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Bioinformatics Advance Access originally published online on February 5, 2008
Bioinformatics 2008 24(7):916-923; doi:10.1093/bioinformatics/btn050
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© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Prediction of recursive convex hull class assignments for protein residues

Michael Stout 1, Jaume Bacardit 1,2, Jonathan D. Hirst 3 and Natalio Krasnogor 1,*

1Automated Scheduling, Optimization and Planning research group, School of Computer Science, 2Multi-disciplinary Centre for Integrative Biology, School of Biosciences and 3School of Chemistry, University of Nottingham, UK

*To whom correspondence should be addressed.


  Abstract

Motivation: We introduce a new method for designating the location of residues in folded protein structures based on the recursive convex hull (RCH) of a point set of atomic coordinates. The RCH can be calculated with an efficient and parameterless algorithm.

Results: We show that residue RCH class contains information complementary to widely studied measures such as solvent accessibility (SA), residue depth (RD) and to the distance of residues from the centroid of the chain, the residues’ exposure (Exp). RCH is more conserved for related structures across folds and correlates better with changes in thermal stability of mutants than the other measures. Further, we assess the predictability of these measures using three types of machine-learning technique: decision trees (C4.5), Naive Bayes and Learning Classifier Systems (LCS) showing that RCH is more easily predicted than the other measures. As an exemplar application of predicted RCH class (in combination with other measures), we show that RCH is potentially helpful in improving prediction of residue contact numbers (CN).

Contact: nxk{at}cs.nott.ac.uk

Supplementary Information: For Supplementary data please refer to Datasets: www.infobiotic.net/datasets, RCH Prediction Servers: www.infobiotic.net

Associate Editor: Burkhard Rost

Received on November 6, 2007; revised on January 28, 2008; accepted on January 30, 2008

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