Predicting nsSNPs that disrupt protein-protein interactions using docking
| Author(s) | Goodacre, Norman | |
| Author(s) | Edwards, Nathan | |
| Author(s) | Danielsen, Mark | |
| Author(s) | Uetz, Peter | |
| Author(s) | Wu, Cathy H. | |
| Ordered Author | Norman Goodacre, Nathan Edwards, Mark Danielsen, Peter Uetz, Cathy Wu | |
| UD Author | Wu, Cathy H. | en_US |
| Date Accessioned | 2016-10-13T14:43:01Z | |
| Date Available | 2016-10-13T14:43:01Z | |
| Copyright Date | Copyright © 2015 IEEE | en_US |
| Publication Date | 2016-01-22 | |
| Description | This 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. | en_US |
| Abstract | 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_US |
| Department | University of Delaware. Center for Bioinformatics & Computational Biology. | en_US |
| Citation | N. 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.2016.2520931 | en_US |
| DOI | doi: 10.1109/TCBB.2016.2520931 | en_US |
| ISSN | 1545-5963 | en_US |
| URL | http://udspace.udel.edu/handle/19716/19809 | |
| Language | en_US | en_US |
| Publisher | IEEE Computational Intelligence Society ; IEEE Computer Society ; IEEE Control Systems Society ; IEEE Engineering in Medicine and Biology Society ; The Association for Computing Machinery | en_US |
| dc.rights | Article 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. | en_US |
| dc.rights | 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. | en_US |
| dc.source | IEEE/ACM Transactions on Computational Biology and Bioinformatics | en_US |
| dc.source.uri | http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8857 | en_US |
| Title | Predicting nsSNPs that disrupt protein-protein interactions using docking | en_US |
| Type | Article | en_US |
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