title: Prediction of side chain orientations in proteins by statistical machine learning methods.
abstract: We develop ways to predict the side chain orientations of residues within a protein structure by using several different statistical machine learning methods. Here side chain orientation of a given residue i is measured by an angle Omega(i) between the vector pointing from the center of the protein structure to the C(i)(alpha) atom and the vector pointing from the C(i)(alpha) atom to the center of its side chain atoms. To predict the Omega(i) angles, we construct statistical models by using several different methods such as general linear regression, a regression tree and bagging, a neural network, and a support vector machine. The root mean square errors for the different models range only from 36.67 to 37.60 degrees and the correlation coefficients are all between 30% and 34%. The performances of different models in the test set are, thus, quite similar, and show the relative predictive power of these models to be significant in comparison with random side chain orientations.
authors.affiliation: Laurence H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, Iowa, USA.
authors.forename: Aimin
authors.initials: A
authors.lastname: Yan
authors.forename: Andrzej
authors.initials: A
authors.lastname: Kloczkowski
authors.forename: Heike
authors.initials: H
authors.lastname: Hofmann
authors.forename: Robert L
authors.initials: RL
authors.lastname: Jernigan
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016571:Neural Networks, Computer; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins