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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...

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Autores principales: Wisitponchai, Tanchanok, Shoombuatong, Watshara, Lee, Vannajan Sanghiran, Kitidee, Kuntida, Tayapiwatana, Chatchai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
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.
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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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