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GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling
Do, Hung N. Wang, Jinan Bhattarai, Apurba Miao, Yinglong
Do, Hung N.
Wang, Jinan
Bhattarai, Apurba
Miao, Yinglong
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Abstract
We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and free energy profiling workflow (GLOW) to predict molecular determinants and map free energy landscapes of biomolecules. All-atom GaMD-enhanced sampling simulations are first performed on biomolecules of interest. Structural contact maps are then calculated from GaMD simulation frames and transformed into images for building DL models using a convolutional neural network. Important structural contacts are further determined from DL models of attention maps of the structural contact gradients, which allow us to identify the system reaction coordinates. Finally, free energy profiles are calculated for the selected reaction coordinates through energetic reweighting of the GaMD simulations. We have also successfully demonstrated GLOW for the characterization of activation and allosteric modulation of a G protein-coupled receptor, using the adenosine A1 receptor (A1AR) as a model system. GLOW findings are highly consistent with previous experimental and computational studies of the A1AR, while also providing further mechanistic insights into the receptor function. In summary, GLOW provides a systematic approach to mapping free energy landscapes of biomolecules. The GLOW workflow and its user manual can be downloaded at http://miaolab.org/GLOW.
Description
This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Theory and Computation, Copyright © 2022 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.jctc.1c01055.
Date
2022-02-24
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American Chemical Society
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Keywords
GLOW, Gaussian accelerated molecular dynamics (GaMD), Deep Learning (DL), Free energy, GPCR, Activation, Allosteric modulation
Citation
Do, H. N., Wang, J., Bhattarai, A., & Miao, Y. (2022). GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling. Journal of chemical theory and computation, 18(3), 1423–1436. https://doi.org/10.1021/acs.jctc.1c01055