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Molecular dynamics simulations on the binding of N-acetyl ornithine to ArgJ of Mycobacterium tuberculosis in the presence of ANS inhibitor

Authors:
1
Molecular dynamics simulations on the binding of N-
acetyl ornithine to ArgJ of Mycobacterium tuberculosis
in the presence of ANS inhibitor
Dissertation submitted to the VIT University in partial fulfillment of the
requirements for the degree of
MASTER OF SCIENCE
IN
BIOTECHNOLOGY
By
Dheeraj Kumar Sarkar
(Reg. No. 15MSB0087)
Department of Bio Sciences
School of Bio Sciences and Technology
VIT University, Vellore 632014
May 2017
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Declaration by the Candidate
I, hereby, declare that the thesis eMolecular dynamics simulations on the binding of N-
acetyl ornithine to ArgJ of Mycobacterium tuberculosis in the presence of ANS inhibitor
submitted by Dheeraj Kumar Sarkar, Reg.No. 15MSB0087 to VIT University, Vellore in
fulfillment of the requirement for the award of the degree of M.Sc Biotechnology is a record of
bonafide research work carried out by me under the guidance of (External guide OR VIT Guide)
during the course of the year 2015 2017. I further declare that the work reported in this
dissertation has not been submitted, and will not be submitted, either in part or in full, for the award
of any other Degree or Diploma of this University or of any other Institute or University.
 Signature of the Candidate
4
Certificate of the External Guide
Molecular dynamics simulations on
the binding of N-acetyl ornithine to ArgJ of Mycobacterium tuberculosis in the
presence of ANS inhibitorMr Dheeraj Kumar Sarkar Reg. No.
15MSB0087 to the School of Bio Sciences and Technology, VIT University, Vellore -
632 014, Tamil Nadu in partial fulfillment of the requirements for the Degree of
M.Sc. Biotechnology is a record of work carried by her/him under my supervision
(Dr. A Surolia, Honorary Professor, Indian Institute of Science, Bangalore). The
contents of this dissertation, in full or in parts, have not been submitted to any other
Institute or University for the award of any Degree or Diploma.
Signature of the Guide
5
Certificate of the Internal Guide
Molecular dynamics simulations on
the binding of N-acetyl ornithine to ArgJ of Mycobacterium tuberculosis in the
presence of ANS inhibitor is submitted by Mr. Dheeraj Kumar SarkarReg. No.
15MSB0087 to the School of Bio Sciences and Technology, VIT University, Vellore-
632 014, Tamil Nadu in partial fulfillment of the requirements for the Degree of
M.Sc. Biotechnology is a record of work carried by her/him under my supervision
(Dr. Sudha Ramaiah, Associate Professor). The contents of this dissertation, in full
or in parts, have not been submitted to any other Institute or University for the award
of any Degree or Diploma.
Signature of the Head, Department of Bio-Sciences Dean of the School
Internal Guide
Internal Examiner External
Examiner
6
Abstract
Mycobacterium tuberculosis is the causative agent of tuberculosis and also co-exists
of tuberculosis with HIV infection in human beings. So far, there are drugs available
in market, but due to multi-drug resistance of Mtb strains, there is a dire for new
potential drugs. In this context, discovering new drug targets and repurposing the
already approved aids in complementing drugs in the existing drug pipeline. In this
context, Arginisuccinate of arginine synthesis pathway provides a potential drug
target. Mycobacterium protein, ArgJ (MtbArgJ) is responsible for the production of
ornithine acetyl transferase which catalyses the formation of L-ornithine and N-acetyl
glutamate which is a vital step for Mycobacterium arginine synthesis pathway.
The present study focuses on analysing the capability of disrupting the catalysis of N-
acetyl ornithine. Hence, MD simulations were performed to study the dynamic
interaction of binding and dissociation of NAO. The simulation results indicated that
the dissociation of N-acetyl ornithine from active site pocket of MtbArgJ protein is
paved by the association of 8-anilinonepthalene-1-sulfonic acid inhibitor to the
allosteric site of the protein. Nevertheless, the direct reason comes from the active site
residues involved in hydrogen bonding interactions between the substrate N-acetyl
ornithine and MtbArgJ protein is more flexible when 8-anilinonepthalene-1-sulfonic
acid inhibitor is bound with it.
The present investigation suggests that the interaction of 8-anilinonepthalene-1-
sulfonic acid to the allosteric site of MtbArgJ protein hampers the binding affinity
between the residues of active site of MtbArgJ protein and N-acetyl ornithine
substrate.
Key words: ArgJ, tuberculosis, multi-drug resistant, inhibitor, substrate
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Acknowledgement
First I would like to express my heartfelt thanks and gratitude towards my parents
and family whose support has been an enormous strength for me throughout the
periodofmywork.
It gives me immense pleasure and gratitude to express my heartfelt thanks to Dr. A
Surolia, Honorary professor, Molecular Biophysics Unit, Indian Institute of Science,
Bangalore, India. I gratefully acknowledge the opportunity that he gave me to work
with him and exposed me to this extremely competent atmosphere of science. His
consistent guidance and support for my work and many things apart from it has made
me learn a lot of things not only from the science but for things away from it.
It gives me immense pleasure to express my gratitude to Dr. M. Asha Latha Sreshty,
PDF @AS-lab, who was the backbone for me in my work. She was the one who taught
me the minute basics of the work, right from the way I handle high performance
computing to everything. It reminds me how she used to ask me note down every
single details of the work to achieve ultimate perfection in it. I learnt a lot by looking
her work and understood the importance of time management in research.
I would like to take the opportunity to acknowledge Dr.Sudha Ramaiah, Associate
Professor, School of Biosciences and Technology, Division of Biological Sciences,
VIT, India. Her consistent follow ups and guidance during my work helped me learn a
lot. I thank her for being a great mentor to me for helping me learn the philosophy of
life and science.
It’s my pleasure to acknowledge other members of the lab, who has been wonderful
people around and made my stay a wonderful experience.
I owe a special thanks to Dr. V. Raju, VC, VIT University;Dr.Seenivasan.R,Professor,
SBST; Mr.SubbajiRao, Assistant Director, IR office, VIT; Mr. JamesOsborne, IR
Office, VIT, and most importantly to Dr. G. Vishwanathan, Chancellor,VIT
University.
And finally I would like to express my heartfelt thanks to my co-project trainee
Nagender and Ishaki, whose inevitable support is inexpressible.
Place:
Date: Dheeraj Kumar Sarkar
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Contents
ABSTRACT……………………………………………………………………………………06
ACKNOWLEDMENT………………………………………………………………………...07
CONTENTS……………………………………………………………………………………08
Chapter1
Introduction…………………………………………………………………………………….15
Chapter2
Aims and Objectives…………………………………………………………………………...18
2.1 Aim of the study…………………………………………………………………..18
2.2 Objectives………………………………………………………………………….18
2.3. Organization of the thesis……………………………………………………......19
Chapter 3
Literature Review………………………………………………………………………………20
3.1 Tuberculosis……………………………………………………………………......20
3.2 Mycobacterium tuberculosis……………………………………………………….22
3.3 TB and AIDS……………………………………………………………………....24
3.4 Present status of anti-TB medications…………………………………………...24
3.5 Arginine biosynthesis pathway…………………………………………………...26
3.6 Fundamentals of Molecular Dynamics Simulations…………………………….27
3.6.1 Pressure and temperature control in MD simulation……………...28
3.6.2 Periodic boundary condition………………………………………......29
3.6.3 Pressure coupling protocols…………………………………………...30
3.6.4 Molecular force field…………………………………………………..31
3.6.5 Periodic cell shape……………………………………………………...33
3.6.6 Water models…………………………………………………………...34
3.7 Binding free energy calculation……………………………………………….......35
3.8 Steered molecular dynamics simulations………………………………………….38
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Chapter 4
Materials and
Methods…………………………………………………………………………………………39
4.1 Model construction……………………………………………………………….39
4.2System preparation and molecular dynamics simulation………………………40
4.3 Preliminary analysis………………………………………………………………40
4.4 Binding free energy calculation…………………………………………………41
4.5 Steered molecular dynamics simulations………………………………………..42
Chapter 5- Results……………………………………………………………………………...44
5.1 Preliminary analysis………………………………………………………………44
5.1.1 RMSD…………………………………………………………………..46
5.1.2 H-bond calculation…………………………………………………….48
5.1.2.1 LigPlot analysis……………………………………………..54
5.1.3 RMSF…………………………………………………………………...58
5.2 Free energy calculation………………………………………………….61
5.3 Steered molecular dynamics simulations………………………………………...63
Chapter 6 Discussion…………………………………………………………………………….69
6.1 Preliminary analysis……………………………………………………………….69
6.2 Free energy calculation…………………………………………………………...70
6.3 Steered molecular dynamics simulations………………………………………...70
