Structure refinement of protein model decoys requires accurate side-chain placement.
In this study, the application of temperature-based replica-exchange (T-ReX) simulations for structure refinement of decoys taken from the I-TASSER dataset was examined. A set of eight nonredundant proteins was investigated using self-guided Langevin dynamics (SGLD) with a generalized Born implicit solvent model to sample conformational space. For two of the protein test cases, a comparison of the SGLD/T-ReX method with that of a hybrid explicit/implicit solvent molecular dynamics T-ReX simulation model is provided. Additionally, the effect of side-chain placement among the starting decoy structures, using alternative rotamer conformations taken from the SCWRL4 modeling program, was investigated. The simulation results showed that, despite having near-native backbone conformations among the starting decoys, the determinant of their refinement is side-chain packing to a level that satisfies a minimum threshold of native contacts to allow efficient excursions toward the downhill refinement regime on the energy landscape. By repacking using SCWRL4 and by applying the RWplus statistical potential for structure identification, the SGLD/T-ReX simulations achieved refinement to an average of 38% increase in the number of native contacts relative to the original I-TASSER decoy sets and a 25% reduction in values of C(α) root-mean-square deviation. The hybrid model succeeded in obtaining a sharper funnel to low-energy states for a modeled target than the implicit solvent SGLD model; yet, structure identification remained roughly the same. Without meeting a threshold of near-native packing of side chains, the T-ReX simulations degrade the accuracy of the decoys, and subsequently, refinement becomes tantamount to the protein folding problem.
Department of Cell Biology and Biochemistry, USAMRIID, Frederick, Maryland 21702, USA. molson@compbiophys.org
Mark A
MA
Olson
Michael S
MS
Lee
D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D030562:Databases, Protein; D008958:Models, Molecular; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D012984:Software; D012997:Solvents / Q000737:chemistry
D011506:Proteins; D012997:Solvents