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Bioinformatics Advance Access published online on February 19, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn055
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

ParCrys: A Parzen Window Density Estimation Approach to Protein Crystallisation Propensity Prediction

Ian M. Overton 1, Gianandrea Padovani 2, Mark A. Girolami 2 and Geoffrey J. Barton 1,*

1 School of Life Sciences Research, University of Dundee, Dow Street, Dundee, DD1 5EH, UK
2 Department of Computing Science, University of Glasgow, Glasgow, GL12 8QQ, UK

*To whom correspondence should be addressed. Geoffrey J. Barton, E-mail: geoff{at}compbio.dundee.ac.uk


  Abstract

The ability to rank proteins by their likely success in crystallisation is useful in current Structural Biology efforts and in particular in high-throughput Structural Genomics initiatives. We present ParCrys, a Parzen Window approach to estimate a protein's propensity to produce diffraction-quality crystals. The Protein Data Bank (PDB) provided training data whilst the databases TargetDB and PepcDB were used to define feature selection data as well as test data independent of feature selection and training. ParCrys outperforms the OB-Score, SECRET and CRYSTALP on the data examined, with accuracy and Matthews correlation coefficient values of 79.1% and 0.582 respectively (74.0% and 0.227 .on data with a ‘real-world’ ratio of positive:negative examples). ParCrys predictions and associated datasets are available from www.compbio.dundee.ac.uk/parcrys

Contact: geoff{at}compbio.dundee.ac.uk

Associate Editor: Prof. John Quackenbush

Received on June 1, 2007; revised on January 21, 2008; accepted on February 6, 2008

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