A probabilistic fragment-based protein structure prediction algorithm.

Conformational sampling is one of the bottlenecks in fragment-based protein structure prediction approaches. They generally start with a coarse-grained optimization where mainchain atoms and centroids of side chains are considered, followed by a fine-grained optimization with an all-atom representat...

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Autores principales:	David Simoncini, Francois Berenger, Rojan Shrestha, Kam Y J Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ac0972fe533943f886e4f1d6b9473e36
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id	oai:doaj.org-article:ac0972fe533943f886e4f1d6b9473e36
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spelling	oai:doaj.org-article:ac0972fe533943f886e4f1d6b9473e362021-11-18T07:11:55ZA probabilistic fragment-based protein structure prediction algorithm.1932-620310.1371/journal.pone.0038799https://doaj.org/article/ac0972fe533943f886e4f1d6b9473e362012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22829868/?tool=EBIhttps://doaj.org/toc/1932-6203Conformational sampling is one of the bottlenecks in fragment-based protein structure prediction approaches. They generally start with a coarse-grained optimization where mainchain atoms and centroids of side chains are considered, followed by a fine-grained optimization with an all-atom representation of proteins. It is during this coarse-grained phase that fragment-based methods sample intensely the conformational space. If the native-like region is sampled more, the accuracy of the final all-atom predictions may be improved accordingly. In this work we present EdaFold, a new method for fragment-based protein structure prediction based on an Estimation of Distribution Algorithm. Fragment-based approaches build protein models by assembling short fragments from known protein structures. Whereas the probability mass functions over the fragment libraries are uniform in the usual case, we propose an algorithm that learns from previously generated decoys and steers the search toward native-like regions. A comparison with Rosetta AbInitio protocol shows that EdaFold is able to generate models with lower energies and to enhance the percentage of near-native coarse-grained decoys on a benchmark of [Formula: see text] proteins. The best coarse-grained models produced by both methods were refined into all-atom models and used in molecular replacement. All atom decoys produced out of EdaFold's decoy set reach high enough accuracy to solve the crystallographic phase problem by molecular replacement for some test proteins. EdaFold showed a higher success rate in molecular replacement when compared to Rosetta. Our study suggests that improving low resolution coarse-grained decoys allows computational methods to avoid subsequent sampling issues during all-atom refinement and to produce better all-atom models. EdaFold can be downloaded from http://www.riken.jp/zhangiru/software.html [corrected].David SimonciniFrancois BerengerRojan ShresthaKam Y J ZhangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 7, p e38799 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q David Simoncini Francois Berenger Rojan Shrestha Kam Y J Zhang A probabilistic fragment-based protein structure prediction algorithm.
description	Conformational sampling is one of the bottlenecks in fragment-based protein structure prediction approaches. They generally start with a coarse-grained optimization where mainchain atoms and centroids of side chains are considered, followed by a fine-grained optimization with an all-atom representation of proteins. It is during this coarse-grained phase that fragment-based methods sample intensely the conformational space. If the native-like region is sampled more, the accuracy of the final all-atom predictions may be improved accordingly. In this work we present EdaFold, a new method for fragment-based protein structure prediction based on an Estimation of Distribution Algorithm. Fragment-based approaches build protein models by assembling short fragments from known protein structures. Whereas the probability mass functions over the fragment libraries are uniform in the usual case, we propose an algorithm that learns from previously generated decoys and steers the search toward native-like regions. A comparison with Rosetta AbInitio protocol shows that EdaFold is able to generate models with lower energies and to enhance the percentage of near-native coarse-grained decoys on a benchmark of [Formula: see text] proteins. The best coarse-grained models produced by both methods were refined into all-atom models and used in molecular replacement. All atom decoys produced out of EdaFold's decoy set reach high enough accuracy to solve the crystallographic phase problem by molecular replacement for some test proteins. EdaFold showed a higher success rate in molecular replacement when compared to Rosetta. Our study suggests that improving low resolution coarse-grained decoys allows computational methods to avoid subsequent sampling issues during all-atom refinement and to produce better all-atom models. EdaFold can be downloaded from http://www.riken.jp/zhangiru/software.html [corrected].
format	article
author	David Simoncini Francois Berenger Rojan Shrestha Kam Y J Zhang
author_facet	David Simoncini Francois Berenger Rojan Shrestha Kam Y J Zhang
author_sort	David Simoncini
title	A probabilistic fragment-based protein structure prediction algorithm.
title_short	A probabilistic fragment-based protein structure prediction algorithm.
title_full	A probabilistic fragment-based protein structure prediction algorithm.
title_fullStr	A probabilistic fragment-based protein structure prediction algorithm.
title_full_unstemmed	A probabilistic fragment-based protein structure prediction algorithm.
title_sort	probabilistic fragment-based protein structure prediction algorithm.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/ac0972fe533943f886e4f1d6b9473e36
work_keys_str_mv	AT davidsimoncini aprobabilisticfragmentbasedproteinstructurepredictionalgorithm AT francoisberenger aprobabilisticfragmentbasedproteinstructurepredictionalgorithm AT rojanshrestha aprobabilisticfragmentbasedproteinstructurepredictionalgorithm AT kamyjzhang aprobabilisticfragmentbasedproteinstructurepredictionalgorithm AT davidsimoncini probabilisticfragmentbasedproteinstructurepredictionalgorithm AT francoisberenger probabilisticfragmentbasedproteinstructurepredictionalgorithm AT rojanshrestha probabilisticfragmentbasedproteinstructurepredictionalgorithm AT kamyjzhang probabilisticfragmentbasedproteinstructurepredictionalgorithm
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A probabilistic fragment-based protein structure prediction algorithm.

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