BMC Bioinformatics | |
AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking | |
Research Article | |
Kuntida Kitidee1  Watshara Shoombuatong2  Chatchai Tayapiwatana3  Tanchanok Wisitponchai3  Vannajan Sanghiran Lee4  | |
[1] Center of Biomolecular Therapy and Diagnostic, Faculty of Associated Medical Sciences, Chiang Mai University, 50200, Chiang Mai, Thailand;Center for Research and Innovation, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand;Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand;Division of Clinical Immunology, Department of Medical Technology, Faculty of Associated Medical Sciences, Chiang Mai University, 50200, Chiang Mai, Thailand;Center of Biomolecular Therapy and Diagnostic, Faculty of Associated Medical Sciences, Chiang Mai University, 50200, Chiang Mai, Thailand;Thailand Center of Excellence in Physics, Commission on Higher Education, 10400, Bangkok, Thailand;Department of Chemistry, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia; | |
关键词: Ankyrin-protein complexes; Near-native docking pose; Machine learning methods; Decision tree; Logistic regression model; AnkPlex; | |
DOI : 10.1186/s12859-017-1628-6 | |
received in 2016-06-30, accepted in 2017-04-07, 发布年份 2017 | |
来源: Springer | |
【 摘 要 】
BackgroundComputational analysis of protein-protein interaction provided the crucial information to increase the binding affinity without a change in basic conformation. Several docking programs were used to predict the near-native poses of the protein-protein complex in 10 top-rankings. The universal criteria for discriminating the near-native pose are not available since there are several classes of recognition protein. Currently, the explicit criteria for identifying the near-native pose of ankyrin-protein complexes (APKs) have not been reported yet.ResultsIn this study, we established an ensemble computational model for discriminating the near-native docking pose of APKs named “AnkPlex”. A dataset of APKs was generated from seven X-ray APKs, which consisted of 3 internal domains, using the reliable docking tool ZDOCK. The dataset was composed of 669 and 44,334 near-native and non-near-native poses, respectively, and it was used to generate eleven informative features. Subsequently, a re-scoring rank was generated by AnkPlex using a combination of a decision tree algorithm and logistic regression. AnkPlex achieved superior efficiency with ≥1 near-native complexes in the 10 top-rankings for nine X-ray complexes compared to ZDOCK, which only obtained six X-ray complexes. In addition, feature analysis demonstrated that the van der Waals feature was the dominant near-native pose out of the potential ankyrin-protein docking poses.ConclusionThe AnkPlex model achieved a success at predicting near-native docking poses and led to the discovery of informative characteristics that could further improve our understanding of the ankyrin-protein complex. Our computational study could be useful for predicting the near-native poses of binding proteins and desired targets, especially for ankyrin-protein complexes. The AnkPlex web server is freely accessible at http://ankplex.ams.cmu.ac.th.
【 授权许可】
CC BY
© The Author(s). 2017
【 预 览 】
Files | Size | Format | View |
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RO202311104066405ZK.pdf | 1463KB | download |
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