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Letters in Drug Design & Discovery

Editor-in-Chief

ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

Research Article

Comparative Proteome Analysis of Mycobacterium Tuberculosis Strains - H37Ra, H37Rv, CCDC5180, and CAS/NITR204: A Step Forward to Identify Novel Drug Targets

Author(s): Shradheya R.R. Gupta, Ekta Gupta, Avnam Ohri, Sandeep Kumar Shrivastava, Sumita Kachhwaha, Vinay Sharma, Rupesh Kumar Mishra and Ravi Ranjan Kumar Niraj*

Volume 17, Issue 11, 2020

Page: [1422 - 1431] Pages: 10

DOI: 10.2174/1570180817999200531165148

Price: $65

Abstract

Background: Mycobacterium tuberculosis is a causative agent of tuberculosis. It is a non-motile, acid-fast, obligatory aerobic bacterium. Finding novel drug targets in Mycobacterium tuberculosis has become extremely important as the bacterium is evolving into a more dangerous multi-drug resistant pathogen. The predominant strains in India belong to the Central-Asian, East- African Indian, and Beijing clad. For the same reason, the whole proteomes of a non-virulent strain (H37Ra), a virulent (H37Rv) and two clinical strains, a Central-Asian clad (CAS/NITR204) and a Beijing clad (CCDC5180) have been selected for comparative study. Selecting a phylogenetically close and majorly studied non-virulent strain is helpful in removing the common and undesired proteins from the study.

Objective: The study compares the whole proteome of non-virulent strain with the other three virulent strains to find a unique protein responsible for virulence in virulent strains. It is expected that the drugs developed against identified targets will be specific to the virulent strains. Additionally, to assure minimal toxicity to the host, we also screened the human proteome.

Methods: Comparative proteome analysis was used for target identification and in silico validation of identified target protein Rv2466c, identification of the respective ligand of the identified target protein and binding interaction study using Molecular docking and Molecular Dynamic Simulation study were used in this study.

Result and Discussions: Finally, eleven proteins were found to be unique in virulent strain only and out of which, Rv2466c (PDB-ID: 4ZIL) was found to be an essential protein and identified as a putative drug target protein for further study. The compound glutathione was found to be a suitable inhibitor for Rv2466c. In this study, we used a comparative proteomics approach to identify novel target proteins.

Conclusion: This study is unique as we are assured that the study will move forward the research in a new direction to cure the deadly disease (tuberculosis) caused by Mycobacterium tuberculosis. Rv2466c was identified as a novel drug target and glutathione as a respective ligand of Rv2466c. Discovery of the novel drug target as well as the drug will provide a solution to drug resistance as well as the infection caused by Mycobacterium tuberculosis.

Keywords: Multi-drug resistance, Mycobacterium tuberculosis, comparative proteomic study, molecular docking, molecular dynamic simulation, novel drug target.

