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BICePs

Project description

BICePs - Bayesian Inference of Conformational Populations

Documentation Status

The BICePs algorithm (Bayesian Inference of Conformational Populations) is a statistically rigorous Bayesian inference method to reconcile theoretical predictions of conformational state populations with sparse and/or noisy experimental measurements and objectively compare different models. Supported experimental observables include:

Citation DOI for Citing BICePs

Check our BICePs website for more details!

Please check out the theory of BICePs to learn more.

Installation (in progress)

BICePs supports Python 2.7 (see tag v1.0) or Python 3.4+ (v2.0 or greater) on Mac, Linux, and Windows.

Dependencies of BICePs

  • pymbar == 3.0.2
  • mdtraj >= 1.5.0
  • matplotlib >= 2.1.2
  • numpy >= 1.14.0
  • multiprocessing (works with Python versions 3.0-3.7)

NOTE: for pymbar, try: $ pip install git+https://github.com/choderalab/pymbar.git@3.0.2


View the workflow of BICePs.

BICePs is research software. If you make use of BICePs in scientific publications, please cite it.

To get started, see biceps/releases for the latest version of BICePs.

Project details


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