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AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking
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 unive...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5395911/ https://www.ncbi.nlm.nih.gov/pubmed/28424069 http://dx.doi.org/10.1186/s12859-017-1628-6 |
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author | Wisitponchai, Tanchanok Shoombuatong, Watshara Lee, Vannajan Sanghiran Kitidee, Kuntida Tayapiwatana, Chatchai |
author_facet | Wisitponchai, Tanchanok Shoombuatong, Watshara Lee, Vannajan Sanghiran Kitidee, Kuntida Tayapiwatana, Chatchai |
author_sort | Wisitponchai, Tanchanok |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1628-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5395911 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53959112017-04-20 AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking Wisitponchai, Tanchanok Shoombuatong, Watshara Lee, Vannajan Sanghiran Kitidee, Kuntida Tayapiwatana, Chatchai BMC Bioinformatics Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1628-6) contains supplementary material, which is available to authorized users. BioMed Central 2017-04-19 /pmc/articles/PMC5395911/ /pubmed/28424069 http://dx.doi.org/10.1186/s12859-017-1628-6 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Wisitponchai, Tanchanok Shoombuatong, Watshara Lee, Vannajan Sanghiran Kitidee, Kuntida Tayapiwatana, Chatchai AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking |
title | AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking |
title_full | AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking |
title_fullStr | AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking |
title_full_unstemmed | AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking |
title_short | AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking |
title_sort | ankplex: algorithmic structure for refinement of near-native ankyrin-protein docking |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5395911/ https://www.ncbi.nlm.nih.gov/pubmed/28424069 http://dx.doi.org/10.1186/s12859-017-1628-6 |
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