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PPT-DB: the protein property prediction and testing database
The protein property prediction and testing database (PPT-DB) is a database housing nearly 30 carefully curated databases, each of which contains commonly predicted protein property information. These properties include both structural (i.e. secondary structure, contact order, disulfide pairing) and...
Autores principales: | , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2238980/ https://www.ncbi.nlm.nih.gov/pubmed/17916570 http://dx.doi.org/10.1093/nar/gkm800 |
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author | Wishart, David S. Arndt, David Berjanskii, Mark Guo, An Chi Shi, Yi Shrivastava, Savita Zhou, Jianjun Zhou, You Lin, Guohui |
author_facet | Wishart, David S. Arndt, David Berjanskii, Mark Guo, An Chi Shi, Yi Shrivastava, Savita Zhou, Jianjun Zhou, You Lin, Guohui |
author_sort | Wishart, David S. |
collection | PubMed |
description | The protein property prediction and testing database (PPT-DB) is a database housing nearly 30 carefully curated databases, each of which contains commonly predicted protein property information. These properties include both structural (i.e. secondary structure, contact order, disulfide pairing) and dynamic (i.e. order parameters, B-factors, folding rates) features that have been measured, derived or tabulated from a variety of sources. PPT-DB is designed to serve two purposes. First it is intended to serve as a centralized, up-to-date, freely downloadable and easily queried repository of predictable or ‘derived’ protein property data. In this role, PPT-DB can serve as a one-stop, fully standardized repository for developers to obtain the required training, testing and validation data needed for almost any kind of protein property prediction program they may wish to create. The second role that PPT-DB can play is as a tool for homology-based protein property prediction. Users may query PPT-DB with a sequence of interest and have a specific property predicted using a sequence similarity search against PPT-DB's extensive collection of proteins with known properties. PPT-DB exploits the well-known fact that protein structure and dynamic properties are highly conserved between homologous proteins. Predictions derived from PPT-DB's similarity searches are typically 85–95% correct (for categorical predictions, such as secondary structure) or exhibit correlations of >0.80 (for numeric predictions, such as accessible surface area). This performance is 10–20% better than what is typically obtained from standard ‘ab initio’ predictions. PPT-DB, its prediction utilities and all of its contents are available at http://www.pptdb.ca |
format | Text |
id | pubmed-2238980 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-22389802008-02-12 PPT-DB: the protein property prediction and testing database Wishart, David S. Arndt, David Berjanskii, Mark Guo, An Chi Shi, Yi Shrivastava, Savita Zhou, Jianjun Zhou, You Lin, Guohui Nucleic Acids Res Articles The protein property prediction and testing database (PPT-DB) is a database housing nearly 30 carefully curated databases, each of which contains commonly predicted protein property information. These properties include both structural (i.e. secondary structure, contact order, disulfide pairing) and dynamic (i.e. order parameters, B-factors, folding rates) features that have been measured, derived or tabulated from a variety of sources. PPT-DB is designed to serve two purposes. First it is intended to serve as a centralized, up-to-date, freely downloadable and easily queried repository of predictable or ‘derived’ protein property data. In this role, PPT-DB can serve as a one-stop, fully standardized repository for developers to obtain the required training, testing and validation data needed for almost any kind of protein property prediction program they may wish to create. The second role that PPT-DB can play is as a tool for homology-based protein property prediction. Users may query PPT-DB with a sequence of interest and have a specific property predicted using a sequence similarity search against PPT-DB's extensive collection of proteins with known properties. PPT-DB exploits the well-known fact that protein structure and dynamic properties are highly conserved between homologous proteins. Predictions derived from PPT-DB's similarity searches are typically 85–95% correct (for categorical predictions, such as secondary structure) or exhibit correlations of >0.80 (for numeric predictions, such as accessible surface area). This performance is 10–20% better than what is typically obtained from standard ‘ab initio’ predictions. PPT-DB, its prediction utilities and all of its contents are available at http://www.pptdb.ca Oxford University Press 2008-01 2007-10-04 /pmc/articles/PMC2238980/ /pubmed/17916570 http://dx.doi.org/10.1093/nar/gkm800 Text en © 2007 The Author(s) http://creativecommons.org/licenses/by-nc/2.0/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Articles Wishart, David S. Arndt, David Berjanskii, Mark Guo, An Chi Shi, Yi Shrivastava, Savita Zhou, Jianjun Zhou, You Lin, Guohui PPT-DB: the protein property prediction and testing database |
title | PPT-DB: the protein property prediction and testing database |
title_full | PPT-DB: the protein property prediction and testing database |
title_fullStr | PPT-DB: the protein property prediction and testing database |
title_full_unstemmed | PPT-DB: the protein property prediction and testing database |
title_short | PPT-DB: the protein property prediction and testing database |
title_sort | ppt-db: the protein property prediction and testing database |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2238980/ https://www.ncbi.nlm.nih.gov/pubmed/17916570 http://dx.doi.org/10.1093/nar/gkm800 |
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