.png){kind=icon}
.png){kind=icon}
Return
Machine-Learned Molecular Surface and Its Application to Implicit Solvent Simulations
.png){kind=icon}
OA
AI
DOI：10.1021/acs.jctc.1c00492.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Implicit solvent models, such as Poisson-Boltz-mann 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.
Keywords:
PROTEIN-PROTEIN INTERACTIONS
POISSON-BOLTZMANN METHODS
RESIDUE CONTACTS
PREDICTION
VISUALIZATION
CALCULATE
ALGORITHM
ACCURACY
PACKING
SYSTEMS
Journal
IF:
5.5
Papers:
1.1W
Citations:
5.4W
