ArticlePLoS computational biology2018
Bayesian inference of protein conformational ensembles from limited structural data.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Equivariant learning leveraging geometric invariances in 3D molecular conformers for accurate prediction of quantum chemical properties.Scientific reports · 2025Article
- Frontiers in integrative structural modeling of macromolecular assemblies.QRB discovery · 2025Review
- Optimizing representations for integrative structural modeling using Bayesian model selection.Bioinformatics (Oxford, England) · 2024Article
- Optimizing representations for integrative structural modeling using Bayesian model selection.bioRxiv : the preprint server for biology · 2023Article
- Autonomous screening of complex phase spaces using Bayesian optimization for SAXS measurements.Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment · 2023Article
- Generation of unfolded outer membrane protein ensembles defined by hydrodynamic properties.European biophysics journal : EBJ · 2023Article
- BICePs v2.0: Software for Ensemble Reweighting Using Bayesian Inference of Conformational Populations.Journal of chemical information and modeling · 2023Article
- Soil Contamination with Metals in Mountainous: A Case Study of Jaworzyna Krynicka in the Beskidy Mountains (Poland).International journal of environmental research and public health · 2023Article
- Myofilament-associated proteins with intrinsic disorder (MAPIDs) and their resolution by computational modeling.Quarterly reviews of biophysics · 2023Review
- Permissive and nonpermissive channel closings in CFTR revealed by a factor graph inference algorithm.Biophysical reports · 2022Article
- Reweighting methods for elucidation of conformation ensembles of proteins.Current opinion in structural biology · 2022Review
- Article
- Resolving Dynamics in the Ensemble: Finding Paths through Intermediate States and Disordered Protein Structures.The journal of physical chemistry. B · 2021Article
- Refining conformational ensembles of flexible proteins against small-angle x-ray scattering data.Biophysical journal · 2021Article
- Molecular insights on CALX-CBD12 interdomain dynamics from MD simulations, RDCs, and SAXS.Biophysical journal · 2021Article
- Automatic Bayesian Weighting for SAXS Data.Frontiers in molecular biosciences · 2021Article
- Reconciling Simulations and Experiments With BICePs: A Review.Frontiers in molecular biosciences · 2021Review
- Article
- Bayesian inference of chromatin structure ensembles from population-averaged contact data.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study multi-domain proteins in solution.PLoS computational biology · 2020Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Many proteins consist of folded domains connected by regions with higher flexibility. The details of the resulting conformational ensemble play a central role in controlling interactions between domains and with binding partners. Small-Angle Scattering (SAS) is well-suited to study the conformational states adopted by proteins in solution. However, analysis is complicated by the limited information content in SAS data and care must be taken to avoid constructing overly complex ensemble models and fitting to noise in the experimental data. To address these challenges, we developed a method based on Bayesian statistics that infers conformational ensembles from a structural library generated by all-atom Monte Carlo simulations. The first stage of the method involves a fast model selection based on variational Bayesian inference that maximizes the model evidence of the selected ensemble. This is followed by a complete Bayesian inference of population weights in the selected ensemble. Experiments with simulated ensembles demonstrate that model evidence is capable of identifying the correct ensemble and that correct number of ensemble members can be recovered up to high level of noise. Using experimental data, we demonstrate how the method can be extended to include data from Nuclear Magnetic Resonance (NMR) and structural energies of conformers extracted from the all-atom energy functions. We show that the data from SAXS, NMR chemical shifts and energies calculated from conformers can work synergistically to improve the definition of the conformational ensemble.
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