ArticleScience advances2016
Metainference: A Bayesian inference method for heterogeneous systems.
Article in Science advances, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers.
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109 citing papers in PubMed.
- A practical guide to investigating biomolecular condensates: a comment from the plant community.Science China. Life sciences · 2026Review
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
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- From possibility to precision in macromolecular ensemble prediction.Nature methods · 2026Review
- Hidden structural states of proteins revealed by conformer selection.Nature communications · 2026Article
- Toward a unified framework for determining conformational ensembles of disordered proteins.Nature methods · 2026Review
- IDPForge: Deep Learning of Proteins with Global and Local Regions of Disorder.bioRxiv : the preprint server for biology · 2026Article
- Automatic forward model parameterization with Bayesian inference of conformational populations.APL machine learning · 2026Article
- Accurate conformational ensembles of intrinsically disordered proteins using reweighting based on NMR chemical shifts.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Self-Assembly of Unconventional Triphenylene-Based Frustrated Amphiphile in Solution.Langmuir : the ACS journal of surfaces and colloids · 2026Article
- Atomic resolution ensembles of intrinsically disordered proteins with Alphafold.Nature communications · 2026Article
- Structural and ensemble-based mechanistic insights into cytoplasmic dynein-1.Frontiers in molecular biosciences · 2026Review
- Implementation of Replica-Averaged Restraints from Nuclear Magnetic Resonance Measurement with UNRES Coarse Grained Model of Polypeptide Chains.Molecules (Basel, Switzerland) · 2025Article
- Determining accurate conformational ensembles of intrinsically disordered proteins at atomic resolution.Nature communications · 2025Article
- Allosteric regulation of proteolytic machines unveiled by the synergy between cryo-EM and solution NMR spectroscopy.The Biochemical journal · 2025Review
- Automatic Forward Model Parameterization with Bayesian Inference of Conformational Populations.ArXiv · 2025Article
- Ensemble refinement of mismodeled cryo-EM RNA structures using all-atom simulations.Nature communications · 2025Article
- The fellowship of the Grignard: 21st century computational tools for hundred-year-old chemistry.Chemical science · 2025Review
- Small Molecules Targeting the Structural Dynamics of AR-V7 Partially Disordered Proteins Using Deep Ensemble Docking.Journal of chemical theory and computation · 2025Article
49 more citing papers are in PubMed but not listed here.
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4 authors.
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Abstract
Modeling a complex system is almost invariably a challenging task. The incorporation of experimental observations can be used to improve the quality of a model and thus to obtain better predictions about the behavior of the corresponding system. This approach, however, is affected by a variety of different errors, especially when a system simultaneously populates an ensemble of different states and experimental data are measured as averages over such states. To address this problem, we present a Bayesian inference method, called "metainference," that is able to deal with errors in experimental measurements and with experimental measurements averaged over multiple states. To achieve this goal, metainference models a finite sample of the distribution of models using a replica approach, in the spirit of the replica-averaging modeling based on the maximum entropy principle. To illustrate the method, we present its application to a heterogeneous model system and to the determination of an ensemble of structures corresponding to the thermal fluctuations of a protein molecule. Metainference thus provides an approach to modeling complex systems with heterogeneous components and interconverting between different states by taking into account all possible sources of errors.
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