Identification of interface residues in protease-inhibitor and antigen-antibody complexes: A support vector machine approach

Changhui Yan, Vasant Honavar, Drena Dobbs

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21 Scopus citations


In this paper, we describe a machine learning approach for sequence-based prediction of protein-protein interaction sites. A support vector machine (SVM) classifier was trained to predict whether or not a surface residue is an interface residue (i.e., is located in the protein-protein interaction surface), based on the identity of the target residue and its ten sequence neighbors. Separate classifiers were trained on proteins from two categories of complexes, antibody-antigen and proteaseinhibitor. The effectiveness of each classifier was evaluated using leave-one-out (jack-knife) cross-validation. Interface and non-interface residues were classified with relatively high sensitivity (82.3% and 78.5%) and specificity (81.0% and 77.6%) for proteins in the antigen-antibody and protease-inhibitor complexes, respectively. The correlation between predicted and actual labels was 0.430 and 0.462, indicating that the method performs substantially better than chance (zero correlation). Combined with recently developed methods for identification of surface residues from sequence information, this offers a promising approach to predict residues involved in protein-protein interactions from sequence information alone.

Original languageEnglish (US)
Pages (from-to)123-129
Number of pages7
JournalNeural Computing and Applications
Issue number2
Publication statusPublished - Jun 2004


All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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