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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

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Journal of Chemical Information and Modeling

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Exploring the Pyrazinamide Drug Resistance Mechanism of Clinical Mutants T370P and

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W403G in Ribosomal Protein S1 of Mycobacterium Tuberculosis

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Ashfaq Ur Rehman†┼║, Muhammad Tahir Khan§║, Hao Liu†, Abdul Wadood┼, Shaukat Iqbal

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Malik§, Hai-Feng Chen†, ‡*

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†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]

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Abstract

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Pyrazinamide (PZA) is an essential first‐line anti-tubercular drug which plays a crucial role in

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tuberculosis treatment. The PZA, which is considered as a pro-drug needs an enzyme of

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mycobacterial Pyrazinamidase (PZase) for its conversion into an active form Pyrazinoic acid.

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Further, this active form of PZA inhibits the ribosomal proteins S1, which facilitates the transfer-

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messenger RNA complex formation throughout the translation. The spontaneous mutations in

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RpsA have been found to be associated with PZA drug resistance. However, the the drug resistance

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mechanism is still unclear. Furthermore, there is no such information available about the structural

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dynamics of RpsA protein due to mutations that confer Pyrazinoic acid resistance. Moreover, total

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18-clinical PZA-resistant isolates were investigated and found as pncAWT that allowed to explore

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the resistance mechanism of RpsA in the mutated state. Samples were repeated for the drug

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susceptibility testing followed by RpsA gene sequencing. Total 11-clinical isolates harbored total

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15-mutations. Almost a half of total strains (07/15) were observed to be in the conserved region of

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RpsA and known as Mycobacterium tuberculosis C-terminal domain. In the current study total

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(02/7) mutation T370P (Mutant-1) and W403G (Mutant-2) were explored to ensure the RpsA

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resistance mechanism through essential dynamics simulation. The essential dynamics study results

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revealed that the consequences of distal loop mutations, drastically altered the conformation of

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RpsA both in the absence (-) and presence (+) of Pyrazinoic acid drug by two reasons. (1) Dramatic

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alteration or reduction in the binding pattern of Pyrazinoic acid with active site residues observed.

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(2) A clear image of the opening and closing switching mechanism was seen upon the distal site

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mutation on nearby 310-helixes beside the Pyrazinoic acid binding site. This switch was found

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consistently remain close only in wild type systems, while open in the mutant systems. We called

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such distance impact an “allosteric effect.” The overall mechanistic investigations will provide

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useful information behind drug resistance for better understanding to manage tuberculosis.

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Introduction

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In 2018, World Health Organization reported that about 1.7 billion individuals of the world’s

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population (23%) are projected to possess a latent Tuberculosis (TB) infection, which indicates a

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risk of developing active TB during their lifetime1. Here 330k cases were multidrug resistance

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(MDR), and Rifampicin resistance (RR) among notified TB patients. According to the surveys

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(Azerbaijan, Bangladesh, Belarus, Pakistan, South Africa, and Ukraine) the average level of

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resistance to all first-line anti-TB drugs, which include rifampicin, isoniazid, pyrazinamide, and

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ethambutol in new and previously treated TB cases was 19% (95% Confidence Interval (CI): 18-

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20%), and 43% (95% CI: 40-46%) respectively. Pyrazinamide (PZA) is an exclusive prodrug

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possessing a diminishing ability of MTB in the latent phase and reducing the period of TB therapy

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from 9 months to 6 months2-4. The prodrug of PZA can be converted by MTB encoded enzyme

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Pyrazinamidase (PZase) into an active state Pyrazinoic acid (POA) which targets ribosomal protein

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S1 (RpsA) protein. The RpsA is involved in trans-translation5, 6 and POA can disrupt the complex

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of RpsA-tmRNA3, 7-9.

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In Mtb, the RpsA has four S1 domains (36–105, 123–188, 209–277 and 294–363)

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Fig 1) 11. The residues 292-363 have been found highly conserved and capable of POA interaction.

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Drug resistance developed due to mutations at the C-terminus domain of mycobacterial species,

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RpsA (Mt-RpsACTD) in, altering the interactions with POA, causing conformational changes in the

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POA binding site 3. Two POA molecules form hydrogen bonds and hydrophobic interactions with

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residues of Lys303, Phe307, Phe310, and Arg357, also known as tmRNA binding site.3,

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Removing alanine of the C-terminal (RpsAΔA438) will induce the PZA resistance due to lack of

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bond formation with RpsA in Mycobacterium smegmatis. The MtRpsACTD is the POA interaction

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site interfering in transfer-messenger RNA (tmRNA) complex formation during protein translation

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11, 12.

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From a structural point of view, an amino acid substitution might have drastic effects on the protein

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structure and function, especially in the active site or distal site near to binding pockets13-15. The

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induced mutation may also possess distal impact at a long-range position from the active site16.

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Exploration of such distal site impact of the mutant on active site or nearby to this region provides

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valuable information for better understanding of the phenomenon. However, these are time-

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consuming analyses and expensive to address by experimental procedure alone. An alternative to

10

(shown in

12

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these experimental procedures of molecular dynamic (MD) simulations has been used extensively

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for exploring these mechanisms (Protein-drug, Protein associated mutations, etc.) caused by

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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

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mechanism among POA-RpsAWT and their mutants (T370PM1 and W403GM2), and the consequent

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indirect impact of these mutants on overall protein conformation, especially on POA binding site.

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These findings could be a useful strategy for deeper understanding and supervision of drug

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resistance mechanism on the atomic level.

