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Building a Better Fragment Library for De Novo Protein Structure Prediction

Fragment-based approaches are the current standard for de novo protein structure prediction. These approaches rely on accurate and reliable fragment libraries to generate good structural models. In this work, we describe a novel method for structure fragment library generation and its application in...

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Detalles Bibliográficos
Autores principales: de Oliveira, Saulo H. P., Shi, Jiye, Deane, Charlotte M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4406757/
https://www.ncbi.nlm.nih.gov/pubmed/25901595
http://dx.doi.org/10.1371/journal.pone.0123998
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author de Oliveira, Saulo H. P.
Shi, Jiye
Deane, Charlotte M.
author_facet de Oliveira, Saulo H. P.
Shi, Jiye
Deane, Charlotte M.
author_sort de Oliveira, Saulo H. P.
collection PubMed
description Fragment-based approaches are the current standard for de novo protein structure prediction. These approaches rely on accurate and reliable fragment libraries to generate good structural models. In this work, we describe a novel method for structure fragment library generation and its application in fragment-based de novo protein structure prediction. The importance of correct testing procedures in assessing the quality of fragment libraries is demonstrated. In particular, the exclusion of homologs to the target from the libraries to correctly simulate a de novo protein structure prediction scenario, something which surprisingly is not always done. We demonstrate that fragments presenting different predominant predicted secondary structures should be treated differently during the fragment library generation step and that exhaustive and random search strategies should both be used. This information was used to develop a novel method, Flib. On a validation set of 41 structurally diverse proteins, Flib libraries presents both a higher precision and coverage than two of the state-of-the-art methods, NNMake and HHFrag. Flib also achieves better precision and coverage on the set of 275 protein domains used in the two previous experiments of the the Critical Assessment of Structure Prediction (CASP9 and CASP10). We compared Flib libraries against NNMake libraries in a structure prediction context. Of the 13 cases in which a correct answer was generated, Flib models were more accurate than NNMake models for 10. “Flib is available for download at: http://www.stats.ox.ac.uk/research/proteins/resources”.
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spelling pubmed-44067572015-05-07 Building a Better Fragment Library for De Novo Protein Structure Prediction de Oliveira, Saulo H. P. Shi, Jiye Deane, Charlotte M. PLoS One Research Article Fragment-based approaches are the current standard for de novo protein structure prediction. These approaches rely on accurate and reliable fragment libraries to generate good structural models. In this work, we describe a novel method for structure fragment library generation and its application in fragment-based de novo protein structure prediction. The importance of correct testing procedures in assessing the quality of fragment libraries is demonstrated. In particular, the exclusion of homologs to the target from the libraries to correctly simulate a de novo protein structure prediction scenario, something which surprisingly is not always done. We demonstrate that fragments presenting different predominant predicted secondary structures should be treated differently during the fragment library generation step and that exhaustive and random search strategies should both be used. This information was used to develop a novel method, Flib. On a validation set of 41 structurally diverse proteins, Flib libraries presents both a higher precision and coverage than two of the state-of-the-art methods, NNMake and HHFrag. Flib also achieves better precision and coverage on the set of 275 protein domains used in the two previous experiments of the the Critical Assessment of Structure Prediction (CASP9 and CASP10). We compared Flib libraries against NNMake libraries in a structure prediction context. Of the 13 cases in which a correct answer was generated, Flib models were more accurate than NNMake models for 10. “Flib is available for download at: http://www.stats.ox.ac.uk/research/proteins/resources”. Public Library of Science 2015-04-22 /pmc/articles/PMC4406757/ /pubmed/25901595 http://dx.doi.org/10.1371/journal.pone.0123998 Text en © 2015 de Oliveira et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
de Oliveira, Saulo H. P.
Shi, Jiye
Deane, Charlotte M.
Building a Better Fragment Library for De Novo Protein Structure Prediction
title Building a Better Fragment Library for De Novo Protein Structure Prediction
title_full Building a Better Fragment Library for De Novo Protein Structure Prediction
title_fullStr Building a Better Fragment Library for De Novo Protein Structure Prediction
title_full_unstemmed Building a Better Fragment Library for De Novo Protein Structure Prediction
title_short Building a Better Fragment Library for De Novo Protein Structure Prediction
title_sort building a better fragment library for de novo protein structure prediction
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4406757/
https://www.ncbi.nlm.nih.gov/pubmed/25901595
http://dx.doi.org/10.1371/journal.pone.0123998
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