ArticleThe journal of physical chemistry. B2018
Model Selection Using BICePs: A Bayesian Approach for Force Field Validation and Parameterization.
Article in The journal of physical chemistry. B, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Article
- Automatic forward model parameterization with Bayesian inference of conformational populations.APL machine learning · 2026Article
- Automatic Forward Model Parameterization with Bayesian Inference of Conformational Populations.ArXiv · 2025Article
- Optimizing representations for integrative structural modeling using Bayesian model selection.Bioinformatics (Oxford, England) · 2024Article
- Minimal Peptoid Dynamics Inform Self-Assembly Propensity.The journal of physical chemistry. B · 2023Article
- Optimizing representations for integrative structural modeling using Bayesian model selection.bioRxiv : the preprint server for biology · 2023Article
- BICePs v2.0: Software for Ensemble Reweighting Using Bayesian Inference of Conformational Populations.Journal of chemical information and modeling · 2023Article
- Metal Cation-Binding Mechanisms of Q-Proline Peptoid Macrocycles in Solution.Journal of chemical information and modeling · 2021Article
- Solution-State Preorganization of Cyclic β-Hairpin Ligands Determines Binding Mechanism and Affinities for MDM2.Journal of chemical information and modeling · 2021Article
- Reconciling Simulations and Experiments With BICePs: A Review.Frontiers in molecular biosciences · 2021Review
- Data-Driven Mapping of Gas-Phase Quantum Calculations to General Force Field Lennard-Jones Parameters.Journal of chemical theory and computation · 2020Article
- BEES: Bayesian Ensemble Estimation from SAS.Biophysical journal · 2019Article
- Advances in coarse-grained modeling of macromolecular complexes.Current opinion in structural biology · 2018Review
- Elucidating the inhibition of peptidoglycan biosynthesis in Staphylococcus aureus by albocycline, a macrolactone isolated from Streptomyces maizeus.Bioorganic & medicinal chemistry · 2018Article
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Authors and funding
2 authors.
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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.
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