title: Sampling Enrichment toward Target Structures Using Hybrid Molecular Dynamics-Monte Carlo Simulations.
abstract: Sampling enrichment toward a target state, an analogue of the improvement of sampling efficiency (SE), is critical in both the refinement of protein structures and the generation of near-native structure ensembles for the exploration of structure-function relationships. We developed a hybrid molecular dynamics (MD)-Monte Carlo (MC) approach to enrich the sampling toward the target structures. In this approach, the higher SE is achieved by perturbing the conventional MD simulations with a MC structure-acceptance judgment, which is based on the coincidence degree of small angle x-ray scattering (SAXS) intensity profiles between the simulation structures and the target structure. We found that the hybrid simulations could significantly improve SE by making the top-ranked models much closer to the target structures both in the secondary and tertiary structures. Specifically, for the 20 mono-residue peptides, when the initial structures had the root-mean-squared deviation (RMSD) from the target structure smaller than 7 Å, the hybrid MD-MC simulations afforded, on average, 0.83 Å and 1.73 Å in RMSD closer to the target than the parallel MD simulations at 310K and 370K, respectively. Meanwhile, the average SE values are also increased by 13.2% and 15.7%. The enrichment of sampling becomes more significant when the target states are gradually detectable in the MD-MC simulations in comparison with the parallel MD simulations, and provide >200% improvement in SE. We also performed a test of the hybrid MD-MC approach in the real protein system, the results showed that the SE for 3 out of 5 real proteins are improved. Overall, this work presents an efficient way of utilizing solution SAXS to improve protein structure prediction and refinement, as well as the generation of near native structures for function annotation.
authors.affiliation: Key Laboratory of Synthetic Rubber & Laboratory of Advanced Power Sources, Changchun Institute of Applied Chemistry (CIAC), Chinese Academy of Sciences, Changchun, 130022, P. R. China.|University of Chinese Academy of Sciences, Beijing, 100049, China.
authors.forename: Kecheng
authors.initials: K
authors.lastname: Yang
authors.affiliation: Institute of Physics, Polish Academy of Sciences, Aleja Lotników 32/46, 02-668, Warsaw, Poland.
authors.forename: Bartosz
authors.initials: B
authors.lastname: Różycki
authors.affiliation: Key Laboratory of Synthetic Rubber & Laboratory of Advanced Power Sources, Changchun Institute of Applied Chemistry (CIAC), Chinese Academy of Sciences, Changchun, 130022, P. R. China.
authors.forename: Fengchao
authors.initials: F
authors.lastname: Cui
authors.affiliation: Key Laboratory of Synthetic Rubber & Laboratory of Advanced Power Sources, Changchun Institute of Applied Chemistry (CIAC), Chinese Academy of Sciences, Changchun, 130022, P. R. China.
authors.forename: Ce
authors.initials: C
authors.lastname: Shi
authors.affiliation: Key Laboratory of Synthetic Rubber & Laboratory of Advanced Power Sources, Changchun Institute of Applied Chemistry (CIAC), Chinese Academy of Sciences, Changchun, 130022, P. R. China.
authors.forename: Wenduo
authors.initials: W
authors.lastname: Chen
authors.affiliation: Key Laboratory of Synthetic Rubber & Laboratory of Advanced Power Sources, Changchun Institute of Applied Chemistry (CIAC), Chinese Academy of Sciences, Changchun, 130022, P. R. China.
authors.forename: Yunqi
authors.initials: Y
authors.lastname: Li
mesh_terms: D000818:Animals; D006801:Humans; D008958:Models, Molecular; D056004:Molecular Dynamics Simulation; D009010:Monte Carlo Method; D010446:Peptide Fragments / Q000737:chemistry*; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*
chemical_list: D010446:Peptide Fragments; D011506:Proteins