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GLOW integrates Gaussian accelerated molecular dynamics (GaMD) and Deep Learning (DL) for free energy profiling of biomolecules. PyReweighting implements a list of commonly used reweighting methods, including (1) exponential average that reweights trajectory frames by the Boltzmann factor of the boost potential and then calculates the ensemble average for each bin, (2) Maclaurin series expansion that approximates the exponential Boltzmann factor, and (3) cumulant expansion that expresses the reweighting factor as summation of boost potential cumulants. Furthermore, the free energy profiles obtained from reweighting of the GaMD simulations allow us to identify distinct low energy states of the biomolecules and characterize the protein folding and ligand binding pathways quantitatively.Ī toolkit of python scripts 'PyReweighting' is provided for reweighting of aMD simulations. Without the need to set predefined reaction coordinates, GaMD enables unconstrained enhanced sampling of these biomolecules.

GaMD has been demonstrated on three biomolecular model systems: alanine dipeptide, chignolin folding and ligand binding to the T4-lysozyme. By constructing a boost potential that follows Gaussian distribution, accurate reweighting of the GaMD simulations is achieved using cumulant expansion to the second order. Gaussian Accelerated Molecular Dynamics (GaMD) is a biomolecular enhanced sampling method that works by adding a harmonic boost potential to smoothen the system potential energy surface. Gaussian Accelerated Molecular Dynamics (GaMD)

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