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.
Original language | English |
---|---|
Article number | 661520 |
Journal | Frontiers in Molecular Biosciences |
Volume | 8 |
DOIs | |
State | Published - May 11 2021 |
Externally published | Yes |
Keywords
- Bayesian inference
- HDX protection factors
- MCMC
- conformational populations
- cyclic peptides
- molecular simulation
- peptidomimetics
- peptoids