Protein Computational Modelling Project - Natan Kalson

8/12/2019 Protein Computational Modelling Project - Natan Kalson

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Ben-Gurion University of the Negev

Department of Chemistry

Molecular Dynamics Study of BmP08 a Short-Chain

Scorpion Toxin fromButhus martensiKarsch

Natan H. Kalson

Final assignment for the course:

Computational Structural Modelling of Protein: Methods and Applications

Under Guidance of Dr. Yifat Miller & Vered Wineman-Fisher


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Table of Contents

1 Abstract ......................................................................................................................... 3

2 Introduction ................................................................................................................... 3

3 Materials and Methods ................................................................................................... 3

3.1 Molecular dynamics (MD) simulations protocol ...................................................... 3

3.2 Analysis details ....................................................................................................... 3

3.2.1 Equilibration and Conservation of Energy ........................................................ 3

3.2.2 Structural Analysis ........................................................................................... 3

3.2.3 Surface Analysis .............................................................................................. 4

4 Results and Discussion ................................................................................................... 4

4.1 Energy Analysis ...................................................................................................... 4

4.2 Root Mean Square Deviation Analysis .................................................................... 8

4.3 Hydrogen Bonds Analysis ....................................................................................... 8

4.4 Distances Between the Backbones of Residues ..................................................... 10

4.5 Salt Bridges .......................................................................................................... 10

4.6 Ramachandran Plot ............................................................................................... 11

4.7 Solvent Accessible Surface Analysis ..................................................................... 11

5 Conclusions ................................................................................................................. 13

6 Appendices .................................................................................................................. 15

6.1 Hydrogen Bonds According To Donor-Acceptor Residues .................................... 15

6.2 Script used to run multiple runs of Surface Racer .................................................. 16

7 References ................................................................................................................... 16


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1 AbstractIn this work, the molecular dynamics study of the short chain venom BmP08 is conducted.

Discoveries of specific changes throughout molecular dynamics indicate about the structural

changes that lead to stabilization of possible active sites on the protein. This work may be of

importance in finding the target channel of this unclassified venom.

2 IntroductionThe study of substances used as traditional medicine has always been appealing to many. Of

these substances, scorpion venoms were used for centuries for the treatment of neural diseases.

The Chinese scorpion Buthus martensiKarsch (BmK) is a source for a number of proteins

which were isolated, identified and characterized. In a recent paper by Chen et al. [ 1] the

purification, characterization and sequence of a short-chain scorpion toxin BmP08 was

reported. In this work, it is reported that this protein shows no inhibitory activity towards all

tested K+ channels. Therefore, making it unclassified as to what type of venom it is.

3 Materials and Methods3.1 Molecular dynamics (MD) simulations protocolAn initial model of the peptide was obtained from the PDB database (PDB ID: 1WT8 [1]),

model 1 was used as a starting point for all following work. The simulations protocol used

herein is as done in works by Miller et al. and as instructed for the assignment [2] [3] [4].

MD simulations of the solvated peptide were performed in the NPT ensemble using the NAMD

[5] with the CHARMM27 force field [6] [7]. Energy minimization of the peptide was done

using the ABNR method followed by solvation in a TIP3P water box [8] [9] with a minimum

distance of 15 , 2.5 . 2 . Preceding the molecular dynamics simulation was theminimization of the peptide at 150K, and heating in two stages to 250K and then to 300K

before the simulation run. The temperature of 300K was controlled by a Langevin thermostat

with a damping coefficient of 10ps-1[5].

3.2 Analysis details3.2.1 Equilibration and Conservation of EnergyAnalysis of the energy components was done by extracting information out of the output files

by given by the NAMD run. Further analysis was done using the namdstats.tcl script that isincluded in the namd-tutorial-files.

3.2.2 Structural AnalysisStructural stability and changes were examined through following the changes in the numbers

of hydrogen bonds exhibited in the peptide throughout the simulations, with the hydrogen bond

cut-off being set to 3.0. Analysis of salt-bridges was conducted with a cut-off of 3.5.


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, . 10101010 11111111.3.2.3 Surface AnalysisSurface analysis of the peptide was conducted using the Surface Racer 5.0 by Tsodikov et al.

[12].

