GLOW
GLOW: A workflow integrating GaMD and Deep Learning for free energy profiling
GLOW integrates Gaussian accelerated molecular dynamics (GaMD) and Deep Learning (DL) for free energy profiling of biomolecules. First, all-atom GaMD enhanced sampling simulations are performed on biomolecules of interest. Structural contact maps are then calculated from GaMD simulation frames and transformed into images for building DL models using convolutional neural network (CNN). Important structural contacts can be determined from DL models of saliency (attention) maps of the residue contact gradients, which allow for the identification of system reaction coordinates . Finally, free energy profiles of these reaction coordinates are calculated through energetic reweighting of GaMD simulations.
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Reference
Do HN, Wang J, Bhattarai A, and Miao Y (2021) 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. (Abstract | PDF)
~~~ Last updated: September 23, 2023 ~~~