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.
Highlights
Bayesian Inference of Conformational Populations (BICePs) is an algorithm developed to reconcile simulated ensembles with sparse experimental measurements
The Bayesian Inference of Conformational Populations (BICePs) algorithm arose from a need to predict conformational ensembles of organic molecules with significant structural heterogeneity in solution, such as natural product macrocycles and peptidomimetics
The ability to “tune” predictions of force fields using sparse experimental restraints can eliminate the need for custom parameterization, which can widen the scope of applications that can be addressed by general-purpose force fields
Summary
Bayesian Inference of Conformational Populations (BICePs) is an algorithm developed to reconcile simulated ensembles with sparse experimental measurements. The inputs to BICePs are: (1) a set of discrete conformational states and corresponding populations predicted from a theoretical prior, and (2) a set of experimental observables. The primary output of BICePs are estimates of reweighted conformational populations that balances the information from theory and experiment using a Bayesian framework. The Bayesian Inference of Conformational Populations (BICePs) algorithm arose from a need to predict conformational ensembles of organic molecules with significant structural heterogeneity in solution, such as natural product macrocycles and peptidomimetics. We aimed to develop an approach that leaned more heavily on high-quality theory/simulation-based conformational ensembles, to be later reconciled with potentially sparse experimental observables
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