BICePs - Bayesian Inference of Conformational Populations
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:
NMR chemical shifts (
J couplings (both small molecules and amino acids) (
Hydrogen--deuterium exchange (
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
NOTE: for pymbar, try:
$ pip install git+https://firstname.lastname@example.org
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.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Hashes for biceps-2.0b0.post0-py3-none-any.whl