msmbuilder

:building_construction: Statistical models for biomolecular dynamics :building_construction:

https://github.com/msmbuilder/msmbuilder

Science Score: 20.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
  • Academic publication links
    Links to: arxiv.org, acs.org
  • Committers with academic emails
    15 of 35 committers (42.9%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (15.0%) to scientific vocabulary

Keywords

analysis clustering dimensionality-reduction feature-extraction hmm markov-model molecular-dynamics msmbuilder pca python tica

Keywords from Contributors

pretty-logo cross-validation hyperparameter-optimization drug-discovery quantum-chemistry optimizing-compiler materials-science biology bayesian-methods hidden-markov-model
Last synced: 6 months ago · JSON representation

Repository

:building_construction: Statistical models for biomolecular dynamics :building_construction:

Basic Info
  • Host: GitHub
  • Owner: msmbuilder
  • License: lgpl-2.1
  • Language: Python
  • Default Branch: master
  • Homepage: http://msmbuilder.org
  • Size: 6.91 MB
Statistics
  • Stars: 158
  • Watchers: 24
  • Forks: 95
  • Open Issues: 103
  • Releases: 0
Archived
Topics
analysis clustering dimensionality-reduction feature-extraction hmm markov-model molecular-dynamics msmbuilder pca python tica
Created over 12 years ago · Last pushed about 5 years ago
Metadata Files
Readme Contributing License

README.md

MSMBuilder

Build Status PyPi version License Documentation

MSMBuilder is a python package which implements a series of statistical models for high-dimensional time-series. It is particularly focused on the analysis of atomistic simulations of biomolecular dynamics. For example, MSMBuilder has been used to model protein folding and conformational change from molecular dynamics (MD) simulations. MSMBuilder is available under the LGPL (v2.1 or later).

Capabilities include:

  • Feature extraction into dihedrals, contact maps, and more
  • Geometric clustering with a variety of algorithms.
  • Dimensionality reduction using time-structure independent component analysis (tICA) and principal component analysis (PCA).
  • Markov state model (MSM) construction
  • Rate-matrix MSM construction
  • Hidden markov model (HMM) construction
  • Timescale and transition path analysis.

Check out the documentation at msmbuilder.org and join the mailing list. For a broader overview of MSMBuilder, take a look at our slide deck.

Installation

The preferred installation mechanism for msmbuilder is with conda:

bash $ conda install -c omnia msmbuilder

If you don't have conda, or are new to scientific python, we recommend that you download the Anaconda scientific python distribution.

Workflow

An example workflow might be as follows:

  1. Set up a system for molecular dynamics, and run one or more simulations for as long as you can on as many CPUs or GPUs as you have access to. There are a lot of great software packages for running MD, e.g OpenMM, Gromacs, Amber, CHARMM, and many others. MSMBuilder is not one of them.

  2. Transform your MD coordinates into an appropriate set of features.

  3. Perform some sort of dimensionality reduction with tICA or PCA. Reduce your data into discrete states by using clustering.

  4. Fit an MSM, rate matrix MSM, or HMM. Perform model selection using cross-validation with the generalized matrix Rayleigh quotient

Owner

  • Name: MSMBuilder
  • Login: msmbuilder
  • Kind: organization
  • Email: msmbuilder-user@lists.stanford.edu

