title: Neighbor-dependent Ramachandran probability distributions of amino acids developed from a hierarchical Dirichlet process model.
abstract: Distributions of the backbone dihedral angles of proteins have been studied for over 40 years. While many statistical analyses have been presented, only a handful of probability densities are publicly available for use in structure validation and structure prediction methods. The available distributions differ in a number of important ways, which determine their usefulness for various purposes. These include: 1) input data size and criteria for structure inclusion (resolution, R-factor, etc.); 2) filtering of suspect conformations and outliers using B-factors or other features; 3) secondary structure of input data (e.g., whether helix and sheet are included; whether beta turns are included); 4) the method used for determining probability densities ranging from simple histograms to modern nonparametric density estimation; and 5) whether they include nearest neighbor effects on the distribution of conformations in different regions of the Ramachandran map. In this work, Ramachandran probability distributions are presented for residues in protein loops from a high-resolution data set with filtering based on calculated electron densities. Distributions for all 20 amino acids (with cis and trans proline treated separately) have been determined, as well as 420 left-neighbor and 420 right-neighbor dependent distributions. The neighbor-independent and neighbor-dependent probability densities have been accurately estimated using Bayesian nonparametric statistical analysis based on the Dirichlet process. In particular, we used hierarchical Dirichlet process priors, which allow sharing of information between densities for a particular residue type and different neighbor residue types. The resulting distributions are tested in a loop modeling benchmark with the program Rosetta, and are shown to improve protein loop conformation prediction significantly. The distributions are available at http://dunbrack.fccc.edu/hdp.
authors.affiliation: Department of Statistics, University of California Berkeley, Berkeley, California, United States of America.
authors.forename: Daniel
authors.initials: D
authors.lastname: Ting
authors.forename: Guoli
authors.initials: G
authors.lastname: Wang
authors.forename: Maxim
authors.initials: M
authors.lastname: Shapovalov
authors.forename: Rajib
authors.initials: R
authors.lastname: Mitra
authors.forename: Michael I
authors.initials: MI
authors.lastname: Jordan
authors.forename: Roland L
authors.initials: RL
authors.lastname: Dunbrack
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry*; D001499:Bayes Theorem; D030562:Databases, Protein; D008954:Models, Biological; D015233:Models, Statistical; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000235:genetics
chemical_list: D000596:Amino Acids; D011506:Proteins