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Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns

Given sufficient large protein families, and using a global statistical inference approach, it is possible to obtain sufficient accuracy in protein residue contact predictions to predict the structure of many proteins. However, these approaches do not consider the fact that the contacts in a protein...

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Autores principales: Skwark, Marcin J., Raimondi, Daniele, Michel, Mirco, Elofsson, Arne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4222596/
https://www.ncbi.nlm.nih.gov/pubmed/25375897
http://dx.doi.org/10.1371/journal.pcbi.1003889
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author Skwark, Marcin J.
Raimondi, Daniele
Michel, Mirco
Elofsson, Arne
author_facet Skwark, Marcin J.
Raimondi, Daniele
Michel, Mirco
Elofsson, Arne
author_sort Skwark, Marcin J.
collection PubMed
description Given sufficient large protein families, and using a global statistical inference approach, it is possible to obtain sufficient accuracy in protein residue contact predictions to predict the structure of many proteins. However, these approaches do not consider the fact that the contacts in a protein are neither randomly, nor independently distributed, but actually follow precise rules governed by the structure of the protein and thus are interdependent. Here, we present PconsC2, a novel method that uses a deep learning approach to identify protein-like contact patterns to improve contact predictions. A substantial enhancement can be seen for all contacts independently on the number of aligned sequences, residue separation or secondary structure type, but is largest for β-sheet containing proteins. In addition to being superior to earlier methods based on statistical inferences, in comparison to state of the art methods using machine learning, PconsC2 is superior for families with more than 100 effective sequence homologs. The improved contact prediction enables improved structure prediction.
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spelling pubmed-42225962014-11-13 Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns Skwark, Marcin J. Raimondi, Daniele Michel, Mirco Elofsson, Arne PLoS Comput Biol Research Article Given sufficient large protein families, and using a global statistical inference approach, it is possible to obtain sufficient accuracy in protein residue contact predictions to predict the structure of many proteins. However, these approaches do not consider the fact that the contacts in a protein are neither randomly, nor independently distributed, but actually follow precise rules governed by the structure of the protein and thus are interdependent. Here, we present PconsC2, a novel method that uses a deep learning approach to identify protein-like contact patterns to improve contact predictions. A substantial enhancement can be seen for all contacts independently on the number of aligned sequences, residue separation or secondary structure type, but is largest for β-sheet containing proteins. In addition to being superior to earlier methods based on statistical inferences, in comparison to state of the art methods using machine learning, PconsC2 is superior for families with more than 100 effective sequence homologs. The improved contact prediction enables improved structure prediction. Public Library of Science 2014-11-06 /pmc/articles/PMC4222596/ /pubmed/25375897 http://dx.doi.org/10.1371/journal.pcbi.1003889 Text en © 2014 Skwark 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
Skwark, Marcin J.
Raimondi, Daniele
Michel, Mirco
Elofsson, Arne
Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns
title Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns
title_full Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns
title_fullStr Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns
title_full_unstemmed Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns
title_short Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns
title_sort improved contact predictions using the recognition of protein like contact patterns
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4222596/
https://www.ncbi.nlm.nih.gov/pubmed/25375897
http://dx.doi.org/10.1371/journal.pcbi.1003889
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