title: Bayesian inference of conformational state populations from computational models and sparse experimental observables.
abstract: We present a Bayesian inference approach to estimating conformational state populations from a combination of molecular modeling and sparse experimental data. Unlike alternative approaches, our method is designed for use with small molecules and emphasizes high-resolution structural models, using inferential structure determination with reference potentials, and Markov Chain Monte Carlo to sample the posterior distribution of conformational states. As an application of the method, we determine solution-state conformational populations of the 14-membered macrocycle cineromycin B, using a combination of previously published sparse Nuclear Magnetic Resonance (NMR) observables and replica-exchange molecular dynamic/Quantum Mechanical (QM)-refined conformational ensembles. Our results agree better with experimental data compared to previous modeling efforts. Bayes factors are calculated to quantify the consistency of computational modeling with experiment, and the relative importance of reference potentials and other model parameters.
authors.affiliation: Department of Chemistry, Temple University, Philadelphia, Pennsylvania.
authors.forename: Vincent A
authors.initials: VA
authors.lastname: Voelz
authors.forename: Guangfeng
authors.initials: G
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D007783:Lactones / Q000737:chemistry*; D047028:Macrocyclic Compounds / Q000737:chemistry*; D018942:Macrolides / Q000737:chemistry*; D009682:Magnetic Resonance Spectroscopy; D008968:Molecular Conformation; D056004:Molecular Dynamics Simulation; D009010:Monte Carlo Method; D011789:Quantum Theory
chemical_list: D007783:Lactones; D047028:Macrocyclic Compounds; D018942:Macrolides
keywords: Bayesian inference; NMR spectroscopy; molecular dynamics; quantum chemistry; structure determination