Subscriber access provided by Washington University | Libraries
Computational Biochemistry
Exploring the Pyrazinamide Drug Resistance Mechanism of Clinical Mutants T370P and W403G in Ribosomal Protein S1 of Mycobacterium Tuberculosis Ashfaq Ur Rehman, Muhammad Tahir Khan, Hao Liu, Abdul Wadood, Shaukat Iqbal Malik, and Hai-Feng Chen J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.8b00956 • Publication Date (Web): 27 Feb 2019 Downloaded from http://pubs.acs.org on February 28, 2019
Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.
is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.
Page 1 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
1
Exploring the Pyrazinamide Drug Resistance Mechanism of Clinical Mutants T370P and
2
W403G in Ribosomal Protein S1 of Mycobacterium Tuberculosis
3
Ashfaq Ur Rehman†┼║, Muhammad Tahir Khan§║, Hao Liu†, Abdul Wadood┼, Shaukat Iqbal
4
Malik§, Hai-Feng Chen†, ‡*
5 6 7 8 9 10 11 12 13 14 15 16 17 18
†State
Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. ‡Shanghai Center for Bioinformation Technology, Shanghai, 200235, China. §Department of Bioinformatics and Biosciences, Capital University of Science and Technology, Islamabad 44000, Pakistan. ┼Department of Biotechnology, Abdul Wali Khan University Marden, 23200, Pakistan. Corresponding Author Hai-Feng Chen (Professor) State Key Laboratory of Microbial Metabolism, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China. Shanghai Center for Bioinformation Technology, Shanghai, 200235, China. Tel: 86-21-34204348; Fax: 86-21-34204348; Email:
[email protected] 19 20 21 22 23 24 25 26 27 28 29 30 31 1
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 2 of 33
32
Abstract
33
Pyrazinamide (PZA) is an essential first‐line anti-tubercular drug which plays a crucial role in
34
tuberculosis treatment. The PZA, which is considered as a pro-drug needs an enzyme of
35
mycobacterial Pyrazinamidase (PZase) for its conversion into an active form Pyrazinoic acid.
36
Further, this active form of PZA inhibits the ribosomal proteins S1, which facilitates the transfer-
37
messenger RNA complex formation throughout the translation. The spontaneous mutations in
38
RpsA have been found to be associated with PZA drug resistance. However, the the drug resistance
39
mechanism is still unclear. Furthermore, there is no such information available about the structural
40
dynamics of RpsA protein due to mutations that confer Pyrazinoic acid resistance. Moreover, total
41
18-clinical PZA-resistant isolates were investigated and found as pncAWT that allowed to explore
42
the resistance mechanism of RpsA in the mutated state. Samples were repeated for the drug
43
susceptibility testing followed by RpsA gene sequencing. Total 11-clinical isolates harbored total
44
15-mutations. Almost a half of total strains (07/15) were observed to be in the conserved region of
45
RpsA and known as Mycobacterium tuberculosis C-terminal domain. In the current study total
46
(02/7) mutation T370P (Mutant-1) and W403G (Mutant-2) were explored to ensure the RpsA
47
resistance mechanism through essential dynamics simulation. The essential dynamics study results
48
revealed that the consequences of distal loop mutations, drastically altered the conformation of
49
RpsA both in the absence (-) and presence (+) of Pyrazinoic acid drug by two reasons. (1) Dramatic
50
alteration or reduction in the binding pattern of Pyrazinoic acid with active site residues observed.
51
(2) A clear image of the opening and closing switching mechanism was seen upon the distal site
52
mutation on nearby 310-helixes beside the Pyrazinoic acid binding site. This switch was found
53
consistently remain close only in wild type systems, while open in the mutant systems. We called
54
such distance impact an “allosteric effect.” The overall mechanistic investigations will provide
55
useful information behind drug resistance for better understanding to manage tuberculosis.
56 57 58 59 60 2
ACS Paragon Plus Environment
Page 3 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
61
Introduction
62
In 2018, World Health Organization reported that about 1.7 billion individuals of the world’s
63
population (23%) are projected to possess a latent Tuberculosis (TB) infection, which indicates a
64
risk of developing active TB during their lifetime1. Here 330k cases were multidrug resistance
65
(MDR), and Rifampicin resistance (RR) among notified TB patients. According to the surveys
66
(Azerbaijan, Bangladesh, Belarus, Pakistan, South Africa, and Ukraine) the average level of
67
resistance to all first-line anti-TB drugs, which include rifampicin, isoniazid, pyrazinamide, and
68
ethambutol in new and previously treated TB cases was 19% (95% Confidence Interval (CI): 18-
69
20%), and 43% (95% CI: 40-46%) respectively. Pyrazinamide (PZA) is an exclusive prodrug
70
possessing a diminishing ability of MTB in the latent phase and reducing the period of TB therapy
71
from 9 months to 6 months2-4. The prodrug of PZA can be converted by MTB encoded enzyme
72
Pyrazinamidase (PZase) into an active state Pyrazinoic acid (POA) which targets ribosomal protein
73
S1 (RpsA) protein. The RpsA is involved in trans-translation5, 6 and POA can disrupt the complex
74
of RpsA-tmRNA3, 7-9.
75
In Mtb, the RpsA has four S1 domains (36–105, 123–188, 209–277 and 294–363)
76
Fig 1) 11. The residues 292-363 have been found highly conserved and capable of POA interaction.
77
Drug resistance developed due to mutations at the C-terminus domain of mycobacterial species,
78
RpsA (Mt-RpsACTD) in, altering the interactions with POA, causing conformational changes in the
79
POA binding site 3. Two POA molecules form hydrogen bonds and hydrophobic interactions with
80
residues of Lys303, Phe307, Phe310, and Arg357, also known as tmRNA binding site.3,
81
Removing alanine of the C-terminal (RpsAΔA438) will induce the PZA resistance due to lack of
82
bond formation with RpsA in Mycobacterium smegmatis. The MtRpsACTD is the POA interaction
83
site interfering in transfer-messenger RNA (tmRNA) complex formation during protein translation
84
11, 12.
85
From a structural point of view, an amino acid substitution might have drastic effects on the protein
86
structure and function, especially in the active site or distal site near to binding pockets13-15. The
87
induced mutation may also possess distal impact at a long-range position from the active site16.
88
Exploration of such distal site impact of the mutant on active site or nearby to this region provides
89
valuable information for better understanding of the phenomenon. However, these are time-
90
consuming analyses and expensive to address by experimental procedure alone. An alternative to
10
(shown in
12
3
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 4 of 33
91
these experimental procedures of molecular dynamic (MD) simulations has been used extensively
92
for exploring these mechanisms (Protein-drug, Protein associated mutations, etc.) caused by
93
conformational changes in protein, especially in drug resistance mechanisms caused by mutations
94
and the consequences of these contents are assured various disease. MD simulation studies of
95
Protein-Ligand (PL) interactions are widely applied approach for explaining the mechanisms of
96
drug resistance. Moreover, it is challenging to collect the drug-mutant combine complex of a
97
protein by single-crystal X-ray diffraction. Comparison of the experimental method, MD
98
simulation has an advantage in exploring the detailed mechanisms of drug resistance.
99
Additionally, it can provide the interaction mechanisms between drug and proteins at the atomic
100
level14, 17, like structural dynamics information of protein complexes which have been found very
101
hard by experimental procedures18-21. In the current study, we explored the drug resistance
102
mechanism among POA-RpsAWT and their mutants (T370PM1 and W403GM2), and the consequent
103
indirect impact of these mutants on overall protein conformation, especially on POA binding site.
104
These findings could be a useful strategy for deeper understanding and supervision of drug
105
resistance mechanism on the atomic level.
106
Material and Methods
107
The clinical isolate of Mycobacterium tuberculosis
108
Total eighteen-isolates were collected from Provincial Tuberculosis Reference Laboratory
109
(PTRL), that were identified previously as PZA resistance and wild-type (pncAWT). All these
110
samples were grown on 7H9 media in the MGIT 960 system 22. Approximately 100µl of the sample
111
of the positive tube was added to TBC ID device that indicates a positive MTB by the emergence
112
of pink to red at the test and control location, confirming the antigen, MPT64 in sample 23, 24. All
113
the positive MTB tubes were subjected to susceptibility testing.
