SIMULATION OF FOLDING PATHWAY STUDIES OF TRP...

SIMULATION OF FOLDING PATHWAY STUDIES OF TRP-CAGE

MINIPROTEIN, AMYLOID A4 PEPTIDE AND α-CONOTOXIN RgIA PEPTIDE

FATAHIYA BTE MOHAMED TAP

UNIVERSITI TEKNOLOGI MALAYSIA

SIMULATION OF FOLDING PATHWAY STUDIES OF TRP-CAGE

MINIPROTEIN, AMYLOID A4 PEPTIDE, AND α-CONOTOXIN RgIA PEPTIDE

FATAHIYA BTE MOHAMED TAP

A thesis submitted in fulfilment of the

requirements for the award of the degree of

Master of Engineering (Bioprocess)

Faculty of Chemical Engineering

Universiti Teknologi Malaysia

DEC 2013

iii

ACKNOWLEDGEMENT

I would like to express the deepest appreciation and sincere gratitude to my

supervisor, Dr. Nurul Bahiyah Ahmad Khairudin for her valuable guidance,

supervision and endless support throughout my study. Her knowledge and advice had

made this study possible.

My special thanks to all the members at UTM and Pharmaceutical Design

and Simulation Laboratory, School of Pharmaceutical, Universiti Sains Malaysia for

their friendship, support and help during my training session.

I also wish to express my gratitude and love to my parents, husband and

family for their encouragement, motivation and understanding during my study.

iv

ABSTRACT

Protein is a sequence of a linear chain of amino acids. Protein folding is a

physical process by which the linear chains of amino acid fold into its functional

tertiary structures. Misfolding of a protein will lead to the problem such as diseases

(cancer and influenza) in protein function. Discovery of protein folding will help the

biologist to find the cause of misfolding and also assist the drug designer to find the

cure for related diseases. Therefore the objective of this study is to investigate the

folding pathways of Trp-cage miniprotein, Amyloid A4 peptide, and α-conotoxin

RgIA. The folding process was simulated using molecular dynamics (MD)

simulation in both explicit and implicit solvent. Amyloid A4 peptide (350ns) and α-

conotoxin (800ns) were simulated in implicit solvent, while the simulation for Trp-

cage (150ns) and α-conotoxin (200ns) were performed in explicit solvent method.

The simulations produced a huge number of trajectories which were further analysed

based on their root mean squared deviation (RMSD) values. The RMSD values

showed that these trajectories approaching their simulated native structure (NMRMD).

Besides that, a few crucial formations of hydrogen bond, disulfide bond, and salt

bridge were involved in stabilizing the folding process. The best structure was

identified by clustering all the trajectories based on RMSD, solvent accessible

surface area (SASA), van der Waals interaction, electrostatic interactions and total

energy of each trajectory. The best structure for Trp-cage miniprotein, Amyloid A4

peptide, α-conotoxin with implicit solvent and, α-conotoxin with explicit solvent

were extracted at 79.76 ns, 224.85 ns, 184.20, and 104.20 ns, respectively.

v

ABSTRAK

Protein merupakan jujukan rantaian asid amino. Lipatan protein adalah satu

proses fizikal di mana rantaian lurus asid amino membentuk kepada lipatan struktur

tiga dimensi. Kesalahan lipatan protein akan mendorong kepada permasalahan

penyakit (kanser dan influenza) dalam fungsi protein. Mengetahui laluan lipatan

protein akan membantu ahli biologi untuk mencari punca kesalahan lipatan protein,

serta membantu pereka ubat untuk mencari penawar sesuatu penyakit. Oleh itu,

objektif kajian ini adalah untuk mengkaji laluan lipatan Trp-cage miniprotein,

Amiloid A4 peptide, dan α-conotoxin RgIA. Proses lipatan protein dilakukan

dengan menggunakan dua kaedah simulasi iaitu melalui kehadiran air sebagai pelarut

dan tanpa kehadiran air sebagai pelarut. Amiloid peptida A4 (350ns) dan α-

conotoxin (800ns) adalah protein yang digunakan untuk kaedah simulasi tanpa

kehadiran air sebagai pelarut, manakala Trp-cage (150ns) dan α-conotoxin (200ns)

