title: Prediction of lipid-binding sites based on support vector machine and position specific scoring matrix.
abstract: Lipid-protein interactions play a vital role in various biological processes, which are involved in cellular functions and can affect the stability, folding and the function of peptides and proteins. In this study, a sequence-based method by using support vector machine and position specific scoring matrix (PSSM) was proposed to predict lipid-binding sites. Considering the influence of surrounding residues of one amino acid, a sliding window was chosen to encode the PSSM profiles. By incorporating the evolutionary information and the local features of residues surrounding one lipid-binding site, the method yielded a high accuracy of 80.86% and the Matthew's Correlation Coefficient of 0.58 by using fivefold cross validation test. The good result indicates the applicability of the method.
authors.affiliation: College of Chemistry, Sichuan University, 610064 Chengdu, China.
authors.forename: Wenjia
authors.initials: W
authors.lastname: Xiong
authors.forename: Yanzhi
authors.initials: Y
authors.lastname: Guo
authors.forename: Menglong
authors.initials: M
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D050356:Lipid Metabolism; D008055:Lipids / Q000737:chemistry*; D054730:Protein Interaction Domains and Motifs; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D012372:ROC Curve; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D008055:Lipids; D011506:Proteins