114
Drug susceptibility testing of Clinical Isolates
115
MTB drug susceptibility testing (DST) is now routinely performed through automated BACTEC
116
Mycobacterium growth indicator tubes (MGIT) 960 system. All the samples were screened
117
according to the standard of positive and negative controls, Mycobacterium tuberculosis strain
118
ATCC 25618 / H37Rv and Mycobacterium Bovis respectively. A sample was marked as PZA-
119
resistance when growth occurred at a critical concentration of PZA (100 g/ml) 25. 4
ACS Paragon Plus Environment
Page 5 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
120
DNA extraction, Amplification, and Sequencing for the selected clinical samples
121
Genomic DNA was extracted from all the PZA resistance samples using the sonication method 26,
122
27.
123
GTGGACAGCAACGACTTC-3′) 28. Each 50µl reaction consists of 34.8µl molecular grade water
124
and 4µl of genomic DNA, 0.1µl DNTs, 3µl MgCl2, 5µl PCR buffer, 0.8µl Taq (New England
125
Biolabs, UK), 01µl forward and reverse primers. The PCR conditions were adjusted as, 94°C for
126
05 minutes as the denaturation step; 30 seconds of 30 cycles of, 30 seconds at 56°C, and for 01
127
minutes at 72°C; an extension step at 72°C for 05 minutes. The PCR product was sequenced using
128
6 Applied Biosystems 3730xl (Macrogen, Korea). The sequence data were analyzed through
129
Mutation Surveyor V5.0.1 29 and compared with RefSeq (NC_000962.3) of RpsA (Rv1630).
130
The Structural Modeling for RpsAWT and RpsAMutant
131
The crystal structure of RpsAWT (PDB code: 4NNI)
132
(www.rcsb.org). RpsAWT crystal structure was used as a template to build the mutant model
133
(T370P and W403G) through Molecular Operating Environment (MOE) v2018® modeling
134
package and named by M1 and M2 systematically. The molecular structure of POA was retrieved
135
from PubChem (PubChem CID: 1047) 30. All the modeled and mutant protein structures of RpsA
136
were processed through molecular dynamics simulation studies for protein structure refinement
137
employing AMBER v2014 31.
138
Molecular docking of RpsAWT, RpsAM1, and RpsAM2 with POA
139
All the native and modeled variant model of RpsA protein was prepared using proteins preparation
140
module in Chimera 32, 33. The polar side chain hydrogen atoms were refined, the modified residue
141
selenomethionines were refined into methionine using MOE® v 2106 modeling package34. The
142
active form of POA was docked into the binding pocket of each RpsA protein (RpsAWT, RpsAM1,
143
and RpsAM2) by using a reliable docking tool Autodock v4.235. Molecular docking grid was
144
specified and centered according to the POA positions in the crystallographic structure deduced
145
by Yang et al3, considering the residues K303, F307, F310, and R357 as the active site. Next, the
146
Grid points were taken as 20×20×20 with 0.375 grid spacing. A total of 50 runs were performed
147
by employing the Lamarckian Genetic Algorithm 36 with a maximum 20000000 number of energy
148
evaluations, and population size of 150, to further explore a broader range of conformational
RpsA gene was amplified using the primers; F-5′CGGAGCAACCCAACAATA-3′), and R-5′-
3
was retrieved from protein data bank
5
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 6 of 33
149
orientations in each pocket. The POA coordinates with the lowest docking energy values in the
150
RpsAWT, RpsAM1, and RpsAM2 protein were acquired for further essential dynamic study. The
151
docking results were shown in Fig 2.
152
Moreover, the online free server PatchDock
153
based on shape complementarity score. The shape complementarity knew as geometric matching,
154
where protein and drug features are compared for the docking purpose. Protein and drug shape
155
complementarity analysis at the interface is a reasonable practice in re-docking. Induced mutations
156
in protein sequence often cause conformational changes affecting the interactions of protein active
157
site residues with a drug 38, 39. The free online server Computed Atlas of Surface Topography of
158
proteins (CASTp) was used to validate the following conformation variation upon induced
159
mutation 40, additionally, to compare the Pocket volumes of bound RpsAWT, bound RpsAM1, and
160
bound RpsAM2. Furthermore, LigPlot
161
interactions between drug and protein.
162
All-atom Molecular Dynamics Simulation
163
Molecular dynamics (MD) simulations provide plenteous dynamical structural and energetic
164
information about the biomacromolecules, target protein and drug interactions in therapy. All MD
165
simulations and possible analysis procedures for the systems were conducted in AMBER v2014
166
software package 31. The LEaP module was used to add hydrogen atoms to the crystal structures.
167
Counter-ions (Na+ and Cl-) were added for maintenance of system neutrality. All the systems were
168
solvated in a truncated octahedral box of TIP3P water model with 8.0 Å buffer. The Particle Mesh
169
Ewald (PME)
170
hydrogen atoms were constrained with SHAKE algorithm 44. All MD simulations were accelerated
171
with the CUDA version of PMEMD in GPU cores of NVIDIA® Tesla K20 45, 46, and were carried
172
out in the NPT ensemble at 298 K and 1 bar an integration time step of 2 fs was used. Temperature
173
control was performed using the Andersen-like temperature coupling scheme, in which imaginary
174
"collisions" randomize the velocities to a distribution corresponding to the simulation temperature
175
every 1000 steps. Pressure control was performed using Berendsen barostat with pressure
176
relaxation time set to 1.0 ps. SHAKE algorithm was implemented to constrain the bonds involving
177
hydrogen atoms. A cutoff of 8.0 Å was used for the Lennard-Jones interactions and short-range
178
electrostatic interactions. Detail simulation conditions are listed in Table 1.
43
41, 42
37
was used to analyze drug and protein structures,
was used to plot the hydrogen and hydrophobic
was used to treat long-range electrostatic interactions. The bonds involving
6
ACS Paragon Plus Environment
Page 7 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
179
Essential Dynamics Simulation Analysis
180
The trajectory files from MD simulations were used to explore the dominant motions in all systems
181
including bound RpsAWT, bound RpsAM1, bound RpsAM2, free RpsAWT, free RpsAM1, and free
182
RpsAM2 through PCA or essential dynamics47. The translational and rotational movements were
183
eliminated from the coordinates and the following superimposition onto a reference structure.
184
Subsequently, positional covariance matrix was calculated of atomic coordinates and its
185
eigenvectors. The matrix was diagonalized by an orthogonal coordinate transformation matrix
186
yielding the diagonal matrix of eigenvalues. Each eigenvector and its eigenvalue typically
187
demonstrate the principal component of the trajectory, which comprises of the dominant
188
significant motion of the protein ensemble.
189
Free energy landscape (FEL) was obtained by plotting the extracted coordinates from the trajectory
190
of total 100ns MD simulation of the first two principal components (PC1 and PC2) against each
191
other. The corresponding Gibbs energy represents the conformation of molecule obtained through
192
the trajectory. The deep valleys in term of the 2D graph represent lowest energy state (stable) and
193
dominant ensemble, while the associated boundaries to the deep valley represent the transition
194
state (meta-stable) or intermediate ensemble of the protein
195
distributed in GROMACS was used for the Gibbs energy calculations. The Origin v9.1 was used
196
to obtain 2D plots. The coordinates on 2D plots were used to predict and find the exact time frames
197
and snapshots of molecules at a time and state. In the present study, the first two principal
198
components were used to calculate the FEL based on the equation 1:
199
ΔG (PC1, PC2) = -KBTlnP (PC1, PC2)
200
Where the PC1 and PC2 represent the reaction coordinates, KB denotes the Boltzmann constant
201
and P(PC1 & PC2) shows the probability distribution of the system along the first two principal
202
components. The dynamics cross-correlation map (DCCM) was designed to explore and identify
203
the dominant correlated motions of each residue in different systems of RpsA. The matrix (Cij)
204
depicts the time-correlated info among the (i) and (j) atoms of a protein 49. The only alpha carbon
205
atoms from the last 5000 snapshots were selected at 0.002ns time intervals to construct the matrix.
