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


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.


BACKGROUND
http://www.insun.hit.edu.cn/~mhli/site_CRFs/index.html.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Li
Ming-Hui
MH
Bioinformatics Research Group, ITNLP Lab, Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. mhli@insun.hit.edu.cn
Lin
Lei
L
Wang
Xiao-Long
XL
Liu
Tao
T
D000465:Algorithms; D001665:Binding Sites; D017124:Conserved Sequence; D030562:Databases, Protein; D008958:Models, Molecular; D011487:Protein Conformation; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry / Q000378:metabolism
D011506:Proteins