telah digunakan dalam kaedah simulasi dengan kehadiran air sebagai pelarut. Proses

simulasi menghasilkan banyak trajektori dan ianya telah dianalisa berdasarkan

kepada nilai punca min sisihan kuasa dua (RMSD). Nilai RMSD menunjukkan

trajektori yang menghampiri struktur sebenar protein. Selain daripada itu, beberapa

pembentukan ikatan hidrogen, ikatan disulfid, dan ikatan jambatan garam yang

penting telah dikenal pasti membantu menstabilkan proses lipatan protein. Struktur

yang terbaik pula telah dikenal pasti dengan mengkelaskan kesemua trajektori

berdasarkan RMSD, luas permukaan pelarut boleh capai (SASA), ikatan van der

Waals, ikatan elektrostatik, dan jumlah tenaga struktur protein untuk setiap trajektori.

Struktur yang terbaik untuk Trp-cage miniprotein, Amiloid A4 peptide, α-conotoxin

RgIA tanpa kehadiran air sebagai pelarut dan α-conotoxin RgIA dengan kehadiran

air sebagai pelarut telah diekstrak pada 79.76 ns, 224.85 ns, 184.20 ns, dan 104.2 ns.

vi

TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

ACKNOWLEDGEMENT iii

ABSTRACT iv

ABSTRAK v

TABLE OF CONTENTS vi

LIST OF TABLES ix

LIST OF FIGURES x

LIST OF ABBREVIATIONS xv

LIST OF SYMBOLS xvi

LIST OF APPENDICES xvii

1 INTRODUCTION 1

1.1 Research background 1

1.2 Problem statement 2

1.3 Objective 3

1.4 Scopes 3

2 LITERATURE REVIEW 4

2.1 Introduction to Peptide 4

2.2 Introduction to Protein 5

2.3 Protein Structure 5

2.3.1 Primary Structure 5

2.3.2 Secondary Structure 7

2.3.3 Tertiary Structure 9

2.3.4 Quaternary Structure 9

vii

2.4 Trp-cage Miniprotein 10

2.5 Amyloid A4 peptide as an Amyloid Plaques in

Alzheimer

11

2.6 α-Conotoxin RgIA 12

2.7 Introduction to Protein Folding 13

2.8 Molecular Dynamics Simulation of Protein Folding 14

2.9 The Limitation of Time Scale in Protein Folding 15

2.9.1 The Protein Folding Simulation of Trp-cage 16

2.10 The Interactions Involved in Protein Folding 17

2.10.1 van der Waals Interaction 17

2.10.2 Electrostatic Interaction 18

2.10.3 Hydrogen Bond Interaction 18

2.10.4 Salt Bridge 19

2.10.5 Disulfide Bond 19

2.11 Parameters of Molecular Dynamics Simulation 19

2.11.1 Force Field 20

2.11.2 Equation of Motion 21

2.11.3 Verlet Algorithm 22

2.11.4 Temperature Constant 23

2.11.4.1 Langevin thermostat 23

2.11.4.2 Berendsen thermostat 24

2.11.5 Pressure Constant 24

3 RESEARCH METHODOLOGY 25

3.1 Introduction to Trp-cage (1L2Y), Amyloid A4 Peptide

(1AML), and α-conotoxin (2JUQ)

25

3.1.1 Trp-cage miniprotein (PDB ID: 1L2Y) 25

3.1.2 The Alzheimer’s disease Amyloid A4 peptide

(PDB ID: 1AML)