206
The following plot from DCCM based on two value positive (+ve), and negative (-ve); the (+ve)-
207
values demonstrate the residue motion in the same direction, while the (-ve)-value indicates the
208
residues displacement in the opposite direction.
48.
Next, the ‘g_sham’ function
(1)
7
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 8 of 33
209
Interaction Analysis of POA-RpsA Complex
210
Protein-ligand (PL) interaction task was conducted with in-house software50. The hydrophobic
211
interaction was considered as the distance between mass centers of side chain for hydrophobic
212
residue and the ligand is less than 6.5 Å. The literature has revealed that charge-charge interactions
213
up to 11 Å have a contribution to protein-protein binding free energies51. Thus, when the distances
214
between the mass centers of charge residues are less than 11Å, the electrostatic (i.e., charge-
215
charge) interactions are assigned. If the distance between the atoms of donor and acceptor is less
216
than 3.5 Å and the bond angle of donor/hydrogen/acceptor is larger than 120°, such interaction
217
would be considered as a hydrogen bond. Average structures were calculated from the structure
218
ensembles of the energy-minimum 51-54. All the representations were conducted in PyMol v1.755.
219
Per-residues Binding Affinity Contribution Calculations
220
To explore the per-residue energy contribution in each system toward the POA drug, binding free
221
energy calculation was conducted for all residues-residues systems by using the molecular
222
mechanics Poisson Boltzmann surface area (MM-PBSA) method56. The snapshots of last 10-ns
223
trajectory were used to estimate the binding free energy. The binding free energies among different
224
residues were calculated as the equation 2.
225
ΔGbinding = ΔGcomplex – [ΔGresiduei + ΔGresiduesj]
226
Here, Gcomplex, Gresidue i, and G residue j was the total free energies of complex, residue i, and residue
227
j in solvent, respectively. Furthermore, the free energies for each Gcomplex, Gresiduei and Gresiduej were
228
estimated by:
229
Gx = EMM - TS + Gsolvation
230
EMM = Ebonded + Enon-bonded = Ebonded + (Evdw + Eelec)
231
Gsolvation = Gpolar + Gnon-polar
232
Here, the x was the residue i, residue j or complex of residue-residue. EMM was the average
233
molecular mechanic's potential energies in vacuum and Gsolvation was free energies of salvation.
234
The T for temperature and S for solute entropy. Ebonded was bonded interactions, and Enon-bonded was
235
non-bonded interactions including Van der Waals (Evdw) and electrostatic (Eelec) interactions. The
236
∆Ebonded was always taken as zero57. The Gpolar and Gnon-polar referred to the electrostatic and non-
237
electrostatic contributions to the solvation free energies, respectively.
(2)
8
ACS Paragon Plus Environment
Page 9 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
238
Results and Discussion
239
From the drug susceptibility testing result, it was evident that all the samples possess resistance to
240
PZA drug. Moreover, these samples were analyzed manually for; whether at a critical
241
concentration of PZA, growth will occur or not. Subsequently, it was observed that out of 18
242
resistant samples, but PncAWT isolates, 11 samples (61%) were found to have total 15 non-
243
synonymous mutations, while seven strains were RpsAWT as shown in Table 2. Later, through
244
visual inspection and confirmation in the protein structure, the variations as mentioned earlier were
245
observed in the conserved region (292-363) of RpsA gene. In the current study, two mutation sites
246
were targeted to explore the indirect impact of these mutations (T370P and W403G) on overall
247
protein structure and following function of RpsA protein. Molecular docking approach was used
248
to dock the active form of PZA (POA) in the active site of RpsA. The docking results revealed that
249
in the bound RpsAWT system, the POA drug potentially adopted various interaction with active
250
site residues including Asp352(OD2), Arg357 (NE and NH2), and -stacking interaction of the
251
Pyrazine moiety of the POA with the benzene ring of F307.
252
In contrast, the bound RpsAM1 and bound RpsAM2 systems, the POA drug dramatically reduced
253
the interaction with the active site residues, the 2D interaction profile were depicted in supporting
254
information Figure S1. Visually, these mutation sites were found on a distance approximately
255
30Å from the binding site (K303, F307, F310, and R357) of the POA active drug (Fig 3).
256
Consequences of post-MD extracted protein structures, it was observed that upon inducing
257
mutation in the RpsAWT protein structure, both the mutated residues showed an indirect impact on
258
nearby loop (αH1& αH2) next to the POA binding site. In the RpsAWT, these loops were in
259
proximity with each other (close ensemble).
260
In contrast, both bound RpsAM1 and RpsAM2 structure, these loops found on distance from each
261
other (open ensemble). The superpose post-MD protein structure revealed that; in the absence of
262
POA drug both the RpsAM1 and RpsAM2 in comparison with RpsAWT express the indirect impact
263
on these loops from the site of mutation. In the presence of POA, both RpsAM1 and RpsAM2 showed
264
impact, interestingly, RpsAWT also similar impact as observed for both RpsAM1 and RpsAM2 as
265
shown in Fig 3.
266
Evaluation of the Stability for free and bound RpsA systems 9
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 10 of 33
267
All the RpsA structures (in the presence and absence of POA drug) were simulated in an explicit
268
water environment for total MD time 100ns. The deviation of backbone atoms was examined by
269
the root mean square deviation (RMSd). The RMSd graphs for both free and bound RpsA systems
270
relative to the original structures show that the simulation time of total 100ns is sufficient to reach
271
dynamics equilibration at temperature 310K. We observed that in the absence of POA drug, the
272
RMSd of RpsAWT systematically steady (~2.5Å) from the beginning to 80ns. However, this stable
273
behavior of deviation then increased dramatically and oscillated around ~5Å after 80ns MD
274
simulations.
275
In contrast, both the RpsAM1 and RpsAM2 systems showed different patterns of backbone deviation
276
in comparison with RpsAWT as shown in Fig 4. The RMSd graph for RpsAM1 revealed dramatical
277
fluctuation like, at the beginning (till 10ns), the RMSd graph gradually reached to around ~5.2Å
278
and then progressively reduced to 3Å, till 80ns MD simulation time, the RMSd showed a slight
279
increase and decrease in comparison with wild-type. After 80ns, the graph gradually reduced to
280
1Å and then increased as compared with the wild-type RpsA, whereas the RpsAM2 mutant showed
281
high RMSd fluctuation throughout the MD run as compared with the wild-type RpsA (Fig 4). The
282
high swing in the backbone RMSd of both the mutants (in the absence of POA drug) points to the
283
involvement of these residues in RpsA de-stability. The consequences of this de-stability further
284
reduce the binding of POA drug with active site residues, and hence showed the distal impact in
285
term of limiting the interaction or the strength of hydrogen bonds with POA drug and thus
286
resistance assured in this phenomenon. Additionally, in the presence of POA drug, such distal loop
287
mutation also showed the allosteric effect on nearby 310-helixes with POA binding site would
288
discuss in the next sections. The superimposed RMSd graphs of all RpsA mutants with free and
289
bound wild-type are shown in Fig 4.
290
To understand the effect of individual amino acids in the free and bound RpsA, we analyzed the
291
C root mean square fluctuations (RMSf). In the absence of POA drug; the RpsAWT system
292
showed slight high fluctuation passion in some regions (Fig 5), but upon distal site mutation in the
293
distal loop, the regions which showed high fluctuation in WT was found less fluctuated in both
294
variant RpsA systems. Additionally, the mutant showed a distal impact on POA binding site
295
residues, and as well as on nearby 310-helixes. While in the presence of POA drug, the RpsAWT
10
ACS Paragon Plus Environment
Page 11 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
296
showed a stable pattern of fluctuation but also showed high fluctuation both in alpha 310-helixes,
297
additionally at the distal impact on POA binding residues.
