Project ID: plumID:25.012
Name: A Machine Learning-Driven, Probability-Based Approach to Enzyme Catalysis
Archive: https://github.com/sudipdas789/Committor_Amylase/raw/main/Committor_Amylase_PLUMED_NEST.zip
Category: bio
Keywords: enzyme catalysis, transition state, structure-activity relationship, free energy surface, reaction mechanism, water, alpha-amylase, sugar, QM/MM MD, OPES, committor function, machine learning
PLUMED version: 2.9
Contributor: Sudip Das
Submitted on: 15 May 2025
Publication: S. Das, U. Raucci, E. Trizio, P. Kang, R. P. P. Neves, M. J. Ramos, M. Parrinello, A Machine Learning-Driven, Probability-Based Approach to Enzyme Catalysis. ACS Catalysis, 9785–9792 (2025)
PLUMED input files
| File | Compatible with |
|---|---|
| OPES_Enhanced_Sampling_with_Committor/plumed.dat |    |
| …_with_Committor/plumed_distances_49descriptors.dat |   |
| …_with_Committor/plumed_positions_49descriptors.dat | |
Last tested: 29 May 2025, 06:50:34
Project description and instructions
This egg contains the input files for replicating committor training and enhanced sampling OPES simulations with a smoother version of the committor as a CV for calculating FES, reaction mechanisms and TS ensembles for alpha-amylase enzyme catalyzing a sugar substrate. The CV was trained with Pytorch version 1.8 and the simulations were run with GROMACS 2021.5, CP2K 9.1, and PLUMED 2.9.
Click here to open manual pages for actions used in this project.
Submission history
[v1] 15 May 2025: original submission
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