Automatic Forward Model Parameterization with Bayesian Inference of Conformational Populations.

ABSTRACT: To quantify how well theoretical predictions of structural ensembles agree with experimental measurements, we depend on the accuracy of forward models. These models are computational frameworks that generate observable quantities from molecular configurations based on empirical relationships linking specific molecular properties to experimental measurements. Bayesian Inference of Conformational Populations (BICePs) is a reweighting algorithm that reconciles simulated ensembles with ensemble-averaged experimental observations, even when such observations are sparse and/or noisy. This is achieved by sampling the posterior distribution of conformational populations under experimental restraints as well as sampling the posterior distribution of uncertainties due to random and systematic error.

SUBMITTER: Raddi RM 

PROVIDER: S-EPMC11160882 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature