Title |
Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions
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Published in |
Nucleic Acids Research, November 2014
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DOI | 10.1093/nar/gku1094 |
Pubmed ID | |
Authors |
Matthew J. Betts, Qianhao Lu, YingYing Jiang, Armin Drusko, Oliver Wichmann, Mathias Utz, Ilse A. Valtierra-Gutiérrez, Matthias Schlesner, Natalie Jaeger, David T. Jones, Stefan Pfister, Peter Lichter, Roland Eils, Reiner Siebert, Peer Bork, Gordana Apic, Anne-Claude Gavin, Robert B. Russell |
Abstract |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 14% |
United States | 1 | 14% |
Spain | 1 | 14% |
Unknown | 4 | 57% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 4 | 57% |
Scientists | 3 | 43% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 4 | 3% |
United Kingdom | 3 | 2% |
Canada | 2 | 1% |
Spain | 2 | 1% |
India | 2 | 1% |
Austria | 1 | <1% |
Brazil | 1 | <1% |
Italy | 1 | <1% |
Colombia | 1 | <1% |
Other | 3 | 2% |
Unknown | 125 | 86% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 39 | 27% |
Researcher | 29 | 20% |
Student > Master | 22 | 15% |
Student > Bachelor | 15 | 10% |
Student > Postgraduate | 6 | 4% |
Other | 21 | 14% |
Unknown | 13 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 58 | 40% |
Biochemistry, Genetics and Molecular Biology | 45 | 31% |
Computer Science | 9 | 6% |
Engineering | 4 | 3% |
Chemistry | 4 | 3% |
Other | 11 | 8% |
Unknown | 14 | 10% |