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An automated stochastic approach to the identification of the protein specificity determinants and functional subfamilies
BACKGROUND: Recent progress in sequencing and 3 D structure determination techniques stimulated development of approaches aimed at more precise annotation of proteins, that is, prediction of exact specificity to a ligand or, more broadly, to a binding partner of any kind. RESULTS: We present a metho...
Autores principales: | , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2914642/ https://www.ncbi.nlm.nih.gov/pubmed/20633297 http://dx.doi.org/10.1186/1748-7188-5-29 |
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author | Mazin, Pavel V Gelfand, Mikhail S Mironov, Andrey A Rakhmaninova, Aleksandra B Rubinov, Anatoly R Russell, Robert B Kalinina, Olga V |
author_facet | Mazin, Pavel V Gelfand, Mikhail S Mironov, Andrey A Rakhmaninova, Aleksandra B Rubinov, Anatoly R Russell, Robert B Kalinina, Olga V |
author_sort | Mazin, Pavel V |
collection | PubMed |
description | BACKGROUND: Recent progress in sequencing and 3 D structure determination techniques stimulated development of approaches aimed at more precise annotation of proteins, that is, prediction of exact specificity to a ligand or, more broadly, to a binding partner of any kind. RESULTS: We present a method, SDPclust, for identification of protein functional subfamilies coupled with prediction of specificity-determining positions (SDPs). SDPclust predicts specificity in a phylogeny-independent stochastic manner, which allows for the correct identification of the specificity for proteins that are separated on a phylogenetic tree, but still bind the same ligand. SDPclust is implemented as a Web-server http://bioinf.fbb.msu.ru/SDPfoxWeb/ and a stand-alone Java application available from the website. CONCLUSIONS: SDPclust performs a simultaneous identification of specificity determinants and specificity groups in a statistically robust and phylogeny-independent manner. |
format | Text |
id | pubmed-2914642 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-29146422010-08-12 An automated stochastic approach to the identification of the protein specificity determinants and functional subfamilies Mazin, Pavel V Gelfand, Mikhail S Mironov, Andrey A Rakhmaninova, Aleksandra B Rubinov, Anatoly R Russell, Robert B Kalinina, Olga V Algorithms Mol Biol Research BACKGROUND: Recent progress in sequencing and 3 D structure determination techniques stimulated development of approaches aimed at more precise annotation of proteins, that is, prediction of exact specificity to a ligand or, more broadly, to a binding partner of any kind. RESULTS: We present a method, SDPclust, for identification of protein functional subfamilies coupled with prediction of specificity-determining positions (SDPs). SDPclust predicts specificity in a phylogeny-independent stochastic manner, which allows for the correct identification of the specificity for proteins that are separated on a phylogenetic tree, but still bind the same ligand. SDPclust is implemented as a Web-server http://bioinf.fbb.msu.ru/SDPfoxWeb/ and a stand-alone Java application available from the website. CONCLUSIONS: SDPclust performs a simultaneous identification of specificity determinants and specificity groups in a statistically robust and phylogeny-independent manner. BioMed Central 2010-07-15 /pmc/articles/PMC2914642/ /pubmed/20633297 http://dx.doi.org/10.1186/1748-7188-5-29 Text en Copyright ©2010 Mazin et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Mazin, Pavel V Gelfand, Mikhail S Mironov, Andrey A Rakhmaninova, Aleksandra B Rubinov, Anatoly R Russell, Robert B Kalinina, Olga V An automated stochastic approach to the identification of the protein specificity determinants and functional subfamilies |
title | An automated stochastic approach to the identification of the protein specificity determinants and functional subfamilies |
title_full | An automated stochastic approach to the identification of the protein specificity determinants and functional subfamilies |
title_fullStr | An automated stochastic approach to the identification of the protein specificity determinants and functional subfamilies |
title_full_unstemmed | An automated stochastic approach to the identification of the protein specificity determinants and functional subfamilies |
title_short | An automated stochastic approach to the identification of the protein specificity determinants and functional subfamilies |
title_sort | automated stochastic approach to the identification of the protein specificity determinants and functional subfamilies |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2914642/ https://www.ncbi.nlm.nih.gov/pubmed/20633297 http://dx.doi.org/10.1186/1748-7188-5-29 |
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