27

3.1.3 The α-conotoxin RgIA peptide (PDB ID: 2JUQ) 28

3.2 Methodology 29

3.2.1 Structure Preparation 30

3.2.2 MinimisationStep 30

3.2.3 MD Simulation Process 31

viii

3.2.3.1 MD Simulation with Implicit Solvent 31

3.2.3.2 Md Simulation with Explicit Solvent 31

3.2.4 Trajectory Analysis 32

3.2.4.1 RMSD 32

3.2.4.2 Secondary Structure Annotation 32

3.2.4.3 Molecular Interaction Viewer 33

3.2.4.4 Clustering Analysis 33

3.2.4.5 Radius of Gyration 33

3.2.4.6 van der Waals Interaction Energy,

Electrostatic Energy, and Total

Energy

33

4 RESULT AND DISCUSSIONS 34

4.1 Simulation of 2JUQ with Implicit Solvent Method 34

4.2 The Folding Pathways of 2JUQ in Explicit Solvent

Method

44

4.3 Folding Process of Amyloid A4 Peptide (1AML) with

Implicit Solvent Method

50

4.4 Molecular Dynamics Simulation of Trp-cage with

Explicit Solvent Method

60

5 CONCLUSION 67

REFERENCES 69

Appendices A – E 76 - 80

ix

LIST OF TABLES

TABLE NO. TITLE PAGE

4.1 Comparison of the best structural properties between

each cluster

40

4.2 The SASA value for the best structure from each cluster. 40

4.3 The evolution of hydrogen bonds for 2JUQ in explicit

solvent methods up to 200 ns.

45

4.4 Comparison of the best structural properties between each

cluster

47

4.5 The SASA value for the best structure from each cluster 48


cluster

56



cluster

63


x

LIST OF FIGURES

FIGURE NO. TITLE PAGE

2.1 General structure of amino acid 6

2.2 Formation of peptide bond 6

2.3 Primary structure 7

2.4 α-helix 8

2.5 a) β pleated sheet, antiparallel and b) β pleated sheet,

parallel 8

2.6 Protein tertiary structure 9

2.7 Quaternary structure 10

2.8 Native structure of Trp-cage miniprotein from NMR 11

2.9 Native structure of Amyloid A4 peptide from NMR 12

2.10 Native structure of α-Conotoxin from NMR 13

2.11 Lennard jones potential graph 18

xi

3.1 The structure of Trp-cage miniprotein. The ribbon and

stick representation are backbone and side chain

respectively.

26

3.2 The structure of Amyloid A4 peptide. The ribbon and

stick representation are backbone and side chain

respectively

27

3.3 The structure of α-conotoxin with 13 residues. The

ribbon and stick representation are backbone and side

chain respectively

28

3.4

Flow diagram of MD simulation process

29

4.1 Representation of the evolution of RMSD values for α-

conotoxin

34

4.2 Representation of the folding event of 2JUQ. a) The

NMRMD structure; b) and c) the formation of 310-helix at

180 ns and 286 ns, respectively; and d) the α-helix

formation at 381 ns.

35

4.3 The formation of 310-helix at residues Cys2 to Asp5. The

cyan and magenta colors show the 310-helix and coil

respectively.

36

4.4 The hydrogen bond formation between residues Arg13

and Arg9 for the structure α-conotoxin at 286 ns.

37

4.5 The disulfide bond link between Cys2 and Cys8 with the

distance 5.21 Å

37

xii

4.6

Evolutions of disulfide bonds between Cys2-Cys8 and

Cys3-Cys12 around 20 ns to 399.8 ns.

38

4.7 The Ramachandran Plot for a) the model at 310 ns

structure; and b) NMRMD structure

39

4.8 The best structure for each clusters, a) cluster 1, b)

cluster 2, c) cluster 3, d) cluster 4, e) cluster 5, f) cluster

6 at 799.09 ns, 74.9 ns, 130.92 ns, 354.35 ns, 519.34 ns,

184.20 ns respectively

41

4.9 The surface presentation for the best structure from each

clusters, a) cluster 1, b) cluster 2, c) cluster 3, d) cluster

4, e) cluster 5, f) cluster 6 at 799.09 ns, 74.9 ns, 130.92

ns, 354.35 ns, 519.34 ns, 184.20 ns respectively. The

blue shaded area shows the polar region of the structure.

42

4.10 The evolution of RMSD of 2JUQ in explicit solvent

44

4.11 Represents a) the hydrogen bond ( Asp5 – Arg7) at 60 ns

with occupancy of 55% and b) the hydrogen bond ( Asp5

– Arg8) at 90 ns with occupancy of 57%.

