Wisitponchai, Tanchanok and Shoombuatong, Watshara and Lee, Vannajan Sanghiran and Kitidee, Kuntida and Tayapiwatana, Chatchai (2017) AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking. BMC Bioinformatics, 18 (1). p. 220. ISSN 1471-2105, DOI https://doi.org/10.1186/s12859-017-1628-6.
Full text not available from this repository.Abstract
Background: Computational 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. Results: In 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. Conclusion: The 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.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | AnkPlex; Ankyrin-protein complexes; Decision tree; Logistic regression model; Machine learning methods; Near-native docking pose |
| Subjects: | Q Science > Q Science (General) Q Science > QD Chemistry R Medicine |
| Divisions: | Faculty of Science > Department of Chemistry |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 22 Oct 2019 04:08 |
| Last Modified: | 22 Oct 2019 04:08 |
| URI: | http://eprints.um.edu.my/id/eprint/22804 |
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