Hybrid methods for macromolecular structure determination: experiment with expectations

Schröder, Gunnar F.

doi:10.1016/j.sbi.2015.02.016

Cited by 38 publications

(29 citation statements)

References 96 publications

(61 reference statements)

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“…, NMR-derived ensembles currently available in the PDB (Clore and Gronenborn, 1991; Snyder et al, 2005; Snyder et al, 2014) and the ensembles generated from SAXS (Tria et al, 2015)). This aspect of the representation allows us to describe model uncertainty and to assess the completeness of input information; such ensembles are distinct from multiple states that represent actual variations in the structure, as implied by experimental information that cannot be accounted for by a single representative structure (Schneidman-Duhovny et al, 2014; Schroder, 2015). …”

Section: Integrative/hybrid Structure Modelingmentioning

confidence: 99%

Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop

et al. 2015

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Summary Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Methods Task Force Workshop held at the European Bioinformatics Institute in Hinxton, UK, October 6 and 7, 2014. At the workshop, experts in various experimental fields of structural biology, experts in integrative modeling and visualization, and experts in data archiving addressed a series of questions central to the future of structural biology. How should integrative models be represented? How should the data and integrative models be validated? What data should be archived? How should the data and models be archived? What information should accompany the publication of integrative models?

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Section: Integrative/hybrid Structure Modelingmentioning

confidence: 99%

Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop

et al. 2015

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“…Structural models built from hybrid data should be as objective as possible and ideally not be biased by a human modeler, therefore automated computational modeling tools are indispensable (Karaca and Bonvin, 2013; Villa and Lasker, 2014; Schröder, 2015). The models should be compatible with all of the available data, which might come from different experimental sources.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Modeling of Biomolecular Assemblies with Cryo-EM Maps

Habeck

2017

Front. Mol. Biosci.

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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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“…If very high resolution has been achieved (2–5 Å) it may be possible to trace the protein backbone, and even perform complete model building based solely on the CyroEM data[28–30]. At subnanometer resolution, secondary structure should be sufficiently visible to at least confirm that the structure is largely correct.…”

Section: Single Particle Reconstruction In Emanmentioning

confidence: 99%

High resolution single particle refinement in EMAN2.1

Bell

Chen

Baldwin

et al. 2016

Methods

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EMAN2.1 is a complete image processing suite for quantitative analysis of greyscale images, with a primary focus on transmission electron microscopy, with complete workflows for performing high resolution single particle reconstruction, 2-D and 3-D heterogeneity analysis, random conical tilt reconstruction and subtomogram averaging, among other tasks. In this manuscript we provide the first detailed description of the high resolution single particle analysis pipeline and the philosophy behind its approach to the reconstruction problem. High resolution refinement is a fully automated process, and involves an advanced set of heuristics to select optimal algorithms for each specific refinement task. A gold standard FSC is produced automatically as part of refinement, providing a robust resolution estimate for the final map, and this is used to optimally filter the final CTF phase and amplitude corrected structure. Additional methods are in-place to reduce model bias during refinement, and to permit cross-validation using other computational methods.

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Hybrid methods for macromolecular structure determination: experiment with expectations

Cited by 38 publications

References 96 publications

Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop

Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop

Bayesian Modeling of Biomolecular Assemblies with Cryo-EM Maps

High resolution single particle refinement in EMAN2.1

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