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PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites
The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3510211/ https://www.ncbi.nlm.nih.gov/pubmed/23209700 http://dx.doi.org/10.1371/journal.pone.0050300 |
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author | Song, Jiangning Tan, Hao Perry, Andrew J. Akutsu, Tatsuya Webb, Geoffrey I. Whisstock, James C. Pike, Robert N. |
author_facet | Song, Jiangning Tan, Hao Perry, Andrew J. Akutsu, Tatsuya Webb, Geoffrey I. Whisstock, James C. Pike, Robert N. |
author_sort | Song, Jiangning |
collection | PubMed |
description | The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s). Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by in silico approaches. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database) with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate sequence using machine learning techniques. It is freely available at http://lightning.med.monash.edu.au/PROSPER/. |
format | Online Article Text |
id | pubmed-3510211 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-35102112012-12-03 PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites Song, Jiangning Tan, Hao Perry, Andrew J. Akutsu, Tatsuya Webb, Geoffrey I. Whisstock, James C. Pike, Robert N. PLoS One Research Article The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s). Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by in silico approaches. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database) with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate sequence using machine learning techniques. It is freely available at http://lightning.med.monash.edu.au/PROSPER/. Public Library of Science 2012-11-29 /pmc/articles/PMC3510211/ /pubmed/23209700 http://dx.doi.org/10.1371/journal.pone.0050300 Text en © 2012 Song 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 Song, Jiangning Tan, Hao Perry, Andrew J. Akutsu, Tatsuya Webb, Geoffrey I. Whisstock, James C. Pike, Robert N. PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites |
title | PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites |
title_full | PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites |
title_fullStr | PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites |
title_full_unstemmed | PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites |
title_short | PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites |
title_sort | prosper: an integrated feature-based tool for predicting protease substrate cleavage sites |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3510211/ https://www.ncbi.nlm.nih.gov/pubmed/23209700 http://dx.doi.org/10.1371/journal.pone.0050300 |
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