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FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information
BACKGROUND: The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is si...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009531/ https://www.ncbi.nlm.nih.gov/pubmed/27587065 http://dx.doi.org/10.1186/s12859-016-1117-3 |
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author | Yaseen, Ashraf Nijim, Mais Williams, Brandon Qian, Lei Li, Min Wang, Jianxin Li, Yaohang |
author_facet | Yaseen, Ashraf Nijim, Mais Williams, Brandon Qian, Lei Li, Min Wang, Jianxin Li, Yaohang |
author_sort | Yaseen, Ashraf |
collection | PubMed |
description | BACKGROUND: The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions. RESULTS: In this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context. The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor. CONCLUSIONS: Incorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called “FLEXc” and available online at: http://hpcr.cs.odu.edu/flexc. |
format | Online Article Text |
id | pubmed-5009531 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-50095312016-09-08 FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information Yaseen, Ashraf Nijim, Mais Williams, Brandon Qian, Lei Li, Min Wang, Jianxin Li, Yaohang BMC Bioinformatics Research BACKGROUND: The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions. RESULTS: In this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context. The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor. CONCLUSIONS: Incorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called “FLEXc” and available online at: http://hpcr.cs.odu.edu/flexc. BioMed Central 2016-08-31 /pmc/articles/PMC5009531/ /pubmed/27587065 http://dx.doi.org/10.1186/s12859-016-1117-3 Text en © The Author(s). 2016 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 Yaseen, Ashraf Nijim, Mais Williams, Brandon Qian, Lei Li, Min Wang, Jianxin Li, Yaohang FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information |
title | FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information |
title_full | FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information |
title_fullStr | FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information |
title_full_unstemmed | FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information |
title_short | FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information |
title_sort | flexc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5009531/ https://www.ncbi.nlm.nih.gov/pubmed/27587065 http://dx.doi.org/10.1186/s12859-016-1117-3 |
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