title: Bayesian statistical analysis of protein side-chain rotamer preferences.
abstract: We present a Bayesian statistical analysis of the conformations of side chains in proteins from the Protein Data Bank. This is an extension of the backbone-dependent rotamer library, and includes rotamer populations and average chi angles for a full range of phi, psi values. The Bayesian analysis used here provides a rigorous statistical method for taking account of varying amounts of data. Bayesian statistics requires the assumption of a prior distribution for parameters over their range of possible values. This prior distribution can be derived from previous data or from pooling some of the present data. The prior distribution is combined with the data to form the posterior distribution, which is a compromise between the prior distribution and the data. For the chi 2, chi 3, and chi 4 rotamer prior distributions, we assume that the probability of each rotamer type is dependent only on the previous chi rotamer in the chain. For the backbone-dependence of the chi 1 rotamers, we derive prior distributions from the product of the phi-dependent and psi-dependent probabilities. Molecular mechanics calculations with the CHARMM22 potential show a strong similarity with the experimental distributions, indicating that proteins attain their lowest energy rotamers with respect to local backbone-side-chain interactions. The new library is suitable for use in homology modeling, protein folding simulations, and the refinement of X-ray and NMR structures.
authors.affiliation: Department of Cellular and Molecular Pharmacology, University of California, San Francisco 94143-0450, USA. dunbrack@cmpharm.ucsf.edu
authors.forename: R L
authors.initials: RL
authors.lastname: Dunbrack
authors.forename: F E
authors.initials: FE
authors.lastname: Cohen
mesh_terms: D001499:Bayes Theorem; D008956:Models, Chemical; D008958:Models, Molecular; D011487:Protein Conformation