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

J Phys Chem B. 2018 May 31;122(21):5610-5622. doi: 10.1021/acs.jpcb.7b11871. Epub 2018 Mar 23.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms*
  • Amino Acid Sequence
  • Bayes Theorem
  • Molecular Dynamics Simulation
  • Nuclear Magnetic Resonance, Biomolecular
  • Peptides / chemistry*
  • Peptides / metabolism
  • Thermodynamics


  • Peptides