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Radial Basis Function Neural Network Ensemble for Predicting Protein-Protein Interaction Sites in Heterocomplexes

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Abstract:

Prediction of protein-protein interaction sites can guide the structural elucidation of protein complexes. We propose a novel method using a radial basis function neural network (RBFNN) ensemble model for the prediction of protein interaction sites in heterocomplexes. We classified protein surface residues into interaction sites or non-interaction sites based on the RBFNNs trained on different datasets, then judged a prediction to be the final output. Only information of evolutionary conservation and spatial sequence profile are used in this ensemble predictor to describe the protein sites. A non-redundant data set of heterodimers used is consisted of 69 protein chains, in which 10329 surface residues can be found. The efficiency and the effectiveness of our proposed approach can be validated by a better performance such as the accuracy of 0.689, the sensitivity of 66.6% and the specificity of 67.6%.





Keywords: Protein interaction sites; ensemble; heterocomplex; radial basis function neural networks; spatial neighboring residue; surface residue

Document Type: Research Article

DOI: http://dx.doi.org/10.2174/092986610791760397

Publication date: September 1, 2010

More about this publication?
  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
ben/ppl/2010/00000017/00000009/art00007
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