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Model Selection Using BICePs: A Bayesian Approach for Force Field Validation and Parameterization.

Yunhui Ge¹, Vincent A Voelz¹

¹Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States.

The Journal of Physical Chemistry. B

|March 9, 2018

Summary

This summary is machine-generated.

The Bayesian Inference of Conformational Populations (BICePs) algorithm validates computational models using experimental data. This approach aids in selecting accurate force fields for molecular simulations, especially with limited measurements.

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Area of Science:

Computational Chemistry
Biophysics
Molecular Dynamics

Background:

Accurate computational models are crucial for understanding molecular behavior.
Reconciling theoretical predictions with experimental data is challenging, especially with sparse or noisy measurements.
The Bayesian Inference of Conformational Populations (BICePs) algorithm offers a framework for this reconciliation.

Purpose of the Study:

To explore the utility of the BICePs score for validating and parametrizing molecular force fields.
To assess the robustness of BICePs against experimental noise and data sparsity.
To apply BICePs for evaluating force fields in all-atom simulations of peptides.

Main Methods:

Utilized the BICePs algorithm and its associated score for model selection.
Employed a 2D lattice protein as a toy model to test parameter selection.
Applied BICePs to analyze NMR chemical shift data for designed β-hairpin peptides in all-atom simulations.

Main Results:

BICePs successfully identified the correct interaction energy parameter in the toy model.
The BICePs score demonstrated robustness to noise and sparsity when conformational states were sufficiently resolved.
BICePs scores proved effective for model selection in all-atom simulations of peptides.

Conclusions:

The BICePs score is a valuable tool for objective model selection in computational chemistry.
BICePs facilitates force field validation and parametrization, even with limited experimental data.
This method holds significant promise for improving computational foldamer design and general-purpose force fields.