Graphical Abstract
[1]
Delogu G, Sali M, Fadda G. The biology of Mycobacterium tuberculosis infection. Mediterr J Hematol Infect Dis 2013; 5(1)e2013070
[http://dx.doi.org/10.4084/mjhid.2013.070 ] [PMID: 24363885]
[2]
Alva A, Aquino F, Gilman RH, et al. Morphological characterization of Mycobacterium tuberculosis in a MODS culture for an automatic diagnostics through pattern recognition. PLoS One 2013; 8(12): e82809-9.
[http://dx.doi.org/10.1371/journal.pone.0082809 ] [PMID: 24358227]
[3]
Smith I. Mycobacterium tuberculosis pathogenesis and molecular determinants of virulence. Clin Microbiol Rev 2003; 16(3): 463-96.
[http://dx.doi.org/10.1128/CMR.16.3.463-496.2003 ] [PMID: 12857778]
[4]
Prasad R, Gupta N, Banka A. 2025 too short time to eliminate tuberculosis from India. Lung India 2017; 34(5): 409-10.
[PMID: 28869222]
[5]
Chai Q. Zhang, Y.; Liu, C.H. Mycobacterium tuberculosis: An adaptable pathogen associated with multiple human diseases. Front Cell Infect Microbiol 2018; 8: 158.
[http://dx.doi.org/10.3389/fcimb.2018.00158 ] [PMID: 29868514]
[6]
Chatterjee S, Poonawala H. Drug-resistant tuberculosis: Is India ready for the challenge? 2018; 3(4)e000971
[http://dx.doi.org/10.1136/bmjgh-2018-000971]
[7]
Devi KR. Bhutia, R.; Bhowmick, S.; Mukherjee, K.; Mahanta, J.; Narain, K. Genetic diversity of Mycobacterium tuberculosis isolates from Assam, India: Dominance of Beijing Family and Discovery of Two New Clades Related to CAS1_Delhi and EAI Family Based on Spoligotyping and MIRU-VNTR Typing. PLoS One 2015; 10(12)e0145860
[http://dx.doi.org/10.1371/journal.pone.0145860 ] [PMID: 26701129]
[8]
Dias HMY, Pai M, Raviglione MC. Ending tuberculosis in India: A political challenge & an opportunity. Indian J Med Res 2018; 147(3): 217-20.
[http://dx.doi.org/10.4103/ijmr.IJMR_660_18 ] [PMID: 29923507]
[9]
Gupta A, Kulkarni S, Rastogi N, Anupurba S. A study of Mycobacterium tuberculosis genotypic diversity & drug resistance mutations in Varanasi, north India. Indian J Med Res 2014; 139(6): 892-902.
[PMID: 25109724]
[10]
Pai M, Daftary A, Satyanarayana S. TB control: Challenges and opportunities for India. Trans R Soc Trop Med Hyg 2016; 110(3): 158-60.
[http://dx.doi.org/10.1093/trstmh/trw003 ] [PMID: 26884494]
[11]
Prasad R, Singh A, Balasubramanian V, Gupta N. Extensively drug-resistant tuberculosis in India: Current evidence on diagnosis & management. Indian J Med Res 2017; 145(3): 271-93.
[PMID: 28749390]
[12]
Zhang M, Gong J, Lin Y, Barnes PF. Growth of virulent and avirulent Mycobacterium tuberculosis strains in human macrophages. Infect Immun 1998; 66(2): 794-9.
[http://dx.doi.org/10.1128/IAI.66.2.794-799.1998 ] [PMID: 9453643]
[13]
Palanisamy GS, DuTeau N, Eisenach KD. Clinical strains of Mycobacterium tuberculosis display a wide range of virulence in guinea pigs. Tuberculosis (Edinb) 2009; 89(3): 203-39.
[http://dx.doi.org/10.1016/j.tube.2009.01.005 ] [PMID: 19251482]
[14]
Petersen TN, Brunak S, von Heijne G, Nielsen H, Signal P. SignalP 4.0: Discriminating signal peptides from transmembrane regions. Nat Methods 2011; 8(10): 785-6.
[http://dx.doi.org/10.1038/nmeth.1701 ] [PMID: 21959131]
[15]
Pennacchio LA, Rubin EM. Comparative genomic tools and databases: Providing insights into the human genome. J Clin Invest 2003; 111(8): 1099-106.
[http://dx.doi.org/10.1172/JCI200317842 ] [PMID: 12697725]
[16]
R, J.; S, P.; K, G., Comparison of the Virulence Factors and Analysis of Hypothetical Sequences of the Strains TIGR4, D39, G54 and R6 of Streptococcus Pneumoniae. J Comput Sci Syst Biol 2008; 1: 103-18.
[17]
Mobley DL, Dill KA. Binding of small-molecule ligands to proteins: “what you see” is not always “what you get”. Structure 2009; 17(4): 489-98.
[http://dx.doi.org/10.1016/j.str.2009.02.010 ] [PMID: 19368882]
[18]
Vyas VK, Ukawala RD, Ghate M, Chintha C. Homology modeling a fast tool for drug discovery: Current perspectives. Indian J Pharm Sci 2012; 74(1): 1-17.
[http://dx.doi.org/10.4103/0250-474X.102537 ] [PMID: 23204616]
[19]
Waterhouse A, Bertoni M, Bienert S. SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Res 2018; 46(W1): W296-303.
[http://dx.doi.org/10.1093/nar/gky427 ] [PMID: 29788355]
[20]
Tang HC, Chen YC. Insight into molecular dynamics simulation of BRAF(V600E) and potent novel inhibitors for malignant melanoma. Int J Nanomedicine 2015; 10: 3131-46.