4 Results and Discussion4.1 Energy AnalysisExamination of the temperature and the kinetic, potential and total energies vs. time reveals

large fluctuations (Figure 1-4). In order to be able to look at trends in these values, a ten period

moving average is shown per graph. There is strong correlation between temperature and

kinetic energy which is expected since there is a linear dependency of kinetic energy in

temperature. The total and potential energies similar behaviors as the potential energy is 4

times more dominant compared to the kinetic energy this is logical. The system retains its totalenergy to a certain extent as can be seen in Figure 4 the simulation starts with a total energy

of around -45,500kcal/mol while the average total energy is (-45,639.7140.6)kcal/mol, theinitial value is within standard deviation meaning it can be said that the system conserves its

total energy.

Figure 1 Temperature changes throughtout molecular dynamics

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0.0 0.5 1.0 1.5 2.0 2.5 3.0

EKinetic[kcal/mol]

Time [ns]

Temperature vs. Time

Temperature Moving average


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Figure 2 Kinetic energy changes throughout molecular dynamics

Figure 3 Potential energy changes throughout molecular dynamics

Figure 4 Total energy changes throughout molecular dynamics

Considering that potential energy arises from both covalent and non-covalent interactions, an

inspection of relative contribution of these interactions might be interesting. It is interesting to

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EKinetic[kcal/mol]

Time [ns]

Kinetic Energy vs. Time

Kinetic Energy Moving average

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EKinetic[kcal/mol]

Time [ns]

Potential Energy vs. Time

Potential energy Moving average

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EKinetic[kcal/mol]

Time [ns]

Total Energy vs. Time

Total Energy Moving average


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note that covalent interactions contribute only little amount in terms of energy compared to

non-covalent interactions. As covalent interactions relate to interactions between atoms that

are close in space such as bonded atoms or atoms that have a maximum of 3 bonds between

them (as in improper interactions) as compared to non-covalent interactions that take into

account interactions that are more long-ranged (somewhere in the range of 8-12

[13]). For

the long range interactions there are many more interactions that are available and thus their

contribution is far more dominant.

Figure 5 Energy of covalent interactions: bonds, angles, dihedrals and improper

Figure 6 Energies of non-covalent interactions: VDW and electrostatic

A statistical analysis of the energies as depicted in Table 1 shows that the deviations oftemperature and kinetic, potential and total energies are relatively small. This is an indication

of a good starting structure for the molecular dynamics as the energy doesnt vary much. In

cases where bad starting geometries are used, it is just a matter of time until a lower energy

domain is reached. Although, the simulation done in this work is relatively short (only 3ns

compared to 30+ in most works) which may not allow for large changed in peptide structure,

or give an appropriate length of time for studying molecular dynamics of the peptide.

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ECovalent

[kcal/mol]

Time [ns]

Covalent interactions

Covalent interactions Moving average

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[kcal/m

ol]

Time [ns]

Non-covalent interactions

Non-covalent interactions Moving average


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Table 1 Statistical analysis of energies throughout molecular dynamics

ParameterAverage value

[kcal/mol]

Standard deviation

[kcal/mol]

Standard Deviation

[%]

Kinetic energy 10418.8 78.1 0.75%

Temperature 298.6 2.2 0.75%

Potential energy -56058.5 122.1 0.22%Total energy -45639.7 140.6 0.31%

Covalent Interactions 486.5 16.3 3.35%

Non-covalent Interactions -56545.0 120.3 0.21%

Figure 7 Energies of VDW interactions

Figure 8 Energies of electrostatic interactions

By the inspection of the components of non-covalent interactions, VDW interactions and

electrostatic interaction, one can infer certain information. Firstly, it can be assumed that VDW

forces, which drop very fast with distance, have mostly a repulsive effect as energies are

positive in average. Secondly, looking at the energies that arise from electrostatic interactions,

which are negative, the molecular dynamics can be described as a fight between electrostatic

interaction, which pull atoms and groups together, against VDW interaction, that pull them

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EVDW[kcal/mol]

Time [ns]

VDW interactions

VDW interactions Moving average

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EElectrostatic[kcal/mol]

Time [ns]

Electrostatic interactions

Electrostatic interactions Moving average


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apart. As the covalent interactions are very low in energy compared to the non-covalent

interactions, they are relatively negligible. Although, as the deviation in the energy of covalent

interaction is much larger compared to other energies in the dynamics, one can learn that non-

covalent interactions are the main interactions that give rise to changes in protein during non-

equilibrium state (both initial folding and during changes that give rise to specific activity) and

that they can alter the energies of the covalent interactions to a somewhat larger extent.