298
Interestingly, these findings conclude that only in the absence of POA drug, the RpsAWT showed
299
a slight fluctuation in the desired regions, while either in the presence of this POA drug, the WT,
300
and all the variants showed a high pattern of changes in the desired regions (310-helixes and POA
301
binding site residues). These findings suggest that distal site mutation not only disturbs the POA
302
binding with active site residues but additionally shows the allosteric effect on nearby 310-helixes,
303
consequently, shows opening and closing pattern on these effected 310-helixes. These variants
304
disturbed the POA binding precisely, and hence the mutated RpsA protein showed drug resistance
305
to POA.
306
POA Binding Pocket Volume and Shape Complementarity Analysis
307
To explore the indirect impact of the mutation on the POA binding site, pocket volume analysis
308
was conducted with CASTp server v3.0. Consequently, it was found that bound states of RpsAWT,
309
RpsAM1, and RpsAM2 showed essential differences in the pocket volume as shown in Fig 6. In the
310
case of bound RpsAWT, the POA binding pocket volume (PV) was 4638.493Å3, while in case of
311
both mutant structures it drastically reduced to 563.383Å3 for bound RpsAM1 and 137.831Å3 for
312
bound RpsAM2. Now it is evident from these pocket analysis that distant mutation site directly
313
affects the POA binding site residues, which consequences of the reduction of PV values. The
314
change of PV will affect the firm interaction between the protein and POA molecule, and hence
315
affect the RpsA activity. On the other hand, the shape complementarity analysis found similar
316
results for bound RpsAWT, RpsAM1, and RpsAM2.
317
Distal Site Mutation Alter the POA Binding
318
The PL interaction analysis was conducted for each system using the lowest energy structures
319
extracted from MD simulations trajectory. The geometric positions of POA resistance compared
320
to the critical residues of RpsA are shown in Fig 7. The results indicate there are hydrogen bond
321
between R357 and POA and -stacking interaction between benzene ring of F307 and POA for
322
wild type, and the residue F310 stabilizes these interactions of R357 and F307. -stacking
323
interaction between F310 and POA was lost for both mutant systems, just remaining the hydrogen
11
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 12 of 33
324
bond. These PL results show the direct impact of the distal mutation on POA binding site, and
325
hence changing the interaction profile, and revealing drug resistance pattern.
326
To further reveal how distal mutation affects the nearby 310-helixes upon mutant and POA binding,
327
we analyze the conformers of six systems. Interestingly, mutant and POA binding showed a
328
tremendous effect on 310-helixes, however, the function of these 310-helixes is unknown. In the
329
current study, we computationally securitized and called this direct impact of distal mutation
330
“allosteric effect.” These 310-helixes showed an opening and closing mechanism. The alpha
331
carbon distance analysis also revealed the same mechanism (Fig 8).
332
Per-Residue Energy Contribution for POA binding
333
MM-PBSA is one of the widely used methods to calculate the binding free energy56. Per-residues
334
contribution and the role of the conserved residues were analyzed upon mutation. The per-residues
335
analysis consequences revealed that the residues of F310 and R357 contributed the highest binding
336
energy upon POA binding for wild type (Fig 9). The contribution of these binding site residues
337
weakened for bound RpsAM1 and RpsAM2 and hence indicated the drug resistance (Fig 9).
338
The consequences of the backbone fluctuations and deviations in the presence and absence of POA
339
drug, both in wild-type and their variants (M1 and M2) of RpsA protein, further exploration
340
regarding the overall compactness in the RpsA-/+-POA-WT, and RpsA-/+-POA-mutants is essential, so,
341
Radius of gyration (Rg) was calculated. The Rg results revealed that all of the free and bound
342
RpsAMutants showed distinct behavior throughout the MD simulation time. In particular, both the
343
mutants M1 and M2, either in the free and bound POA drug molecule were discrete as compared
344
to the RpsAWT regarding Rg values, likely due to the single distal point mutations (Fig 10). These
345
results justified that both in free RpsAM1, and RpsAM2, the overall compactness reduced compare
346
with RpsAWT. Interestingly, in bound RpsA, both the mutants system, the overall compactness
347
gradually increase throughout MD simualtion time compared with bound RpsAWT.
348
Exploring the Dominant Motions for Free and Bound Systems
349
To further identify the dominant motion, we performed the principal component analysis (PCA)
350
for free and bound systems, in which the first ten eigenvectors captured most of the combined
351
dominant motions. Generally, in this PCA analysis, the amplitude of the corresponding
352
eigenvalues progressively declined and finally reached a constrained and more localized 12
ACS Paragon Plus Environment
Page 13 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
353
fluctuations, the ratio of all the ten principal components depicted in supporting information
354
Figure S2. The first 10-eigenvectors were produced from the MD trajectory and examined for
355
their influence to the total fluctuation of RpsA systems. The final figure for PCA was plotted by
356
using the first two eigenvectors. The 2D plots were made to compare the probably credited
357
motions. These plots demonstrate the alteration in the ensemble distribution of the RpsA diverse
358
systems specified by each dot in the particular plot. The color gradient representation (from brown
359
to light yellow) highlights the periodic jumps among these conformations. The PCA plot showed
360
compactness and steady for free wild type and two-fold twist in the conformation throughout MD
361
simulation time for free mutant systems (Fig 11A). More interestingly, both the mutant systems
362
showed a drastic variation in structure upon the binding of POA (Fig 12A). These findings indicate
363
a better distal impact of the mutation on the overall protein structure and hence reveal the resistance
364
toward the POA first glance drug.
365
The dynamics cross-correlation matrix (DCCM) was also constructed to investigate the functional
366
residues displacements for all RpsA systems. The results indicate that all the mutant systems
367
showed a different pattern of residues correlated motions comparing with free and bound RpsAWT.
368
However, the difference in the (+)/(-)-correlation of atomic displacements was most prominent in
369
all RpsA systems. DCCM of Free RpsAWT showed a distinct pattern of correlation mostly, and
370
reduced correlations among the residues for the 310-helixes regions. DCCM of bound RpsAWT
371
showed a slight positive correlation in the 310-helixes regions and insignificant negative
372
associations for the binding site residues. The following negative correlation or slightly positive
373
in the desired regions and residues are the consequences of inducing distal mutation, which further
374
reveal the drug resistance mechanism upon the mutant ( shown in Fig 13).
375
Exploring the Transition Pathway from Meta-stable to the Native States
376
The first two eigenvectors (PC1 and PC2) were extracted from the entire 100ns MD trajectory and
377
were used to plot the landscape for free energy for all RpsA systems. The FEL analysis would help
378
us to determine the refine (stable) native and transition (meta-stable) states of all the RpsA systems.
379
Consequences of FEL plot, intensive, in-depth study were conducted to further extract the
380
structural coordinates from the lowest energy states (highlighted in the extreme central region of
381
every valley plot) to also understand the structural evolution in the presence and absence of POA
382
drug, and additionally, the distal impact of mutation on overall ensemble of protein. In the 13
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 14 of 33
383
following Fig 11B, the dark central black color represents the lowest Gibbs energy states. The FEL
384
results illustrate that free RpsAWT and RpsA M1 attained single energy states (all were found native
385
state), while free RpsAM2 attained two state (one meta-stable and native state) and separated by
386
low-energy barriers. It was recorded while calculating the Cα-RMSd, the RpsAWT remained in the
387
stable state throughout the MD time, while the other RpsAMA, & M2 showed deviation initially, and
388
later showed calm behavior. The FEL plots of bound systems revealed drastic conformational
389
changes. This analysis further illustrates that bound RpsAWT shows three metastable and one native
390
state, separated by low energy barrier during total 100 MD trajectory, while in case of bound RpsA
391
M1
392
separated from each other by moderates and high energy barrier (Fig 12B). These results indicate
393
that these energy states separated by low, moderate and high energy barrier states jumped from
394
one state to another, etc., and periodically changing the conformational behavior. Most
395
interestingly this periodic variation were observed for mutation systems. The resistance for bound
396
RpsAM1 & M2 are the consequences of this instability in structural evaluation which was explored
397
in the current study.
