Amino acid features for prediction of protein-protein interface residues with support vector machines

Authors:
Minh N. Nguyen;Jagath C. Rajapakse;Kai-Bo Duan
Affiliations:
BioInformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore;BioInformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore and Singapore-MIT Alliance, Singapore;BioInformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Year:
2007

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Abstract

Knowledge of protein-protein interaction sites is vital to determine proteins' function and involvement in different pathways. Support Vector Machines (SVM) have been proposed over the recent years to predict protein-protein interface residues, primarily based on single amino acid sequence inputs. We investigate the features of amino acids that can be best used with SVM for predicting residues at proteinprotein interfaces. The optimal feature set was derived from investigation into features such as amino acid composition, hydrophobic characters of amino acids, secondary structure propensity of amino acids, accessible surface areas, and evolutionary information generated by PSI-BLAST profiles. Using a backward elimination procedure, amino acid composition, accessible surface areas, and evolutionary information generated by PSI-BLAST profiles gave the best performance. The present approach achieved overall prediction accuracy of 74.2% for 77 individulal proteins collected from the Protein Data Bank, which is better than the previously reported accuracies.