Evidence map›Paper›PMID 38451205›Full record

ArticleActa crystallographica. Section D, Structural biology2024

AlphaFold-assisted structure determination of a bacterial protein of unknown function using X-ray and electron crystallography.

Justin E Miller, Matthew P Agdanowski, Joshua L Dolinsky, Michael R Sawaya, Duilio Cascio, Jose A Rodriguez, Todd O Yeates

Abstract read
In one paragraph

Article in Acta crystallographica. Section D, Structural biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. GraPhAI: Neural Networks for Solving Centrosymmetric Crystal Structures.Journal of the American Chemical Society · 2026
    Article
  2. Review
  3. Review
  4. Standards for MicroED.Acta crystallographica. Section C, Structural chemistry · 2025
    Article
  5. Review
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

7 authors.

Justin E MillerMolecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0001-5224-2237
Matthew P AgdanowskiDepartment of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Joshua L DolinskyMolecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0002-8042-9645
Michael R SawayaInstitute for Genomics and Proteomics, UCLA-DOE, Los Angeles, CA 90095, USA.ORCID 0000-0003-0874-9043
Duilio CascioInstitute for Genomics and Proteomics, UCLA-DOE, Los Angeles, CA 90095, USA.
Jose A RodriguezMolecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Todd O YeatesMolecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0001-5709-9839

Funding

User Training and OutreachP30GM124165 · NIGMS · CORNELL UNIVERSITY · PI Nozomi Ando, J Christopher Fromme · 2018 to 2026
$34.2M
Pixel Array Detector for Macromolecular CrystallographyS10OD021527 · OD · CORNELL UNIVERSITY · PI EALICK, STEVEN E · 2016 to 2016
$2.0M
NIGMS NIH HHS P30 GM124165NIH HHS S10 OD021527
6 · The paper itself

Abstract

Macromolecular crystallography generally requires the recovery of missing phase information from diffraction data to reconstruct an electron-density map of the crystallized molecule. Most recent structures have been solved using molecular replacement as a phasing method, requiring an a priori structure that is closely related to the target protein to serve as a search model; when no such search model exists, molecular replacement is not possible. New advances in computational machine-learning methods, however, have resulted in major advances in protein structure predictions from sequence information. Methods that generate predicted structural models of sufficient accuracy provide a powerful approach to molecular replacement. Taking advantage of these advances, AlphaFold predictions were applied to enable structure determination of a bacterial protein of unknown function (UniProtKB Q63NT7, NCBI locus BPSS0212) based on diffraction data that had evaded phasing attempts using MIR and anomalous scattering methods. Using both X-ray and micro-electron (microED) diffraction data, it was possible to solve the structure of the main fragment of the protein using a predicted model of that domain as a starting point. The use of predicted structural models importantly expands the promise of electron diffraction, where structure determination relies critically on molecular replacement.

Indexed as

Bacterial ProteinsElectronsCrystallography, X-RayProtein ConformationX-RaysBacterial ProteinsAlphaFoldbacterial proteinselectron diffractionmolecular replacementprotein structure prediction

Identifiers

PMID38451205
PMCPMC10994174

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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.