basicrta

Bayesian nonparametric inference of ligand binding kinetics from molecular dynamics simulations.

https://github.com/becksteinlab/basicrta

Science Score: 75.0%

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Keywords

bayesian-inference lipids md mdanalysis moleculardynamics python

Last synced: 6 months ago · JSON representation ·

Repository

Bayesian nonparametric inference of ligand binding kinetics from molecular dynamics simulations.

Basic Info

Host: GitHub
Owner: Becksteinlab
License: gpl-3.0
Language: Python
Default Branch: main
Homepage: https://basicrta.readthedocs.io
Size: 33.1 MB

Statistics

Stars: 0
Watchers: 1
Forks: 2
Open Issues: 10
Releases: 8

Topics

bayesian-inference lipids md mdanalysis moleculardynamics python

Created about 2 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Citation Authors

Bayesian Single-Cutoff Residence Time Analysis (basicrta)

A package to extract binding kinetics from molecular dynamics simulations based on Sexton (2025) [^1].

[^1]: Sexton, R.; Fazel, M.; Schweiger, M.; Pressé, S.; Beckstein, O. Bayesian Nonparametric Analysis of Residence Times for Protein-Lipid Interactions in Molecular Dynamics Simulations. Journal of Chemical Theory and Computation 2025 21 (8), 4203-4220 DOI: 10.1021/acs.jctc.4c01522 <http://doi.org/10.1021/acs.jctc.4c01522>_

basicrta is bound by a Code of Conduct.

Installation

To build basicrta from source, we highly recommend using virtual environments. If possible, we strongly recommend that you use Anaconda as your package manager. Below we provide instructions both for conda and for pip.

With conda

Ensure that you have conda installed.

Create a virtual environment and activate it:

conda create --name basicrta conda activate basicrta

Install the development and documentation dependencies:

conda env update --name basicrta --file devtools/conda-envs/test_env.yaml conda env update --name basicrta --file docs/requirements.yaml

Build this package from source:

pip install -e .

If you want to update your dependencies (which can be risky!), run:

conda update --all

And when you are finished, you can exit the virtual environment with:

conda deactivate

With pip

To build the package from source, run:

pip install .

If you want to create a development environment, install the dependencies required for tests and docs with:

pip install ".[test,doc]"

Copyright

The basicrta source code is hosted at https://github.com/becksteinlab/basicrta and is available under the GNU General Public License, version 3 (see the file LICENSE).

Acknowledgements

Project based on the MDAnalysis Cookiecutter version 0.1. Please cite MDAnalysis when using basicrta in published work.

Owner

Name: Becksteinlab
Login: Becksteinlab
Kind: organization
Email: obeckste@asu.edu
Location: Tempe, AZ

Website: https://becksteinlab.physics.asu.edu
Repositories: 56
Profile: https://github.com/Becksteinlab

