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Identification of interface residues in protease-inhibitor and antigen-antibody complexes: a support vector machine approach
Neural Computing & Applications
  • Changhui Yan, Iowa State University
  • Vasant Honavar, Iowa State University
  • Drena Dobbs, Iowa State University
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In this paper, we describe a machine learning approach for sequence-based prediction of proteinprotein 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 protease-inhibitor. 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.


This is a manuscript of an article from Neural Computing & Applications 13 (2004): 123. The final publication is available at Springer via

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Springer-Verlag London Limited
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Changhui Yan, Vasant Honavar and Drena Dobbs. "Identification of interface residues in protease-inhibitor and antigen-antibody complexes: a support vector machine approach" Neural Computing & Applications Vol. 13 Iss. 2 (2004) p. 123 - 129
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