Journal Article

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

in Bioinformatics

Volume 25, issue 12, pages 1513-1520
Published in print June 2009 | ISSN: 1367-4803
Published online April 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp240
Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy

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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@ku.edu.tr; okeskin@ku.edu.tr

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6147 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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