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

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

SUBMITTER: Ge Y 

PROVIDER: S-EPMC6473793 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature