Nucleic Acids Research Advance Access published online on October 4, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm800
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Database Issue |
PPT-DB: the protein property prediction and testing database
1Department of Biological Sciences, 2Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada T6G 2E8 and 3National Institute for Nanotechnology, 11421 Saskatchewan Drive, Edmonton, AB, Canada T6G 2M9
*To whom correspondence should be addressed. Tel: 780 492 0383; Fax: 780 492 1071; Email: david.wishart{at}ualberta.ca
Received August 14, 2007. Revised September 15, 2007. Accepted September 17, 2007.
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
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