title: Prediction of fatty acid-binding residues on protein surfaces with three-dimensional probability distributions of interacting atoms.
abstract: Protein-fatty acid interaction is vital for many cellular processes and understanding this interaction is important for functional annotation as well as drug discovery. In this work, we present a method for predicting the fatty acid (FA)-binding residues by using three-dimensional probability density distributions of interacting atoms of FAs on protein surfaces which are derived from the known protein-FA complex structures. A machine learning algorithm was established to learn the characteristic patterns of the probability density maps specific to the FA-binding sites. The predictor was trained with five-fold cross validation on a non-redundant training set and then evaluated with an independent test set as well as on holo-apo pair's dataset. The results showed good accuracy in predicting the FA-binding residues. Further, the predictor developed in this study is implemented as an online server which is freely accessible at the following website, http://ismblab.genomics.sinica.edu.tw/.
authors.affiliation: Genomics Research Center, Academia Sinica, Taipei 115, Taiwan. Electronic address: rajasekaran.mahalingam@case.edu.
authors.forename: Rajasekaran
authors.initials: R
authors.lastname: Mahalingam
authors.affiliation: Genomics Research Center, Academia Sinica, Taipei 115, Taiwan; Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan; Bioinformatics Program, Taiwan International Graduate Program, Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.
authors.forename: Hung-Pin
authors.initials: HP
authors.lastname: Peng
authors.affiliation: Genomics Research Center, Academia Sinica, Taipei 115, Taiwan. Electronic address: yangas@gate.sinica.edu.tw.
authors.forename: An-Suei
authors.initials: AS
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D005227:Fatty Acids / Q000737:chemistry*; D008958:Models, Molecular; D011336:Probability; D011506:Proteins / Q000737:chemistry*; D016008:Statistical Distributions; D013499:Surface Properties
chemical_list: D005227:Fatty Acids; D011506:Proteins
keywords: Functional annotation; Machine learning; Probability density map; Protein-fatty acid interaction; Structure-based prediction