https://github.com/drorlab/pensa
PENSA - a collection of python methods for exploratory analysis and comparison of biomolecular conformational ensembles.
Science Score: 46.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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✓DOI references
Found 9 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org, zenodo.org -
✓Committers with academic emails
6 of 14 committers (42.9%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (8.7%) to scientific vocabulary
Keywords
Repository
PENSA - a collection of python methods for exploratory analysis and comparison of biomolecular conformational ensembles.
Basic Info
Statistics
- Stars: 142
- Watchers: 14
- Forks: 21
- Open Issues: 4
- Releases: 12
Topics
Metadata Files
README.md
PENSA - Python Ensemble Analysis
A collection of Python methods for exploratory analysis and comparison of biomolecular conformational ensembles, e.g., from molecular dynamics simulations. All functionality is available as a Python package.
To get started, see the documentation which includes a tutorial for the PENSA library, or read our article or the corresponding preprint.
If you would like to contribute, check out our contribution guidelines and our to-do list.
Functionality
With PENSA, you can (currently): - compare structural ensembles of biomolecules (proteins, DNA or RNA) via the relative entropy of their features or statistical tests and visualize deviations on a reference structure. - project several ensembles on a joint reduced representation using principal component analysis (PCA) or time-lagged independent component analysis (tICA) and sort the structures along the obtained components. - cluster structures across ensembles via k-means or regular-space clustering and write out the resulting clusters as trajectories. - trace allosteric information flow through a protein using state-specific information analysis methods.
Biomolecules can be featurized using backbone torsions, sidechain torsions, or arbitrary distances (e.g., between all backbone C-alpha atoms). We also provide density-based methods to featurize water and ion pockets as well as a featurizer for hydrogen bonds. The library is modular so you can easily write your own feature reader.
PENSA also includes trajectory processing tools based on MDAnalysis and plotting functions using Matplotlib.
Documentation
PENSA's documentation pages are here, where you find installation instructions, API documentation, and a tutorial.
Example Scripts
For the most common applications, example Python scripts are provided. We show how to run the example scripts in a short separate tutorial.
Citation
General citation, representing the "concept" of the software:
Martin Vögele, Neil Thomson, Sang Truong, Jasper McAvity. (2021). PENSA. Zenodo. http://doi.org/10.5281/zenodo.4362136
To get the citation and DOI for a particular version, see Zenodo.
Please also consider citing our our article:
Systematic Analysis of Biomolecular Conformational Ensembles with PENSA
M. Vögele, N. J. Thomson, S. T. Truong, J. McAvity, U. Zachariae, R. O. Dror
J. Chem. Phys. 162, 014101 (2025)
If you lack a JCP subscription, you can read the corresponding preprint.
Acknowledgments
Contributors
Martin Vögele, Neil Thomson, Sang Truong, Jasper McAvity
Beta-Testers
Alexander Powers, Lukas Stelzl, Nicole Ong, Eleanore Ocana, Emma Andrick, Callum Ives, Bu Tran, and Luca Morlok
Funding & Support
This project was started by Martin Vögele at Stanford University, supported by an EMBO long-term fellowship (ALTF 235-2019), as part of the INCITE computing project 'Enabling the Design of Drugs that Achieve Good Effects Without Bad Ones' (BIP152). Neil Thomson was supported by a BBSRC EASTBIO PhD studentship and Jasper McAvity by the Stanford Computer Science department via the CURIS program. Stanford University, the Stanford Research Computing Facility, and the University of Dundee provided additional computational resources and support that contributed to these research results.
