https://github.com/aspirincode/md-analysis-tools-scripts
Tools and scripts for analyses of molecular dynamics simulations
https://github.com/aspirincode/md-analysis-tools-scripts
Last synced: 3 months ago
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Tools and scripts for analyses of molecular dynamics simulations
- Host: GitHub
- URL: https://github.com/aspirincode/md-analysis-tools-scripts
- Owner: AspirinCode
- License: mit
- Created: 2019-10-20T13:36:26.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-10-18T15:00:10.000Z (over 6 years ago)
- Last Synced: 2025-01-29T07:30:30.963Z (about 1 year ago)
- Homepage:
- Size: 6.29 MB
- Stars: 8
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MD simulations set-up and data analysis
This repository contains tools and scripts for analyses of molecular dynamics simulations.
(An example of allosteric network analysis. Left, protein network visualized in PyMOL; Right, entropy transfer between different residues and statistices on each residue.)
## Topics
### System preparation:
- Model the missing loop
### Simulation configuration & submission:
- Configuration files for vanilla MD
- Configuration files for enhanced sampling MD
- Job submission
- Shell scripting to automate jobs
### Analyses of simulations
- Visualization in VMD & PyMOL
- Membrane-related analyses: hydrophobic thickness, acyl chain order parameter etc;
- Protein-related analyses: RMSD, RMSF, contact map, secondary structure analyses, allosteric network etc;
- Enhanced sampling-related anlysis: MBAR, WHAM, EXP, TI etc;
## Contact
If you have any questions, comments, or would like to report a bug, please file a Github issue or contact me at shaalltime@gmail.com.
## About author
Shasha Feng is currently a PhD student at Dr. Wonpil Im's group at Lehigh University. She did her undergraduate study at Peking University with Dr. Chen Song (membrane protein biophysics) and Dr. Peng Zou (voltage sensor engineering). The research interests focus on understanding the molecular interactions in biological systems, employing tools like MD simulations, statistics, and machine learning.