Abstract:
: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.
journal_name
Nucleic Acids Resjournal_title
Nucleic acids researchauthors
Wishart DS,Arndt D,Berjanskii M,Guo AC,Shi Y,Shrivastava S,Zhou J,Zhou Y,Lin Gdoi
10.1093/nar/gkm800subject
Has Abstractpub_date
2008-01-01 00:00:00pages
D222-9issue
Database issueeissn
0305-1048issn
1362-4962pii
gkm800journal_volume
36pub_type
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