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      DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability

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          Abstract

          Proteins are highly dynamic molecules, whose function is intrinsically linked to their molecular motions. Despite the pivotal role of protein dynamics, their computational simulation cost has led to most structure-based approaches for assessing the impact of mutations on protein structure and function relying upon static structures. Here we present DynaMut, a web server implementing two distinct, well established normal mode approaches, which can be used to analyze and visualize protein dynamics by sampling conformations and assess the impact of mutations on protein dynamics and stability resulting from vibrational entropy changes. DynaMut integrates our graph-based signatures along with normal mode dynamics to generate a consensus prediction of the impact of a mutation on protein stability. We demonstrate our approach outperforms alternative approaches to predict the effects of mutations on protein stability and flexibility ( P-value < 0.001), achieving a correlation of up to 0.70 on blind tests. DynaMut also provides a comprehensive suite for protein motion and flexibility analysis and visualization via a freely available, user friendly web server at http://biosig.unimelb.edu.au/dynamut/.

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          Most cited references37

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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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            Molecular dynamics and protein function.

            A fundamental appreciation for how biological macromolecules work requires knowledge of structure and dynamics. Molecular dynamics simulations provide powerful tools for the exploration of the conformational energy landscape accessible to these molecules, and the rapid increase in computational power coupled with improvements in methodology makes this an exciting time for the application of simulation to structural biology. In this Perspective we survey two areas, protein folding and enzymatic catalysis, in which simulations have contributed to a general understanding of mechanism. We also describe results for the F(1) ATPase molecular motor and the Src family of signaling proteins as examples of applications of simulations to specific biological systems.
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              iMODS: internal coordinates normal mode analysis server

              Normal mode analysis (NMA) in internal (dihedral) coordinates naturally reproduces the collective functional motions of biological macromolecules. iMODS facilitates the exploration of such modes and generates feasible transition pathways between two homologous structures, even with large macromolecules. The distinctive internal coordinate formulation improves the efficiency of NMA and extends its applicability while implicitly maintaining stereochemistry. Vibrational analysis, motion animations and morphing trajectories can be easily carried out at different resolution scales almost interactively. The server is versatile; non-specialists can rapidly characterize potential conformational changes, whereas advanced users can customize the model resolution with multiple coarse-grained atomic representations and elastic network potentials. iMODS supports advanced visualization capabilities for illustrating collective motions, including an improved affine-model-based arrow representation of domain dynamics. The generated all-heavy-atoms conformations can be used to introduce flexibility for more advanced modeling or sampling strategies. The server is free and open to all users with no login requirement at http://imods.chaconlab.org.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                02 July 2018
                30 April 2018
                30 April 2018
                : 46
                : Web Server issue
                : W350-W355
                Affiliations
                [1 ]Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Australia
                [2 ]Instituto René Rachou, Fundação Oswaldo Cruz, Brazil
                [3 ]Department of Biochemistry, University of Cambridge, UK
                Author notes
                To whom correspondence should be addressed. Tel: +61 90354794; Email: david.ascher@ 123456unimelb.edu.au . Correspondence may also be addressed to Douglas E. V. Pires. Email: douglas.pires@ 123456minas.fiocruz.br
                Author information
                http://orcid.org/0000-0002-3004-2119
                http://orcid.org/0000-0003-2948-2413
                Article
                gky300
                10.1093/nar/gky300
                6031064
                29718330
                e411097c-d50d-45e7-b8b9-57df103ff035
                © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 16 April 2018
                : 03 April 2018
                : 31 January 2018
                Page count
                Pages: 6
                Funding
                Funded by: Jack Brockhoff Foundation
                Award ID: JBF 4186
                Funded by: Pesquisa do Estado de Minas Gerais 10.13039/501100004901
                Award ID: MR/M026302/1
                Funded by: National Health and Medical Research Council 10.13039/501100000925
                Award ID: APP1072476
                Categories
                Web Server Issue

                Genetics
                Genetics

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