Motivation: 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.#R##N##R##N#Results: 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.#R##N##R##N#Availability: http://www.insun.hit.edu.cn/~mhli/site_CRFs/index.html#R##N##R##N#Contact: mhli@insun.hit.edu.cn#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online.
['Minghui Li', 'Lei Lin', 'Xiaolong Wang', 'Tao Liu']
Protein--protein interaction site prediction based on conditional random fields