Comparison between self‐guided Langevin dynamics and molecular dynamics simulations for structure refinement of protein loop conformations

Abstract

This article presents a comparative analysis of two replica-exchange simulation methods for the structure refinement of protein loop conformations, starting from low-resolution predictions. The methods are self-guided Langevin dynamics (SGLD) and molecular dynamics (MD) with a Nosé-Hoover thermostat. We investigated a small dataset of 8- and 12-residue loops, with the shorter loops placed initially from a coarse-grained lattice model and the longer loops from an enumeration assembly method (the Loopy program). The CHARMM22 + CMAP force field with a generalized Born implicit solvent model (molecular-surface parameterized GBSW2) was used to explore conformational space. We also assessed two empirical scoring methods to detect nativelike conformations from decoys: the all-atom distance-scaled ideal-gas reference state (DFIRE-AA) statistical potential and the Rosetta energy function. Among the eight-residue loop targets, SGLD out performed MD in all cases, with a median of 0.48 Å reduction in global root-mean-square deviation (RMSD) of the loop backbone coordinates from the native structure. Among the more challenging 12-residue loop targets, SGLD improved the prediction accuracy over MD by a median of 1.31 Å, representing a substantial improvement. The overall median RMSD for SGLD simulations of 12-residue loops was 0.91 Å, yielding refinement of a median 2.70 Å from initial loop placement. Results from DFIRE-AA and the Rosetta model applied to rescoring conformations failed to improve the overall detection calculated from the CHARMM force field. We illustrate the advantage of SGLD over the MD simulation model by presenting potential-energy landscapes for several loop predictions. Our results demonstrate that SGLD significantly outperforms traditional MD in the generation and populating of nativelike loop conformations and that the CHARMM force field performs comparably to other empirical force fields in identifying these conformations from the resulting ensembles.

Membrane insertion of fusion peptides from Ebola and Marburg viruses studied by replica‐exchange molecular dynamics simulations

“…The implicit solvent model implemented by Im and Brooks is the generalized Born smoothed switching function (GBSW) approximation . This solvent model is an efficient approximation of Poisson–Boltzmann theory for calculating solvation energies and has been successfully applied to a wide range of problems focused on modeling proteins and peptides . Other implicit membrane models have been developed (see, e.g., those noted in Ref.…”

Protein Structure Refinement through Structure Selection and Averaging from Molecular Dynamics Ensembles

“…16,19–21 Although successful examples of MD-based refinement have been reported in the past, 2,11,19–26 consistent success appears to be hindered by a combination of insufficient sampling, 11,27,28 force field inaccuracies, 20,29 and an inability to reliably identify refined structures that may be generated during the course of an MD simulation. 11,23,29–32 To address these issues, statistical potentials 21,33–35 and optimized force fields 20,36,37 have been used as well as effective sampling techniques such as replica-exchange 19,24,25,33 and self-guided Langevin dynamics 38 simulations. In some studies it was possible to generate improved structures by as much as 0.5 Å in root-mean-square deviation (RMSD) in one out of five models, 25,33 but reliable identification of a single refined structure remained difficult.…”

Application of replica exchange umbrella sampling to protein structure refinement of nontemplate models

“…Figure illustrates scatter plots of comparing the three potentials for structure identification taken from two of the targets. While our dataset is relatively limited, the trend is consistent of empirical‐based functions and their contrast with CHARMM22 . Among the overall results, the target 1cy5A showed the most significant difference among the potentials, where the force field yielded a noticeably worst f N than the top‐ranked starting decoy.…”