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dc.contributor.authorCao, Renzhi
dc.contributor.authorAdhikari, Badri
dc.contributor.authorBhattacharya, Debswapna
dc.contributor.authorSun, Miao
dc.contributor.authorHou, Jie
dc.contributor.authorCheng, Jianlin
dc.identifier.citationCao, Renzhi; Adhikari, Badri; Bhattacharya, Debswapna; Sun, Miao; Hou, Jie; Cheng, Jianlin. 2017. QAcon: single model quality assessment using protein structural and contact information with machine learning techniques. Bioinformatics, vol. 33:no. 4:pp 586-588en_US
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractMotivation: Protein model quality assessment (QA) plays a very important role in protein structure prediction. It can be divided into two groups of methods: single model and consensus QA method. The consensus QA methods may fail when there is a large portion of low quality models in the model pool. Results: In this paper, we develop a novel single-model quality assessment method QAcon utilizing structural features, physicochemical properties, and residue contact predictions. We apply residue residue contact information predicted by two protein contact prediction methods PSICOV and DNcon to generate a new score as feature for quality assessment. This novel feature and other 11 features are used as input to train a two-layer neural network on CASP9 datasets to predict the quality of a single protein model. We blindly benchmarked our method QAcon on CASP11 dataset as the MULTICOM-CLUSTER server. Based on the evaluation, our method is ranked as one of the top single model QA methods. The good performance of the features based on contact prediction illustrates the value of using contact information in protein quality assessment.en_US
dc.description.sponsorshipUS National Institutes of Health (NIH) grant (R01GM093123)en_US
dc.publisherOxford University Pressen_US
dc.subjectTertiary structure predictionen_US
dc.subjectSupport vector machinesen_US
dc.titleQAcon: single model quality assessment using protein structural and contact information with machine learning techniquesen_US
dc.rights.holder© The Author 2016. Published by Oxford University Press. All rights reserved.en_US

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