Appendix………………………………………………………………………………..72
References……………………………………………………………………………..74
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Lists of Figures
Fig 1.1 Catalytic reaction of ArgJ enzyme
Fig 1.2 Cartoon monomeric structure of MtbArgJ protein
Fig 3.1.Countries in the three TB high-burden country lists
Fig 3.2 Estimated TB incident rates, 2015
Fig 3.3 Estimated incident of MDR/RR-TB in 2015
Fig 3.4 Genome of Mycobacterium tuberculosis and functional categories are shown
in colour 
Fig 3.5 Stages of Tuberculosis infection in human
Fig 3.6 Percentage of new and relapse TB cases with HIV status, 2015
Fig 3.7 Arginine biosynthesis showing both linear and cyclic pathways
Fig 3.8 Periodic boundary conditions
Fig 3.9 Pressure coup
Fig 3.10 Types of force field
Fig 3.11 Schematic diagram of MM-PBSA technique adopted in
g_mmpbsamethod

Fig 4.1 Cartoon image of modelled structure of Mt
Fig 5.1 Cartoon structure of MtbArgJ protein bound with N-acetyl ornithine at
substrate pocket 1 and pocket 2 in absence of ANS
Fig 5.2 Cartoon structure of MtbArgJ protein bound with N-acetyl ornithine at
substrate pocket 1 and pocket 2 in presence of ANS 
Fig 5.3 RMSD plot of free MtbArgJ protein bound with substrate NAO
Fig 5.4 RMSD plot of MtbArgJ protein complexed with ANS inhibitor and NAO
substrate
Fig 5.5a LigPlot analysis of H-bond interactions between NAO and MtbArgJ at
substrate pocket-1 in the absence of ANS inhibitor
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Fig 5.5b LigPlot analysis of H-bond interactions between NAO and MtbArgJ at
substrate pocket-1 in the presence of ANS inhibitor
Fig 5.6a LigPlot analysis of H-bond interactions between NAO and MtbArgJ at
substrate pocket-2 in the absence of ANS inhibitor
Fig 5.6b LigPlot analysis of H-bond interactions between NAO and MtbArgJ at
substrate pocket-2 in the presence of ANS inhibitor 
Fig 5.7 LigPlot analysis of H-bond interactions between ANS and MtbArgJ at
allosteric pocket
Fig 5.5 RMS fluctuation of residues involved in hydrogen bond interactions in
substrate pocket 1 in absences and presence of ANS inhibitor
Fig 5.6 RMS fluctuation of residues involved in hydrogen bond interactions in
substrate pocket 2 in absences and presence of ANS inhibitor
Fig 5.7 SMD analysis of MtbArgJ protein bound with NAO in presence of ANS and
in absence of ANS for substrate pocket 1
Fig 5.11a Dissociation of N-acetyl ornithine from substrate pocket-1 of MtbArgJ in
the absence of ANS
Fig 5.11b Dissociation of N-acetyl ornithine from substrate pocket-1 of MtbArgJ in
the presence of ANS 
Fig 5.12 SMD analysis of MtbArgJ protein bound with NAO in presence of ANS and
in absence of ANS for substrate pocket 2
Fig 5.13a Dissociation of N-acetyl ornithine from substrate pocket-2 of MtbArgJ in
the presence of ANS
Fig 5.13b Dissociation of N-acetyl ornithine from substrate pocket-2 of MtbArgJ in
the presence of ANS
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List of Tables
Table 1: Residues involved in hydrogen bond interactions between MtbArgJ protein
and N-acetyl ornithine (NAO) in substrate pocket 1 in absence of ANS
inhibitor49
Table 2: Residues involved in hydrogen bond interactions between MtbArgJ protein
and N-acetyl ornithine (NAO) in substrate pocket 1 in presence of ANS
inhibitor
Table 3: Residues involved in hydrogen bond interactions between MtbArgJ protein
and N-acetyl ornithine (NAO) in substrate pock
Table 4: Residues involved in hydrogen bond interactions between MtbArgJ protein
and N-acetyl ornithine (NAO) in substrate pocket 2 in presence of AN52
Table 5: Residues involved in hydrogen bond interactions between MtbArgJ protein
and ANS
Table 6: Results of MM-PBSA calculations
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Symbols and Notations
:Angstrom
AFM : Atomic Force Microscopy
ANS : 8-anilinonepthalene-1-sulfonic acid
ArgJ :Ornithine acetyltransferase
AIDS :acquired immune deficiency syndrome
BCG :BacilleCalmette Guerin
COM : Centre of Mass
HIV :human immunodeficiency virus
H-Bond : Hydrogen Bonding
INH : isoniazid
KJ/mol : Kilo Joule per molar
Mtb :Mycobacterium tuberculosis
MtbArgJ :Mycobacterium tuberculosis ArgJ protein
MDR-TB : Multi-drug Resistant tubercilosis
MD : Molecular Dynamics
nm :nano-meter
ns :nano-second
NAGS : N-acetyl glutamate synthase
NAO : N-acetyl ornithine
NPT : Normal Pressure and Temperature
NVT : Normal Volume and Temperature
OAT : Ornithine acetyltransferase
PDB : Protein Data Bank
RCSB : Research Collaboratory for StructuralBioinformatics
RIF : rifampicin
RMSD : Root Mean Square Deviation
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RMSF : Root Mean Square Fluctuation
SMD : Steered Molecular Dynamics
TB : Tuberculosis
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Chapter 1
Introduction
Mycobacterium tuberculosis (Mtb) is the most pathogeneous micro-organism which is
responsible for causing tuberculosis. According to a statistical survey done by WHO,
3.7% of new cases reported were MDR-TB and about 60% of these cases were noted
in Brazil, China, India, the Russian Federation and South Africa alone.In 2015 alone
the percentage of TB patients in co-existence to with HIV infection were reported to
be 64% on average in countries like India and other high TB/HIV burden countries
while 81% and 82% in African region and Americas respectively (WHO,
2016).Multidrug resistances strains of Mycobacterium tuberculosis (Mtb) is resistant
to some of the most powerful first-line and second-line anti-tuberculosis drugs
namely, rifampicin (RIF) and isoniazid (INH). The co-existance of TB with AIDS is
one of the key factors behind the resurgence of TB despite of devolopment of drug-
       (Shankaranarayananet.al., 2010).
The strains of the obligate human pathogen Mycobacterium tuberculosis with
supporting theory of phylogenetic and phylogeographic analysis has shown that the
bacterium has evolved and migrated with the human host (Comas Iet.al, 2013). Since,
antibiotics have been implemented for the first time for the treatment of tuberculosis
and after the era of predictive effective tuberculosis chemotherapy began in 1952
some strains of TB bacteria have developed resistances to standard drug through some
genetic changes (Iseman,1993). The most widely used vaccine for TB is BCG which
is sometimes found unreliable against highly infectious pulmonary tuberculosis (Haile
M and Kallenius G, 2005). The various factors which may be responsible for low
efficacy of BCG vaccine may be due to environmental, genetic or immunological
(Young D and Dye C, 2006) Therefore, the alarming rate of this disease have called
for the development of effective anti-bacterial drugs which can perturb the
metabolism of Mycobacterium tuberculosis (Mtb).
Mycobacterium tuberculosisornithine acetyltransferase (OAT) has been noted to be a
key enzyme involved in acetyl recycling in the arginine synthesis pathway of Mtb.
Mycobacterium argJ gene product OAT catalyses the formation of L-ornithine and N-
acetyl glutamate by recycling acetyl group from acetylornithine therefore
ArgJencoded products exhibits both N-acetyl glutamate synthase(NAGS) and OAT
16
activity. While, in some organisms like C. Glutamicum, the ArgJ encoded product is
considered as monofunctional enzyme and lacks NAGS activity.
Fig 1.1: Catalytic reaction of ArgJ enzyme
According to the study of structural analysis of OAT performed byShankaranarayanan
et al (2013), the active site of Mycobacterium tuberculosis OAT comprises residues
Gly128, Thr166, Lys189, Thr200, Glu280, Asn399 and Ser404 and the presence of
ornithine OAT enzyme causes stabilization of disordered C-terminal residues that
are disordered and not observed in the native structure. Also, stabilization of the C-
terminal residues reduces the size of the active-site pocket volume in complex with
ORN complex. The interactions of ORN and the protein
residues of Mtb OAT unambiguously delineate the active-site residues of this enzyme
in Mtb. (Shankaranarayananet.al., 2010)
Fig 1.2: Cartoon monomeric structure of Mycobacterium tuberculosis ArgJ
protein showing Chain A (domain I) and Chain B (domain II)
17
Here, in the present study, investigation of the the impact of ANS, an inhibitor on the
substrate catalysis of MtArgJ was studied. For this, MD simulation was applied to
unleash the binding dynamics of N-acetyl ornithine in MtbArgJ with and without
ANS. ANS is a fluorescent molecular probe which binds to the hydrophobic region of
a protein surface and the binding of ligand to the protein causes ANS fluorescent
properties to change as it interacts with the hydrophobic region on a protein. (Gains N
et al.,1975)
Molecular dynamics (MD) simulation is a classical method, which involves the
calculation of forces on all atoms during simulation provides the positions and
velocities of atoms after a definite time interval using these forces and gives the
detailed information on structural changes. However, applying such a technique to
studying MtbArgJ- substrate interactions for N-acetyl-ornithine would provide
minimal information as the complete release of substrate from the MtbArgJ active-site
pocket would not be accomplished in the time scale of a conventional MD simulation.
