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Docking features for predicting binding loss due to protein mutation

Published: 20 September 2014 Publication History

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 that uses docking simulations to predict whether variants have altered interactions with their binding partners. A novel docking score normalization that compares the docking of mutant-containing protein pairs to that of the wild-type pair is introduced. Using the SKEMPI database and CAPRI, a training set of 167 mutant pairs (87 binders, 80 non-binders) were identified and docked using the docking program, HADDOCK. A random forest classifier that uses the differences in resulting docking scores for the 167 mutant pairs, to distinguish between variants that have either completely or partially lost binding ability, was used. 50% of non-binders were correctly predicted with a false discovery rate of only 2%. This allows for the rapid identification of a large number of protein polymorphisms that are likely to have a physiological consequence. 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%.

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  • (2017)Predicting nsSNPs that Disrupt Protein-Protein Interactions Using DockingIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2016.252093114:5(1082-1093)Online publication date: 1-Sep-2017

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  1. Docking features for predicting binding loss due to protein mutation

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        cover image ACM Conferences
        BCB '14: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
        September 2014
        851 pages
        ISBN:9781450328944
        DOI:10.1145/2649387
        • General Chairs:
        • Pierre Baldi,
        • Wei Wang
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 20 September 2014

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        Author Tags

        1. PPI
        2. interface
        3. machine learning
        4. mutant
        5. non-synonymous polymorphism
        6. protein docking

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        September 20 - 23, 2014
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        • (2017)Predicting nsSNPs that Disrupt Protein-Protein Interactions Using DockingIEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)10.1109/TCBB.2016.252093114:5(1082-1093)Online publication date: 1-Sep-2017

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