GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules

no code implementations • 12 Jan 2022 • Mahdi Ghorbani, Samarjeet Prasad, Jeffery B. Klauda, Bernard R. Brooks

In this contribution, we combine VAMPNet and graph neural networks to generate an end-to-end framework to efficiently learn high-level dynamics and metastable states from the long-timescale molecular dynamics trajectories.

Graph Representation Learning molecular representation +1

Variational embedding of protein folding simulations using gaussian mixture variational autoencoders

no code implementations • 27 Aug 2021 • Mahdi Ghorbani, Samarjeet Prasad, Jeffery B. Klauda, Bernard R. Brooks

We show that GMVAE can learn a reduced representation of the free energy landscape of protein folding with highly separated clusters that correspond to the metastable states during folding.

Dimensionality Reduction Protein Folding

Best Practices for Alchemical Free Energy Calculations

1 code implementation • 7 Aug 2020 • Antonia S. J. S. Mey, Bryce Allen, Hannah E. Bruce Macdonald, John D. Chodera, Maximilian Kuhn, Julien Michel, David L. Mobley, Levi N. Naden, Samarjeet Prasad, Andrea Rizzi, Jenke Scheen, Michael R. Shirts, Gary Tresadern, Huafeng Xu

Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another.