Hence, SMD was performed to evaluate the difference in binding of NAO in the
presence and absence of ANS; which provided a theoretical way of computing the
dissociation odf substrate from the enzyme, MtbArgJ. Steered molecular dynamics
(SMD) is a way to imitate atomic force microscopy (AFM) experiments and can
provide insight to the molecular mechanisms underlying the dissociation process of a
protein and associated substrate performed in explicit solvent at physiological
temperature (Michel A. Cuendet and Olivier Michielin, 2008).Further, the free energy
of binding can be analysed from the relative stabilities of different biomolecular
structures and to understand the biomolecular associations by decomposing the total
binding energy into a series of components Thus, the free energy of binding can be
calculated using the method of molecular mechanics Poisson Boltzmann surface
area(MM/PBSA)(K Reshmiet al., 2014).
18
Chapter 2
Aims and Objectives
2.1 Aim of the study:
To study the impact of ANS, an allosteric inhibitor on the ability of substrate catalysis
in MtbArgJ enzyme of arginine synthesis pathway.
2.2 Objectives:
A. To perform MD simulations study on substrate bound protein with and
without ANS in the hydrophobic pocket.
B. To carry out preliminary analysis through RMSD calculations, hydrogen bond
interaction calculations and RMSF of active site residues on MD trajectories.
C. To perform free energy calculation of MtbArgJ bound to substrate by
association study using MM-PBSA method.
D. Predicting the rupture force of substrate binding toMtbArgJ in presence and
absence of ANS inhibitor by applyingSMD simulations method.
19
2.3 Organization of the thesis:
MtbArgJ
protein
Preliminary
Analysis
RMSD
Calculation
H-Bond
Calculation
Free energy
Calculation
SMD
Analysis
In absence
of ANS
In
presence
of ANS
In
absence of
ANS
In
presence
of ANS
20
Chapter 3
Literature Review
3.1 Tuberculosis:
Approximately, with an occurrence of 1% new TB infection each year (WHO,
tuberculosis, 2002) it is estimated that nearly one-
been infected with M. tuberculosis. (Tuberculosis fact sheet, WHO, 2008) In 2015
alone, 10.4 million new TB cases were estimated and in countries like India,
Indonesia, China, Nigeria, Pakistan and South Africa 60% new cases were noted.
Moreover, 480 000 new cases of MDR-TB has been reported world-wide of which
from 2013-2015, Indian alone contributed 34% increase in TB cases. Thus, a time of
difficulty in terms of MDR-TB detection and treatment has immerged. Moreover,
countries like India, Indonesia, China, Nigeria, South Africa are reported in three TB
high-burden country list, shown in figure 3.1(TB report, WHO, 2016)
Fig 3.1.Countries in the three TB high-burden country lists. Here a indicates
countries that are included in the list of 30 high-burden countries for TB on the
basis of the severity of their TB burden.
21
WHO has introduced three terms to represent the burden of TB, they are incidence i.e.
the number of new cases of TB arising at a given period of time; prevalence i.e. the
number of cases of TB at a given point of time; and mortality i.e. the number of death
caused TB at a given period of time. Moreover, 200-299 new TB cases out of per
100,000 populations per year have been reported in the updated report of World
Health Organisation in 2016. The annual report published by WHO the TB cases
relative to population size varied ranging from 150-300 in per100000 population for
most high income countries and above 500 in few countries like Lesotho, South
Africa, etc.(Fig 3.2)
Fig 3.2 Estimated TB incident rates, 2015
22
About 580 000 incident cases of MDR/RR-TB were reported in 2015 from which
83% of the total cases were MDR-TB and 130 000 incident cases were noted in India
alone. About 250 000 total deaths were reported from MDR/RR-TB. (Fig 3.3)
Fig 3.3 Estimated incident of MDR/RR-TB in 2015, for countries with at least
1000 incident cases (shown in pink circles)
3.2 Mycobacterium tuberculosis:
The complete genome of Mtb of the H37Rv strain was sequenced in 1998(Sanger
Institute, 2007) with a total genome size of 4 million base pairs and approximately
4060 genes (Fig 3.4) Robert Koch first described M. tuberculosis in the year 1882 as
           
(Robert Koch and TB, 2008) M. tuberculosis can be identified by Acid-fast strains
because of the presence of a waxy coating generated by polyketide metabolism which
involves 39 genes out of the 250 genes which are involved in fatty acid metabolism
(Bloch H and Segal W, 1956) M. tuberculosis can grow on lipid and use it as its
carbon source. The unusual cell wall of MTB with mycolic acid makes it resistance to
desiccation and also contributes to its virulence factor (Murray et al., 2005)
23
Fig 3.4 Genome of Mycobacterium tuberculosis and functional categories are
shown in colour(Source: Karthik Raman, October 2008)
Fig 3.5 Stages of Tuberculosis infection in human (Source:
http://www.oxfordimmunotec.com)
24
3.3 TB and AIDS:
Co-existence of different strains of the obligate human pathogen M. tuberculosis with
supporting theory of M. tuberculosis has shown by phylogenetic and phylogeographic
analysis that the bacterium has evolved and migrated with the human host. (Comas I,
et al, 2013) In 2015 alone the percentage of TB patients in co-existence to with HIV
infection were reported to be 64% on average in countries like India and other high
TB/HIV burden countries while 81% and 82% in African region and Americas
respectively (TB report WHO, 2016) as shown in Fig 3.6
Fig 3.6 Percentage of new and relapse TB cases with documented HIV status,
2015
3.4 Present status of anti-TB medications:
The bacteria that cause tuberculosis (TB) can develop resistance to the antimicrobial
drugs used to cure the disease. Multidrug-resistant TB (MDR-TB) is TB that does not
respond to at least isoniazid and rifampicin, the 2 most powerful anti-TB drugs. The 2
reasons why multidrug resistance continues to emerge and spread are mismanagement
25
of TB treatment and person-to-person transmission. Most people with TB are cured
by a strictly followed, 6-month drug regimen that is provided to patients with support
and supervision. Inappropriate or incorrect use of antimicrobial drugs, or use of
ineffective formulations of drugs (such as use of single drugs, poor quality medicines
or bad storage conditions), and premature treatment interruption can cause drug
resistance, which can then be transmitted, especially in crowded settings such as
prisons and hospitals.
In some countries, it is becoming increasingly difficult to treat MDR-TB. Treatment
options are limited and expensive, recommended medicines are not always available,
and patients experience many adverse effects from the drugs. In some cases even
more severe drug-resistant TB may develop. Extensively drug-resistant TB, XDR-TB,
is a form of multidrug-resistant TB with additional resistance to more anti-TB drugs
that therefore responds to even fewer available medicines. It has been reported in 117
countries worldwide.
The most widely used vaccine against TB i.e. BCG is sometimes found unreliable
against highly infectious pulmonary tuberculosis which is mostly found in adults
(Haile M and Kallenius G, 2005) The various factors which may be responsible for
low efficacy of BCG vaccine may be due to environmental, genetic or immunological
(Young D and Dye C, 2006)
26
3.5 Arginine biosynthesis pathway:
M. tuberculosis gene ArgJ product ornithine acetyl transferase (OAT) catalyses the
formation of L-ornithine and N-acetyl glutamate by recycling acetyl group from
acetyl ornithine and thus exhibiting the activity of both N-acetyl glutamate synthase
(NAGS) and OAT (Shankaranarayananet al, 2010) And also ArgJ encoded protein
has been reported as mono- and bifunctional enzymes (Sakanyan. Vet.al., 1993) The
arginine biosynthesis pathway is shown in (Fig 3.7)
Fig 3.7 Arginine biosynthesis showing both linear and cyclic pathways.This
figure is adopted from the paper (Shankaranarayananet al., 2010)
According to the study of structural analysis of OAT performed by
Shankaranarayanan and his group, the active site of Mycobacterium tuberculosis OAT
comprises residues Gly128, Thr166, Lys189, Thr200, Glu280, Asn399 and Ser404
protein and residue Met193 is involved in van der Waals interactions.Whereas,
Arg362 and Asp132 were involved in salt bridge formation. Moreover, Ala191,
Met193, Leu194, Ala195 and Pro196 were noted as hydrophobic residues that flanks
27
to the active site of Mtb OAT protein and the presence of ornithine OAT enzyme
causes stabilization of disordered C-terminal residues that are disordered and not
observed in the native structure. Also, stabilization of the C-terminal residues by
reduces the size of the active-site pocket volume in the structure of the ORN complex.
The interactions of ORN and the protein residues of Mtb OAT unambiguously
delineate the active-site residues of this enzyme in Mtb. Here, in the present study,
MtbOAT is named as MtbArgJ as OAT is a product of argJ gene.
(Shankaranarayananet al., 2010)
3.6 Fundamentals of Molecular Dynamics Simulations:
Molecular dynamics (MD) is a computer simulation technique, where the classical
equations of motion of atoms or molecules are used to calculate the time evolution of
the system leading to a deeper understanding of their functions and biological
processes. The protein structures generated by X-ray crystallography and NMR
experiments provide a static interpretation of the data. Molecular Dynamics
simulation studies serves as bridging theoretical and experimental data by providing
insight rational drug design by understanding the molecular systems in context of
space and time and by calculating the atomic fluctuations of the molecular systems.
