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Bioinformatics. 2007 Mar 1;23(5):597-604. Epub 2007 Jan 18.

Protein-protein interaction site prediction based on conditional random fields.

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  • 1Bioinformatics Research Group, ITNLP Lab, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.



We are motivated by the fast-growing number of protein structures in the Protein Data Bank with necessary information for prediction of protein-protein interaction sites to develop methods for identification of residues participating in protein-protein interactions. We would like to compare conditional random fields (CRFs)-based method with conventional classification-based methods that omit the relation between two labels of neighboring residues to show the advantages of CRFs-based method in predicting protein-protein interaction sites.


The prediction of protein-protein interaction sites is solved as a sequential labeling problem by applying CRFs with features including protein sequence profile and residue accessible surface area. The CRFs-based method can achieve a comparable performance with state-of-the-art methods, when 1276 nonredundant hetero-complex protein chains are used as training and test set. Experimental result shows that CRFs-based method is a powerful and robust protein-protein interaction site prediction method and can be used to guide biologists to make specific experiments on proteins.



Supplementary data are available at Bioinformatics online.

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