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Automatic forward model parameterization with Bayesian inference of conformational populations.

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This study enhances the Bayesian Inference of Conformational Populations (BICePs) algorithm to optimize forward model (FM) parameters, improving theoretical predictions of molecular structures and enabling better force field validation and neural network model training.

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Area of Science:

  • Computational Chemistry
  • Molecular Dynamics
  • Biophysics

Background:

  • Accurate theoretical predictions of molecular structural ensembles rely on precise forward models (FMs).
  • Existing methods struggle to reconcile simulated molecular dynamics with experimental data, especially with sparse or noisy observations.
  • Refining empirical FM parameters is crucial for improving the accuracy of these predictions.

Purpose of the Study:

  • To enhance the Bayesian Inference of Conformational Populations (BICePs) algorithm for optimizing forward model (FM) parameters.
  • To introduce and evaluate novel methods for refining empirical FM parameters.
  • To facilitate force field validation and the training of neural network-based FMs.

Main Methods:

  • Developed two novel methods for optimizing FM parameters within the BICePs framework.
  • Method 1: Treats FM parameters as nuisance parameters, integrating over them in the full posterior distribution.
  • Method 2: Employs variational minimization of the BICePs score, representing the free energy of experimental restraints.
  • Incorporated improved likelihood functions for handling experimental outliers.

Main Results:

  • Successfully refined parameters modulating the Karplus relation, essential for predicting nuclear spin-spin coupling constants (J-couplings).
  • Validated the approach using a toy model and human ubiquitin, predicting six sets of Karplus parameters for various couplings.
  • Demonstrated the framework's generalizability to any differentiable FM, including neural network-based models.

Conclusions:

  • The enhanced BICePs algorithm effectively refines FM parameters, improving the agreement between theoretical predictions and experimental measurements.
  • This approach offers a robust method for force field validation and optimization.
  • The generalized framework provides a promising direction for training and validating advanced neural network-based FMs.