398
The significances of essential dynamics simulation study showed that currently subjected mutation
399
which was explored for RpsA resistance mechanism was found in the conserved region, and these
400
mutations further reduced the binding pattern of RpsA active site residues with active form of PZA
401
drug (POA). From the visual inspection, it was observed that the site of mutation is far (~30 Å)
402
from the POA binding site, but due to allosteric effect, these mutations showed a drastic impact
403
on two essential regions. Firstly, it dramatically disturbed the conformation of binding site residues
404
site chain which further limits the interaction of POA, and hence showed resistance. Secondly,
405
these mutated residues showed the distal impact on 310-helixes exist beside POA binding site. The
406
consequences of a distal impact on these 310-helixes further revealed an opening and closing
407
phenomena. The effect of a mutation in term of showing resistance of POA drug might be due to
408
the additional reason which is the change in volume and the size of the entry to the pocket 58. The
409
binding affinity between RpsA and POA might also reduced due to alteration in pocket size which
410
is consistent with the previous work54. That a drastic change in the binding pocket of mutants,
411
where PZA resistance was measured due to mutation K96R in PZase 59, 60.
412
Conclusion
and RpsAM2 various meta-stable and stable states were observed and these all states further
14
ACS Paragon Plus Environment
Page 15 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
413
Global health security is affected by drug-resistant TB and has been considered as the main
414
obstacle towards the global milestones, end TB strategy 203061,
415
regarding reductions in the burden of tuberculosis disease set for 2020, 2025, 2030, and 2035.
416
Additionally better understanding attracts the attention of the researcher to explore the insight
417
mechanism of drug resistance for better supervision of global TB control 63. PZA is the only drug
418
that diminishes the persisted MTB under the latent state. In the current study, we explored the
419
distal impact of our novel clinical mutations T370P (M1) and W403G (M2) on RpsA activity by
420
comparing them with wild-type in PZA-resistant pncAWT MTB isolates. The drastic changes of
421
these mutations were assured from various dynamics analysis which includes bringing alteration
422
in RpsA activity by transforming the total energy, flexibility, and stability. Hence, the
423
consequences of these mutations directly affect and alter at first glance the binding pattern of POA
424
with active residues. Additional essential dynamics analysis revealed (free energy landscape) that
425
the dynamics of mutants structures, seemed to be variable to maintain a stable conformation,
426
subsequently, observed many the meta-stable ensembles, which further shows the unstable behave
427
of RpsA. In summary, all these essential dynamics analysis supports the role of these mutations in
428
reducing the RpsA activity tremendously, and subsequently findings play a key role in RpsA drug
429
resistance. To the best of our knowledge, we presented a first comprehensive investigation of such
430
kind where multiple dynamic characteristics have been investigated for better insight into
431
mutations, affecting target activity. The current study could be used as a useful model and would
432
open a new window for better understanding of the management of drug resistance of TB.
433 434 435 436 437 438 439 440 441 442 443 444 445 446
║These
447
Acknowledgment and Funding
62.
The intensive research
ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website. (PDF ) Conflict of interest The authors declare no competing financial interests. Author Contribution HFC and AUR designed the study. AUR performed molecular dynamics simulation study and wrote the manuscript. LH performed the energy analysis. Experimental data belongs to MTK and SIM. AW critically reviewed the manuscript. HFC revised and approved the manuscript. authors contributed equally to this study.
15
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
448 449 450 451 452
Page 16 of 33
This work was supported by Center for HPC at Shanghai Jiao Tong University, the National Natural Science Foundation of China (31770771 and 31620103901), the National Key Research and Development Program of China (2017YFE0103300), Medical Engineering Cross Fund of Shanghai Jiao Tong University (YG2017MS08). We are thankful to Pinky Sultana of Department of pharmacy, SJTU, china for reviewing the manuscript for technical errors.
453 454 455
References
456 457
1. World Health Organization-Global tuberculosis report 2018; World Health Organization: UN, 2018.
458 459
2. Mitchison, D. A., The action of antituberculosis drugs in short-course chemotherapy. Tubercle. 1985, 66, 219-225.
460 461 462
3. Yang, J.; Liu, Y.; Bi, J.; Cai, Q.; Liao, X.; Li, W.; Guo, C.; Zhang, Q.; Lin, T.; Zhao, Y.; Wang, H.; Liu, J.; Zhang, X.; Lin, D., Structural basis for targeting the ribosomal protein S1 of Mycobacterium tuberculosis by pyrazinamide. Mol. Microbiol. 2015, 95, 791-803.
463 464 465
4. Yadon, A. N.; Maharaj, K.; Adamson, J. H.; Lai, Y. P.; Sacchettini, J. C.; Ioerger, T. R.; Rubin, E. J.; Pym, A. S., A comprehensive characterization of PncA polymorphisms that confer resistance to pyrazinamide. Nat. Commun. 2017, 8, 588.
466 467 468
5. Lu, P.; Haagsma, A. C.; Pham, H.; Maaskant, J. J.; Mol, S.; Lill, H.; Bald, D., Pyrazinoic acid decreases the proton motive force, respiratory ATP synthesis activity, and cellular ATP levels. Antimicrob. Agents. Chemother. 2011, 55, 5354-5357.
469 470 471
6. Zhang, Y.; Wade, M. M.; Scorpio, A.; Zhang, H.; Sun, Z., Mode of action of pyrazinamide: disruption of Mycobacterium tuberculosis membrane transport and energetics by pyrazinoic acid. J. Antimicrob. Chemother. 2003, 52, 790-795.
472 473
7. Sorensen, M. A.; Fricke, J.; Pedersen, S., Ribosomal protein S1 is required for translation of most, if not all, natural mRNAs in Escherichia coli in vivo. J. Mol Biol. 1998, 280, 561-569.
474 475
8. Simons, S. O.; Mulder, A.; van Ingen, J.; Boeree, M. J.; van Soolingen, D., Role of rpsA gene sequencing in diagnosis of pyrazinamide resistance. J. Clin Microbiol. 2013, 51, 382.
476 477 478
9. Tan, Y.; Hu, Z.; Zhang, T.; Cai, X.; Kuang, H.; Liu, Y.; Chen, J.; Yang, F.; Zhang, K.; Tan, S.; Zhao, Y., Role of pncA and rpsA gene sequencing in detection of pyrazinamide resistance in Mycobacterium tuberculosis isolates from southern China. J. Clin Microbiol. 2014, 52, 291-297.
479 480 481
10. Salah, P.; Bisaglia, M.; Aliprandi, P.; Uzan, M.; Sizun, C.; Bontems, F., Probing the relationship between Gram-negative and Gram-positive S1 proteins by sequence analysis. Nucleic Acids Res. 2009, 37 (16), 5578-5588. 16
ACS Paragon Plus Environment
Page 17 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
482 483 484
11. Bycroft, M.; Hubbard, T. J.; Proctor, M.; Freund, S. M.; Murzin, A. G., The solution structure of the S1 RNA binding domain: a member of an ancient nucleic acid–binding fold. Cell. 1997, 88 (2), 235-242.
485 486 487
12. Shi, W.; Zhang, X.; Jiang, X.; Yuan, H.; Lee, J. S.; Barry, C. E.; Wang, H.; Zhang, W.; Zhang, Y., Pyrazinamide inhibits trans-translation in Mycobacterium tuberculosis. Science. 2011, 333 (6049), 1630-1632.
488 489
13. Worth, C. L.; Gong, S.; Blundell, T. L., Structural and functional constraints in the evolution of protein families. Nat Rev Mol Cell Biol. 2009, 10 (10), 709.
