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Exhaustive search of linear information encoding protein-peptide recognition
High-throughput in vitro methods have been extensively applied to identify linear information that encodes peptide recognition. However, these methods are limited in number of peptides, sequence variation, and length of peptides that can be explored, and often produce solutions that are not found in...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5417721/ https://www.ncbi.nlm.nih.gov/pubmed/28426660 http://dx.doi.org/10.1371/journal.pcbi.1005499 |
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author | Kelil, Abdellali Dubreuil, Benjamin Levy, Emmanuel D. Michnick, Stephen W. |
author_facet | Kelil, Abdellali Dubreuil, Benjamin Levy, Emmanuel D. Michnick, Stephen W. |
author_sort | Kelil, Abdellali |
collection | PubMed |
description | High-throughput in vitro methods have been extensively applied to identify linear information that encodes peptide recognition. However, these methods are limited in number of peptides, sequence variation, and length of peptides that can be explored, and often produce solutions that are not found in the cell. Despite the large number of methods developed to attempt addressing these issues, the exhaustive search of linear information encoding protein-peptide recognition has been so far physically unfeasible. Here, we describe a strategy, called DALEL, for the exhaustive search of linear sequence information encoded in proteins that bind to a common partner. We applied DALEL to explore binding specificity of SH3 domains in the budding yeast Saccharomyces cerevisiae. Using only the polypeptide sequences of SH3 domain binding proteins, we succeeded in identifying the majority of known SH3 binding sites previously discovered either in vitro or in vivo. Moreover, we discovered a number of sites with both non-canonical sequences and distinct properties that may serve ancillary roles in peptide recognition. We compared DALEL to a variety of state-of-the-art algorithms in the blind identification of known binding sites of the human Grb2 SH3 domain. We also benchmarked DALEL on curated biological motifs derived from the ELM database to evaluate the effect of increasing/decreasing the enrichment of the motifs. Our strategy can be applied in conjunction with experimental data of proteins interacting with a common partner to identify binding sites among them. Yet, our strategy can also be applied to any group of proteins of interest to identify enriched linear motifs or to exhaustively explore the space of linear information encoded in a polypeptide sequence. Finally, we have developed a webserver located at http://michnick.bcm.umontreal.ca/dalel, offering user-friendly interface and providing different scenarios utilizing DALEL. |
format | Online Article Text |
id | pubmed-5417721 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-54177212017-05-27 Exhaustive search of linear information encoding protein-peptide recognition Kelil, Abdellali Dubreuil, Benjamin Levy, Emmanuel D. Michnick, Stephen W. PLoS Comput Biol Research Article High-throughput in vitro methods have been extensively applied to identify linear information that encodes peptide recognition. However, these methods are limited in number of peptides, sequence variation, and length of peptides that can be explored, and often produce solutions that are not found in the cell. Despite the large number of methods developed to attempt addressing these issues, the exhaustive search of linear information encoding protein-peptide recognition has been so far physically unfeasible. Here, we describe a strategy, called DALEL, for the exhaustive search of linear sequence information encoded in proteins that bind to a common partner. We applied DALEL to explore binding specificity of SH3 domains in the budding yeast Saccharomyces cerevisiae. Using only the polypeptide sequences of SH3 domain binding proteins, we succeeded in identifying the majority of known SH3 binding sites previously discovered either in vitro or in vivo. Moreover, we discovered a number of sites with both non-canonical sequences and distinct properties that may serve ancillary roles in peptide recognition. We compared DALEL to a variety of state-of-the-art algorithms in the blind identification of known binding sites of the human Grb2 SH3 domain. We also benchmarked DALEL on curated biological motifs derived from the ELM database to evaluate the effect of increasing/decreasing the enrichment of the motifs. Our strategy can be applied in conjunction with experimental data of proteins interacting with a common partner to identify binding sites among them. Yet, our strategy can also be applied to any group of proteins of interest to identify enriched linear motifs or to exhaustively explore the space of linear information encoded in a polypeptide sequence. Finally, we have developed a webserver located at http://michnick.bcm.umontreal.ca/dalel, offering user-friendly interface and providing different scenarios utilizing DALEL. Public Library of Science 2017-04-20 /pmc/articles/PMC5417721/ /pubmed/28426660 http://dx.doi.org/10.1371/journal.pcbi.1005499 Text en © 2017 Kelil 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 (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Kelil, Abdellali Dubreuil, Benjamin Levy, Emmanuel D. Michnick, Stephen W. Exhaustive search of linear information encoding protein-peptide recognition |
title | Exhaustive search of linear information encoding protein-peptide recognition |
title_full | Exhaustive search of linear information encoding protein-peptide recognition |
title_fullStr | Exhaustive search of linear information encoding protein-peptide recognition |
title_full_unstemmed | Exhaustive search of linear information encoding protein-peptide recognition |
title_short | Exhaustive search of linear information encoding protein-peptide recognition |
title_sort | exhaustive search of linear information encoding protein-peptide recognition |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5417721/ https://www.ncbi.nlm.nih.gov/pubmed/28426660 http://dx.doi.org/10.1371/journal.pcbi.1005499 |
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