45

4.12 The formation of hydrogen bond ( Cys3 – Arg11) at 100

ns with occupancy of 73 %.

46



6 at 32.48 ns, 60.04 ns, 19.56 ns, 104.20 ns, 197.96 ns

and 121.16 ns respectively

48

xiii



4, e) cluster 5, f) cluster 6 at 32.48 ns, 60.04 ns, 19.56 ns,

104.20 ns, 197.96 ns and 121.16 ns respectively. The red

and green shaded area shows the polar region of the

structure

49

4.15 The formation of hydrogen bond at Asp23-Gly29

51

4.16 The evolution of the RMSD values for 350 ns of the

simulation time.

52

4.17 Representative conformations of the 1AML trajectories.

This figure presents the turn extended and changed to

helix as the simulation proceeded. The cyan and purple

colours represent turn and coil, respectively, while the

new cartoon represents helix formation.

53

4.18 a), b) and c) Ramachandran plot for trajectories at 5 ns,

224 ns, and NMRMD structure respectively.

55

4.19 The best structure for each cluster, a) cluster 1, b) cluster

2, c) cluster 3, d) cluster 4, e) cluster 5, f) cluster 6 at

89.4 ns, 224.85 ns, 274.18 ns, 5.37 ns, 324.64 ns and

149.36 ns respectively.

57


cluster, a) cluster 1, b) cluster 2, c) cluster 3, d) cluster 4,

e) cluster 5, f) cluster 6 at 89.4 ns, 224.85 ns, 274.18 ns,

5.37 ns, 324.64 ns and 149.36 ns respectively.

58

4.21 The formation of secondary structure at 224.85 ns

59

xiv

4.22 The evolution of the RMSD values between 30 ns to 120

ns. The red circle shows the stable region of RMSD

values during the process of simulation from 60 ns to 100

ns.

60

4.23 The formation of hydrogen bond at Gly11-Ser14

61

4.24 The evolution of the 1L2Y conformation throughout the

150 ns simulation time. The conformation a, b, c, and d

were extracted at 40 ns, 80 ns, 135 ns, and 145 ns,

respectively

62



6 at 29.39 ns, 79.76 ns, 144.08 ns, 129.48 ns, 34.71 ns

and 39.61 ns respectively

64



4, e) cluster 5, f) cluster 6 at 29.39 ns, 79.76 ns, 144.08

ns, 129.48 ns, 34.71 ns and 39.61 ns respectively

65

4.27 The secondary structure formation at 79.76 ns

66

xv

LIST OF ABBREVIATIONS

3D - Three dimensional structure

AMBER11 - Assisted model building with energy refinement version 11

CHARMM - Chemistry at Harvard molecular mechanics

COM - Center of mass

DNA - Deoxyribonucleic acid

GB - Generalize bond

GROMACS - Groningen machine for chemical simulations

MD - Molecular dynamics

MMPBSA - Molecular modelling poison Boltzmann surface area

MMTSB - Multiscale modelling tools for structural biology

NMR - Nuclear magnetic resonance

NMRMD - Simulated nuclear magnetic resonance structure

PDB - Protein data bank

RMSD - Root mean square deviation

RMSDc - RMSD between the best structure

RMSDc-best - RMSD between the centroid structure and the best structure

RMSDbest-NMRMD - RMSD between the best structure and NMR structure.

RMSDc-NMRMD - RMSD between the centroid structure and NMR structure

SASA - Solvent accessible surface area

VMD - Visual molecular dynamics

xvi

LIST OF SYMBOLS

% - Percentage

Å - Angstrom

ai - Acceleration of particle i

Ekin - Kinetic energy

Fi - Force exerted on particle i

K - Isothermal compressibility

KB - Boltzmann constant

Ɩi,o - Bond length

mi - Mass of particle i

N - Number of particles

n - Number of moles

r - Radius

Ri - Frictional force

t - Time

T - Temperature

u - Potential energy

α - Alpha

β - Beta

εi,j - Well depth for Lennad Jone Potential

σi,j - Collision diameter for Lennard Jones Potential

Φ - Dihedral angle in protein structure (phi angle)

