Reconciling Simulations and Experiments With BICePs: A Review

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.

Metal Cation-Binding Mechanisms of Q-Proline Peptoid Macrocycles in Solution

“…While we have not systematically explored using BICePs alongside other force fields beyond GAFF, we expect the reweighting procedure to work well for any reasonably accurate force field, as long as all relevant conformational states can be sampled. A future direction is to use the BICePs algorithm to evaluate and further optimize force fields for specific foldamers …”

“…A future direction is to use the BICePs algorithm to evaluate and further optimize force fields for specific foldamers. 53 Explicit-solvent simulations of the predicted macrocycle conformations show metal cation binding to an all-trans amide backbone. Impressively, simulations are able to correctly predict the solvent-, sequence-, and ion-dependence of binding for all of the macrocycles studied in this work, in agreement with the experiment.…”

“…There have been many attempts to parameterize all-atom force fields for peptoids ,, and other foldamers, , but the chemical diversity of peptidomimetics makes the development of a general-purpose force field for foldamers difficult. Here, we have opted to pursue a slightly different philosophy for foldamer simulation which utilizes our Bayesian Inference of Conformational Populations (BICePs) algorithm. , Instead of having to perform a custom force field parameterization for each new class of foldamer, we first perform molecular simulations using a general-purpose force field for organic molecules (in this case GAFF) and then use Bayesian inference to refine the predicted conformational populations using sparse 1 H NMR ROESY correlations implemented as distance restraints. An advantage of the BICePs approach is that the refinement of conformational populations can be performed as a postprocessing step, with Bayesian inference used to optimally combine both computational and experimental information, implicitly taking into account unknown uncertainties in the experimental measurements.…”

BICePs v2.0: Software for Ensemble Reweighting using Bayesian Inference of Conformational Populations

“…11 While conceptually similar to other Bayesian approaches, 3,5,12,13 BICePs has several unique advantages. 14 For example: Unlike methods in which experimental restraints are enforced during the course of a molecular simulation, 3,5,6,[15][16][17][18][19] BICePs is instead a post-processing reweighting algorithm, using experimental information to reweight conformational populations obtained from some prior theoretical method.…”

“…A summary of the theory of BICePs, and a discussion of closely related methods, can be found in a recent review article. 14 To date, BICePs has been applied to a number of molecular systems ranging from small macrocycles, 11,20,21 to peptides and peptidomimetics, [22][23][24] and larger proteins like apomyoglobin. 25 Despite the availability of BICePs, and many published examples of its use, researchers may still find it difficult to apply to their system of interest.…”