2 11.05±2.41

p(DTB succinate) AR1_102604_10 7.02 5.57±0.20

p(DTE glutarate) AR1_012604_3 19.6 19.33±10.25p(DtiP succinate) AR1_042004_1 6.54

p(DTE methyl adipate) AR1_012604_2 9.52

p(DTBn succinate) AR1_040504_1 6.33

p(DTD sebacate) AR1_040504_1 2.44

p(DTO diglycolate) AR1_012604_4 6.64

p(HTH suberate) AR1_030204_8 2.92

p(DTB methyl adipate) AR1_012604_6 2.45

p(DTH succinate) AR1_042004_3 3.13

p(DTiB adipate) AR1_012604_12 6.59

p(DTH glutarate) AR2_062504_1 2.49

p(DTsB adipate) AR1_012604_1 3.46

Water uptake and flux in polymers

• Preliminary QSPR model based on the water uptake for 13 polyarylates and suggested values for two outliers

Row

TargetModel

1Model

2Model

3

16.59000

05.30914

26.17507

76.07988

1

23.46000

02.46631

33.64946

23.73314

7

36.64000

05.02278

14.26242

44.42449

8

42.49000

03.28614

33.22668

73.46275

7

52.45000

05.19198

55.24796

15.21428

5

69.52000

08.20179

37.74789

67.66929

3

72.44000

02.84355

92.53044

82.37406

4

87.02000

07.53540

86.50268

26.33198

2

96.33000

05.98618

16.22562

26.32370

8

103.13000

04.91349

64.35497

14.26154

1

116.54000

06.75340

77.78529

57.89694

7

123.20000

02.92232

62.71227

52.68476

0

132.92000

02.50807

02.40024

12.36861

0

1419.6000

004.91349

64.29853

24.17912

8

1546.0000

005.13249

34.29994

44.32523

2

ResultsWater uptake and flux in polymers

• Water uptake data analysis

R2 = 0.87

2

3

4

5

6

7

8

9

10

2 3 4 5 6 7 8 9 10

13 model #1

Linear (13 model #1)

-3

-2

-1

0

1

2

0 5 10 15

residuals 13

Data are segregated into three isolated islands which yields a poor distribution for QSPR modeling. Suggestion: add more experimental values to void space

Scoring Function Based Approach to

Predict Protein-Surface Interactions

D. Sun, K. Fears, R.A. Latour, W.J. Welsh

Lysozyme adsorption on surfaces with polymeric functional groups

OH-functional groups CH3-functional groups COOH-functional groups

Sun Y., Welsh W.J., and Latour R.A., Prediction of the orientations of adsorbed protein using an empirical energy function with implicit solvation, Langmuir, 21: 5616-5626 (2005).

Scoring Function Based Approach to

Predict Protein-Surface Interactions

Simulation of Protein Adsorption Using A Scoring Function Approach

• Docking & Scoring methods are used by biopharma industry to evaluate ligand-receptor binding affinity for drug design.

• Research Question: Can similar methods be developed for use with surrogate modeling to accurately predict protein adsorption to polymer surfaces?

• Critical issues:– Efficient sampling of multiple low-energy orientations– Calculation of overall adsorption energy– Implementation with surrogate modeling for polyarylate library of polymers

Initial Studies• Model systems

– Surface: Alkanethiol self assembled monolayers (SAMs) on gold• Well characterized – easily simulated• Easily synthesized for experimental use

– Proteins: Small bioactive proteins (enzymes)• Provide systems that can be experimentally validated

• Evaluate energy function of selected scoring method (AutoDock)– Existing parameterization – designed for ligand-receptor binding– Reparameterization required for residue-surface adsorption behavior

• Application of new scoring function: – Develop efficient methods to map energy as function of orientation– Identify probable adsorbed orientations & overall adsorption energy

Protein Adsorption Simulation with AutoDock

Preferred orientations and energy of adsorbed lysozyme vs. SAM surface

CH3 SAM

30%CH3/OH SAM 30%COO-/OH SAM

50%CH3/OH SAM

( ) )2/(

1012612

2

)(

)( σ

εijr

ijijjisol

ijentrentr

ijij

jielec

ij ij

ij

ij

ijhbond

ij ij

ij

ij

ijvdW eVSVSCNC

rr

qqC

r

D

r

CtEC

r

B

r

ACG −∑∑∑∑ ++++⎟

⎟⎠

⎞⎜⎜⎝

⎛−+⎟

⎟⎠

⎞⎜⎜⎝

⎛−=Δ

(arrows indicate bioactive site)

(Energy function: G = Gvdw + GH-bond + Gelectrostatic + Gentropy + Gsolvation)

• Map of energy vs. orientation angles of protein on surface (θ and ).

• For each combination of θ and , a minimum energy value was searched as a function of y andψ.

• Boltzmann distribution of 90,792 orientation states (T = 310 K) generated by sampling. Average adsorption free energy: -3.62 kcal/mol.

Boltzmann Distribution ( T=310K)

Energy Levels ( kcal/mol )

-6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -0.5

Percent ( % )

0

10

20

30

40

50

-6

-5

-4

-3

-2

-1

0

020406080100120140160180

50100

150200

250300

350

Energey (kcal/mol)

Phi Angle (Degree)

Theta Angle (Degree)

Lysozyme on CH3 SAM

-6 -5 -4 -3 -2

-1

0

x,

y,

z,

Lysozyme on CH3 SAM with Modified Force Field

Orientations of Adsorbed Lysozymeon CH3 SAM: Sideways Orientation Preferred

side-onLys96

end-onGly126

end-onPro70

Orientations Angles (phi, theta)

Point of attachment

Energy( kcal/mol)

Side-on (110,155) LYS96 -5.297

End -on (80,240) GLY126 -2.092

End -on (100,45) PRO70 -2.226

Protein Adsorption Simulation with AutoDock

Preferred orientations and energy of adsorbed lysozyme vs. SAM surface

CH3 SAM

30%CH3/OH SAM 30%COO-/OH SAM

50%CH3/OH SAM

( ) )2/(

1012612

2

)(

)( σ

εijr

ijijjisol

ijentrentr

ijij

jielec

ij ij

ij

ij

ijhbond

ij ij

ij

ij

ijvdW eVSVSCNC

rr

qqC

r

D

r

CtEC

r

B

r

ACG −∑∑∑∑ ++++⎟

⎟⎠

⎞⎜⎜⎝

⎛−+⎟

⎟⎠

⎞⎜⎜⎝

⎛−=Δ

(arrows indicate bioactive site)

(Energy function: G = Gvdw + GH-bond + Gelectrostatic + Gentropy + Gsolvation)

• Map of energy vs. orientation angles of protein on surface (θ and ).

• For each combination of θ and , a minimum energy value was searched as a function of y andψ.

• Boltzmann distribution of 90,792 orientation states (T = 310 K) generated by sampling. Average adsorption free energy: -3.62 kcal/mol.

Boltzmann Distribution ( T=310K)

Energy Levels ( kcal/mol )

-6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -0.5

Percent ( % )

0

10

20

30

40

50

-6

-5

-4

-3

-2

-1

0

020406080100120140160180

50100

150200

250300

350

Energey (kcal/mol)

Phi Angle (Degree)

Theta Angle (Degree)

Lysozyme on CH3 SAM

-6 -5 -4 -3 -2

-1

0

x,

y,

z,

Lysozyme on CH3 SAM with Modified Force Field