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Prediction of Protein-Protein Interactions from Protein Sequence Using Local Descriptors

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With a huge amount of protein sequence data, the computational method for protein-protein interaction (PPI) prediction using only the protein sequences information have drawn increasing interest. In this article, we propose a sequence- based method based on a novel representation of local protein sequence descriptors. Local descriptors account for the interactions between residues in both continuous and discontinuous regions of a protein sequence, so this method enables us to extract more PPI information from the sequence. A series of elaborate experiments are performed to optimize the prediction model by varying the parameter k and the distance measuring function of the k-nearest neighbors learning system and the ways of coding a protein pair. When performed on the PPI data of Saccharomyces cerevisiae, the method achieved 86.15% prediction accuracy with 81.03% sensitivity at the precision of 90.24%. An independent data set of 986 Escherichia coli PPIs was used to evaluate this prediction model and the prediction accuracy is 73.02%. Given the complex nature of PPIs, the performance of our method is promising, and it can be a helpful supplement for PPIs prediction.





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Keywords: Feature representation; KNNs; PPIs prediction; local descriptors; protein sequence; sequence-based method

Document Type: Research Article

Publication date: 01 September 2010

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  • 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.
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