Owner
- Name: Dror Lab
- Login: drorlab
- Kind: organization
- Location: Stanford, CA
- Repositories: 13
- Profile: https://github.com/drorlab
Ron Dror's computational biology laboratory at Stanford University
GitHub Events
Total
- Create event: 7
- Issues event: 16
- Release event: 1
- Watch event: 58
- Delete event: 6
- Issue comment event: 24
- Push event: 30
- Pull request event: 9
- Fork event: 4
Last Year
- Create event: 7
- Issues event: 16
- Release event: 1
- Watch event: 58
- Delete event: 6
- Issue comment event: 24
- Push event: 30
- Pull request event: 9
- Fork event: 4
Committers
Last synced: about 3 years ago
All Time
- Total Commits: 771
- Total Committers: 14
- Avg Commits per committer: 55.071
- Development Distribution Score (DDS): 0.556
Top Committers
| Name | Commits | |
|---|---|---|
| martinvoegele | m****9@g****m | 342 |
| neilsyt | n****n@d****k | 209 |
| Martin Vögele | m****e@u****m | 182 |
| Sang Truong | s****1@d****u | 10 |
| Sang T. Truong | s****g@s****u | 9 |
| Jasper McAvity | j****y@s****u | 7 |
| Neil J Thomson | 5****n@u****m | 3 |
| Jasper McAvity | j****y@g****m | 2 |
| Sang T. Truong | s****g@c****u | 2 |
| Jasper McAvity | 6****y@u****m | 1 |
| Jasper J McAvity | j****y@s****u | 1 |
| Jasper J McAvity | j****y@s****t | 1 |
| Ikko Ashimine | e****r@g****m | 1 |
| Michael Ward | m****9@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 29
- Total pull requests: 48
- Average time to close issues: 4 months
- Average time to close pull requests: 4 days
- Total issue authors: 14
- Total pull request authors: 8
- Average comments per issue: 1.17
- Average comments per pull request: 0.04
- Merged pull requests: 47
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 8
- Pull requests: 7
- Average time to close issues: about 20 hours
- Average time to close pull requests: less than a minute
- Issue authors: 7
- Pull request authors: 1
- Average comments per issue: 2.25
- Average comments per pull request: 0.0
- Merged pull requests: 7
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- martinvoegele (12)
- NeilJ-Thomson (4)
- piia600 (2)
- sangttruong (2)
- ShubhamM96 (1)
- izaguirr (1)
- LMorlok (1)
- MauricioMartil (1)
- H-EKE (1)
- erolic (1)
- chyannn06 (1)
- lucyraven (1)
- Nithishwer (1)
- orbeckst (1)
Pull Request Authors
- martinvoegele (32)
- sangttruong (9)
- NeilJ-Thomson (5)
- martinakaduc (2)
- LMorlok (2)
- jmcavity (1)
- eltociear (1)
- Mickdub (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 2
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Total downloads:
- pypi 61 last-month
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Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 1
(may contain duplicates) - Total versions: 31
- Total maintainers: 1
proxy.golang.org: github.com/drorlab/pensa
- Documentation: https://pkg.go.dev/github.com/drorlab/pensa#section-documentation
- License: mit
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Latest release: v0.6.0
published about 1 year ago
Rankings
pypi.org: pensa
exploratory analysis and comparison of biomolecular conformational ensembles.
- Documentation: https://pensa.readthedocs.io/en/latest/
- License: MIT
-
Latest release: 0.6.0
published about 1 year ago
Rankings
Maintainers (1)
Dependencies
- MDAnalysis *
- Sphinx ==3.1.2
- biotite *
- cython *
- matplotlib *
- mdshare *
- mdtraj ==1.9.3
- numpy *
- pensa *
- pyemma *
- scipy >=1.2
- sphinx_rtd_theme ==0.5.1
- sphinxcontrib-applehelp ==1.0.2
- sphinxcontrib-devhelp ==1.0.2
- sphinxcontrib-htmlhelp ==1.0.3
- sphinxcontrib-jsmath ==1.0.1
- sphinxcontrib-qthelp ==1.0.3
- sphinxcontrib-serializinghtml ==1.1.4
- MDAnalysis *
- biotite *
- gpcrmining *
- matplotlib *
- mdshare *
- mdtraj ==1.9.3
- numpy *
- pyemma *
- scipy >=1.2
- actions/first-interaction v1 composite
- actions/checkout v2 composite
- conda-incubator/setup-miniconda v2 composite