(Laxmikant K et al., 1998) In MD simulation a force field method is used which
controls the position, velocity and acceleration of each atom in the molecular system

F = ma = m dv/dt = m d2x/dt2
Where, F is the applied force, m is the mass of atoms, a is the acceleration, v is the
velocity, x is the distance travelled in time, t
Molecular dynamics simulation consists of jiggling and wiggling of atoms which once
     th
century it is known that matter consists of interacting particles in motion. (M. P. Allen
28
and D. J. Tildesley, 1989) Molecular dynamics uses the laws of mathematics, physics
and chemistry and algorithms from computer sciences. (D. C. Rapaport, 1996)
3.6.1 Pressure and temperature control in MD simulations:
The most commonmethod used for control of pressure and temperature in MD
simulations is the Hamiltonian system to control the baro- and thermostats. There are
also other ways of pressure and temperature control they are Berendsenbarostat and
Berendsen thermostat. The approaches are explained as follows-
Barostats and thermostats using the extended Hamiltonian approach:
The control of pressure by extended system method was previously developed by
Andersen (H. C. Andersen, 1980).In this method in order to control the pressure,
volume is adjusted in such a way that it is free for fluctuationand physical system is
extended to a composite system consisting of system of interest. If we consider a unit
cell box of edge length L, where L= V1/3, and two sets of variables are considered viz.
      
expressed as-
  
 

 
Where,  are the scaled position and momentum of the ith particle, V is
the volume of the system, p is the barostat,W is the barostat inertia parameter
corresponding to the piston mass and Pext is the external pressure and PextV
corresponds to the potential energy associated with the volume.
Berendsenbarostat:
In this method, the volume of the system is measured by µ3 and µ can be expressed
as
29
Where, P is the instantaneous pressure, P0 
and pis a defined time constant which is used to adjust the pressure coupling.
Berendsen thermostat:
               
equation is given by-
Where, Ti is the instantaneous temperature, T0   
time step and T is a defined time constant which can be used to adjust the coupling of
hypothetical heat bath.
3.6.2 Periodic boundary condition:
Periodic boundary condition defines the bulky nature of the molecular systems which
provides the realistic view of the system. Here, particles are replicated in such a
manner that one particle is located in a specific position will be represented in three
Cartesian dimensions. Hence, the water molecule located on the right will reappear on
the left.
Fig 3.8Periodic boundary conditions. Particles in the colored in dotted
circle are replicated infinitely in three dimensions
30
3.6.3 Pressure coupling protocols:
Basically, three terms are used for representing pressure coupling in
systems, they are-
Isotropic: Here, the volume of a unit cell in increased in all three
dimensions.
Semi-isotropic: Here, the volume is increased in only two dimensions(x
and y).
Anisotropic:Here, there is no change in dimensions.
Fig 3.9 Pressure coupling protocols showing isotropic(left), semi-
isotropic(middle) and anisotropic(right). This figure has been taken from (C.
Kandtet al., 2007)
31
3.6.4 Molecular force field:
A molecular dynamics simulation requires the definition of a potential function that
describes the physical interactions involved in molecular systems that is it is the
potential by which the particles in the simulation will interact. In chemistry and
biology this is usually referred to as a force field. There are generally two types of
force field bonded and non-bonded. Bonded interactions includesbond length, angle
bending and torsions. Non-bonded interactions includes van der waals and Coulombic
electrostatic potential.
Fig 3.10 Types of force field. This figure has been taken from (A. Kukol, 2008)
The different force fields are used in MD simulations. A force field is built in two
conditions-
The potential energies and derivatives used to generate a set of equations
The parameters used in this set of equations
The different kinds of force fields used in Gromacs(http://www.gromacs.org/) MD
simulations are-
32
GROMOS-96 (GROningenMOlecular Simulation)
OPLS/AA (Optimized Potential for Liquid Simulations)
AMBER (Assisted Model Building and Energy Refinement)
CHARMM (Chemistry at HARvard Molecular Mechanics)
Coarse-grained force fields
MARTINI (S.J. Marrink, et al, 2007 )
PLUM
Here, in the present study CHARMM36 force field was used to define the empirical
interactions in the molecular systems. The molecular force fields can also be called as
empirical interactions which are described below-
Non-bonded interaction:
Van der Waals Interactions
The most commonly used potential to calculate van der Waals interactions is
Lennard-Jones 12-6 potential. Calculation of van der Waals energy is important to get
insight into the interactions between particles in the molecular systems.
Coulombic electrostatic potential
The electrostatic potential betweens two particles is measure here according to
           
approach. Ewaldsuimulation technique has many variations and the most common
variation used is particle mesh Ewald(PME).
33
Bonded interactions:
Bond length
Harmonic spring potential is used to measure the bond length between two particles in
a molecular system.
Angle bending
It also uses harmonic spring potential to measure the angle formed by three particles.
Torsions
To measure torsions specification of four atomic positions is required and here
dihedral angle potential is used.
3.6.5 Periodic cell shapes
There are several periodic cell shapes that are used in defining the unit cells
boundaries in MD simulations, they are
Cubic,
Rectangular,
Truncated Octahedron, and
Rhombic Dodecahedron
34
3.6.6 Water models:
There are many variations in Water models in MD simulation in order to speed up the
simulation process. Here, in the present work TIP3 water model is used. The mostly
commonly used water models are-
SPC
TIP3
TIP4P
3.6.7 Steps in a typical simulation:
A typical simulation is consists of the following steps
Retrieve of protein structure from RCSB PDB
Removal of water molecules and fixing of missing segments and side-chains
Preparation of a Gromacs topology file of the protein structure
Addition of solvent and ions
Energy minimization
Equilibration simulation (NPT and NVT)
Position restraint
Run production simulation
Analysis of the output trajectory data
35
           
http://www.nmr.chem.uu.nl/) has been followed for molecular dynamics
simulation of the protein structure MtbArgJ. This tutorial used
Gromacs(http://www.gromacs.org/) for performing and analysis of molecular
dynamics simulation. As mentioned above the preparation of the protein part includes
the retrieval of protein structure and generating the topology file in order to define the
system in terms of atom types, charges, bonds, etc and also the topology of a protein
is specific to certain force fields. Moreover, the addition of solvent and ions may
cause the atoms of the protein to come more close, so energy minimization step is
performed in order to relax the atoms of the protein to some extends. To perform
energy minimization of a molecular system the parameter file minim.mdp is used.
Next position restraint step is performed to ensure that the solvent configuration is
suitable for the protein conformation. This step is followed by equilibration step
where the system goes through heat bath and pressure coupling. And finally
production simulation is run by specifying the steps in order to get the trajectories for
analysis.
3.7 Binding free energy calculations:
Various approaches have been applied in order to calculate the potential of
biomolecularinterations involved in catalysis and protein-ligand interactions like free
energy perturbation (FEP), thermodynamic integration (TI), linear interaction energies
(LIE), molecular mechanics Poisson-Boltzmann surface area (MM-PBSA), and
molecular mechanics Generalized Born surface area (MM-GBSA).The most widely
used binding free energy calculation, the molecular mechanics Poisson Boltzmann
surface area (MM/PBSA) method (K Rashmiet al.,2014) by using g_mmpbsa package
(http://rashmikumari.github.io) and MM-PBSA has been widely used for to evaluate
relative stabilities for different biomolecular structures. Here, in the present study, the
MD trajectories for free ArgJ and complexedArgJ(ArgJ-ANS) were taken for
estimation of interaction free energies with the aim to integrate molecular dynamics
with binding energy calculations.
36
Particularly, the binding free energy of ligand-protein complex in solvent was
expressed as:
binding = Gcomplex (Gprotein + Gligand)
where G complex is the total free energy of the protein-ligand complex, G protein and
G ligand are total energy of separated protein and ligand in solvent, respectively. The
free energy for each individual Gcomplex, Gprotein and Gligand were estimated by:
Gx= EMM TS + Gsolvation
where x is the protein, ligand, or complex. EMM is the average molecular mechanics
potential
energy in vacuum and Gsolvation is free energy of solvation. The molecular mechanics
potential energy was calculated in vacuum as following:
EMM = Ebonded + Enonbonded= Ebonded+ (EvdW+ Eelec)
where E bonded is bonded interaction including of bond, angle, dihedral and improper
interac-
tions and E non-bonded is non-bonded interactions consisting of van der Waals (E
vdw ) and electrostatic (E  
Rashmi,et.al.,2014)
The solvation free energy (G solvation ) was estimated as the sum of electrostatic
solvation free
energy (Gpolar) and apolar solvation free energy (Gnonpolar):
Gsolvation = Gpolar+ Gnonpolar
Where Gpolar was computed using the Poisson-Boltzmann (PB) equation (K Rashmiet
al.,2014) and Gnonpolarestimated from the solvent-accessible surface area (SASA) as
equation following:
Gnonpolar
37
               
parameter and A is SASA (V Sharadet al.,2016)
Fig 3.11 Schematic diagram of MM-PBSA technique adopted in g_mmpbsa
method. This figure has been adopted from the paper (K Rashmiet al.,2014)
38
3.8 Steered molecular dynamics simulations:
Since last decades, the studies were carried out starting from ligand unbinding studies
followed by protein unfolding and protein-protein interaction. Earlier SMD studies
were used to characterize dissociation mechanism and calculate rupture force in order
to reproduce atomic force microscopy (AFM) experiments. (Michel A. Cuendet and
Olivier Michielin, 2008) In order to conduct Steered molecular dynamics simulations
it is necessary to choose a constant-force SMD that has to be loaded on the SMD
atoms and a reaction coordinated has to be loaded along which the pulling simulation
will take place. In the present study, constant-velocity SMD was applied where the
centre of mass (COM) of the SMD atoms was linked to a spring with a given force
constant, which moved at a given constant velocity. Let us consider a force F is
applied to pull a ligand that is bound with a protein, than the generalised equation of
pulled force within a given time can be expressed as-
F(t) = a[vt (
)
]
Where, a is the force constant, v is the velocity by which the ligand is pulled away
from the protein,
is the direction of pulling the ligand, 
 are the final and
initial position of the ligand respectively and t defines the time interval. As (
)
increases, the force of pulling decreases and if the ligand is not unboung than the
force of pulling F increases due to increase of t.
