Prediction of Protein-Protein Interface Residues Using Sequence Neighborhood and Surface Properties

  • Yasir Arafat
  • Joarder Kamruzzaman
  • Gour Karmakar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


An increasing number of protein structures with unknown functions have been solved in the recent years. But understanding the mechanism of protein-protein association still remains one of the biggest problems in structural bioinformatics. Significant research efforts have been dedicated to the identification of protein binding sites and detecting specific amino acid residues which have important connotations ranging from rational drug design to analysis of metabolic and signal transduction networks. In this paper, we present a support vector machine (SVM) based model to predict interface residues from amino acid sequences using sequence neighborhood and surface properties. Experiments with a number of surface properties reveal that the prediction accuracy enhances when residue interface propensity and coil interface propensity of amino acid residues are incorporated in the prediction model which is an improvement over the previously proposed model based on sequence neighborhood only. We also analyzed the effectiveness of a recently proposed coding scheme [1] of secondary structures on the proposed model.


Support Vector Machine Receiver Operating Characteristic Interface Residue Sequence Neighborhood Target Residue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yasir Arafat
    • 1
  • Joarder Kamruzzaman
    • 1
  • Gour Karmakar
    • 1
  1. 1.Faculty of Information TechnologyMonash UniversityAustralia

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