Statistical models for biomolecular dynamics

GitHub Events

Total
  • Watch event: 5
Last Year
  • Watch event: 5

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 2,446
  • Total Committers: 35
  • Avg Commits per committer: 69.886
  • Development Distribution Score (DDS): 0.622
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Robert McGibbon r****o@g****m 924
Matthew Harrigan h****n@s****u 584
Bharath Ramsundar b****r@g****m 207
kyleabeauchamp k****p@g****m 140
Carlos Hernandez c****h@s****u 132
Brooke Husic b****c@s****u 125
Mohammad Muneeb Sultan m****n@g****m 74
Christian Schwantes s****r@s****u 64
msultan m****n@s****u 40
Carlos Hernández c****z@u****m 34
Peter Eastman p****n@s****u 29
Patrick Riley p****r@g****m 10
Stephen Liu s****9@g****m 10
Jade Shi j****i@v****u 8
Jade Shi j****i@s****u 8
Nate Stanley n****y@g****m 7
brookehus b****c@g****m 7
Juan Eiros j****z@g****m 6
Robert Arbon r****n@g****m 6
skearnes k****s@s****u 4
Joshua L. Adelman j****n@g****m 4
Nate Stanley d****e@u****m 3
Hannah Wayment-Steele h****1@s****u 3
Steven Kearnes s****s@g****m 2
Unknown r****n@b****k 2
Sunhwan Jo s****j@g****m 2
Mohammad Sultan m****b@v****u 2
gkiss k****t@g****m 2
Brooke Husic b****s@u****m 1
Brooke Husic b****c@v****u 1
and 5 more...

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 79
  • Total pull requests: 21
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 2 months
  • Total issue authors: 43
  • Total pull request authors: 9
  • Average comments per issue: 2.87
  • Average comments per pull request: 0.57
  • Merged pull requests: 17
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • orthonalmatrix (9)
  • sbhakat (6)
  • jeiros (6)
  • girodat (4)
  • ghost (4)
  • Nidane (4)
  • AlirezaTafazzol (3)
  • cxhernandez (3)
  • momarzouksobeh (3)
  • Avon07 (2)
  • brookehus (2)
  • asgharrazavi (2)
  • vshiv18 (1)
  • oni-sama14 (1)
  • ahy3nz (1)
Pull Request Authors
  • cxhernandez (6)
  • brookehus (4)
  • nhstanley (3)
  • RobertArbon (2)
  • rmcgibbo (2)
  • sunhwan (1)
  • koileee (1)
  • HWaymentSteele (1)
  • jeiros (1)
Top Labels
Issue Labels
docs (1)
Pull Request Labels
bug (2) docs (1)

Packages

  • Total packages: 4
  • Total downloads:
    • pypi 189 last-month
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 11
    (may contain duplicates)
  • Total versions: 26
  • Total maintainers: 5
pypi.org: msmbuilder

MSMBuilder: Statistical models for Biomolecular Dynamics

  • Versions: 15
  • Dependent Packages: 0
  • Dependent Repositories: 9
  • Downloads: 142 Last month
Rankings
Forks count: 4.6%
Dependent repos count: 4.9%
Stargazers count: 5.9%
Average: 7.8%
Dependent packages count: 10.0%
Downloads: 13.5%
Last synced: 6 months ago
pypi.org: testmsm

MSMBuilder: Statistical models for Biomolecular Dynamics

  • Versions: 6
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 13 Last month
Rankings
Forks count: 4.7%
Stargazers count: 5.9%
Dependent packages count: 10.0%
Average: 18.1%
Dependent repos count: 21.7%
Downloads: 48.3%
Maintainers (1)
Last synced: 6 months ago
pypi.org: msmbuilder2022

MSMBuilder: Statistical models for Biomolecular Dynamics

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 34 Last month
Rankings
Forks count: 4.7%
Stargazers count: 5.9%
Dependent packages count: 9.1%
Average: 22.0%
Dependent repos count: 68.3%
Maintainers (1)
Last synced: 6 months ago
conda-forge.org: msmbuilder

MSMBuilder is an application and python library. It builds statistical models for high-dimensional time-series. The particular focus of the package is on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change.

  • Versions: 3
  • Dependent Packages: 0
  • Dependent Repositories: 1
Rankings
Forks count: 18.7%
Dependent repos count: 24.3%
Stargazers count: 30.1%
Average: 31.2%
Dependent packages count: 51.6%
Last synced: 6 months ago

Dependencies

docs/requirements.txt pypi
  • jinja2 *
  • jupyter *
  • matplotlib *
  • msmb_data *
  • msmexplorer *
  • nbconvert *
  • notebook *
  • numpydoc *
  • openmm *
  • pyparsing *