  We developed a multiscale approach (MultiSCAAL) that integrates the potential
of mean force (PMF) obtained from all-atomistic molecular dynamics simulations
with a knowledge-based energy function for coarse-grained molecular simulations
in better exploring the energy landscape of a small protein under chemical
interference such as chemical denaturation. An excessive amount of water
molecules in all-atomistic molecular dynamics simulations often negatively
impacts the sampling efficiency of some advanced sampling techniques such as
the replica exchange method and it makes the investigation of chemical
interferences on protein dynamics difficult. Thus, there is a need to develop
an effective strategy that focuses on sampling structural changes in protein
conformations rather than solvent molecule fluctuations. In this work, we
address this issue by devising a multiscale simulation scheme (MultiSCAAL) that
bridges the gap between all-atomistic molecular dynamics simulation and
coarse-grained molecular simulation. The two key features of this scheme are
the Boltzmann inversion and a protein atomistic reconstruction method we
previously developed (SCAAL). Using MultiSCAAL, we were able to enhance the
sampling efficiency of proteins solvated by explicit water molecules. Our
method has been tested on the folding energy landscape of a small protein
Trp-cage with explicit solvent under 8M urea using both the all-atomistic
replica exchange molecular dynamics (AA-REMD) and MultiSCAAL. We compared
computational analyses on ensemble conformations of Trp-cage with its available
experimental NOE distances. The analysis demonstrated that conformations
explored by MultiSCAAL better agree with the ones probed in the experiments
because it can effectively capture the changes in side chain orientations that
can flip out of the hydrophobic pocket in the presence of urea and water
molecules.