In SMD, the dissociation mechanism of molecular complexes like protein-protein,
protein- ligand, etc are performed by applying a time-dependent external force for
unbinding of the molecular complexes, which cannot be achieved by convensional
MD simulation. Here, in SMD a standard Hamiltonian which is a time-dependent
potential that acts on the descriptor, for example protein-ligand distance is used to
achieve the transition between two states, here the bound and unbound state (Jagdish
Suresh Patel et al., 2014). Thus, in SMD the dissociation of a ligand bound to a
protein is investigated by exploring the bound states and the unbound states of a
variety of biomolecular complexes as well as their responses to external mechanical
manipulations at atomic level (Li-Jun yang et al, 2009).
39
Chapter 4
Materials and Methods
4.1 Model construction:
The crystal structure of MtbArgJ with 2.4 Å resolution was retrieved from RCSB
PDB (PDB ID 3IT6). The protein was bound with ornithine. Ornithine was replaced
with N-acetyl ornithine (NAO) by modelling with PyMol molecular graphics viewer
(https://www.pymol.org). Two molecular systems of MtArgJ were prepared- one with
only substrate bound in both the active site pockets (Fig 4.2) namely, (MtArgJ-NAO)
and the other with substrate bound with ANS in hydrophobic pocket (Fig 4.1) namely,
(MtArgJ-NAO-ANS).
Fig 4.1 Cartoon image of modelled structure of MtbArgJ protein showing
different chain viz. Chain A (Red), Chain B (Yellow), Chain C (Green), Chain D
(Blue) and ligands (Purple)
40
4.2 System preparation and Molecular Dynamic Simulations:
The substrate bound and inhibitor bound complexes of MtArgJwas used for all atom
molecular dynamic simulations. GROMACS 5.1.1(Abraham et al., 2015) was used to
perform molecular dynamics simulations of free ArgJ system. Molecular dynamics
simulations of the MtbArgJ system was carried out using CHARMM36 force field
and TIP3P water model(Mackerellet al., 2004). Vacuum energy minimization step
was performed by employing steepest descent algorithm for 1000 steps and conjugate
gradient minimization was performed for 500 steps. Sodium (Na+) and chloride(Cl-)
counter-ions were added at physiological conditions to neutralize the system after
solvent addition by assuming normal charge state of ionisable groups with reference
to pH 7 and by adjusting the boundaries of cubic box by 10A in a unit cell. The free
MtbArgJ system in solvant was again subjected to energy minimization for 5000 steps
for stabilization. Position restrained and unrestrained dynamic simulations of the
solvated system was performed and Parinello-Rahman pressure coupling bath was
used for equilibration step(NVT and NPT) of the system at temperature 300K using
Berendesen thermostat under pressure of 1 atm for 200ps. For simulations, all bonds
were constrained using LINCS algorithm. Particle-mesh ewald(PME) method was
used for electrostatic calculations by maintaining a cut-off distance of 1.4nm for
Coulomb and van der Waals interactions and finally MD run step for 50ns was
performed for the simulations free ArgJ system. Further, ArgJ complexes were
perpared with two N-acetyl ornithine molecules docked with in the two substrate
pockets (MtbArgJ-NAO) and one inhibitor namely, 8-Anilinonapthalene-1-sulfonic
acid(MtbArgJ-NAO-ANS) bound into the hydrophobic binding site and simulated for
50ns. SwissParam webserver (http://www.swissparam.ch/) was used for generation of
topological parameters for substrate and inhibitor molecules (Zoeteet al., 2011).
4.3 Preliminary analysis:
RMSD was computed for the 50 ns MD trajectories to study the molecular stability.
For this,gmxrms module ofGROMACS 5.1.1was used to generate the RMSD of
MtbArgJ protein bound with NAO in presence and absence of ANS
inhibitor.Hydrogen bonds in the molecular systems were computedby using
gmxhbond module of GROMACS program. Further,
LigPlot(http://www.ebi.ac.uk/thornton-srv/software/LIGPLOT/) program was used to
41
visualise the prominent hydrogen bond interactions and other hydrophobic residues
associated.RMS fluctuation were also calculated using gmxrmsf module of
GROMACS for only active site residues which are involved in hydrogen bond
interactions.
4.4 Binding free energy calculation:
The most widely used binding free energy calculation, the molecular mechanics
Poisson Boltzmann surface area (MM/PBSA) method (K Rashmiet al.,2014) was used
for free energy calculation. For this,g_mmpbsa package
(http://rashmikumari.github.io) and the MD trajectories for substrate boundMtbArgJ
and complexedMtbArgJ were taken for estimation of interaction free energies with
the aim to investigate the free energies.
Particularly, the binding free energy of ligand-protein complex in solvent was
expressed as:
binding = Gcomplex (Gprotein + Gligand)
Where, Gcomplex is the total free energy of the protein-ligand complex, Gprotein and
Gligand are total energy of separated protein and ligand in solvent, respectively. The
free energy for each individual Gcomplex, Gprotein and Gligand were estimated by:
Gx= EMM TS + Gsolvation
Where, x is the protein, ligand, or complex. EMM is the average molecular mechanics
potential energy in vacuum and Gsolvation is free energy of solvation. The molecular
mechanics potential energy was calculated in vacuum as following:
EMM = Ebonded + Enonbonded= Ebonded+ (EvdW+ Eelec)
where E bonded is bonded interaction including of bond, angle, dihedral and improper
interactions and E non-bonded is non-bonded interactions consisting of van der Waals
(Evdw ) and electrostatic (Ealways taken as zero. (K
Rashmiet al.,2014) The solvation free energy (G solvation) was estimated as the sum
42
of electrostatic solvation free energy (Gpolar) and apolar solvation free energy
(Gnonpolar):
Gsolvation = Gpolar+ Gnonpolar
Where Gpolar was computed using the Poisson-Boltzmann (PB) equation (K
Rashmi,et.al.,2014) and Gnonpolarestimated from the solvent-accessible surface area
(SASA) as equation following:
Gnonpolar
Where,       surface tension of the solvent and b is fitting
parameter and A is the SASA (V Sharadet al.,2016)
4.5 Steered molecular dynamics simulations:
The complexedMtbArgJ bound with N-acetyl ornithine(NAO) with 8-
Anilinonapthalene-1-sulfonic acid(ANS) and without ANS atom coordinates were
taken for steered molecular dynamics simulations(SMD) as implemented in
GROMACS 5.1.1 (R. Bevan David et al.,2009). The molecular systems were
subjected to molecular dynamics simulations by using CHARMM36 force field and
TIP3P water model as applied previously for conventional MD simulations. The unit
cell dimension was defined with centre of the box at 6.290x6.290x6.290 and with a
box dimension of 12.580x12.580x12.580 allowing enough space in pulling direction.
The systems were solvated and sodium(Na+) and chloride (Cl-) counter-ions were
added to neutralize the molecular systems at physiological concentration of 0.1
Mol/L. Energy minimization was performed as conventional MD simulations for
5000 steps by employing steepest descent algorithm and position restrained and
unrestrained dynamic simulations of the solvated system was performed and
Parinello-Rahman pressure coupling bath was used for equilibration step (NVT and
NPT) of the system at temperature 300K using Berendesen thermostat under pressure
of 1 atm for 200ps. After equilibration, the molecular systems were submitted for
pull-chord molecular dynamics simulations. Pulling simulations were performed by
43
setting the pulling speed to 0.02 A/ps and with a spring constant of  1
 -axis for 5000ps to generate a series of configurations along a reaction
coordinate corresponding to the frames generated in pulling simulations.
