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DeepConPred2: An Improved Method for the Prediction of Protein Residue Contacts

Information of residue-residue contacts is essential for understanding the mechanism of protein folding, and has been successfully applied as special topological restraints to simplify the conformational sampling in de novo protein structure prediction. Prediction of protein residue contacts has exp...

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Detalles Bibliográficos
Autores principales: Ding, Wenze, Mao, Wenzhi, Shao, Di, Zhang, Wenxuan, Gong, Haipeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6247404/
https://www.ncbi.nlm.nih.gov/pubmed/30505403
http://dx.doi.org/10.1016/j.csbj.2018.10.009
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author Ding, Wenze
Mao, Wenzhi
Shao, Di
Zhang, Wenxuan
Gong, Haipeng
author_facet Ding, Wenze
Mao, Wenzhi
Shao, Di
Zhang, Wenxuan
Gong, Haipeng
author_sort Ding, Wenze
collection PubMed
description Information of residue-residue contacts is essential for understanding the mechanism of protein folding, and has been successfully applied as special topological restraints to simplify the conformational sampling in de novo protein structure prediction. Prediction of protein residue contacts has experienced amazingly rapid progresses recently, with prediction accuracy approaching impressively high levels in the past two years. In this work, we introduce a second version of our residue contact predictor, DeepConPred2, which exhibits substantially improved performance and sufficiently reduced running time after model re-optimization and feature updates. When testing on the CASP12 free modeling targets, our program reaches at least the same level of prediction accuracy as the best contact predictors so far and provides information complementary to other state-of-the-art methods in contact-assisted folding.
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spelling pubmed-62474042018-11-30 DeepConPred2: An Improved Method for the Prediction of Protein Residue Contacts Ding, Wenze Mao, Wenzhi Shao, Di Zhang, Wenxuan Gong, Haipeng Comput Struct Biotechnol J Short Review Information of residue-residue contacts is essential for understanding the mechanism of protein folding, and has been successfully applied as special topological restraints to simplify the conformational sampling in de novo protein structure prediction. Prediction of protein residue contacts has experienced amazingly rapid progresses recently, with prediction accuracy approaching impressively high levels in the past two years. In this work, we introduce a second version of our residue contact predictor, DeepConPred2, which exhibits substantially improved performance and sufficiently reduced running time after model re-optimization and feature updates. When testing on the CASP12 free modeling targets, our program reaches at least the same level of prediction accuracy as the best contact predictors so far and provides information complementary to other state-of-the-art methods in contact-assisted folding. Research Network of Computational and Structural Biotechnology 2018-11-10 /pmc/articles/PMC6247404/ /pubmed/30505403 http://dx.doi.org/10.1016/j.csbj.2018.10.009 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Short Review
Ding, Wenze
Mao, Wenzhi
Shao, Di
Zhang, Wenxuan
Gong, Haipeng
DeepConPred2: An Improved Method for the Prediction of Protein Residue Contacts
title DeepConPred2: An Improved Method for the Prediction of Protein Residue Contacts
title_full DeepConPred2: An Improved Method for the Prediction of Protein Residue Contacts
title_fullStr DeepConPred2: An Improved Method for the Prediction of Protein Residue Contacts
title_full_unstemmed DeepConPred2: An Improved Method for the Prediction of Protein Residue Contacts
title_short DeepConPred2: An Improved Method for the Prediction of Protein Residue Contacts
title_sort deepconpred2: an improved method for the prediction of protein residue contacts
topic Short Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6247404/
https://www.ncbi.nlm.nih.gov/pubmed/30505403
http://dx.doi.org/10.1016/j.csbj.2018.10.009
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