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Description
The dataset includes input files, simulation parameters, and analysis scripts used in Repulsive Scaling Replica Exchange Molecular Dynamics (RS-REMD) simulations to study protein–glycosaminoglycan (GAG) interactions. In this study, the RS-REMD method was applied for molecular docking of GAGs and carbohydrates to selected protein targets. Molecular Mechanics Generalized Born Surface Area (MM-GBSA) served as the scoring function, and a Fully Connected Neural Network (FCNN) model was subsequently trained using MM-GBSA energies and structural properties to predict the Root Mean Square Atom Type Deviation (RMSatd), a metric that quantifies structural similarity. The dataset includes structures selected based on MM-GBSA and FCNN-predicted RMSatd values, supporting binding site identification and energy evaluation. The provided Python scripts facilitate force field modification, RMSatd calculations, and machine learning model training and application.
Dataset file
hexmd5(md5(part1)+md5(part2)+...)-{parts_count} where a single part of the file is 512 MB in size.Example script for calculation:
https://github.com/antespi/s3md5
File details
- License:
-
open in new tab
CC BYAttribution - Software:
- VMD, python3, gromacs, gmx_MMPBSA
Details
- Year of publication:
- 2025
- Verification date:
- 2025-02-10
- Dataset language:
- English
- Fields of science:
-
- chemical sciences (Natural sciences)
- DOI:
- DOI ID 10.34808/v6dw-7x21 open in new tab
- Funding:
- Verified by:
- Gdańsk University of Technology
Keywords
- machine learning
- molecular dynamics
- molecular docking
- protein-ligand interactions
- glycosaminoglycans
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