4.2 Root Mean Square Deviation AnalysisRoot mean square deviation is an indicator of how closely related are structures during a

molecular dynamics simulations (and in general). It is calculated according to the following

equation:

=1 , , Results clearly show that the structure isnt in equilibrium. Right after the simulation start there

is a large change in structure in less than 0.3ns. Another large change occurs after 0.75ns and

even after that the peptide doesnt keep its structure in equilibrium as can be seen in the RMSD

plot after 1ns. The RMSD keeps changing in the range of 7-10. In order to explain thesechanges there is a need of further analysis.

Figure 9 Root Mean Square Deviation throughout molecular dynamics

4.3 Hydrogen Bonds AnalysisAnalysis of the hydrogen bonds during the simulation gives an indications of a very large

involvement of hydrogen bonds in the changes during the simulation. The percentage of

hydrogen bonds from the initial number drops in average to less than 50%, though there are

peaks of ~80% in certain occurrences.

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0.0 0.5 1.0 1.5 2.0 2.5 3.0

R

MSD[]

Time [ns]

RMSD vs. Time


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Figure 10 Percentage of hydrogen bonds compared to t=0ns throughout molecular dynamics

As can be seen in Figure 11, at the beginning there is a noticeable occurrence: after a large

increase in RMSD, where hydrogen bonds break, there is some formation of hydrogen bondswith a decrease in RMSD. It is important to realize that these arent the same hydrogen bonds,

as the bonds that break in the first half of the simulation are mostly ones related to salt bridges,

as will be shown in section 4.5. In the second half of the simulation this trend is less apparent,

meaning that something else is happening to stabilize the protein.

Figure 11 Correlation between RMSD and percentage of hydrogen bonds

It is important to mention that the number of hydrogen bonds refer to the absolute number but

doesnt take into account the identity of the bonds. In total, hydrogen analysis counts all the

hydrogen bonds during the molecular dynamics and it is revealed that there are 30 different

bonds when counting residues that interact using hydrogen bonds, while differentiating

between main and side-chain hydrogen bonds. There are hydrogen bonds related to salt bridges

that change during the molecular dynamics as will be discussed in the next section.

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%Hydrogen

Bonds

Time [ns]

%Hydrogen Bonds vs. Time

%Hydrogen bonds Moving average

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RMSD[]

Time [ns]

%Hydrogen

Bonds

Correlation between RMSD and %Hydrogen Bonds

%Hydrogen bonds RMSD Moving average


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4.4 Distances Between the Backbones of ResiduesA change in distance between backbones of residues can indicate of a process that is happening

during molecular dynamics. Looking at the alpha carbons of residues 11-15 and their distance

to the alpha carbon of residue 3 shows an interesting trend. After approximately 2ns, there is a

sudden decrease in distance for all distances. A change as large as this indicates a change in

the surface of protein, and a surface analysis should reveal more, as is shown in section 4.7.

Figure 12 Distance of -Carbons throughout molecular dynamics

4.5 Salt BridgesAnother important factor determining protein structural stability is salt bridges. In this case, the

salt bridges are breaking apart throughout the molecular dynamics as depicted in Figure 13 Salt

Bridge Distances throughout molecular dynamicsFigure 13. This is possible as a result of a

stabilizing interactions of the charged side chains and bulk water. Although, breakage of salt

bridges isnt always favorable. This might indicate that salt bridge breakage allows for other

changes in the protein as a result of decrease in stress of the backbone. One important factor

that salt bridges are related to is the number of hydrogen bonds. In this case, the salt bridges

have a number of hydrogen bonds with which they can interact, shown in appendix 6.1.

Figure 13 Salt Bridge Distances throughout molecular dynamics

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AlphaCarbonDistance[]

Time [ns]

Alpha Carbon Distances vs. Time

TYR3-ARG1 1 TYR3-ASP12 TYR3-CYS13

TYR3-VAL14 TYR3-MET15

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Distance[]

Time [ns]

Salt Bridge Distances vs. Time

ASP12-LYS8

ASP12-ARG11


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4.6 Ramachandran PlotThe Ramachandran plot is an indication of the secondary structure that an amino acid is part

of. Changes in the psi and phi angles that define the backbone structure will result in amino

acids changing their location on the Ramachandran plot. Analysis of the Ramachandran plot

reveals that most amino acids dont change very much. Residues that do change somewhat are

LEU18 and GLY19. LEU18 is mostly in a turn or an undefined secondary structure, but

sometimes is in a region of a beta sheet structure. GLY19 is able to change as much as it does

(at least in terms of the psi angle) as a consequence of its small side chain. This allows for

LEU18 some flexibility as well as both residues are neighbors.

