Automatic Forward Model Parameterization with Bayesian Inference of Conformational Populations
Year: 2026
Type: other
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 they sparse and/or noisy. This is achieved by sampling the posterior distribution of conformational populations under experimental restraints as well as their uncertainties due to random and systematic error. Here, we extend BICePs to optimize empirical forward model (FM) parameters. We examine two methods for optimizing FM parameters. The first method treats FM parameters as nuisance parameters, integrating over them in the full posterior distribution. The second method employs variational minimization of a quantity called the BICePs score that reports the free energy of ``turning on' the experimental restraints. This technique, coupled with improved likelihood functions for handling experimental outliers (Raddi et al. 2025), facilitates force field validation and optimization. Using this approach, we refine parameters of Karplus models that predict $J$-coupling constants based on protein backbone dihedral angles. We validate this approach first with a toy model system, and then for human ubiquitin, predicting six sets of Karplus parameters. Finally, we demonstrate generalization to any differentiable forward model, by performing a similar optimization with neural networks. This approach provides a promising direction for training and validating neural network-based forward models.
Source: AIP Publishing
Authors: Voelz, Vincent, Marshall, Tim, Raddi, Robert
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7119506715
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