490 491 492
14. Ganesan, P.; Ramalingam, R. Investigation of structural stability and functionality of homodimeric gramicidin towards peptide‐based drug: a molecular simulation approach. J. Cell Biochem. 2018, 1‐9.
493 494
15. Bartlett, G. J.; Borkakoti, N.; Thornton, J. M., Catalysing new reactions during evolution: economy of residues and mechanism. J. Mol Biol. 2003, 331 (4), 829-860.
495 496
16. Kosloff, M.; Kolodny, R., Sequence-similar, structure-dissimilar protein pairs in the PDB. Proteins. 2008, 71, 891-902.
497 498 499
17. Liu, H.; Yao, X., Molecular basis of the interaction for an essential subunit PA− PB1 in influenza virus RNA polymerase: insights from molecular dynamics simulation and free energy calculation. Mol Pharm. 2009, 7 (1), 75-85.
500 501 502
18. Xue, W.; Pan, D.; Yang, Y.; Liu, H.; Yao, X., Molecular modeling study on the resistance mechanism of HCV NS3/4A serine protease mutants R155K, A156V and D168A to TMC435. Antiviral Res. 2012, 93 (1), 126-137.
503 504
19. Hou, T.; McLaughlin, W. A.; Wang, W., Evaluating the potency of HIV‐1 protease drugs to combat resistance. Proteins. 2008, 71 (3), 1163-1174.
505 506 507
20. Ding, B.; Li, N.; Wang, W., Characterizing binding of small molecules. II. Evaluating the potency of small molecules to combat resistance based on docking structures. J. Chem Inf. Model. 2013, 53 (5), 1213-1222.
508 509 510
21. Xue, W.; Liu, H.; Yao, X., Molecular mechanism of HIV‐1 integrase–vDNA interactions and strand transfer inhibitor action: A molecular modeling perspective. J. Comput Chem. 2012, 33 (5), 527-536.
511 512 513
22. Pardini, M.; Varaine, F.; Bonnet, M.; Orefici, G.; Oggioni, M. R.; Fattorini, L.; Group, L.D. S., Usefulness of the BACTEC MGIT 960 system for isolation of Mycobacterium tuberculosis from sputa subjected to long-term storage. J. Clin Microbiol. 2007, 45 (2), 575-576.
17
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 18 of 33
514 515 516
23. Said, H. M.; Ismail, N.; Osman, A.; Velsman, C.; Hoosen, A. A., Evaluation of TBc Identification Immunochromatographic Assay for Rapid Identification of Mycobacterium tuberculosis Complex in Samples from Broth Cultures. J. Clin Microbiol. 2011, 49, 1939.
517 518 519 520
24. Machado, D.; Ramos, J.; Couto, I.; Cadir, N.; Narciso, I.; Coelho, E.; Viegas, S.; Viveiros, M., Assessment of the BD MGIT TBc identification test for the detection of Mycobacterium tuberculosis complex in a network of mycobacteriology laboratories. Biomed Res Int. 2014, 2014, 398108.
521 522 523 524
25. Aono, A.; Hirano, K.; Hamasaki, S.; Abe, C., Evaluation of BACTEC MGIT 960 PZA medium for susceptibility testing of Mycobacterium tuberculosis to pyrazinamide (PZA): compared with the results of Pyrazinamidase assay and Kyokuto PZA test. Diagn. Microbiol. Infect Dis. 2002, 44 (4), 347-352.
525 526 527
26. Buck, G. E.; O Hara, L. C.; Summersgill, J. T., Rapid, simple method for treating clinical specimens containing Mycobacterium tuberculosis to remove DNA for polymerase chain reaction. J. Clin Microbiol. 1992, 30 (5), 1331-1334.
528 529 530
27. Kirschner, P.; Springer, B.; Vogel, U.; Meier, A.; Wrede, A.; Kiekenbeck, M.; Bange, F.; Bottger, E., Genotypic identification of mycobacteria by nucleic acid sequence determination: report of a 2-year experience in a clinical laboratory. J. Clin Microbiol. 1993, 31 (11), 2882-2889.
531 532 533 534
28. Xia, Q.; Zhao, L.-l.; Li, F.; Fan, Y.-m.; Chen, Y.-y.; Wu, B.-b.; Liu, Z.-w.; Pan, A.-z.; Zhu, M., Phenotypic and Genotypic Characterization of Pyrazinamide Resistance among Multidrugresistant Tuberculosis isolates in Zhejiang, China. Antimicrob Agents. Chemother. 2015, AAC. 04541-14.
535 536 537
29. Dong, C.; Yu, B. Mutation Surveyor: An In-Silico Tool for Sequencing Analysis. In In Silico Tools for Gene Discovery, Yu, B.; Hinchcliffe, M., Eds.; Humana Press: Totowa, NJ, 2011, pp 223-237.
538 539 540
30. Kim, S.; Thiessen, P. A.; Bolton, E. E.; Chen, J.; Fu, G.; Gindulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B. A.; Wang, J.; Yu, B.; Zhang, J.; Bryant, S. H., PubChem Substance and Compound databases. Nucleic Acids Res. 2016, 44, D1202-13.
541 542
31. Salomon-Ferrer, R.; Case, D. A.; Walker, R. C. An Overview of the Amber Biomolecular Simulation Package. Comput. Mol. Sci. 2013, 3, 198-210.
543 544 545
32. Pettersen, E. F.; Goddard, T. D.; Huang, C. C.; Couch, G. S.; Greenblatt, D. M.; Meng, E. C.; Ferrin, T. E., UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput Chem. 2004, 25 (13), 1605-1612.
546 547
33. Rodriguez-Guerra Pedregal, J.; Marechal, J.-D., PyChimera: use UCSF Chimera modules in any Python 2.7 project. Bioinformatics. 2018, 34 (10), 1784-1785. 18
ACS Paragon Plus Environment
Page 19 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
548 549
34. Molecular Operating Environment (MOE), 2013.08; Chemical Computing Group ULC, 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2019.
550 551 552
35. Morris, G. M.; Huey, R.; Lindstrom, W.; Sanner, M. F.; Belew, R. K.; Goodsell, D. S.; Olson, A. J., AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput Chem. 2009, 30 (16), 2785-2791.
553 554 555
36. Morris, G. M.; Goodsell, D. S.; Halliday, R. S.; Huey, R.; Hart, W. E.; Belew, R. K.; Olson, A. J., Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput Chem. 1998, 19 (14), 1639-1662.
556 557
37. Schneidman-Duhovny, D.; Inbar, Y.; Nussinov, R.; Wolfson, H. J., PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Res. 2005, 33, W363-7.
558 559
38. Norel, R.; Petrey, D.; Wolfson, H. J.; Nussinov, R., Examination of shape complementarity in docking of unbound proteins. Proteins. 1999, 36, 307-317.
560 561
39. Zhang, Q.; Sanner, M.; Olson, A. J., Shape complementarity of protein-protein complexes at multiple resolutions. Proteins. 2009, 75, 453-67.
562 563
40. Binkowski, T. A.; Naghibzadeh, S.; Liang, J., CASTp: computed atlas of surface topography of proteins. Nucleic Acids Res. 2003, 31 (13), 3352-3355.
564 565 566
41. Aggarwal, M.; Singh, A.; Grover, S.; Pandey, B.; Kumari, A.; Grover, A., Role of pncA gene mutations W68R and W68G in pyrazinamide resistance. J. C Biochem. 2018, 119 (3), 25672578.
567 568 569 570
42. Friesner, R. A.; Banks, J. L.; Murphy, R. B.; Halgren, T. A.; Klicic, J. J.; Mainz, D. T.; Repasky, M. P.; Knoll, E. H.; Shelley, M.; Perry, J. K., Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 2004, 47 (7), 1739-1749.
571 572
43. Darden, T.; York, D.; Pedersen, L., Particle mesh Ewald: An N⋅ log (N) method for Ewald sums in large systems. J. Chem Phys. 1993, 98 (12), 10089-10092.
573 574 575
44. Ryckaert, J.-P.; Ciccotti, G.; Berendsen, H. J., Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput Phys 1977, 23 (3), 327-341.