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Material and Methods

107

The clinical isolate of Mycobacterium tuberculosis

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Total eighteen-isolates were collected from Provincial Tuberculosis Reference Laboratory

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(PTRL), that were identified previously as PZA resistance and wild-type (pncAWT). All these

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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

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of pink to red at the test and control location, confirming the antigen, MPT64 in sample 23, 24. All

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the positive MTB tubes were subjected to susceptibility testing.

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Drug susceptibility testing of Clinical Isolates

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MTB drug susceptibility testing (DST) is now routinely performed through automated BACTEC

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Mycobacterium growth indicator tubes (MGIT) 960 system. All the samples were screened

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according to the standard of positive and negative controls, Mycobacterium tuberculosis strain

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ATCC 25618 / H37Rv and Mycobacterium Bovis respectively. A sample was marked as PZA-

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resistance when growth occurred at a critical concentration of PZA (100 g/ml) 25. 4


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DNA extraction, Amplification, and Sequencing for the selected clinical samples

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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

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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

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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

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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

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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

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orientations in each pocket. The POA coordinates with the lowest docking energy values in the

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RpsAWT, RpsAM1, and RpsAM2 protein were acquired for further essential dynamic study. The

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docking results were shown in Fig 2.

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Moreover, the online free server PatchDock

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based on shape complementarity score. The shape complementarity knew as geometric matching,

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where protein and drug features are compared for the docking purpose. Protein and drug shape

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complementarity analysis at the interface is a reasonable practice in re-docking. Induced mutations

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in protein sequence often cause conformational changes affecting the interactions of protein active

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site residues with a drug 38, 39. The free online server Computed Atlas of Surface Topography of

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proteins (CASTp) was used to validate the following conformation variation upon induced

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mutation 40, additionally, to compare the Pocket volumes of bound RpsAWT, bound RpsAM1, and

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bound RpsAM2. Furthermore, LigPlot

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interactions between drug and protein.

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All-atom Molecular Dynamics Simulation

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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.

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Counter-ions (Na+ and Cl-) were added for maintenance of system neutrality. All the systems were

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solvated in a truncated octahedral box of TIP3P water model with 8.0 Å buffer. The Particle Mesh

169

Ewald (PME)

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hydrogen atoms were constrained with SHAKE algorithm 44. All MD simulations were accelerated

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with the CUDA version of PMEMD in GPU cores of NVIDIA® Tesla K20 45, 46, and were carried

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out in the NPT ensemble at 298 K and 1 bar an integration time step of 2 fs was used. Temperature

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control was performed using the Andersen-like temperature coupling scheme, in which imaginary

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"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

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relaxation time set to 1.0 ps. SHAKE algorithm was implemented to constrain the bonds involving

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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


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Essential Dynamics Simulation Analysis

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The trajectory files from MD simulations were used to explore the dominant motions in all systems

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including bound RpsAWT, bound RpsAM1, bound RpsAM2, free RpsAWT, free RpsAM1, and free

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RpsAM2 through PCA or essential dynamics47. The translational and rotational movements were

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eliminated from the coordinates and the following superimposition onto a reference structure.

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Subsequently, positional covariance matrix was calculated of atomic coordinates and its

185

eigenvectors. The matrix was diagonalized by an orthogonal coordinate transformation matrix

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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

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significant motion of the protein ensemble.

189

Free energy landscape (FEL) was obtained by plotting the extracted coordinates from the trajectory

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of total 100ns MD simulation of the first two principal components (PC1 and PC2) against each

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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

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state (meta-stable) or intermediate ensemble of the protein

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distributed in GROMACS was used for the Gibbs energy calculations. The Origin v9.1 was used

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to obtain 2D plots. The coordinates on 2D plots were used to predict and find the exact time frames

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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:

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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

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components. The dynamics cross-correlation map (DCCM) was designed to explore and identify

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the dominant correlated motions of each residue in different systems of RpsA. The matrix (Cij)

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depicts the time-correlated info among the (i) and (j) atoms of a protein 49. The only alpha carbon

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atoms from the last 5000 snapshots were selected at 0.002ns time intervals to construct the matrix.

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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)

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Interaction Analysis of POA-RpsA Complex

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Protein-ligand (PL) interaction task was conducted with in-house software50. The hydrophobic

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interaction was considered as the distance between mass centers of side chain for hydrophobic

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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

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ensembles of the energy-minimum 51-54. All the representations were conducted in PyMol v1.755.

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Per-residues Binding Affinity Contribution Calculations

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To explore the per-residue energy contribution in each system toward the POA drug, binding free

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energy calculation was conducted for all residues-residues systems by using the molecular

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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.

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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


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Results and Discussion

239

From the drug susceptibility testing result, it was evident that all the samples possess resistance to

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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



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All the RpsA structures (in the presence and absence of POA drug) were simulated in an explicit

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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


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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



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


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


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



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


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


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



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

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626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 21



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


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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



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


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742 743

Fig 1.

744 745 746

Fig 2.

25



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747 748

Fig 3.

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26


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750 751

Fig 4.

752

753 754

Fig 5.

27



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755 756

Fig 6.

757 28


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758


Fig 7.

759

760 761

Fig 8.

762 763

Fig 9. 29



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764 765

Fig 10.

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30


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767 768

Fig 11.

769 770

Fig 12.

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773 774

Fig 13.

775 776 777 778 779 780 781 782 783 784 785 786 787 788 32


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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


Exploring the Pyrazinamide Drug Resistance ... - ACS Publications

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