[PMID: 25960652]
[21]
Copps J, Murphy F R, Lovas S. Molecular dynamics simulations of peptides 2008; 494: 115-26.
[22]
Khan F, Srivastava V, Kumar A. computational identification and characterization of potential T-cell epitope for the utility of vaccine design against enterotoxigenic Escherichia coli. Int J Pept Res Ther 2018; 25(1): 289-302.
[http://dx.doi.org/10.1007/s10989-018-9671-3]
[23]
Markowitz VM, Chen IM, Palaniappan K. IMG: The Integrated Microbial Genomes database and comparative analysis system. Nucleic Acids Res 2012; 40(Database issue): D115-22.
[http://dx.doi.org/10.1093/nar/gkr1044 ] [PMID: 22194640]
[24]
Wattam AR, Abraham D, Dalay O. PATRIC, the bacterial bioinformatics database and analysis resource Nucleic Acids Res, 2014; 42(database issue): D581-91.
[http://dx.doi.org/10.1093/nar/gkt1099] [PMID: 24225323]
[25]
Davis JJ, Gerdes S, Olsen GJ, et al. Pattyfams: Protein families for the microbial genomes in the PATRIC Database. Front Microbiol 2016; 7: 118.
[http://dx.doi.org/10.3389/fmicb.2016.00118 ] [PMID: 26903996]
[26]
Szklarczyk D,, Morris JH, Cook H. The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible 2017; 45(D1): D362-d368.
[27]
Wang S, Li W, Liu S, Xu J. RaptorX-Property: A web server for protein structure property prediction. Nucleic Acids Res 2016; 44(W1): W430-5.
[http://dx.doi.org/10.1093/nar/gkw306 ] [PMID: 27112573]
[28]
Morris GM, Lim-Wilby M. Molecular docking. Methods Mol Biol 2008; 443: 365-82.
[http://dx.doi.org/10.1007/978-1-59745-177-2_19 ] [PMID: 18446297]
[29]
Cosconati S, Forli S, Perryman AL, Harris R, Goodsell DS, Olson AJ. Virtual screening with AutoDock: Theory and Practice. Expert Opin Drug Discov 2010; 5(6): 597-607.
[http://dx.doi.org/10.1517/17460441.2010.484460 ] [PMID: 21532931]
[30]
Rizvi SM, Shakil S, Haneef M. A simple click by click protocol to perform docking: AutoDock 4.2 made easy for non-bioinformaticians. EXCLI J 2013; 12: 831-57.
[PMID: 26648810]
[31]
Sharma A, Sharma A, Rana A, Niraj RRK. Insilico repurposing of anticancer drug (5-FU) as an antimicrobial agent against methicillin-resistant Staphylococcus aureus (MRSA). Int J Pept Res Ther 2020.
[http://dx.doi.org/10.1007/s10989-019-10010-9] [http://dx.doi.org/10.1007/s10989-019-10010-9]
[32]
Gupta E, Gupta SRR, Niraj RRK. identification of drug and vaccine target in Mycobacterium leprae: A reverse vaccinology approach. Int J Pept Res Ther 2020; 1313-26.
[http://dx.doi.org/10.1007/s10989-019-09936-x]
[33]
Abraham MJ, Murtola T, Schulz R, Smith JC, Hess B, Lindhal E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015; 1-2: 19-25.
[http://dx.doi.org/10.1016/j.softx.2015.06.001]
[34]
Kufareva I, Abagyan R. Methods of protein structure comparison. Methods Mol Biol 2012; 857: 231-57.
[http://dx.doi.org/10.1007/978-1-61779-588-6_10 ] [PMID: 22323224]
[35]
Mandlik V, Singh S. Molecular docking and molecular dynamics simulation study of inositol phosphorylceramide synthase - inhibitor complex in leishmaniasis: Insight into the structure based drug design. F1000 Res 2016; 5: 1610.
[http://dx.doi.org/10.12688/f1000research.9151.1 ] [PMID: 27853511]
[36]
Cressler CE. McLEOD, D.V.; Rozins, C.; VAN, DEN.; Hoogen, J.; Day, T. The adaptive evolution of virulence: A review of theoretical predictions and empirical tests. Parasitology 2016; 143(7): 915-30.
[http://dx.doi.org/10.1017/S003118201500092X ] [PMID: 26302775]
[37]
Pavlopoulos GA, Soldatos TG, Barbosa-Silva A, Schneider R. A reference guide for tree analysis and visualization. BioData Min 2010; 3(1): 1.
[http://dx.doi.org/10.1186/1756-0381-3-1 ] [PMID: 20175922]
[38]
Dannenberg JJ. An Introduction to Hydrogen Bonding By George A. Jeffrey (University of Pittsburgh). Oxford University Press: New York and Oxford. 1997. Ix, 303 pp. $60.00. ISBN 0-19-509549-9. J Am Chem Soc 1998; 120(22): 5604-4.
[http://dx.doi.org/10.1021/ja9756331]
[39]
Martínez L. Automatic identification of mobile and rigid substructures in molecular dynamics simulations and fractional structural fluctuation analysis. PLoS One 2015; 10(3)e0119264
[http://dx.doi.org/10.1371/journal.pone.0119264 ] [PMID: 25816325]

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