Figure 14 Ramachandran plot of LEU18 (right) and GLY18 (left)

4.7 Solvent Accessible Surface AnalysisAnalysis of the solvent accessible surface area (solvent ASA) can give an indication of what

changes in the core of the protein lead to the formation of surface phenomena which might be

of interest. As shown in Figure 15, the ASA varies to a large degree throughout the moleculardynamics. as shown before, a little before 1ns the second salt bridge starts to break and thus

the ASA increases rapidly. As show both in Figure 16 and in Figure 17, polar ASA doesnt

vary a lot, while non-polar ASA changes in the same trend as the total ASA. This is explained

in that the decrease in contact between non-polar areas and water can happen only with certain

strains in the structure are relived through the breakage of salt bridges on the other side of the

protein, as can be indicated by Figure 18 that shows an increase in charged ASA, as well as

earlier discussed in sections 4.4 and 4.5.


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Figure 15 Total accessible surface area throughout molecular dynamics

Figure 16 Polar\non-polar accessible surface area throughout molecular dynamics

Figure 17 Polar\non-polar side chains accessible surface area throughout molecular dynamics

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SolventAccessibleSu

rfaceArea[2]

Time [ns]

Total ASA vs. Time

Tota l ASA Moving average

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Polar\non-polar ASA vs. Time

Non-polar ASA Polar ASA

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Time [ns]

Polar\non-polar side chains ASA vs. Time

Non-polar side chain A SA Polar side chain ASA


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Figure 18 Charged accessible surface area throughout molecular dynamics

5 ConclusionsThe changes occurring throughout the molecular dynamics show that there are many factors to

the structure of a protein. At the beginning, the salt bridges between ASP12 and ARG11, and

between ASP12 and LYS8 break, probably as a result of stabilizing interactions with water. As

this happens, the backbone of the protein is allowed to move more freely, allowing for the

formation of cavities and specifically the cavity involving residue 3 and 11-15. During this

process non-polar residues decrease their contact with water and turn into the core of the

protein, while charged residues increase their surface area with water. As a matter of summing

things up, all these changes allow for the increase in ASA even though it might increase the

potential energy, in order to allow for changes in the residues 11-15&3 area in order to create

a cavity which allows for non-polar surface areas to hide from water, and thus stabilizing theprotein structure. The total change is portrayed in Figure 19 that shows the structures of the

protein in selected timestaps.

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PositivelyChargedASA[2]

NegativelyCharg

edASA[2]

Time [ns]

Charged ASA vs. Time

Negatively charged

Positively charged


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Figure 19 blue indicates side chains, green indicates backbone. the white indicates residues 1 and 11-15. Top: structure at

t=0.03ns; Bottom Right: structure at t=2.81ns; Bottom Left: structure at t=2.47ns. It is clrealy seen that these residues getcloser and form a cavity during the molecular dynamics.


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6 Appendices6.1 Hydrogen Bonds According To Donor-Acceptor Residues

