title: Improved prediction of protein binding sites from sequences using genetic algorithm.
abstract: We undertook this project in response to the rapidly increasing number of protein structures with unknown functions in the Protein Data Bank. Here, we combined a genetic algorithm with a support vector machine to predict protein-protein binding sites. In an experiment on a testing dataset, we predicted the binding sites for 66% of our datasets, made up of 50 testing hetero-complexes. This classifier achieved greater sensitivity (60.17%), specificity (58.17%), accuracy (64.08%), and F-measure (54.79%), and a higher correlation coefficient (0.2502) than those of the support vector machine. This result can be used to guide biologists in designing specific experiments for protein analysis.
authors.affiliation: Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, 230039, Hefei, China. duxiuquan2@sina.com
authors.forename: Xiuquan
authors.initials: X
authors.lastname: Du
authors.forename: Jiaxing
authors.initials: J
authors.lastname: Cheng
authors.forename: Jie
authors.initials: J
authors.lastname: Song
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D008954:Models, Biological; D008958:Models, Molecular; D011485:Protein Binding; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*
chemical_list: D011506:Proteins