BICePs - Bayesian Inference of Conformational Populations

— BICePs with replica averaging (BICePs v3.0a)

- BICePs v2.0

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 nuclear Overhauser effect (NOE).

• NMR chemical shifts (HA,NH, CA and N).

• J couplings (both small molecules and amino acids) (J).

• Hydrogen--deuterium exchange (HDX).

Installation (BICePs v2.0)

We recommend that you install biceps via pip:

    $ pip install BICePs

Installation (BICePs v3.0a)

Please navigate to the BICePs v3.0a branch using this link: biceps_v3.0a

Some dependencies of BICePs

• pymbar >= 4.0.1
• mdtraj

Documentation

https://biceps.readthedocs.io/en/latest/

Citing BICePs

@article{doi:10.1021/acs.jctc.5c00044,
author = {Raddi, Robert M. and Marshall, Tim and Ge, Yunhui and Voelz, Vincent A.},
title = {Model Selection Using Replica Averaging with Bayesian Inference of Conformational Populations},
journal = {Journal of Chemical Theory and Computation},
doi = {10.1021/acs.jctc.5c00044},
URL = {https://doi.org/10.1021/acs.jctc.5c00044}
}