Ψ - Dihedral angle in protein structure (psi angle)

ω - Dihedral angle in protein structure (omega angle)

xvii

LIST OF APPENDICES

APPENDIX TITLE PAGE

A Input file for minimisation step of Trp-cage

miniprotein

76

B Input file for minimisation step of Amyloid A4

peptide

77

C Input file for minimisation step of α-conotoxin 78

D Input file for implicit solvent simulation 79

E Input file for explicit solvent simulation 80

CHAPTER 1

INTRODUCTION

1.1 Research Background

Protein is composed of one or more chains of amino acids. Protein carries out

important function in every cell. In order for the protein to function correctly, it must

fold into its three-dimensional structure. Therefore, understanding the protein

folding process is vital because several diseases such as Alzheimer and cancer are

directly related to the misfolding of protein. All these diseases have no cure until

today and this problem has not been solved for more than 4 decades.

The causes for those diseases such as Alzheimer, Parkinson and Influenza can

be found if the folding process of protein is known. This is the major challenge in

science today since nobody knows how the protein folds. Theoretically, protein

folding is a process in which the sequence of amino acids folds naturally into its

three-dimensional structure. The formation of the three-dimensional structure is

related to the interaction among amino acid residues. The most important finding in

understanding protein folding was carried out by Anfinsen (1972) and his colleagues;

they claimed that the structure of the protein is determined by the sequence of amino

acids. Findings by Anfinsen and colleagues have inspired researchers to continue

investigating the pathways of protein folding. Therefore the evolution in studying

protein folding ha increased very rapidly, researchers have come up with various

methods and they have proven that protein folding can be simulated using computer

(Levitt and Warshel, 1975a). Computational method such as molecular dynamics

(MD) simulation is a powerful tool due to its high resolutions and detailed atomic

2

level representation. Furthermore, the increase in computer speed and improvements

in force field along with more efficient computation algorithms have brought realistic

computational simulation of the folding process within reach (Pande et al., 2003,

Scheraga et al., 2007).

The aim of this study was to investigate the pathway of the protein folding

towards their native or near native state using MD simulation. MD simulation was

employed using Amber11 (Case et al., 2010). Several studies using this programme

shown promising result (Sonavane et al., 2008, Best, 2012). There are two types of

simulations that can be applied; they are implicit solvent method and explicit solvent

method. For this research, both simulations were used. The protein α-Conotoxin

(PDB ID: 2JUQ) was simulated using both methods, while Trp-cage (PDB ID:

1L2Y) and Amyloid (PDB ID: 1AML) were simulated using explicit solvent method

and implicit solvent method, respectively.

1.2 Problem Statement

Researchers have defined how the amino acid sequence of a protein is coded

into DNA. However, the secret on how the protein folds into its three-dimensional

structure still remains unsolved. Many theoreticians and biologists have huge

interests to investigate the pathway of protein folding. This is proven by the

increasing number of new findings on the fundamental, knowledge, and theory of

protein folding.

On the experimental front, artificially designed autonomous-folding mini

protein has been solved. These findings have helped researchers to address the

fundamental issues regarding protein folding. However, protein has marginally

stable non-native states that are difficult to observe experimentally. In order to

identify this structure, the best method is to use computational simulation. This is

because this method has high resolution and provides detailed atomic-level

presentations.

3

1.3 Objective

The objective of this study is to study the folding pathway of Trp-cage

miniprotein, Amyloid A4 peptide and α-conotoxin RgIA peptide using molecular

dynamics simulation.

1.4 Scopes

There are five scopes for this study. The first scope is to use the Trp-cage

miniprotein, Amyloid A4 peptide, and α-conotoxin as subjects for investigating the

folding pathway. The second scope is to investigate the hydrogen bond formation,

disulfide bond and salt bridge formation from the trajectories. The third scope is to

develop clusters from the trajectories based on the RMSD value using clustering

analysis. The forth scope is to identify the SASA value for each cluster. The fifth

scope is the find the best structure based on RMSD value, radius of gyration, total

energy, van der Waals interaction energy, and electrostatic interaction energy.

69

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