Computational Biophysics at Arizona State University

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: "basicrta: Bayesian Single-cutoff Residence Time Analysis"
abstract: basicrta is an open-source Python package developed for the
  analysis of binding events in Molecular Dynamics (MD) simulations. Basicrta
  uses an MD trajectory to collect a set of binding contact durations (residence
  times) using a user-specified cutoff between any two atom groups.  Using an
  exponential mixture model and Markov Chain Monto Carlo simuulation, binding
  events are assigned to each of the mixture components with an associated
  probability. Such an analysis characterizes binding processes at different
  timescales (quantified by their kinetic off-rate) and assigns to each trajectory
  frame a probability of belonging to a specific process. In this way, we classify
  trajectory frames in an unsupervised manner and obtain, for example, different
  binding poses or molecular densities based on the timescale of the process.  The
  nonparametric Bayesian analysis allows us to connect the coarse binding time
  series data to the underlying molecular picture and, thus, not only infers
  accurate binding kinetics with error distributions from MD simulations but also
  describes molecular events responsible for the broad range of kinetic rates.
message: >-
  If you use this software, please cite it using the
  preferred citation (JCTC DOI 10.1021/acs.jctc.4c01522) 
  together with any other references.  
authors:
  - given-names: Ricky
    family-names: Sexton
    email: rsexton2@asu.edu
    orcid: 'https://orcid.org/0009-0007-0599-5958'
    affiliation: Arizona State University
  - given-names: Mohamadreza
    family-names: Fazel
    orcid: 'https://orcid.org/0000-0002-6215-1336'
    affiliation: Arizona State University
  - given-names: Maxwell
    family-names: Schweiger
    affiliation: Arizona State University
    orcid: 'https://orcid.org/0000-0002-0795-9826'
  - given-names: Steve
    family-names: Pressé
    email: spresse@asu.edu
    affiliation: Arizona State University
    orcid: 'https://orcid.org/0000-0002-5408-0718'
  - given-names: Oliver
    family-names: Beckstein
    email: obeckste@asu.edu
    affiliation: Arizona State University
    orcid: 'https://orcid.org/0000-0003-1340-0831'
type: software
preferred-citation:
  authors:
    - given-names: Ricky
      family-names: Sexton
      email: rsexton2@asu.edu
      orcid: 'https://orcid.org/0009-0007-0599-5958'
      affiliation: Arizona State University
    - given-names: Mohamadreza
      family-names: Fazel
      orcid: 'https://orcid.org/0000-0002-6215-1336'
      affiliation: Arizona State University
    - given-names: Maxwell
      family-names: Schweiger
      affiliation: Arizona State University
      orcid: 'https://orcid.org/0000-0002-0795-9826'
    - given-names: Steve
      family-names: Pressé
      email: spresse@asu.edu
      affiliation: Arizona State University
      orcid: 'https://orcid.org/0000-0002-5408-0718'
    - given-names: Oliver
      family-names: Beckstein
      email: obeckste@asu.edu
      affiliation: Arizona State University
      orcid: 'https://orcid.org/0000-0003-1340-0831'
  type: 'article'
  year: 2025
  journal: 'Journal of Chemical Theory and Computation'
  doi: '10.1021/acs.jctc.4c01522'
  pages: '4203-4220'
  volume: '21'
  number: '8'
identifiers:
  - type: doi
    value: 10.1021/acs.jctc.4c01522
    description: JCTC Publication
repository-code: 'https://github.com/Becksteinlab/basicrta'
abstract: >-
  Bayesian nonparametric analysis of binding event times in
  MD simulations.
license: GPL-3.0
commit: 974cd92c102a90e587421fcb43a12d8b6e26e3e0
version: '1.0'
date-released: '2025-05-24'

GitHub Events

Total

Create event: 30
Release event: 6
Issues event: 30
Delete event: 9
Member event: 1
Issue comment event: 49
Push event: 165
Pull request review comment event: 33
Pull request review event: 34
Pull request event: 35
Fork event: 2

Last Year

Create event: 30
Release event: 6
Issues event: 30
Delete event: 9
Member event: 1
Issue comment event: 49
Push event: 165
Pull request review comment event: 33
Pull request review event: 34
Pull request event: 35
Fork event: 2

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 23
Total pull requests: 21
Average time to close issues: 10 days
Average time to close pull requests: 1 day
Total issue authors: 2
Total pull request authors: 5
Average comments per issue: 1.13
Average comments per pull request: 0.86
Merged pull requests: 14
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 22
Pull requests: 20
Average time to close issues: 11 days
Average time to close pull requests: 1 day
Issue authors: 2
Pull request authors: 5
Average comments per issue: 1.14
Average comments per pull request: 0.9
Merged pull requests: 13
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

orbeckst (16)
rjoshi44 (7)

Pull Request Authors

orbeckst (14)
rsexton2 (3)
ianmkenney (2)
rjoshi44 (2)

Top Labels

Issue Labels

bug (4) enhancement (2) help wanted (1) documentation (1)

Pull Request Labels

Packages

Total packages: 1
Total downloads:
- pypi 41 last-month

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 8
Total maintainers: 2

pypi.org: basicrta

A package to extract binding kinetics from molecular dynamics simulations

Documentation: https://basicrta.readthedocs.io/
License: gpl-3.0
Latest release: 1.1.2
published 7 months ago

Versions: 8
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 41 Last month

Rankings

Dependent packages count: 10.0%

Average: 33.3%

Dependent repos count: 56.5%

Maintainers (2)

Oliver.Beckstein rsexton2