44
Chapter 5
Results
5.1 Preliminary Analysis:
MD trajectories of the two molecular systems of MtbArgJ bound to NAO with and
without ANS wereused for the preliminary analysis as shown in fig 5.1 and 5.2.The
molecular complexes of MtbArgJ protein bound with NAO at substrate pocket
1(MtbArgJ-NAO1) and substrate pocket 2(MtbArgJ-NAO2); MtbArgJ protein bound
with NAO in substrate pocket 1(MtbArgJ-NAO1-ANS) and substrate pocket 2
(MtbArgJ-NAO2-ANS) in presence of ANS inhibitor were studied for RMSD, RMSF
and substrate and inhibitor interactions with MtbArgJ by computing the percentage
existence of hydrogen bonds.
Fig 5.1 Cartoon structure of MtbArgJ protein (pale green) bound with N-acetyl
ornithine at substrate pocket 1(green) and pocket 2 (yellow) in absence of ANS.
45
Fig 5.2 Cartoon structure of MtbArgJ protein (pale green) bound with N-acetyl
ornithine at substrate pocket 1(green) and pocket 2 (yellow )in presence of ANS
(violet).
5.1.1 RMSD:
RMSD of molecular systems compares two structures by calculating the average
deviation distances between the residues along a given run of simulation. The RMSD
profile of NAO bound with MtbArgJ protein is represented in fig 5.3 shows that the
initial RMSD was recordedand gradually increased and stabilised with an average
RMSD of 0.216 ± 0.089 nm. But when MtbArgJ bound with NAO substrate is
complex with ANS inhibitor, the RMSD of MD trajectories was stable with an
average RMSD of 0.189± 0.027 nm as shown in fig 5.4. This RMSD plots indicates
that the MD trajectories have stabilized during the course of simulation.
46
Fig 5.3 RMSD plot of free MtbArgJ protein bound with substrateNAO
Fig 5.4 RMSD plot of MtbArgJ protein complexed with ANS inhibitor and NAO
substrate
47
5.1.2 H-bond calculation:
Percentage occurrence of hydrogen bond interactions were calculated for both NAO
and ANS with MtArgJ. The binding of NAO substrate in both substrate pocket 1 and
pocket 2 in presence and absence of ANS inhibitor is shown in Table 1,2,3& 4. Table
5 reports the hydrogen bonds of ANS in hydrophobic pocket. Hydrogen bond
calculation for MtbArgJ protein bound with NAO at substrate pocket 1 showed
interactions with residues Gly128, Thr166, Thr167, Lys189, Ala191, Gly192, Thr200,
Glu280 Arg313, Glu398, Asn399 and Ser404. It was noted that in substrate pocket 1,
residues Thr127, Gly128, Thr166, Thr200 and Glu280 showed more than 50%of
percentage of existence of hydrogen bond interaction with NAO in absence of ANS
inhibitor (Table 1). Whereas, the hydrogen bonding interactions of residuesThr127,
Gly128 and Thr166 were lost up to some extends for NAO bound to MtbArgJ in
presence of ANS (Table 2) which clearly resembles that the binding of ANS inhibitor
to the allosteric site of MtbArgJ protein hampers the hydrogen bond interactions
between NAO and MtbArgJ at substrate pocket 1. While, the results of percentage of
existence of hydrogen bond interactions between NAO and MtbArgJ in substrate
pocket 2 is vice-versa than that of in substrate pocket 1 i.e. more residues were seen to
have more than 50% of hydrogen bond existence in substrate pocket 2 between NAO
and MtbArgJ in presence of ANS inhibitor than compared to in absence of ANS
inhibitor which resembles that ANS inhibitor has no significant impact on the other
active site.
48
Table 1: Residues involved in hydrogen bond interactions between MtbArgJ
protein and N-acetyl ornithine (NAO) in substrate pocket 1 in absence of ANS
inhibitor showing donor and acceptor residues and their percentage of existence
(Residues having more than 50% existence hydrogen bonds are coloured red)-
Donor
Acceptor
% Exist
THR127OG1
THR127OG1
GLY128N
THR166OG1
LYS189NZ
LYS183NZ
THR200N
THR200N
THR200N
THR200OG1
THR200OG1
THR200OG1
NAO1NE
NAO1NE
NAO1NE
NAO1NE
NAO1NE
NAO1NE
NAO1NE
NAO1NE
NAO1NE
NAO1NE
NAO1NE
NAO1O
NAO1O1
NAO1O1
NAO1O
NAO1O
NAO1OXT
NAO1O
NAO1OXT
NAO1O1
NAO1OXT
NAO1N1
NAO1O1
THR167OG1
GLY192O
MET193N
GLU280OE1
GLU280OE2
ASN399OD1
ASN399ND2
TYR402OH
SER404OG
SER404OT1
SER404OT2
5.439
78.012
97.05
97.55
30.317
0.15
0.03
89.711
0.02
32.557
21.738
26.617
0.36
20.028
0.01
37.606
50.895
39.036
0.01
0.01
0.08
0.61
4.2
49
Table 2: Residues involved in hydrogen bond interactions between MtbArgJ
protein and N-acetyl ornithine (NAO) in substrate pocket 1 in presence of ANS
inhibitor showing donor and acceptor residues and their percentage of existence
(Residues having more than 50% existence hydrogen bonds are coloured red)-
Donor
Acceptor
%Exist
GLY128N
GLY128N
THR166OG1
THR166OG1
THR166OG1
THR166OG1
THR167N
THR167N
THR167OG1
THR167OG1
THR167OG1
LYS189NZ
LYS189NZ
LYS189NZ
ALA191N
GLY192N
THR200N
THR200N
THR200OG1
THR200OG1
SER404OG
ARG313NH2
NAO405NE
NAO405NE
NAO405NE
NAO405NE
NAO405NE
NAO405NE
NAO405NE
NAO405NE
NAO405NE
NAO405N1
NAO405N1
NAO405O
NAO405O1
NAO405O
NAO405OXT
NAO405N1
NAO405O1
NAO405O
NAO405O1
NAO405O
NAO405OXT
NAO405O1
NAO405O
NAO405OXT
NAO405O1
NAO405O
NAO405OXT
NAO405O
NAO405OXT
NAO405O
NAO405OXT
NAO405O1
NAO405O1
THR167OG1
ALA191O
GLY192O
GLU280OE1
GLU280OE2
GLU398OE1
GLU398OE2
ASN399OD1
ASN399ND2
THR167OG1
THR200OG1
0.03
0.04
37.196
2.84
0.41
28.077
0.09
0.01
0.19
0.01
0.4
87.991
7.379
13.009
0.01
0.01
50.785
57.975
5.099
39.286
0.02
0.04
1.81
2.96
0.09
57.415
40.466
11.789
11.499
30.297
0.05
0.03
32.347
50
Table 3: Residues involved in hydrogen bond interactions between MtbArgJ
protein and N-acetyl ornithine (NAO) in substrate pocket 2 in absence of ANS
inhibitor showing donor and acceptor residues and their percentage of existence
(Residues having more than 50% existence hydrogen bonds are coloured red)-
Donor
Acceptor
%Exist
THR127OG1
THR127OG1
THR127OG1
GLY128N
GLY128N
THR166OG1
THR166OG1
LYS189NZ
LYS189NZ
THR200N
THR200N
THR200N
THR200OG1
THR200OG1
THR200OG1
THR200OG1
ASN240ND2
NAO2NE
NAO2NE
NAO2NE
NAO2NE
NAO2NE
NAO2NE
NAO2NE
NAO2NE
NAO2NE
NAO2NE
NAO2O
NAO2OXT
NAO2O1
NAO2N1
NAO2O1
NAO2O
NAO2OXT
NAO2O
NAO2OXT
NAO2O
NAO2OXT
NAO2N1
NAO2O
NAO2OXT
NAO2N1
NAO2O1
NAO2O1
THR155OG1
ALA191O
GLY192O
GLU280OE1
GLU280OE2
ASN399OD1
ASN399ND2
SER404OG
SER404OT1
SER404OT2
0.06
0.2
96.54
0.9
95.06
18.568
0.22
2.52
55.274
9.889
82.062
0.01
1.18
22.568
1.95
1.88
0.01
11.589
2.46
2.35
9.169
9.039
13.879
0.01
44.636
0.19
0.09
51
Table 4: Residues involved in hydrogen bond interactions between MtbArgJ
protein and N-acetyl ornithine (NAO) in substrate pocket 2 in presence of ANS
inhibitor showing donor and acceptor residues and their percentage of existence
(Residues having more than 50% existence hydrogen bonds are coloured red)-
Donor
Acceptor
%Exist
THR127OG1
THR127OG1
GLY128N
GLY128N
LEU129N
THR166OG1
THR166OG1
LYS189NZ
LYS189NZ
THR200N
THR200N
THR200OG1
THR200OG1
THR200OG1
THR200OG1
SER238OG
ASN399ND2
NAO406NE
NAO406NE
NAO406NE
NAO406NE
NAO406NE
NAO406N1
NAO406N1
NAO406N1
NAO406N1
NAO406O
NAO406O1
NAO406O
NAO406O1
NAO406N1
NAO406O
NAO406OXT
NAO406O
NAO406OXT
NAO406OXT
NAO406O1
NAO406O
NAO406OXT
NAO406N1
NAO406O1
NAO406O1
NAO406NE
ARG313NH2
GLU280OE1
GLU280OE2
ASN399OD1
SER404OG
GLY128O
SER404OG
SER404OT1
SER404OT2
3.86
0.23
0.11
0.73
0.01
99.69
1.95
0.91
0.56
97.35
93.821
0.17
0.09
0.01
14.839
2.54
0.05
0.01
47.725
54.335
78.192
84.002
0.05
0.01
3.7
98.61
52
Table 5: Residues involved in hydrogen bond interactions between MtbArgJ
protein and ANS inhibitor showing donor and acceptor residues and their
percentage of existence (Residues having more than 50% existence hydrogen
bonds are coloured red)-
Donor
Acceptor
% Exists
ARG57NH1
ARG57NH1
ARG57NH2
ARG57NH2
ARG57NH2
ARG57NH2
GLN305NE2
GLN305NE2
GLN305NE2
ARG308NH1
ARG54NH1
ARG54NH2
ARG54NH2
GLN305NE2
GLN305NE2
GLN305NE2
GLN305NE2
ARG308NH1
ARG308NH1
ARG308NH1
ARG308NH2
ANS407N1
ANS407O1
ANS407O3
ANS407N1
ANS407O1
ANS407O2
ANS407O3
ANS407O1
ANS407O2
ANS407O3
ANS407O1
