Machine-Learned Molecular Surface and Its Application to Implicit Solvent Simulations

Implicit
solvent models, such as Poisson–Boltzmann models,
play important roles in computational studies of biomolecules. A vital
step in almost all implicit solvent models is to determine the solvent–solute
interface, and the solvent excluded surface (SES) is the most widely
used interface definition in these models. However, classical algorithms
used for computing SES are geometry-based, so that they are neither
suitable for parallel implementations nor convenient for obtaining
surface derivatives. To address the limitations, we explored a machine
learning strategy to obtain a level set formulation for the SES. The
training process was conducted in three steps, eventually leading
to a model with over 95% agreement with the classical SES. Visualization
of tested molecular surfaces shows that the machine-learned SES overlaps
with the classical SES in almost all situations. Further analyses
show that the machine-learned SES is incredibly stable in terms of
rotational variation of tested molecules. Our timing analysis shows
that the machine-learned SES is roughly 2.5 times as efficient as
the classical SES routine implemented in Amber/PBSA on a tested central
processing unit (CPU) platform. We expect further performance gain
on massively parallel platforms such as graphics processing units
(GPUs) given the ease in converting the machine-learned SES to a parallel
procedure. We also implemented the machine-learned SES into the Amber/PBSA
program to study its performance on reaction field energy calculation.
The analysis shows that the two sets of reaction field energies are
highly consistent with a 1% deviation on average. Given its level
set formulation, we expect the machine-learned SES to be applied in
molecular simulations that require either surface derivatives or high
efficiency on parallel computing platforms.