Improving contact prediction along three dimensions.

Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members. The typical pipeline to address this task, which we in this paper refer to as the three dimensions of contact prediction, is to (i) f...

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Autores principales:	Christoph Feinauer, Marcin J Skwark, Andrea Pagnani, Erik Aurell
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/218963cc1828459ea88ef83f9aaee8f5
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id	oai:doaj.org-article:218963cc1828459ea88ef83f9aaee8f5
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spelling	oai:doaj.org-article:218963cc1828459ea88ef83f9aaee8f52021-11-25T05:40:41ZImproving contact prediction along three dimensions.1553-734X1553-735810.1371/journal.pcbi.1003847https://doaj.org/article/218963cc1828459ea88ef83f9aaee8f52014-10-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1003847https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members. The typical pipeline to address this task, which we in this paper refer to as the three dimensions of contact prediction, is to (i) filter and align the raw sequence data representing the evolutionarily related proteins; (ii) choose a predictive model to describe a sequence alignment; (iii) infer the model parameters and interpret them in terms of structural properties, such as an accurate contact map. We show here that all three dimensions are important for overall prediction success. In particular, we show that it is possible to improve significantly along the second dimension by going beyond the pair-wise Potts models from statistical physics, which have hitherto been the focus of the field. These (simple) extensions are motivated by multiple sequence alignments often containing long stretches of gaps which, as a data feature, would be rather untypical for independent samples drawn from a Potts model. Using a large test set of proteins we show that the combined improvements along the three dimensions are as large as any reported to date.Christoph FeinauerMarcin J SkwarkAndrea PagnaniErik AurellPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 10, p e1003847 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Christoph Feinauer Marcin J Skwark Andrea Pagnani Erik Aurell Improving contact prediction along three dimensions.
description	Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members. The typical pipeline to address this task, which we in this paper refer to as the three dimensions of contact prediction, is to (i) filter and align the raw sequence data representing the evolutionarily related proteins; (ii) choose a predictive model to describe a sequence alignment; (iii) infer the model parameters and interpret them in terms of structural properties, such as an accurate contact map. We show here that all three dimensions are important for overall prediction success. In particular, we show that it is possible to improve significantly along the second dimension by going beyond the pair-wise Potts models from statistical physics, which have hitherto been the focus of the field. These (simple) extensions are motivated by multiple sequence alignments often containing long stretches of gaps which, as a data feature, would be rather untypical for independent samples drawn from a Potts model. Using a large test set of proteins we show that the combined improvements along the three dimensions are as large as any reported to date.
format	article
author	Christoph Feinauer Marcin J Skwark Andrea Pagnani Erik Aurell
author_facet	Christoph Feinauer Marcin J Skwark Andrea Pagnani Erik Aurell
author_sort	Christoph Feinauer
title	Improving contact prediction along three dimensions.
title_short	Improving contact prediction along three dimensions.
title_full	Improving contact prediction along three dimensions.
title_fullStr	Improving contact prediction along three dimensions.
title_full_unstemmed	Improving contact prediction along three dimensions.
title_sort	improving contact prediction along three dimensions.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/218963cc1828459ea88ef83f9aaee8f5
work_keys_str_mv	AT christophfeinauer improvingcontactpredictionalongthreedimensions AT marcinjskwark improvingcontactpredictionalongthreedimensions AT andreapagnani improvingcontactpredictionalongthreedimensions AT erikaurell improvingcontactpredictionalongthreedimensions
_version_	1718414504611545088

Improving contact prediction along three dimensions.

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