Bioinformatics Advance Access first published online on February 5, 2008
This version published online on February 12, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn050
Prediction of Recursive Convex Hull Class Assignments for Protein Residues
1Automated Scheduling, Optimization and Planning research group, School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, UK
2School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK
*To whom correspondence should be addressed. Natalio Krasnogor, E-mail: nxk{at}cs.nott.ac.uk
| Abstract |
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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.
Contact: nxk{at}cs.nott.ac.uk
Supplementary Information: Datasets: www.infobiotic.net/datasets, RCH Prediction Servers: www.infobiotic.net
Associate Editor: Prof. Burkhard Rost
Received on November 6, 2007; revised on January 28, 2008; accepted on January 30, 2008