Supervised machine learning approach to molecular dynamics forecast of SARS-CoV-2 spike glycoproteins at varying temperatures.

ABSTRACT:

Abstract

Molecular dynamics (MD) simulations are a widely used technique in modeling complex nanoscale interactions of atoms and molecules. These simulations can provide detailed insight into how molecules behave under certain environmental conditions. This work explores a machine learning (ML) solution to predicting long-term properties of SARS-CoV-2 spike glycoproteins (S-protein) through the analysis of its nanosecond backbone RMSD (root-mean-square deviation) MD simulation data at varying temperatures. The simulation data were denoised with fast Fourier transforms. The performance of the models was measured by evaluating their mean squared error (MSE) accuracy scores in recurrent forecasts for long-term predictions. The models evaluated include k-nearest neighbors (kNN) regress

SUBMITTER: Liang D 

PROVIDER: S-EPMC7888691 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature