title: Prediction of hot spots in protein interfaces using extreme learning machines with the information of spatial neighbour residues.
abstract: The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming increasingly important for the research on protein-protein interaction and drug design. For each interface residue or target residue to be predicted, the authors extract hybrid features which incorporate a wide range of information of the target residue and its spatial neighbor residues, that is, the nearest contact residue in the other face (mirror-contact residue) and the nearest contact residue in the same face (intra-contact residue). Here, feature selection is performed using random forests to avoid over-fitting. Thereafter, the extreme learning machine is employed to effectively integrate these hybrid features for predicting hot spots in protein interfaces. By the 5-fold cross validation in the training set, their method can achieve accuracy (ACC) of 82.1% and Matthew's correlation coefficient (MCC) of 0.459, and outperforms some alternative machine learning methods in the comparison study. Furthermore, their method achieves ACC of 76.8% and MCC of 0.401 in the independent test set, and is more effective than the major existing hot spot predictors. Their prediction method offers a powerful tool for uncovering candidate residues in the studies of alanine scanning mutagenesis for functional protein interaction sites.
authors: School of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300222, People's Republic of China. linwang@amss.ac.cn.
authors: Lin
authors: L
authors: Wang
authors: Faculty of Fundamental Courses, Tianjin Foreign Studies University, Tianjin 300204, People's Republic of China.
authors: Wenjuan
authors: W
authors: Zhang
authors: Key Lab of Industrial Fermentation Microbiology, Ministry of Education & Tianjin City, College of Biotechnology, Tianjin University of Science and Technology, Tianjin 300457, People's Republic of China.
authors: Qiang
authors: Q
authors: Gao
authors: School of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300222, People's Republic of China.
authors: Congcong
authors: C
authors: Xiong
mesh_terms: D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D001665:Binding Sites; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods; D030562:Databases, Protein; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins