ArticleFrontiers in molecular biosciences2017
Bayesian Modeling of Biomolecular Assemblies with Cryo-EM Maps.
Article in Frontiers in molecular biosciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Bayesian reweighting of biomolecular structural ensembles using heterogeneous cryo-EM maps with the cryoENsemble method.Scientific reports · 2024Article
- Gentle and fast all-atom model refinement to cryo-EM densities via a maximum likelihood approach.PLoS computational biology · 2023Article
- NMR-Based Configurational Assignments of Natural Products: Gibbs Sampling and Bayesian Inference Using Floating Chirality Distance Geometry Calculations.Marine drugs · 2021Article
- Bayesian Inference: The Comprehensive Approach to Analyzing Single-Molecule Experiments.Annual review of biophysics · 2021Review
- Bayesian Random Tomography of Particle Systems.Frontiers in molecular biosciences · 2021Article
- Architecture of the flexible tail tube of bacteriophage SPP1.Nature communications · 2020Article
- Hyper-Molecules: on the Representation and Recovery of Dynamical Structures for Applications in Flexible Macro-Molecules in Cryo-EM.Inverse problems · 2020Article
- Bayesian Weighing of Electron Cryo-Microscopy Data for Integrative Structural Modeling.Structure (London, England : 1993) · 2019Article
- Use of Computational Biochemistry for Elucidating Molecular Mechanisms of Nitric Oxide Synthase.Computational and structural biotechnology journal · 2019Review
- Advances in coarse-grained modeling of macromolecular complexes.Current opinion in structural biology · 2018Review
- CryoEM-based hybrid modeling approaches for structure determination.Current opinion in microbiology · 2018Review
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Abstract
A growing array of experimental techniques allows us to characterize the three-dimensional structure of large biological assemblies at increasingly higher resolution. In addition to X-ray crystallography and nuclear magnetic resonance in solution, new structure determination methods such cryo-electron microscopy (cryo-EM), crosslinking/mass spectrometry and solid-state NMR have emerged. Often it is not sufficient to use a single experimental method, but complementary data need to be collected by using multiple techniques. The integration of all datasets can only be achieved by computational means. This article describes Inferential structure determination, a Bayesian approach to integrative modeling of biomolecular complexes with hybrid structural data. I will introduce probabilistic models for cryo-EM maps and outline Markov chain Monte Carlo algorithms for sampling model structures from the posterior distribution. I will focus on rigid and flexible modeling with cryo-EM data and discuss some of the computational challenges of Bayesian inference in the context of biomolecular modeling.
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