Structural Constraints on the Covariance Matrix Derived from Multiple Aligned Protein Sequences

Residue contact predictions were calculated based on the mutual information observed between pairs of positions in large multiple protein sequence alignments. Where previously only the statistical properties of these data have been considered important, we introduce new measures to impose constraint...

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Main Authors:	Taylor, William R., Sadowski, Michael I.
Format:	Online
Language:	English
Published:	Public Library of Science 2011
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3237328/

id	pubmed-3237328
recordtype	oai_dc
spelling	pubmed-32373282011-12-22 Structural Constraints on the Covariance Matrix Derived from Multiple Aligned Protein Sequences Taylor, William R. Sadowski, Michael I. Research Article Residue contact predictions were calculated based on the mutual information observed between pairs of positions in large multiple protein sequence alignments. Where previously only the statistical properties of these data have been considered important, we introduce new measures to impose constraints that make the contact map more consistent with a three dimensional structure. These included global (bulk) properties and local secondary structure properties. The latter allowed the contact constraints to be employed at the level of filtering pairs of secondary structure contacts which led to a more efficient (lower-level) implementation in the PLATO structure prediction server. Where previously the measure of success with this method had been whether the correct fold was predicted in the top 10 ranked models, with the current implementation, our summary statistic is the number of correct folds included in the top 10 models — which is on average over 50 percent. Public Library of Science 2011-12-05 /pmc/articles/PMC3237328/ /pubmed/22194819 http://dx.doi.org/10.1371/journal.pone.0028265 Text en Taylor, Sadowski. 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.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Taylor, William R. Sadowski, Michael I.
spellingShingle	Taylor, William R. Sadowski, Michael I. Structural Constraints on the Covariance Matrix Derived from Multiple Aligned Protein Sequences
author_facet	Taylor, William R. Sadowski, Michael I.
author_sort	Taylor, William R.
title	Structural Constraints on the Covariance Matrix Derived from Multiple Aligned Protein Sequences
title_short	Structural Constraints on the Covariance Matrix Derived from Multiple Aligned Protein Sequences
title_full	Structural Constraints on the Covariance Matrix Derived from Multiple Aligned Protein Sequences
title_fullStr	Structural Constraints on the Covariance Matrix Derived from Multiple Aligned Protein Sequences
title_full_unstemmed	Structural Constraints on the Covariance Matrix Derived from Multiple Aligned Protein Sequences
title_sort	structural constraints on the covariance matrix derived from multiple aligned protein sequences
description	Residue contact predictions were calculated based on the mutual information observed between pairs of positions in large multiple protein sequence alignments. Where previously only the statistical properties of these data have been considered important, we introduce new measures to impose constraints that make the contact map more consistent with a three dimensional structure. These included global (bulk) properties and local secondary structure properties. The latter allowed the contact constraints to be employed at the level of filtering pairs of secondary structure contacts which led to a more efficient (lower-level) implementation in the PLATO structure prediction server. Where previously the measure of success with this method had been whether the correct fold was predicted in the top 10 ranked models, with the current implementation, our summary statistic is the number of correct folds included in the top 10 models — which is on average over 50 percent.
publisher	Public Library of Science
publishDate	2011
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3237328/
_version_	1611493857249198080

Structural Constraints on the Covariance Matrix Derived from Multiple Aligned Protein Sequences

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