1Cell Networks, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany, 2Bioquant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany, 3Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany, 4Department for Bioinformatics and Functional Genomics, Institute for Pharmacy and Molecular Biotechnology (IPMB), University of Heidelberg, Heidelberg, Germany, 5Institut für Humangenetik, Universitätsklinikum Schleswig-Holstein, Christian-Albrechts-Universität zu Kiel, Arnold Heller Straße 3, 24105 Kiel, Germany, 6EMBL, Meyerhofstrasse 1, 69117 Heidelberg, Germany and 7Cambridge Cell Networks Ltd, St John's Innovation Centre, Cowley Road, CB3 0WS, Cambridge, UK
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Systematic interrogation of mutation or protein modification data is important to identify sites with functional consequences and to deduce global consequences from large data sets. Mechismo (mechismo.russellab.org) enables simultaneous consideration of thousands of 3D structures and biomolecular interactions to predict rapidly mechanistic consequences for mutations and modifications. As useful functional information often only comes from homologous proteins, we benchmarked the accuracy of predictions as a function of protein/structure sequence similarity, which permits the use of relatively weak sequence similarities with an appropriate confidence measure. For protein–protein, protein–nucleic acid and a subset of protein–chemical interactions, we also developed and benchmarked a measure of whether modifications are likely to enhance or diminish the interactions, which can assist the detection of modifications with specific effects. Analysis of high-throughput sequencing data shows that the approach can identify interesting differences between cancers, and application to proteomics data finds potential mechanistic insights for how post-translational modifications can alter biomolecular interactions.