ANS407O1
ANS407O1
ANS407O3
ANS407N1
ANS407O1
ANS407O2
ANS407O3
ANS407N1
ANS407O2
ANS407O3
ANS407O2
GLN305OE1
0.46
0.44
0.03
0.2
0.06
0.56
0.29
0.31
17.258
0.46
0.09
0.33
0.01
0.01
9.029
0.33
0.54
0.02
34.287
0.16
0.05
0.02
53
5.1.2.1 Ligplot Analysis:
Further analysis of hydrogen bond interaction was done using LigPlot program for
both the substrate pockets bound with N-acetyl ornithine (NAO) in the absence and
presence of ANS inhibitor. LigPlot analysis resembled a prominent interaction of
hydrogen bonds between the residues of MtbArgJ active site or substrate pocket 1 and
NAO in the absence of ANS inhibitor (Fig 5.5a).Whereas, when ANS inhibitor is
bound with MtbArgJ, the hydrogen interactions decreased in substrate pocket 1 (Fig
5.5b). But, for substrate pocket 2, the hydrogen bond interactions in presence of ANS
inhibitor were found to be similar to that of ANS free MtArgJ complex. Fig 5.6a and
Fig 5.6b shows the hydrogen bond interactions between NAO and MtbArgJ at
substrate pocket 1 in the absence and presence of ANS inhibitor respectively. And,
the hydrophobic and hydrogen bond interaction of the MtArgJ protein with ANS
inhibitor in the allosteric site is shown in Fig 5.7 and residues Gln305, Arg304,
Ser197 and Ala195 were observed to involve in hydrophobic interactions at the
allosteric site of the MtbArgJ protein.
54
Fig 5.5aLigPlot analysis of H-bond interactions between NAO and MtbArgJ at
substrate pocket-1 in the absence of ANS inhibitor
Fig 5.5bLigPlot analysis of H-bond interactions between NAO and MtbArgJ at
substrate pocket-1 in the presence of ANS inhibitor
55
Fig 5.6aLigPlot analysis of H-bond interactions between NAO and MtbArgJ at
substrate pocket-2 in the absence of ANS inhibitor
Fig 5.6b LigPlot analysis of H-bond interactions between NAO and MtbArgJ at
substrate pocket-2 in the presence of ANS inhibitor
56
Fig 5.7LigPlot analysis of H-bond interactions between ANS and MtbArgJ at
allosteric pocket
57
5.1.3 RMSF:
The residues that were involved in hydrogen bond interactions were further analysed
for root mean square fluctuation (RMSF) to study their behaviour under the influence
of ANS. RMSF results also showed that the fluctuations of residues involved in
hydrogen bonding between NAO and MtbArgJ were more in presence of ANS
inhibitor for substrate pocket 1 (Fig 5.8) while the fluctuations of residues that were
involved hydrogen bonding between NAO and MtbArgJ for substrate pocket 2 was
vice-versa (Fig 5.9) i.e. less fluctuations of residues were observed when NAO is
bound to MtbArgJ in presence of ANS in pocket 2. Thus, it is seen that the interaction
of ANS inhibitor with MtbArgJ perturbs the binding affinity of NAO to MtbArgJ in
substrate pocket 1 but has no notable effect on the binding of NAO to MtbArgJ at
substrate pocket 2.
58
Fig 5.8 RMS fluctuation of residues involved in hydrogen bond interactions in substrate pocket 1 in absences
and presence of ANS inhibitor.
59
Fig 5.9 RMS fluctuation of residues involved in hydrogen bond interactions in substrate pocket 2 in absences
and presence of ANS inhibitor.
60
5.2 Free energy calculation:
Free energy calculations were performed by using g_mmpbsa package
(http://rashmikumari.github.io) in order to predict the substrate binding affinities in
presence and absence of ANS inhibitor.
Table 6 shows the van der Waals and electrostatic energies computed for the binding
of NAO in the two active sites, with and without ANS. It was noted that in the
presence of ANS, the van der Waals energy of binding of NAO were unfavourable
with increase in energies as noted in both substrate pocket-1 and substrate pocket-2.
On the contrary, reverse was true for electrostatic energy. It was found that
electrostatic energies have decreased in both the substrate binding pockets upon
interactions with ANS.
61
Table 6: Results of MM-PBSA calculations showing van der Waals energy and
electrostatic energies involved:
Molecular Systems
van der Waals (KJ/mol)
Electrostatic (KJ/mol)
MtbArgJ-NAO1
-52.219 +/- 18.689
-529.579 +/- 57.211
MtbArgJ-NAO1-ANS
-41.170 +/- 17.431
-548.544 +/- 75.223
MtbArgJ-NAO2
-59.469 +/- 16.634
-341.663 +/- 108.784
MtbArgJ-NAO2-ANS
-50.249 +/- 17.725
-579.240 +/- 38.041
62
5.3 Steered molecular dynamics simulations:
The two molecular systems were studied for dissociation of NAO from the active site
pocket under the influence of NAO. MtbArgJ protein bound with NAO at substrate
pocket-1(MtbArgJ-NAO1) and substrate pocket-2(MtbArgJ-NAO2) ;MtbArgJ protein
bound with NAO in substrate pocket-1(MtbArgJ-NAO1-ANS) and substrate pocket-2
(MtbArgJ-NAO2-ANS) in presence of ANS inhibitor were analysed for force profile
and dissociation pathway along the reaction coordinate of ligand dissociation and
binding analysis.
Computation of Rupture force and dissociation of NAO in MtbArgJ-
NAO1 and MtbArgJ-NAO1-ANS systems:
Fig 5.10 represents the pulling simulation profile of MtbArgJ-NAO1 and MtbArgJ-
NAO1-ANS showing the rupture force and dissociation of NAO in presence and
absence of ANS inhibitor from substrate pocket-1. The force vs distance graphical
representation in Fig 5.10 shows a pull-force of maximum 550 kJ/mol was needed to
dissociate NAO from substrate pocket-1 of MtbArgJ in presence of ANS and after a
pull-distance of 0.07 nm the NAO substrate was completely pulled out from the
substrate pocket-1 followed by a drop in the pull force returning to zero kJ/mol.
Whereas, a force of 650 KJ/mol was required to completely unbound NAO from
substrate pocket 1 in absence of ANS inhibitor. Hence, the binding of also perturb the
binding of NAO in substrate pocket-1. Thus, dissociation of NAO in presence of ANS
ismore easier than that of MtbArgJ bound with NAO in absence of ANS inhibitor.Fig
5.11 and 5.12 depicts the dissociation of NAO from substrate pocket-1 of
MtbArgJalong a series of constant pull-force and velocity in the absence and presence
of ANS inhibitor prepared by taking snapshots at different intervals of SMD
simulation run.
63
Fig 5.10 SMD analysis of MtbArgJ protein bound with NAO in presence of ANS
(Black) and in absence of ANS (Red) for substrate pocket-1.
64
Fig 5.11a Dissociation of N-acetyl ornithine from substrate pocket-1 of
MtbArgJ(violet) in the absence of ANS inhibitor
Fig 5.11b Dissociation of N-acetyl ornithine from substrate pocket-1 of
MtbArgJ(violet) in the presence of ANS inhibitor (orange)
65
Comp Rupture force and dissociation of MtbArgJ-NAO2 and
MtbArgJ-NAO2-ANS systems:
Summarized in Fig 5.13 is the pulling simulation profile of MtbArgJ-NAO2 and
MtbArgJ-NAO2-ANS showing the rupture force and dissociation of N-acetyl
ornithine in presence and absence of ANS inhibitor from substrate pocket-2. The
force vs distance graphical representation in Fig 5.13 shows a pull-force of maximum
700 kJ/mol was needed to dissociate NAO from substrate pocket-2 of MtbArgJ in
presence of ANS and at a pull-distance of about 0.05 nm the N-acetyl ornithine
substrate was completely dissociated from the substrate pocket-2 followed by a drop
in the pull force returning to zero kJ/mol. Whereas, a force of 350 KJ/mol was
required to completely unbound N-acetyl ornithine from substrate pocket-2 in absence
of ANS inhibitor after a pulling distance of around 0.03nm which is with 350 KJ/mol
less force and roughly a 0.02 nm distance less than that of ANS bound system of
MtbArgJ. Hence, the binding of ANS might have increased the binding strength
resulting in tight interaction than usual. Represented in Fig 5.14 and 5.15 are the
dissociation of N-acetyl ornithine from the substrate pocket-2 of MtbArgJ taken at a
different interval of time of SMD simulation run along the reaction coordinate of the
dissociation of N-acetyl ornithine.