576 577 578
45. Salomon-Ferrer, R.; Götz, A. W.; Poole, D.; Le Grand, S.; Walker, R. C., Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J. Chem. Theory Comput 2013, 9 (9), 3878-3888.
579 580 581
46. Salomon-Ferrer, R.; Gootz, A. W.; Poole, D.; Le Grand, S.; Walker, R. C., Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J. Chem. Theory Comput 2013, 9 (9), 3878-3888. 19
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 20 of 33
582 583
47. Maisuradze, G. G.; Liwo, A.; Scheraga, H. A., Principal component analysis for protein folding dynamics. J Mol Biol. 2009, 385 (1), 312-329.
584 585 586
48. Hoang, T. X.; Trovato, A.; Seno, F.; Banavar, J. R.; Maritan, A., Geometry and symmetry presculpt the free-energy landscape of proteins. Proc. Natl. Acad. Sci. USA. 2004, 101 (21), 79607964.
587 588 589 590
49. Chillemi, G.; Dannessa, I.; Fiorani, P.; Losasso, C.; Benedetti, P.; Desideri, A., Thr729 in human topoisomerase I modulates anti-cancer drug resistance by altering protein domain communications as suggested by molecular dynamics simulations. Nucleic Acids Res. 2008, 36 (17), 5645-5651.
591 592
50. Chen, H.-F.; Luo, R., Binding induced folding in p53− MDM2 complex. J. Am. Chem. Soc 2007, 129 (10), 2930-2937.
593 594
51. Qin, F.; Chen, Y.; Wu, M.; Li, Y.; Zhang, J.; Chen, H.-F., Induced fit or conformational selection for RNA/U1A folding. RNA 2010, 16 (5), 1053-1061.
595 596
52. Chen, H.-F., Mechanism of coupled folding and binding in the siRNA-PAZ complex. J. Chem. Theory Comput 2008, 4 (8), 1360-1368.
597 598 599
53. Rehman, A. U.; Rahman, M. U.; Khan, M. T.; Saud, S.; Liu, H.; Song, D.; Sultana, P.; Wadood, A.; Chen, H. F., The Landscape of Protein Tyrosine Phosphatase (Shp2) and Cancer. Curr Pharm Des. 2018, 24, 3767-3777.
600 601 602
54. Khan, M. T.; Rehaman, A. U.; Junaid, M.; Malik, S. I.; Wei, D.-Q., Insight into novel clinical mutants of RpsA-S324F, E325K, and G341R of Mycobacterium tuberculosis associated with pyrazinamide resistance. Comput. Struct Biotechnol J 2018, 16, 379-387.
603 604
55. DeLano, W. L. The Pymol Molecular Graphics System; DeLano Scientific: San Carlos, CA, 2002; pp 2.40-2.44
605 606 607
56. Kollman, P. A.; Massova, I.; Reyes, C.; Kuhn, B.; Huo, S.; Chong, L.; Lee, M.; Lee, T.; Duan, Y.; Wang, W., Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc. Chem. Res 2000, 33 (12), 889-897.
608 609
57. Homeyer, N.; Gohlke, H., Free energy calculations by the molecular mechanics Poisson− Boltzmann surface area method. Mol Inform 2012, 31 (2), 114-122.
610 611
58. Coleman, R. G.; Sharp, K. A., Protein pockets: inventory, shape, and comparison. J Chem Inf Model 2010, 50 (4), 589-603.
612 613 614
59. Vats, C.; Dhanjal, J. K.; Goyal, S.; Gupta, A.; Bharadvaja, N.; Grover, A. In Mechanistic analysis elucidating the relationship between Lys96 mutation in Mycobacterium tuberculosis pyrazinamidase enzyme and pyrazinamide susceptibility, BMC. Genomics. 2015; p S14. 20
ACS Paragon Plus Environment
Page 21 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
615 616 617
60. Swier, L.; Monjas, L.; Reeßing, F.; Oudshoorn, R.; Primke, T.; Bakker, M.; van Olst, E.; Ritschel, T.; Faustino, I.; Marrink, S., Insight into the complete substrate-binding pocket of ThiT by chemical and genetic mutations. Med. Chem. Comm 2017, 8 (5), 1121-1130.
618 619
61. Suthar, A. B.; Zachariah, R.; Harries, A. D., Ending tuberculosis by 2030: can we do it?. Int. J. Tuberc. Lung Dis. 2016, 20, 1148-54.
620 621 622
62. Floyd, K.; Glaziou, P.; Houben, R.; Sumner, T.; White, R.; Raviglione, M., Global tuberculosis targets and milestones set for 2016–2035: definition and rationale. Int. J. Tuberc. Lung Dis 2018, 22(7), 723-730.
623 624 625
63. Floyd, K.; Glaziou, P.; Zumla, A.; Raviglione, M., The global tuberculosis epidemic and progress in care, prevention, and research: an overview in year 3 of the End TB era. Lancet. Respir Med. 2018, 6 (4), 299-314.
626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 21
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 22 of 33
641
Captions
642
Table 1. All atom-molecular dynamics simulation conditions for all RpsA systems.
643 644
Table 2. Mutations in RpsA gene in PZA resistant pncAWT isolates 24. The red colored lines were the focus of the current study.
645 646 647 648 649
Fig 1. The structural domain organization of RpsA of Mtb. The boxes denote the predicted S1 domains based on sequence analysis. The residue numbers corresponding to each S1 domain and the predicted α-helix are labeled. The C-terminal domain (MtRpsACTD, residues 285–481), which includes the fourth S1 domain and the C-terminus, is indicated. The residues at locations, Phe307, Phe310, His322, Asp352, and Arg357, are present in RNA binding sites
650 651
Fig 2. The RpsA wild-type and variants modeled based on the crystal structure of RpsA WT. The active site residues labeled bold.
652 653 654 655 656 657 658 659
Fig 3. The superposed protein structures of RpsA free and bound with POA drug molecule. The RMSD (Å) were calculated among the post-MD protein structure and were labeled in each model. Various color indicates a different model. The point of mutation was represented as a black circle. The opening and closing of both the loop (αH1 and αH2) indicated by double arrows in the opposite direction (open ensemble) and same direction (close ensemble) conformation. (A) represent the superposed model among free RpsAWT and bound RpsAWT, (B) for free RpsAWT and free RpsAM1, (C) for free RpsAWT and free RpsAM2, (D) for bound RpsAWT and bound RpsAM1, (E) for bound RpsAWT and bound RpsAM1 and (F) bound RpsAWT and bound RpsAM2.
660 661 662 663 664 665
Fig 4. The Root means square deviation (RMSd) of the RpsAWT wild-type and their mutants in the presence (+) and absence (-) of POA drug. The RMSd graph shows the deviation of the backbone atoms (from the initial state) of the RpsA protein over the 100 ns simulation time. (A) the superposed RMSd graph for free RpsAWT (black) and free RpsAM1 (red), (B) free RpsAWT (black) and free RpsAM2 (blue), (C) bound RpsAWT(brown) and bound RpsAM1 (green), and (D) bound RpsAWT(brown) and bound RpsAM2 (dark pick). The black line indicates the fluctuation.
666 667 668 669 670 671
Fig 5. The Root means square fluctuation (RMSF) of the RpsAWT wild type and their mutants in the presence (+) and absence (-) of POA drug. Residue fluctuations monitored for the Cα atoms of the protein over the entire trajectory. Each panel represents variants of RpsA in comparison with RpsAWT. The distal site of mutation was highlighted with red-(M1) and blue-(M2) color. (A) represent the superposed RMSF graph of RpsA in the absence (-) of POA drug. (D) shows the superposed RMSF graph of RpsA in the presence (+) of POA drug.
