title: A generative, probabilistic model of local protein structure.
abstract: Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state. Our method represents a significant theoretical and practical improvement over the widely used fragment assembly technique by avoiding the drawbacks associated with a discrete and nonprobabilistic approach.
authors.affiliation: Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen N, Denmark.
authors.forename: Wouter
authors.initials: W
authors.lastname: Boomsma
authors.forename: Kanti V
authors.initials: KV
authors.lastname: Mardia
authors.forename: Charles C
authors.initials: CC
authors.lastname: Taylor
authors.forename: Jesper
authors.initials: J
authors.lastname: Ferkinghoff-Borg
authors.forename: Anders
authors.initials: A
authors.lastname: Krogh
authors.forename: Thomas
authors.initials: T
authors.lastname: Hamelryck
mesh_terms: D020816:Amino Acid Motifs; D008958:Models, Molecular; D015233:Models, Statistical; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins