title: Monte Carlo sampling of near-native structures of proteins with applications.
abstract: Since a protein's dynamic fluctuation inside cells affects the protein's biological properties, we present a novel method to study the ensemble of near-native structures (NNS) of proteins, namely, the conformations that are very similar to the experimentally determined native structure. We show that this method enables us to (i) quantify the difficulty of predicting a protein's structure, (ii) choose appropriate simplified representations of protein structures, and (iii) assess the effectiveness of knowledge-based potential functions. We found that well-designed simple representations of protein structures are likely as accurate as those more complex ones for certain potential functions. We also found that the widely used contact potential functions stabilize NNS poorly, whereas potential functions incorporating local structure information significantly increase the stability of NNS.
authors.affiliation: Department of Statistics, Harvard University, Cambridge, Massachusetts, USA.
authors.forename: Jinfeng
authors.initials: J
authors.lastname: Zhang
authors.forename: Ming
authors.initials: M
authors.lastname: Lin
authors.forename: Rong
authors.initials: R
authors.lastname: Chen
authors.forename: Jie
authors.initials: J
authors.lastname: Liang
authors.forename: Jun S
authors.initials: JS
authors.lastname: Liu
mesh_terms: D003198:Computer Simulation; D009010:Monte Carlo Method; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry; D013816:Thermodynamics
chemical_list: D011506:Proteins