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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...

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Detalles Bibliográficos
Autores principales: Yaseen, Ashraf, Nijim, Mais, Williams, Brandon, Qian, Lei, Li, Min, Wang, Jianxin, Li, Yaohang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
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.
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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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