Evidence map›Paper›PMID 36012140›Full record

ArticleInternational journal of molecular sciences2022

Comprehensive Collection and Prediction of ABC Transmembrane Protein Structures in the AI Era of Structural Biology.

Hedvig Tordai, Erzsebet Suhajda, Ian Sillitoe, Sreenath Nair, Mihaly Varadi, Tamas Hegedus

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 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
2.7field-weighted citation impact, top 8% 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, 26 citations in OpenAlex.

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  12. Genetics ofCancers · 2023
    Review
  13. Article
  14. Article
  15. AI-Based Protein Interaction Screening and Identification (AISID).International journal of molecular sciences · 2022
    Article
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

6 authors at 3 institutions in 2 countries.

Hedvig TordaiDepartment of Biophysics and Radiation Biology, Semmelweis University, 1085 Budapest, Hungary.
Erzsebet SuhajdaDepartment of Biophysics and Radiation Biology, Semmelweis University, 1085 Budapest, Hungary.ORCID 0000-0001-8159-5007
Ian SillitoeDepartment of Structural and Molecular Biology, University College London, London WC1E 6BT, UK.ORCID 0000-0003-1091-9144
Sreenath NairEuropean Bioinformatics Institute, European Molecular Biology Laboratory, Hinxton CB10 1SD, UK.ORCID 0000-0002-6861-7627
Mihaly VaradiEuropean Bioinformatics Institute, European Molecular Biology Laboratory, Hinxton CB10 1SD, UK.ORCID 0000-0002-3687-0839
Tamas HegedusDepartment of Biophysics and Radiation Biology, Semmelweis University, 1085 Budapest, Hungary.ORCID 0000-0002-5195-749X
Semmelweis University · HUEuropean Bioinformatics Institute · GBInstitute of Structural and Molecular Biology · GB

Funding

Cystic Fibrosis Foundation HEGEDU20I0National Research, Development and Innovation Office K127961, K137610, and 2020-2.1.1-ED-2021-00179
6 · The paper itself

Abstract

The number of unique transmembrane (TM) protein structures doubled in the last four years, which can be attributed to the revolution of cryo-electron microscopy. In addition, AlphaFold2 (AF2) also provided a large number of predicted structures with high quality. However, if a specific protein family is the subject of a study, collecting the structures of the family members is highly challenging in spite of existing general and protein domain-specific databases. Here, we demonstrate this and assess the applicability and usability of automatic collection and presentation of protein structures via the ABC protein superfamily. Our pipeline identifies and classifies transmembrane ABC protein structures using the PFAM search and also aims to determine their conformational states based on special geometric measures, conftors. Since the AlphaFold database contains structure predictions only for single polypeptide chains, we performed AF2-Multimer predictions for human ABC half transporters functioning as dimers. Our AF2 predictions warn of possibly ambiguous interpretation of some biochemical data regarding interaction partners and call for further experiments and experimental structure determination. We made our predicted ABC protein structures available through a web application, and we joined the 3D-Beacons Network to reach the broader scientific community through platforms such as PDBe-KB.

Indexed as

ATP-Binding Cassette TransportersFurylfuramideArtificial IntelligenceBiologyCryoelectron MicroscopyHumansModels, MolecularProtein ConformationATP-Binding Cassette TransportersFurylfuramide3D-BeaconsABC transportersAF-multimerAlphaFold2protein complexprotein structurestructure database

Identifiers

PMID36012140
PMCPMC9408558
OpenAlexW4292074325

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.