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      MutaBind2: Predicting the Impacts of Single and Multiple Mutations on Protein-Protein Interactions

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          Summary

          Missense mutations may affect proteostasis by destabilizing or over-stabilizing protein complexes and changing the pathway flux. Predicting the effects of stabilizing mutations on protein-protein interactions is notoriously difficult because existing experimental sets are skewed toward mutations reducing protein-protein binding affinity and many computational methods fail to correctly evaluate their effects. To address this issue, we developed a method MutaBind2, which estimates the impacts of single as well as multiple mutations on protein-protein interactions. MutaBind2 employs only seven features, and the most important of them describe interactions of proteins with the solvent, evolutionary conservation of the site, and thermodynamic stability of the complex and each monomer. This approach shows a distinct improvement especially in evaluating the effects of mutations increasing binding affinity. MutaBind2 can be used for finding disease driver mutations, designing stable protein complexes, and discovering new protein-protein interaction inhibitors.

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          Highlights

          • A new method to predict binding affinity changes upon single and multiple mutations

          • Improved performance in evaluating the effects of mutations increasing binding affinity

          • Generation of the structural model of a mutant complex

          Abstract

          Protein Folding; Bioinformatics; 3D Reconstruction of Protein

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          Most cited references33

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          Three-dimensional reconstruction of protein networks provides insight into human genetic disease.

          To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies.
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            mCSM-PPI2: predicting the effects of mutations on protein–protein interactions

            Abstract Protein–protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein–protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at http://biosig.unimelb.edu.au/mcsm_ppi2/.
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              BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations

              The ability of proteins to establish highly selective interactions with a variety of (macro)molecular partners is a crucial prerequisite to the realization of their biological functions. The availability of computational tools to evaluate the impact of mutations on protein–protein binding can therefore be valuable in a wide range of industrial and biomedical applications, and help rationalize the consequences of non-synonymous single-nucleotide polymorphisms. BeAtMuSiC (http://babylone.ulb.ac.be/beatmusic) is a coarse-grained predictor of the changes in binding free energy induced by point mutations. It relies on a set of statistical potentials derived from known protein structures, and combines the effect of the mutation on the strength of the interactions at the interface, and on the overall stability of the complex. The BeAtMuSiC server requires as input the structure of the protein–protein complex, and gives the possibility to assess rapidly all possible mutations in a protein chain or at the interface, with predictive performances that are in line with the best current methodologies.
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                27 February 2020
                27 March 2020
                27 February 2020
                : 23
                : 3
                : 100939
                Affiliations
                [1 ]Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
                [2 ]National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD 20894, USA
                Author notes
                []Corresponding author panch@ 123456ncbi.nlm.nih.gov
                [∗∗ ]Corresponding author minghui.li@ 123456suda.edu.cn
                [3]

                Present address: Department of Pathology and Molecular Medicine, School of Medicine, Queen's University, ON, Canada

                [4]

                Present address: Ontario Institute of Cancer Research, Toronto, ON, Canada

                [5]

                Present address: Translational and Functional Genomics Branch, National Human Genome Research, National Institutes of Health, Bethesda, MD 20892, USA

                [6]

                These authors contributed equally

                [7]

                Lead Contact

                Article
                S2589-0042(20)30123-1 100939
                10.1016/j.isci.2020.100939
                7068639
                32169820
                5c637382-561d-4c8a-bd21-4422262c2480
                © 2020 The Author(s)

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

                History
                : 15 August 2019
                : 21 November 2019
                : 20 February 2020
                Categories
                Article

                protein folding,bioinformatics,3d reconstruction of protein

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