672 673 674 675 676 677 678 679 680
Fig 6. Post-MD Structure presentation of RpsAWT and their Mutants. The Illustration of the angle measurement among bound RpsAWT complex, bound RpsAM1, and bound RpsAM2,(A) among RpsAWT and RpsAM1, (B) among RpsAWT and RpsAM2, and (C) superposition of all three RpsA model, In both RpsA mutant models, the only R357; which have a crucial role in RpsA drug resistance mechanism, showed different patter in term of an angle measurement, both in RpsAWT and RpsAMutants. (B) surface representation of the POA binding site pocket volume (PV) analysis, indicate that the PV reduced dramatically upon inducing distant mutation. (A) for RpsAWT (PV = 4638.493Å), (B) for RpsAM1, PV = 563.383Å, and (C) for RpsAM2 (PV = 137.831Å). The black line on the surface indicates the POA binding site in term of PV. 22
ACS Paragon Plus Environment
Page 23 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
681 682 683 684 685 686
Fig 7. The binding pattern of POA drug in RpsA protein. (A) moreover, (B) shows the Cα distance (Å) among the POA drug and binding site residues of RpsA, less the distance, more stable should be the binding, while high distance, less stable should be the binding. (C) indicate the PL interaction profile for RpsAWT, (D) for RpsAM1, and (E) for RpsAM2, clearly show that in RpsAWT, the POA adopted various interaction and let stabilized their self in the pocket, while in RpsAM1 and RpsAM2 the POA reduced interaction with active site residues.
687 688 689 690 691 692 693 694 695
Fig 8. The alpha carbon distance (Å) among αH1 and αH2. In the absence of POA in RpsAWT showed zero impact, while in the presence of POA in all the rest of RpsA systems showed tremendous direct impact, which called here allosteric effect. The initial 50ns simulation data was discarded to prove a particular impact. (A) the distance analysis graph in the absence of POA drug between WT and M1, (B) between WT and M2, (C) ribbon representation of the 310-helixes in the absence of POA, the first one represents RpsAWT, 2nd RpsAM1, 3rd RpsAM2, and the last one represents the superposition of all the RpsAWT and their mutant. (D) moreover, in the presence of POA. (E) indicate the distance analysis graph in the presence of POA between free RpsAWT and free RpsAM1, and (F) bound RpsAWT and bound RpsAM2
696 697 698 699 700 701
Fig 9. Per-residue MMPBSA energy contribution of all bound RpsA systems. The active site residue R357 and F310 showed various energy level toward POA drug. In the presence of POA drug in RpsAWT system, the energy is enough high but upon induce distal site mutation the residue energy gradually decreased and showed resistance toward the POA drug. Additionally, the 310helixes showed dramatic variation in energy. The total energy was shown in each panel, the energy measure in KJ/M.
702 703 704 705 706
Fig 10. The superposed plot for Radius of gyration (Rg), in the presence (+) and absence (-) of POA drug molecule of wild-type RpsA and variants (M1 and M2). The plots revealed that the Rg of all RpsA systems oscillated around 18.5Å during MD simulation. Each color shows the corresponding RpsA system. The minimum difference among two systems throughout the MD simulation time was labeled in term of approximation.
707 708 709 710 711 712 713 714 715 716 717 718 719
Fig 11. (A) The upper panel 2D plot representation for principal component analysis (PCA) for RpsAWT, RpsAM1, and RpsAM2 in the absence of POA drug. These plots represent the projection of motions of various systems of RpsA conformation and its variants by plotting the first two eigenvectors obtained from the total 100 ns trajectories. Color scale tracks the movement of eigenvectors during the trajectory from deep brown to dark green. The dash lines show the total fraction of the expected ensemble. The flow of the changing the conformation throughout the MD simulation time is from initial to intermediate and then to refine state. (B) Free energy landscape (FEL) of the RpsAWT, RpsAM1, and RpsAM2 in the absence of POA drug, which represents the FEL obtained from the first two eigenvectors. The dark black and the proximity deep brown color represents the lowest energy state while a continuous change from light brown to green color highlights an increase in the Gibbs energy of the respective systems. The numbers on each FEL plot corresponded to the coordinates extracted from a specific time frame and used such lower energy state for structural analysis.
720 721 722 723
Fig 12. (A) The 2D plot representation for principal component analysis (PCA) for RpsAWT, RpsAM1, and RpsAM2 in the presence of POA drug. These plots represent the projection of motions of various systems of RpsA conformation and its variants by plotting the first two eigenvectors obtained from the total 100 ns trajectories. Color scale tracks the movement of eigenvectors during 23
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 24 of 33
724 725
the trajectory from dark brown to dark green. (B) Free energy landscape (FEL) of the RpsAWT, RpsAM1, and RpsAM2 in the absence of POA drug.
726 727 728 729 730 731 732
Fig 13. Dynamic cross-correlation map (DCCM). Each DCCM map for RpsA system was labeled. It shows the correlated motions of RpsA protein residues in wild-type and mutant in the (+) and (-) of POA drug. The dark, slight, and light brown color represent high, moderate, and weak negative correlation, while the dark, slight, and light green color represents high, moderate, and weak positive correlation. The binding site residues were labeled to (K303, F307, F310, and R357), and showed a different correlation in different systems of RpsA. The mutation site was also labeled.
733
Table 1.
734
RpsA system Free RpsA WT Free RpsAM1 Free RpsAM2 Bound RpsAWT Bound RpsAM1 Bound RpsAM2
Temp (K) 298 298 298 298 298 298
Counter ions 12 Na+ 12 Na+ 12 Na+ 11 Na+ 11 Na+ 11Na+
Waters 9889 9114 9121 8353 8581 8767
Time (ns) 250 250 250 250 250 250
Trajectories 1 1 1 1 1 1
Table 2
735 NO 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Nucleotide 76delA 220G>A 278A>G 618G>A 636A>C 830A>G 971C>T 973G>A 1021G>C 1024G>A 1027G>A 1030G>C 1051A>T 1108A>C 1207T>G
Codon No 26 74 93 206 212 277 324 325 341 342 343 344 351 370 403
Codon Change ATA GTC>ATC AAG>AGG TTG>TTA CGA>CGC AAG>AGG TCC>TTC GAG>AAG GGC>CGC GAC>AAC GAC>AAC GCG>CCG ATC>TTC ACC>CCC TGG>GGG
Residue Change Ile26FRAME Val74Ile Lys93Arg Leu206Leu Arg212Arg Lys277Arg Ser324Phe Glu325Lys Gly341Arg Asp342Asn Asp343Asn Ala344Pro Ile351Phe Thr370Pro Trp403Gly
Frequency 1 1 1 2 2 1 1 3 1 4 6 6 3 1 1
736 737 738 739 740 741
24
ACS Paragon Plus Environment
Page 25 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
742 743
Fig 1.
744 745 746
Fig 2.
25
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 26 of 33
747 748
Fig 3.
749
26
ACS Paragon Plus Environment
Page 27 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
750 751
Fig 4.
752
753 754
Fig 5.
27
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 28 of 33
755 756
Fig 6.
757 28
ACS Paragon Plus Environment
Page 29 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
758
Journal of Chemical Information and Modeling
Fig 7.
759
760 761
Fig 8.
762 763
Fig 9. 29
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 30 of 33
764 765
Fig 10.
766
30
ACS Paragon Plus Environment
Page 31 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
767 768
Fig 11.
769 770
Fig 12.
771 772 31
ACS Paragon Plus Environment
Journal of Chemical Information and Modeling 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Page 32 of 33
773 774
Fig 13.
775 776 777 778 779 780 781 782 783 784 785 786 787 788 32
ACS Paragon Plus Environment
Page 33 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Journal of Chemical Information and Modeling
789
Exploring the Pyrazinamide Drug Resistance Mechanism of Clinical Mutants T370P and
790
W403G in Ribosomal Protein S1 of Mycobacterium Tuberculosis
791
Ashfaq Ur Rehman†, ┼║, Muhammad Tahir Khan§║, Hao Liu†, Abdul Wadood┼, Shaukat Iqbal
792
Malik§, Hai-Feng Chen†, ‡*
793 794
Graphical Abstract
795
33
ACS Paragon Plus Environment