title: Molecular dynamics in the endgame of protein structure prediction.
abstract: In order adequately to sample conformational space, methods for protein structure prediction make necessary simplifications that also prevent them from being as accurate as desired. Thus, the idea of feeding them, hierarchically, into a more accurate method that samples less effectively was introduced a decade ago but has not met with more than limited success in a few isolated instances. Ideally, the final stages should be able to identify the native state, show a good correlation with native similarity in order to add value to the selection process, and refine the structures even further. In this work, we explore the possibility of using state-of-the-art explicit solvent molecular dynamics and implicit solvent free energy calculations to accomplish all three of those objectives on 12 small, single-domain proteins, four each of alpha, beta and mixed topologies. We find that this approach is very successful in ranking the native and also enhances the structure selection of predictions generated from the Rosetta method.
authors.affiliation: Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA 94143, USA. mathew.lee@lionbioscience.com
authors.forename: M R
authors.initials: MR
authors.lastname: Lee
authors.forename: J
authors.initials: J
authors.lastname: Tsai
authors.forename: D
authors.initials: D
authors.lastname: Baker
authors.forename: P A
authors.initials: PA
authors.lastname: Kollman
mesh_terms: D001331:Automation; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D008958:Models, Molecular; D019906:Nuclear Magnetic Resonance, Biomolecular; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D012984:Software; D012997:Solvents; D055672:Static Electricity; D013816:Thermodynamics
chemical_list: D011506:Proteins; D012997:Solvents