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Voelz VA , Ge Y , Raddi RM
Reconciling Simulations and Experiments With BICePs: A Review
Front Mol Biosci. 2021 ;8 :661520
PMID: 34046431    PMCID: PMC8144449   
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Abstract
Bayesian Inference of Conformational Populations (BICePs) is an algorithm developed to reconcile simulated ensembles with sparse experimental measurements. The Bayesian framework of BICePs enables population reweighting as a post-simulation processing step, with several advantages over existing methods, including the proper use of reference potentials, and the estimation of a Bayes factor-like quantity called the BICePs score for model selection. Here, we summarize the theory underlying this method in context with related algorithms, review the history of BICePs applications to date, and discuss current shortcomings along with future plans for improvement.
Notes
2296-889x Voelz, Vincent A Ge, Yunhui Raddi, Robert M R01 GM123296/GM/NIGMS NIH HHS/United States S10 OD020095/OD/NIH HHS/United States Journal Article Review Front Mol Biosci. 2021 May 11;8:661520. doi: 10.3389/fmolb.2021.661520. eCollection 2021.