Project ID: plumID:24.025
Name: Correlating Enzymatic Reactivity for Different Substrates using Transferable Data-Driven Collective Variables
Archive: https://github.com/sudipdas789/Transferable_DeepCVs/archive/refs/heads/main.zip
Category: bio
Keywords: enzymatic reactivity, k_cat, transfer learning, data-driven CVs, catalysis, ligand-binding modes, water, alpha-amylase, sugar, classical MD, OPES, machine learning, Deep TDA CV, path CV
PLUMED version: 2.9
Contributor: Sudip Das
Submitted on: 26 Oct 2024
Publication: S. Das, U. Raucci, R. P. P. Neves, M. J. Ramos, M. Parrinello, Correlating enzymatic reactivity for different substrates using transferable data-driven collective variables. Proceedings of the National Academy of Sciences. 121 (2024)
PLUMED input files
| File | Compatible with |
|---|---|
| …_pathCV_allcontacts1+torsion+Z_waterCV_align2z.dat |   |
| …_pathCV_allcontacts1+torsion+Z_waterCV_align2z.dat | |
| …_pathCV_allcontacts1+torsion+Z_waterCV_align2z.dat | |
Last tested: 23 Apr 2025, 10:10:50
Project description and instructions
This egg contains the input files for replicating the unbiased classical MD simulations and enhanced sampling OPES simulations using Deep TDA CVs, and path CV for alpha-amylase enzyme with four different sugar substrates. The path CV used in OPES simulations enhances the sampling (in the Deep TDA CVs’ space) of different substrate-binding modes within the enzyme’s active site. For more information about Deep TDA, we refer to plumID:21.028. The CVs were trained with Pytorch version 1.8 and the simulations were run with GROMACS 2021.5, and PLUMED 2.9.
Submission history
[v1] 26 Oct 2024: original submission
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