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

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Abstract

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 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.

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Acknowledgements

This research was supported in part by grants from the National Science Foundation (0219699), the National Institute of Health (GM066387), and the Iowa State University Plant Science Institute.

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Authors and Affiliations

  1. Artificial Intelligence Research Laboratory, Iowa State University, Atanasoff Hall 226, Ames, IA, 50011-1040, USA

    Changhui Yan & Vasant Honavar

  2. Department of Computer Science, Iowa State University, Atanasoff Hall 226, Ames, IA, 50011-1040, USA

    Changhui Yan & Vasant Honavar

  3. Department of Genetics, Development and Cell Biology, Iowa State University, 2114 Molecular Biology Building, Ames, IA, 50011-1040, USA

    Drena Dobbs

  4. Laurence H Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA, 50011-1040, USA

    Vasant Honavar & Drena Dobbs

  5. Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, IA, 50011-1040, USA

    Changhui Yan, Vasant Honavar & Drena Dobbs

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  1. Changhui Yan
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  2. Vasant Honavar
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  3. Drena Dobbs
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Correspondence to Changhui Yan.

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Yan, C., Honavar, V. & Dobbs, D. Identification of interface residues in protease-inhibitor and antigen-antibody complexes: a support vector machine approach. Neural Comput & Applic 13, 123–129 (2004). https://doi.org/10.1007/s00521-004-0414-3

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  • DOI: https://doi.org/10.1007/s00521-004-0414-3

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