Dataset Information

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) regression models, as well as GRU (gated recurrent unit) neural networks and LSTM (long short-term memory) autoencoder models. Results demonstrated that the kNN model achieved the greatest accuracy in forecasts with MSE scores over around 0.01 nm less than those of the GRU model and the LSTM autoencoder. Furthermore, it demonstrated that the kNN model accuracy increases with data size but can still forecast relatively well when trained on small amounts of data, having achieved MSE scores of around 0.02 nm when trained on 10,000 ns of simulation data. This study provides valuable information on the feasibility of accelerating the MD simulation process through training and predicting supervised ML models, which is particularly applicable in time-sensitive studies.

Graphic abstract

SARS-CoV-2 spike glycoprotein molecular dynamics simulation. Extraction and denoising of backbone RMSD data. Evaluation of k-nearest neighbors regression, GRU neural network, and LSTM autoencoder models in recurrent forecasting for long-term property predictions.

SUBMITTER: Liang D

PROVIDER: S-EPMC7888691 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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

Liang David D Song Meichen M Niu Ziyuan Z Zhang Peng P Rafailovich Miriam M Deng Yuefan Y

MRS advances 20210217 13

<h4>Abstract</h4>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 va ...[more]

PMID: 33619443

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Project description:Machine learning techniques including neural networks are popular tools for chemical, physical and materials applications searching for viable alternative methods in the analysis of structure and energetics of systems ranging from crystals to biomolecules. Efforts are less abundant for prediction of kinetics and dynamics. Here we explore the ability of three well established recurrent neural network architectures for reproducing and forecasting the energetics of a liquid solution of ethyl acetate containing a macromolecular polymer-lipid aggregate at ambient conditions. Data models from three recurrent neural networks, ERNN, LSTM and GRU, are trained and tested on half million points time series of the macromolecular aggregate potential energy and its interaction energy with the solvent obtained from molecular dynamics simulations. Our exhaustive analyses convey that the recurrent neural network architectures investigated generate data models that reproduce excellently the time series although their capability of yielding short or long term energetics forecasts with expected statistical distributions of the time points is limited. We propose an in silico protocol by extracting time patterns of the original series and utilizing these patterns to create an ensemble of artificial network models trained on an ensemble of time series seeded by the additional time patters. The energetics forecast improve, predicting a band of forecasted time series with a spread of values consistent with the molecular dynamics energy fluctuations span. Although the distribution of points from the band of energy forecasts is not optimal, the proposed in silico protocol provides useful estimates of the solvated macromolecular aggregate fate. Given the growing application of artificial networks in materials design, the data-based protocol presented here expands the realm of science areas where supervised machine learning serves as a decision making tool aiding the simulation practitioner to assess when long simulations are worth to be continued.

Dataset Information

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

Abstract

Graphic abstract

Publications

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

Similar Datasets

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