66
Fig 5.12 SMD analysis of MtbArgJ protein bound with NAO in presence of ANS
(Black) and in absence of ANS (Red) for substrate pocket 2.
67
Fig 5.13a Dissociation of N-acetyl ornithine from substrate pocket-2 of
MtbArgJ(violet) in the presence of ANS inhibitor
Fig 5.13b Dissociation of N-acetyl ornithine from substrate pocket-2 of MtbArgJ
(violet) in the presence of ANS inhibitor (sky blue)
68
Chapter 6
Discussion
Through this study, it is evident that ANS inhibitor has effective role in perturbing the
binding interactions of NAO in substrate pocket-1 of MtbArgJ protein but has no such
effective role in substrate pocket-2.
6.1 Preliminary analysis:
RMSDs computed showed that the MD trajectories obtained through MD simulations
were found to be stable, thus enabling to carry out further studies (Fig 5.3 and
5.4).Hydrogen bond analysis showed that NAO in substrate pocket-1 interacted with
residues Gly128, Thr166, Thr167, Lys189, Ala191, Gly192, Thr200 Glu280, Arg313,
Asn399 and Ser404 were prominent in the absence of ANS but in the presence of
ANS, the hydrogen bonding interactions of residuesThr127, Gly128 and Thr166 were
lost in substrate pocket-1 whereas, no significant changes in hydrogen bond
interactions were reported on the other substrate pocket in presence of ANS.Previous
structural analysis carried out by Shankaranarayananet al., (2013) showed residues
Gly128, Thr166, Lys189, Thr200 Glu280, Asn399 and Ser404 of Mycobacterium
tuberculosisArgJ (MtbArgJ) were mainly involved in hydrogen bond interactions and
were present in the active site of the protein and residue Met193 is involved in van der
Waals interactions. Whereas, Arg362 and Asp132 were involved in salt bridge
formation. Moreover, Ala191, Met193, Leu194, Ala195 and Pro196 were noted as
hydrophobic residues that flanks to the active site of MtbArgJ protein. It has also been
reported thatresidues Thr127, Gly128 and Thr200 are involved in the formation of
oxyanion on the active site of MtArgJ and binding of NAO to the active site stabilizes
the oxyanion formation during enzymatic reaction (Shankaranarayananet al.,
2010).LigPlot analysis further showed prominent hydrogen bond interactions in
absence of ANS inhibitor for both the substrate pockets bound with N-acetyl
ornithine. Further, analysis confirmed that the fluctuations of active site residues in
substrate pocket of monomer-1 has increased in the presence of ANS thus
strengthening the present findings that ANS that ANS has disrupted the interaction
network of NAO and active site resudues of binding pocket monomer-1.However, the
69
fluctuations were less for residues involved in hydrogen bonding for substrate pocket-
2. Thus, it is evident from preliminary analysis that ANS inhibitor has effective role in
perturbing the binding interactions of NAO in substrate pocket-1 of MtbArgJ protein
but has no significant role in substrate pocket-2.
6.2 Free energy calculation:
Upon calculating the binding affinity of NAO in both the substrate binding pockets, it
was observed that the van der Waals energies have increased for both the substrate
pockets and thus were unfavourable for the binding of NAO. But, reverse was noted
with electrostatic energies which were favourable for both the substrate pockets upon
interactions with ANS. This may also indicate that the NAO tightly binds than usual
which may not be beneficial in the substrate catalysis.
6.3 Steered molecular dynamics simulation:
Subsequently, Steered MD simulations were performed to analyse the force required
to dissociate the substrate from its binding pocket under the impact of ANS inhibitor.
It has been observed that the binding of ANS inhibitor to the allosteric site of
MtbArgJ protein hampers some of the interactions in substrate pocket-1 of MtbArgJ
protein bound with NAO and so,the force required to pull the substrate from pocket 1
is lesser in presence of ANS than compared to in the absence of ANS inhibitor. When
ANS is bound with MtbArgJ protein, during the pulling simulation, the NAO
substrate is pulled with a constant increase of force, at a maximum force of 550
KJ/mol, the substrate NAO comes out of the pocket-1 completely. Whereas, the force
required to completely unbound NAO from substrate pocket 1in absence of ANS
inhibitor is 650 KJ/mol, i.e. 100 KJ/mol force was additionally required to dissociate
NAO which clearly explains that ANS inhibitor has some notable effect on MtbArgJ
protein which hinders its interaction with NAO bound in substrate pocket 1 (Fig 5.9).
But, in case of substrate pocket-2, the effect of ANS inhibitor had an opposite impact
than that of pocket-1 i.e. the unbinding force of NAO from substrate pocket-2 in
absence of ANS is 350KJ/mol whereas, in presence of ANS, the force required is
nearly twice i.e. 700KJ/mol (Fig5.10). This also collaborates our finding of decreased
electrostatic energies from -341.66 KJ/mol in absence of ANS but -579.24 KJ/mol in
the presence of ANS.
70
Hence, it is explained clearly from the above discussions that the interaction of ANS
in the hydrophobic allosteric site of MtbArgJ hinders binding of NAO to substrate
pocket-1 of MtbArgJ whereas, binding of NAO in substrate pocket-2 was tightened
under the effect of ANS thus also perturbing the substrate catalysis in substrate
pocket-2.Thus, the present computation approach has helped to successfully unleash
the mechanism of allosteric inhibition of ANS to perturb the substrate interactions of
catalytic residues of MtbArgJ protein.
71
Appendix
Calculation of binding free energy:
The g_mmpbsa package has three modules for calculation of binding free energy.The
binding energy consists of three energetic terms, (a) potential energy in vacuum, (b)
polar-solvation energy and (c) non-polar solvation energy. These energetic terms
could be calculated in either three or one step.
(a) Calculation of potential energy in Vacuum
Execute the following command, select 1 and 13 group number for protein and ligand,
respectively:
g_mmpbsa -f 1EBZ.xtc -s 1EBZ.tpr -n 1EBZ.ndx -pdie 2 -decomp
Two files energy_MM.xvg and contrib_MM.dat are generated as outputs. Both files
could be generated with different name by -mm filename1.xvg and -mmcon
filename2.dat. energy_MM.xvg file contains van der Waals, electrostatic interactions,
and net non-bonded potential energy between the protein and inhibitor.
contrib_MM.dat contains contribution of each residue to the calculated net non-
bonded interaction energy.
(b) Calculation of polar solvation energy
To calculate the polar solvation energy, an input file (polar.mdp) is required. This file
contains input parameters that are used in the calculation of polar solvation energy.
The details of input are in (http://rashmikumari.github.io/g_mmpbsa/parameters.html)
link.
Execute the following command, select 1 and 13 group number for protein and ligand,
respectively:
g_mmpbsa -f 1EBZ.xtc -s 1EBZ.tpr -n 1EBZ.ndx -i ../polar.mdp -nomme -pbsa -
decomp
Two files polar.xvg and contrib_pol.dat are generated as outputs. Both files could be
generated with different name by -pol filename1.xvg and -pcon filename2.dat.
polar.xvg contains polar solvation energies for unbound protein, unbound inhibitor
and protein-inhibtor complex. contrib_pol.dat contains contribution of each residue to
the calculated net polar solvation energy.
(c) Calculation of non-polar solvation energy
To calculate the non-polar solvation energy, an input file (apolar_sasa.mdp) is
required. This file contains parameters that are used in the calculation of non-polar
72
solvation energy. The details of input parameter files and method of execution are
available in (http://rashmikumari.github.io/g_mmpbsa/parameters.html) link.
Here, SASA-only and SAV-only model are used for which input parameter files are
provided.
For SASA-only model:
Execute the following command, select 1 and 13 group number for protein and ligand,
respectively:
g_mmpbsa -f 1EBZ.xtc -s 1EBZ.tpr -n 1EBZ.ndx -i ../apolar_sasa.mdp -nomme -pbsa
-decomp -apolsasa.xvg -apcon sasa_contrib.dat`
Two files sasa.xvg and sasa_contrib.dat are generated as outputs. sasa.xvg
contains non-polar solvation energy for unbound protein, unbound inhibitor and
protein-inhibtor complex. sasa_contrib.dat contains contribution of each residue to
the calculated net polar-solvation energy.
For SAV-only model:
Execute the following command, select 1 and 13 group number for protein and ligand,
respectively:
g_mmpbsa -f 1EBZ.xtc -s 1EBZ.tpr -n 1EBZ.ndx -i ../apolar_sav.mdp -nomme -pbsa
-decomp -apolsav.xvg -apcon sav_contrib.dat`
73
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