Evidence map›Paper›PMID 31589784›Full record

ArticleProteins2019

Small angle X-ray scattering-assisted protein structure prediction in CASP13 and emergence of solution structure differences.

Greg L Hura, Curtis D Hodge, Daniel Rosenberg, Dmytro Guzenko, Jose M Duarte, Bohdan Monastyrskyy, Sergei Grudinin, Andriy Kryshtafovych, John A Tainer, Krzysztof Fidelis and 1 more

Abstract read
In one paragraph

Article in Proteins, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Greg L HuraMolecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California.
Curtis D HodgeMolecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California.
Daniel RosenbergMolecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California.
Dmytro GuzenkoResearch Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, California.ORCID 0000-0002-8688-7460
Jose M DuarteResearch Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, California.ORCID 0000-0002-9544-5621
Bohdan MonastyrskyyProtein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California.
Sergei GrudininUniv. Grenoble Alpes, CNRS, Inria, Grenoble INP, LJK, 38000, Grenoble, France.ORCID 0000-0002-1903-7220
Andriy KryshtafovychProtein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California.ORCID 0000-0001-5066-7178
John A TainerMolecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California.ORCID 0000-0003-1659-2429
Krzysztof FidelisProtein Structure Prediction Center, Genome and Biomedical Sciences Facilities, University of California, Davis, California.ORCID 0000-0002-8061-412X
Susan E TsutakawaMolecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California.ORCID 0000-0002-4918-4571

Funding

Transcription-Coupled & Replication-Associated Excision RepairP01CA092584 · NCI · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI WALTER J. CHAZIN · 2001 to 2026
$89.6M
User Training and OutreachP30GM124169 · NIGMS · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI PAUL David ADAMS, Gregory L Hura · 2017 to 2026
$28.6M
PDB MANAGEMENT BY THE RESEARCH COLLABORATORY FOR STRUCTURAL BIOINFORMATICSR01GM133198 · NIGMS · RUTGERS, THE STATE UNIV OF N.J. · PI BURLEY, STEPHEN K · 2019 to 2023
$19.3M
Prospective analysis to determine model accuracy performance and boundaries in the post-AlphaFold2 environmentR01GM100482 · NIGMS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI FIDELIS, KRZYSZTOF A · 2012 to 2025
$11.1M
Resource for Biocomputing Visualization and InformaticsP41GM103311 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI FERRIN, THOMAS E · 2012 to 2017
$8.2M
Mesoscale and Nanoscale Technologies Integrated by Structures for DNA Repair Complexes (MANTIS-DRC)R35CA220430 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI John A. Tainer · 2018 to 2026
$7.6M
Integrative modeling of PCNA assemblies engaged in genome duplication and repairR01GM110387 · NIGMS · GEORGIA STATE UNIVERSITY · PI IVANOV, IVAYLO NIKOLAEV · 2015 to 2019
$1.7M
NCI NIH HHS P01 CA092584NCI NIH HHS P01CA092584NCI NIH HHS R35 CA220430NCI NIH HHS R35CA220430NIGMS NIH HHS P30 GM124169NIGMS NIH HHS P30GM124169NIGMS NIH HHS P41 GM103311NIGMS NIH HHS R01 GM100482NIGMS NIH HHS R01GM100482NIGMS NIH HHS R01 GM110387NIGMS NIH HHS R01GM110387NIGMS NIH HHS R01 GM133198
6 · The paper itself

Abstract

Small angle X-ray scattering (SAXS) measures comprehensive distance information on a protein's structure, which can constrain and guide computational structure prediction algorithms. Here, we evaluate structure predictions of 11 monomeric and oligomeric proteins for which SAXS data were collected and provided to predictors in the 13th round of the Critical Assessment of protein Structure Prediction (CASP13). The category for SAXS-assisted predictions made gains in certain areas for CASP13 compared to CASP12. Improvements included higher quality data with size exclusion chromatography-SAXS (SEC-SAXS) and better selection of targets and communication of results by CASP organizers. In several cases, we can track improvements in model accuracy with use of SAXS data. For hard multimeric targets where regular folding algorithms were unsuccessful, SAXS data helped predictors to build models better resembling the global shape of the target. For most models, however, no significant improvement in model accuracy at the domain level was registered from use of SAXS data, when rigorously comparing SAXS-assisted models to the best regular server predictions. To promote future progress in this category, we identify successes, challenges, and opportunities for improved strategies in prediction, assessment, and communication of SAXS data to predictors. An important observation is that, for many targets, SAXS data were inconsistent with crystal structures, suggesting that these proteins adopt different conformation(s) in solution. This CASP13 result, if representative of PDB structures and future CASP targets, may have substantive implications for the structure training databases used for machine learning, CASP, and use of prediction models for biology.

Indexed as

Computational BiologyProtein ConformationAlgorithmsModels, MolecularProtein FoldingProteinsScattering, Small AngleSolutionsX-Ray DiffractionProteinsSolutionscomplexesdisorderexperimental restraintsflexibilitymodelingSASSAXSsolution scatteringstructure predictionunstructured regions

Identifiers

PMID31589784
PMCPMC6851496

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.