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 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

[1]  David R. Westhead,et al.  TMB-Hunt: a web server to screen sequence sets for transmembrane β-barrel proteins , 2005, Nucleic Acids Res..

[2]  Yaoqi Zhou,et al.  Real‐SPINE: An integrated system of neural networks for real‐value prediction of protein structural properties , 2007, Proteins.

[3]  Tin Wee Tan,et al.  SPdb – a signal peptide database , 2005, BMC Bioinformatics.

[4]  F M Richards,et al.  Areas, volumes, packing and protein structure. , 1977, Annual review of biophysics and bioengineering.

[5]  Jennifer A. Siepen,et al.  β Edge strands in protein structure prediction and aggregation , 2003, Protein science : a publication of the Protein Society.

[6]  B Rost,et al.  EVA: Large‐scale analysis of secondary structure prediction , 2001, Proteins.

[7]  A. Krogh,et al.  Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. , 2001, Journal of molecular biology.

[8]  David S. Wishart,et al.  VADAR: a web server for quantitative evaluation of protein structure quality , 2003, Nucleic Acids Res..

[9]  A. Guzzo,et al.  The influence of amino-acid sequence on protein structure. , 1965, Biophysical journal.

[10]  David S. Wishart,et al.  SuperPose: a simple server for sophisticated structural superposition , 2004, Nucleic Acids Res..

[11]  G. Fasman.  Prediction of Protein Structure and the Principles of Protein Conformation , 2012, Springer US.

[12]  J. Thornton,et al.  Analysis and prediction of the different types of β-turn in proteins , 1988 .

[13]  Amy E. Keating,et al.  Paircoil2: improved prediction of coiled coils from sequence , 2006, Bioinform..

[14]  Pierre Baldi,et al.  SCRATCH: a protein structure and structural feature prediction server , 2005, Nucleic Acids Res..

[15]  Maria Jesus Martin,et al.  High-quality Protein Knowledge Resource: SWISS-PROT and TrEMBL , 2002, Briefings Bioinform..

[16]  David S. Wishart,et al.  Tools for Protein Technologies , 2001 .

[17]  Guoli Wang,et al.  PISCES: a protein sequence culling server , 2003, Bioinform..

[18]  S. Brunak,et al.  Improved prediction of signal peptides: SignalP 3.0. , 2004, Journal of molecular biology.

[19]  William J. Welsh,et al.  Improved method for predicting ?-turn using support vector machine , 2005, Bioinform..

[20]  J. Thornton,et al.  Analysis and prediction of the different types of beta-turn in proteins. , 1988, Journal of molecular biology.

[21]  David S. Wishart,et al.  PREDITOR: a web server for predicting protein torsion angle restraints , 2006, Nucleic Acids Res..

[22]  Burkhard Rost,et al.  Static benchmarking of membrane helix predictions , 2003, Nucleic Acids Res..

[23]  William J Welsh,et al.  Improved method for predicting beta-turn using support vector machine. , 2005, Bioinformatics.

[24]  D. Baker,et al.  Contact order, transition state placement and the refolding rates of single domain proteins. , 1998, Journal of molecular biology.

[25]  Haruki Nakamura,et al.  The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data , 2006, Nucleic Acids Res..

[26]  David S. Wishart,et al.  Improving the accuracy of protein secondary structure prediction using structural alignment , 2006, BMC Bioinformatics.

[27]  Gajendra P. S. Raghava,et al.  BhairPred: prediction of β-hairpins in a protein from multiple alignment information using ANN and SVM techniques , 2005, Nucleic Acids Res..

[28]  Robert F. Boyko,et al.  Automated 1H and 13C chemical shift prediction using the BioMagResBank , 1997, Journal of biomolecular NMR.

[29]  Rafael Brüschweiler,et al.  Contact model for the prediction of NMR N-H order parameters in globular proteins. , 2002, Journal of the American Chemical Society.

[30]  B. Rost,et al.  Protein flexibility and rigidity predicted from sequence , 2005, Proteins.

[31]  Ashley M. Buckle,et al.  Protein Folding Database (PFD 2.0): an online environment for the International Foldeomics Consortium , 2006, Nucleic Acids Res..