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Bioinformatics Advance Access originally published online on April 8, 2009
Bioinformatics 2009 25(12):1513-1520; doi:10.1093/bioinformatics/btp240
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy

Nurcan Tuncbag , Attila Gursoy * and Ozlem Keskin *

Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey

*To whom correspondence should be addressed.


  Abstract

Motivation:Hot spots are residues comprising only a small fraction of interfaces yet accounting for the majority of the binding energy. These residues are critical in understanding the principles of protein interactions. Experimental studies like alanine scanning mutagenesis require significant effort; therefore, there is a need for computational methods to predict hot spots in protein interfaces.

Results:We present a new intuitive efficient method to determine computational hot spots based on conservation (C), solvent accessibility [accessible surface area (ASA)] and statistical pairwise residue potentials (PP) of the interface residues. Combination of these features is examined in a comprehensive way to study their effect in hot spot detection. The predicted hot spots are observed to match with the experimental hot spots with an accuracy of 70% and a precision of 64% in Alanine Scanning Energetics Database (ASEdb), and accuracy of 70% and a precision of 73% in Binding Interface Database (BID). Several machine learning methods are also applied to predict hot spots. Performance of our empirical approach exceeds learning-based methods and other existing hot spot prediction methods. Residue occlusion from solvent in the complexes and pairwise potentials are found to be the main discriminative features in hot spot prediction.

Conclusion:Our empirical method is a simple approach in hot spot prediction yet with its high accuracy and computational effectiveness. We believe that this method provides insights for the researchers working on characterization of protein binding sites and design of specific therapeutic agents for protein interactions.

Availability:The list of training and test sets are available as Supplementary Data at http://prism.ccbb.ku.edu.tr/hotpoint/supplement.doc

Contact: agursoy{at}ku.edu.tr; okeskin{at}ku.edu.tr

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Burkhard Rost

Received on January 12, 2009; revised on March 19, 2009; accepted on April 3, 2009

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