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      Insight into novel clinical mutants of RpsA-S324F, E325K, and G341R of Mycobacterium tuberculosis associated with pyrazinamide resistance

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          Abstract

          Pyrazinamide (PZA) is an important component of first-line anti-tuberculosis drugs which is converted into active form, pyrazinoic acid (POA), by Mycobacterium tuberculosis (MTB) pncA gene encoded, pyrazinamidase (PZase). Mutations in pncA are detected in >70% of PZA resistant isolates but, noticeably, not in all. In this study, we selected 18 PZA-resistant but wild type pncA (pncA WT) MTB isolates. Drug susceptibility testing (DST) of all the isolates were repeated at the critical concentration of PZA drug. All these PZA-resistance but pncA WT isolates were subjected to RpsA sequencing. Fifteen different mutations were identified in eleven isolates, where seven were present in a conserved region including, Ser324Phe, Glu325Lys, Gly341Arg. As the molecular mechanism of resistance behind these variants has not been reported earlier, we have performed multiple analysis to unveil the mechanisms of resistance behind mutations S324F, E325K, and G341R. The mutant and wild type RpsA structures were subjected to comprehensive computational molecular dynamic simulations at 50 ns. Root mean square deviation (RMSD), Root mean square fluctuation (RMSF), and Gibbs free energy of mutants were analyzed in comparison with wild type. Docking score of wild type -RpsA has been found to be maximum, showing a strong binding affinity in comparison with mutants. Pocket volume, RMSD and RMSF have also been found to be altered, whereas total energy, folding effect (radius of gyration) and shape complimentarily analysis showed that variants S324F, E325K, and G341R have been playing a significant role behind PZA-resistance. The study offers valuable information for better management of drug resistance tuberculosis.

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          Radius of gyration as an indicator of protein structure compactness

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            Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery.

            The search for new compounds with a given biological activity requires enormous effort in terms of manpower and cost. This effort arises from the large number of compounds that need to be synthesized and subsequently biologically evaluated. For this reason the pharmaceutical industry has shown great interest in theoretical methods that enable the rational design of pharmaceutical agents. In the last years bioinformatics has experienced a great evolution due to the development of specialized software and to the increasing computer power. The codification of the structural information of molecules through molecular descriptors and the subsequent data analysis allow establishing QSAR models (Quantitative Structure-Activity Relationship) that can be applied to the design and the virtual screening of new drugs. The development of sophisticated Docking methodologies also allows a more accurate predict of the biological activity of molecules. Moreover, through this type of computational techniques and theoretical approaches, it is possible to develop explanatory hypothesis on the mechanism of action of drugs. This work provides a brief description of a series of studies implemented in the software MOE (Molecular Operating Environment) with particular attention to the medicinal chemistry aspects.
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              The action of antituberculosis drugs in short-course chemotherapy.

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                Author and article information

                Contributors
                Journal
                Comput Struct Biotechnol J
                Comput Struct Biotechnol J
                Computational and Structural Biotechnology Journal
                Research Network of Computational and Structural Biotechnology
                2001-0370
                04 October 2018
                2018
                04 October 2018
                : 16
                : 379-387
                Affiliations
                [a ]Department of Bioinformatics and Biosciences, Capital University of Science and Technology, Pakistan.
                [b ]College of Life Sciences and Biotechnology, The State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, China
                Author notes
                [* ]Correspondence to: D. Q. Wei, Department of Bioinformatics and Biostatistics College of Life Sciences and Biotechnology, The State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, 800 Dongchuan Road Shanghai, Minhang District, China, 200240. dqwei@ 123456sjtu.edu.cn
                [** ]Correspondence to: S. I. Malik, Department of Bioinformatics and Biosciences Capital University of Science and Technology, 44000 Islamabad Expressway, Kahuta Road, Zone-V, Islamabad.
                [1]

                These authors equally contributed to this work.

                Article
                S2001-0370(18)30098-9
                10.1016/j.csbj.2018.09.004
                6205349
                30402208
                b4b4b7ab-6975-4e96-b5a2-6404149cda30
                © 2018 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 16 August 2018
                : 21 September 2018
                : 25 September 2018
                Categories
                Research Article

                mutations,rpsa,poa,simulations,resistance,rmsd
                mutations, rpsa, poa, simulations, resistance, rmsd

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