TY - JOUR
T1 - BICePs v2.0
T2 - Software for Ensemble Reweighting Using Bayesian Inference of Conformational Populations
AU - Raddi, Robert M.
AU - Ge, Yunhui
AU - Voelz, Vincent A.
N1 - Publisher Copyright:
© 2023 American Chemical Society.
PY - 2023/4/24
Y1 - 2023/4/24
N2 - Bayesian Inference of Conformational Populations (BICePs) version 2.0 (v2.0) is a free, open-source Python package that reweights theoretical predictions of conformational state populations using sparse and/or noisy experimental measurements. In this article, we describe the implementation and usage of the latest version of BICePs (v2.0), a powerful, user-friendly and extensible package which makes several improvements upon the previous version. The algorithm now supports many experimental NMR observables (NOE distances, chemical shifts, J-coupling constants, and hydrogen-deuterium exchange protection factors), and enables convenient data preparation and processing. BICePs v2.0 can perform automatic analysis of the sampled posterior, including visualization, and evaluation of statistical significance and sampling convergence. We provide specific coding examples for these topics, and present a detailed example illustrating how to use BICePs v2.0 to reweight a theoretical ensemble using experimental measurements.
AB - Bayesian Inference of Conformational Populations (BICePs) version 2.0 (v2.0) is a free, open-source Python package that reweights theoretical predictions of conformational state populations using sparse and/or noisy experimental measurements. In this article, we describe the implementation and usage of the latest version of BICePs (v2.0), a powerful, user-friendly and extensible package which makes several improvements upon the previous version. The algorithm now supports many experimental NMR observables (NOE distances, chemical shifts, J-coupling constants, and hydrogen-deuterium exchange protection factors), and enables convenient data preparation and processing. BICePs v2.0 can perform automatic analysis of the sampled posterior, including visualization, and evaluation of statistical significance and sampling convergence. We provide specific coding examples for these topics, and present a detailed example illustrating how to use BICePs v2.0 to reweight a theoretical ensemble using experimental measurements.
KW - Algorithms
KW - Magnetic Resonance Spectroscopy
KW - Molecular Conformation
KW - Bayes Theorem
KW - Software
UR - http://www.scopus.com/inward/record.url?scp=85152200160&partnerID=8YFLogxK
U2 - 10.1021/acs.jcim.2c01296
DO - 10.1021/acs.jcim.2c01296
M3 - Article
C2 - 37027181
SN - 1549-9596
VL - 63
SP - 2370
EP - 2381
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
IS - 8
ER -