Donor Acceptor Occupancy Atoms

CYS16-Main CYS13-Main 1.67% 232 198 233

CYS27-Main VAL5-Main 24.33% 379 80 380CYS22-Main GLY19-Main 2.00% 305 274 306

LYS8-Main ASP12-Side 20.00% 105 186 106

LYS8-Side ASP12-Side 12.00%

121 185 122

121 185 123

121 186 122

121 186 123

ARG11-Side ASP12-Side 0.67% 169 185 170

GLY17-Main VAL14-Main 1.00% 242 214 243

SER21-Main ASP10-Side 32.00%294 149 295

294 150 295

CYS30-Main CYS27-Main 4.67% 413 388 414

GLY19-Main ASP10-Side 2.33% 268 149 269

THR31-Main GLN28-Main 2.00% 423 405 424

TYR3-Main TYR26-Side 0.67% 30 371 31

CYS7-Main GLY25-Main 10.33% 95 357 96

ASP12-Main THR9-Main 2.33% 177 140 178

TYR26-Side THR31-Side 1.00% 371 429 372

GLY17-Main CYS13-Main 6.00% 242 198 243

CYS13-Main ASP10-Main 7.67% 189 152 190

LYS23-Side ASN24-Side 3.33%

331 345 332

331 345 333

331 345 334

THR31-Main CYS27-Main 0.33% 423 388 424

THR31-Side CYS27-Main 6.00% 429 388 430

VAL14-Main ARG11-Main 0.33% 199 176 200

THR9-Main ASP12-Side 3.33% 127 186 128

TYR26-Main LYS23-Main 1.67% 358 336 359

LYS23-Side GLN28-Side 0.33% 331 400 332

ASN6-Side GLY25-Main 11.67% 90 357 92

THR31-Side GLN28-Main 3.67% 429 405 430

THR31-Side TYR26-Side 2.00% 429 371 430

LYS23-Main GLY19-Main 1.00% 315 274 316

GLN28-Main TYR26-Main 0.67% 389 378 390

SER21-Side ASP10-Side 0.67% 301 150 302


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6.2 Script used to run multiple runs of Surface RacerAs the program input allow only for single frames input, there is a need to divide multiple-

frame PDBs (or PSF with DCD trajectories) into multiple files each holding a single frame.

Dividing the multiple frame file into separate files was done using the splitmultiframepdb.tcl

script from the VMD project website. In order to automate the process of the analysis using

Surface Racer, the following Batch script was used:

7 References[1] Xiang Chen et al., "Solution structure of BmP08, a novel short-chain scorpion toxin from

Buthus martensi Karsch,"Biochemical and Biophysical Research Communications, vol.

330, no. 4, pp. 1116-1126, 2005.

[2] Yifat Miller, Buyong Ma, and Ruth Nussinov, "Polymorphism in Alzheimer Abeta

amyloid organization reflects conformational selection in a rugged energy landscape,"

Chemical Reviews, vol. 110, no. 8, pp. 4820-4838, 2010.

[3] Yoav Raz and Yifat Miller, "Interactions between A and Mutated Tau Lead to

Polymorphism and Induce Aggregation of A-Mutated Tau Oligomeric Complexes,"

PLoS One, vol. 8, no. 8, p. e73303, 2013.

[4] Yoav Raz et al., "Effects of mutations in de novo designed synthetic amphiphilic -sheet

peptides on self-assembly of fibrils," Chemical Communications, vol. 49, pp. 6561-6563,

2013.

[5] Laxmikant Kal et al., "NAMD2: Greater Scalability for Parallel Molecular Dynamics,"

Journal of Computational Physics, vol. 151, no. 1, pp. 283-312, 1999.

[6] Brooks BR et al., "CHARMM: The Biomolecular Simulation Program," Journal o

Computational Chemistry, vol. 30, no. 10, pp. 1545-1615, 2009.

[7] A. D., Jr. MacKerell et al., "All - Atom Empirical Potential for Molecular Modeling and

Dynamics Studies of Proteins,"Journal of Physical Chemistry B, vol. 102, no. 18, pp.

3586-3616, 1998.


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[8] William L. Jorgensen, Jayaraman Chandrasekhar, Jeffry D. Madura, Roger W. Impey,

and Michael L. Klein, "Comparison of simple potential functions for simulating liquid

water," The Journal of Chemical Physics, vol. 79, no. 2, pp. 926-935, 1983.

[9] Michael W. Mahoney and William L. Jorgensen, "A five-site model for liquid water and

the reproduction of the density anomaly by rigid, nonpolarizable potential functions," The

Journal of Chemical Physics, vol. 112, no. 20, pp. 8910-8922, 2000.

[10] W Humphrey, A Dalke, and K Schulten, "VMD - Visual Molecular Dynamics,"Journal

of Molecular Graphics, vol. 14, no. 1, pp. 33-38, 1996.

[11] M Heinig and D Frishman, "Knowledge-based protein secondary structure assignment,"

PROTEINS, vol. 23, no. 4, pp. 566-579, 1995.

[12] OV Tsodikov, MT Jr. Record, and Sergeev YV, "A novel computer program for fast

exact calculation of accessible and molecular surface areas and average surface

curvature,"Journal of Computational Chemistry, vol. 23, no. 6, pp. 600-609, 2002.

[13] Piana Stefano et al., "Evaluating the Effects of Cutoffs and Treatment of Long-range

Electrostatics in Protein Folding Simulations,"PLoS ONE, vol. 7, no. 6, p. e33918, 2012.

[14] B Knapp, N Lederer, U Omastis, and W Schreiner, "vmdICE: a plug-in for rapid

evaluation of molecular dynamics simulations using VMD,"Journal of Computational

Chemistry, vol. 31, no. 16, pp. 2868-2873, 2010.

Protein Computational Modelling Project - Natan Kalson

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