期刊
NUCLEIC ACIDS RESEARCH
卷 47, 期 W1, 页码 W338-W344出版社
OXFORD UNIV PRESS
DOI: 10.1093/nar/gkz383
关键词
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资金
- Australian Government Research Training Program Scholarship
- Jack Brockhoff Foundation [JBF 4186]
- Newton Fund RCUK-CONFAP Grant - Medical Research Council (MRC) [MR/M026302/1]
- Newton Fund RCUK-CONFAP Grant - Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG) [MR/M026302/1]
- National Health and Medical Research Council of Australia [APP1072476]
- Victorian Life Sciences Computation Initiative (VLSCI), an initiative of the Victorian Government, Australia [UOM0017]
- Instituto Rene Rachou (IRR/FIOCRUZ Minas), Brazil
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)
- Department of Biochemistry and Molecular Biology, University of Melbourne
- Victorian Government's OIS Program
- MRC
- MRC [MR/M026302/1] Funding Source: UKRI
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/mcsmppi2/.
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