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

Author:
Ge, Yunhui, Voelz, Vincent A.
Source:
The Journal of physical chemistry 2018 v.122 no.21 pp. 5610-5622
ISSN:
1520-5207
Subject:
Bayesian theory, Gibbs free energy, algorithms, energy, models, nuclear magnetic resonance spectroscopy, peptides, physical chemistry, prediction
Abstract:
The Bayesian Inference of Conformational Populations (BICePs) algorithm reconciles theoretical predictions of conformational state populations with sparse and/or noisy experimental measurements. Among its key advantages is its ability to perform objective model selection through a quantity we call the BICePs score, which reflects the integrated posterior evidence in favor of a given model, computed through free energy estimation methods. Here, we explore how the BICePs score can be used for force field validation and parametrization. Using a 2D lattice protein as a toy model, we demonstrate that BICePs is able to select the correct value of an interaction energy parameter given ensemble-averaged experimental distance measurements. We show that if conformational states are sufficiently fine-grained, the results are robust to experimental noise and measurement sparsity. Using these insights, we apply BICePs to perform force field evaluations for all-atom simulations of designed β-hairpin peptides against experimental NMR chemical shift measurements. These tests suggest that BICePs scores can be used for model selection in the context of all-atom simulations. We expect this approach to be particularly useful for the computational foldamer design as a tool for improving general-purpose force fields given sparse experimental measurements.
Agid:
6055646