Evidence map›Paper›PMID 36576775›Full record

ArticleeLife2022

Deep learning-driven insights into super protein complexes for outer membrane protein biogenesis in bacteria.

Mu Gao, Davi Nakajima An, Jeffrey Skolnick

Open access · goldAbstract read
In one paragraph

Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.7field-weighted citation impact, top 15% of its field
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

15 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Article
  6. AF3Complex Yields Improved Structural Predictions of Protein Complexes.bioRxiv : the preprint server for biology · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Improved deep learning prediction of antigen-antibody interactions.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  11. Article
  12. Article
  13. Enhancing CytochromeACS omega · 2024
    Article
  14. Article
  15. 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

3 authors at 1 institution in 1 country.

Mu GaoCenter for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States.ORCID 0000-0002-0378-3704
Davi Nakajima AnSchool of Computer Science, Georgia Institute of Technology, Atlanta, United States.
Jeffrey SkolnickCenter for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, United States.ORCID 0000-0002-1877-4958
Georgia Institute of Technology · US

Funding

Purchase of a GPU cluster for deep learning applications in protein-protein interaction and supercomplex prediction and biochemical literature annotation.R35GM118039 · NIGMS · GEORGIA INSTITUTE OF TECHNOLOGY · PI JEFFREY SKOLNICK · 2016 to 2026
$5.8M
NIGMS NIH HHS R35 GM118039
6 · The paper itself

Abstract

To reach their final destinations, outer membrane proteins (OMPs) of gram-negative bacteria undertake an eventful journey beginning in the cytosol. Multiple molecular machines, chaperones, proteases, and other enzymes facilitate the translocation and assembly of OMPs. These helpers usually associate, often transiently, forming large protein assemblies. They are not well understood due to experimental challenges in capturing and characterizing protein-protein interactions (PPIs), especially transient ones. Using AF2Complex, we introduce a high-throughput, deep learning pipeline to identify PPIs within the

Indexed as

Deep LearningEscherichia coli ProteinsBacterial Outer Membrane ProteinsEscherichia coliMembrane ProteinsMolecular ChaperonesPeptide HydrolasesBacterial Outer Membrane ProteinsEscherichia coli ProteinsMembrane ProteinsMolecular ChaperonesPeptide Hydrolasescomputational biologydeep learningE. colimolecular biophysicsouter membrane protein biogenesisprotein complex structure predictionprotein-protein interactionstructural biologysystems biologytransloconvirtual screening

Identifiers

PMID36576775
PMCPMC9797188
OpenAlexW4313305657

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.