Goodacre, NormanEdwards, NathanDanielsen, MarkUetz, PeterWu, Cathy H.2016-10-132016-10-13Copyright2016-01-22N. Goodacre; N. Edwards; M. Danielsen; P. Uetz; C. Wu, "Predicting nsSNPs that disrupt protein-protein interactions using docking," in IEEE/ACM Transactions on Computational Biology and Bioinformatics , vol.PP, no.99, pp.1-1 doi: 10.1109/TCBB.2016.25209311545-5963http://udspace.udel.edu/handle/19716/19809This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.The human genome contains a large number of protein polymorphisms due to individual genome variation. How many of these polymorphisms lead to altered protein-protein interaction is unknown. We have developed a method to address this question. The intersection of the SKEMPI database (of affinity constants among interacting proteins) and CAPRI 4.0 docking benchmark was docked using HADDOCK, leading to a training set of 166 mutant pairs. A random forest classifier that uses the differences in resulting docking scores between the 166 mutant pairs and their wild-types was used, to distinguish between variants that have either completely or partially lost binding ability. 50% of non-binders were correctly predicted with a false discovery rate of only 2%. The model was tested on a set of 15 HIV-1 - human, as well as 7 human - human glioblastoma-related, mutant proteins pairs: 50% of combined non-binders were correctly predicted with a false discovery rate of 10%. The model was also used to identify 10 protein-protein interactions between human proteins and their HIV-1 partners that are likely to be abolished by rare non-synonymous single-nucleotide polymorphisms (nsSNPs). These nsSNPs may represent novel and potentially therapeutically-valuable targets for anti-viral therapy by disruption of viral binding.en-USArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.Articledoi: 10.1109/TCBB.2016.2520931