title: Generative probabilistic models extend the scope of inferential structure determination.
abstract: Conventional methods for protein structure determination from NMR data rely on the ad hoc combination of physical forcefields and experimental data, along with heuristic determination of free parameters such as weight of experimental data relative to a physical forcefield. Recently, a theoretically rigorous approach was developed which treats structure determination as a problem of Bayesian inference. In this case, the forcefields are brought in as a prior distribution in the form of a Boltzmann factor. Due to high computational cost, the approach has been only sparsely applied in practice. Here, we demonstrate that the use of generative probabilistic models instead of physical forcefields in the Bayesian formalism is not only conceptually attractive, but also improves precision and efficiency. Our results open new vistas for the use of sophisticated probabilistic models of biomolecular structure in structure determination from experimental data.
authors: Bioinformatics Center, University of Copenhagen, Department of Biology, Ole Maaløes Vej 5, DK-2200 Copenhagen N, Denmark. solsson@binf.ku.dk
authors: Simon
authors: S
authors: Olsson
authors: Wouter
authors: W
authors: Boomsma
authors: Jes
authors: J
authors: Frellsen
authors: Sandro
authors: S
authors: Bottaro
authors: Tim
authors: T
authors: Harder
authors: Jesper
authors: J
authors: Ferkinghoff-Borg
authors: Thomas
authors: T
authors: Hamelryck
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D004574:Electromagnetic Fields; D008958:Models, Molecular; D015233:Models, Statistical; D019906:Nuclear Magnetic Resonance, Biomolecular / Q000379:methods*; D011487:Protein Conformation; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D013696:Temperature
chemical_list: D011506:Proteins