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      Empirical ways to identify novel Bedaquiline resistance mutations in AtpE

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          Abstract

          Clinical resistance against Bedaquiline, the first new anti-tuberculosis compound with a novel mechanism of action in over 40 years, has already been detected in Mycobacterium tuberculosis. As a new drug, however, there is currently insufficient clinical data to facilitate reliable and timely identification of genomic determinants of resistance. Here we investigate the structural basis for M. tuberculosis associated bedaquiline resistance in the drug target, AtpE. Together with the 9 previously identified resistance-associated variants in AtpE, 54 non-resistance-associated mutations were identified through comparisons of bedaquiline susceptibility across 23 different mycobacterial species. Computational analysis of the structural and functional consequences of these variants revealed that resistance associated variants were mainly localized at the drug binding site, disrupting key interactions with bedaquiline leading to reduced binding affinity. This was used to train a supervised predictive algorithm, which accurately identified likely resistance mutations (93.3% accuracy). Application of this model to circulating variants present in the Asia-Pacific region suggests that current circulating variants are likely to be susceptible to bedaquiline. We have made this model freely available through a user-friendly web interface called SUSPECT-BDQ, StrUctural Susceptibility PrEdiCTion for bedaquiline ( http://biosig.unimelb.edu.au/suspect_bdq/). This tool could be useful for the rapid characterization of novel clinical variants, to help guide the effective use of bedaquiline, and to minimize the spread of clinical resistance.

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          DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach

          Cancer genome and other sequencing initiatives are generating extensive data on non-synonymous single nucleotide polymorphisms (nsSNPs) in human and other genomes. In order to understand the impacts of nsSNPs on the structure and function of the proteome, as well as to guide protein engineering, accurate in silicomethodologies are required to study and predict their effects on protein stability. Despite the diversity of available computational methods in the literature, none has proven accurate and dependable on its own under all scenarios where mutation analysis is required. Here we present DUET, a web server for an integrated computational approach to study missense mutations in proteins. DUET consolidates two complementary approaches (mCSM and SDM) in a consensus prediction, obtained by combining the results of the separate methods in an optimized predictor using Support Vector Machines (SVM). We demonstrate that the proposed method improves overall accuracy of the predictions in comparison with either method individually and performs as well as or better than similar methods. The DUET web server is freely and openly available at http://structure.bioc.cam.ac.uk/duet.
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            NGL viewer: web-based molecular graphics for large complexes.

            The interactive visualization of very large macromolecular complexes on the web is becoming a challenging problem as experimental techniques advance at an unprecedented rate and deliver structures of increasing size.
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              SDM—a server for predicting effects of mutations on protein stability and malfunction

              The sheer volume of non-synonymous single nucleotide polymorphisms that have been generated in recent years from projects such as the Human Genome Project, the HapMap Project and Genome-Wide Association Studies means that it is not possible to characterize all mutations experimentally on the gene products, i.e. elucidate the effects of mutations on protein structure and function. However, automatic methods that can predict the effects of mutations will allow a reduced set of mutations to be studied. Site Directed Mutator (SDM) is a statistical potential energy function that uses environment-specific amino-acid substitution frequencies within homologous protein families to calculate a stability score, which is analogous to the free energy difference between the wild-type and mutant protein. Here, we present a web server for SDM (http://www-cryst.bioc.cam.ac.uk/~sdm/sdm.php), which has obtained more than 10 000 submissions since being online in April 2008. To run SDM, users must upload a wild-type structure and the position and amino acid type of the mutation. The results returned include information about the local structural environment of the wild-type and mutant residues, a stability score prediction and prediction of disease association. Additionally, the wild-type and mutant structures are displayed in a Jmol applet with the relevant residues highlighted.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draft
                Role: SoftwareRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: ResourcesRole: ValidationRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                29 May 2019
                2019
                : 14
                : 5
                : e0217169
                Affiliations
                [1 ] Victorian Tuberculosis Program, Melbourne Health, Victoria, Australia
                [2 ] Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Victoria, Australia
                [3 ] Department of Microbiology and Immunology, University of Melbourne, Melbourne, Victoria, Australia
                [4 ] Structural Biology and Bioinformatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
                [5 ] The Peter Doherty Institute for Infection and Immunity, University of Melbourne, Victoria, Australia
                [6 ] Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom
                St Petersburg Pasteur Institute, RUSSIAN FEDERATION
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-0303-2895
                http://orcid.org/0000-0002-4420-6401
                http://orcid.org/0000-0003-3949-2471
                http://orcid.org/0000-0001-7873-933X
                Article
                PONE-D-19-03087
                10.1371/journal.pone.0217169
                6541270
                31141524
                1e1e45a5-72e5-4e3d-a9c4-0049b44e99ab
                © 2019 Karmakar et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 31 January 2019
                : 1 May 2019
                Page count
                Figures: 5, Tables: 1, Pages: 14
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MR/M026302/1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: APP1072476
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: APP1056689
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100012698, Jack Brockhoff Foundation;
                Award ID: JBF 4186, 2016
                Award Recipient :
                M.K was funded by the Melbourne Research Scholarship. D.B.A was funded by a Newton Fund RCUK-CONFAP Grant awarded by The Medical Research Council (MRC) and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) (MR/M026302/1), the Jack Brockhoff Foundation (JBF 4186, 2016), and a C. J. Martin Research Fellowship from the National Health and Medical Research Council (NHMRC) of Australia (APP1072476). The Vietnam genomic dataset was funded by a NHMRC Australia grant (APP1056689) to SJD and KEH. This work was supported in part by the Victorian Government's OIS Program. No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Mutation
                Point Mutation
                Biology and Life Sciences
                Organisms
                Bacteria
                Actinobacteria
                Mycobacterium Tuberculosis
                Physical Sciences
                Physics
                Condensed Matter Physics
                Solid State Physics
                Crystallography
                Crystal Structure
                Biology and Life Sciences
                Genetics
                Gene Identification and Analysis
                Mutation Detection
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Machine Learning Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Machine Learning Algorithms
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Machine Learning Algorithms
                Biology and Life Sciences
                Molecular Biology
                Macromolecular Structure Analysis
                Protein Structure
                Biology and Life Sciences
                Biochemistry
                Proteins
                Protein Structure
                Medicine and Health Sciences
                Infectious Diseases
                Bacterial Diseases
                Tuberculosis
                Medicine and Health Sciences
                Tropical Diseases
                Tuberculosis
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                All relevant data are within the manuscript and its Supporting Information files.

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