title: Bayesian estimation of Karplus parameters and torsion angles from three-bond scalar couplings constants.
abstract: We apply Bayesian inference to analyze three-bond scalar coupling constants in an objective and consistent way. The Karplus curve and a Gaussian error law are used to model scalar coupling measurements. By applying Bayes' theorem, we obtain a probability distribution for all unknowns, i.e., the torsion angles, the Karplus parameters, and the standard deviation of the Gaussian. We infer all these unknowns from scalar coupling data using Markov chain Monte Carlo sampling and analytically derive a probability distribution that only involves the torsion angles.
authors.affiliation: Unité de Bioinformatique Structurale, Institut Pasteur, CNRS URA 2185, Paris, France. habeck@pasteur@.fr
authors.forename: Michael
authors.initials: M
authors.lastname: Habeck
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Rieping
authors.forename: Michael
authors.initials: M
authors.lastname: Nilges
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D008390:Markov Chains; D008958:Models, Molecular; D019906:Nuclear Magnetic Resonance, Biomolecular / Q000379:methods*; D010455:Peptides / Q000737:chemistry*
chemical_list: D010455:Peptides