Evidence map›Paper›PMID 35439658›Full record

ReviewCurrent opinion in structural biology2022

AlphaFold illuminates half of the dark human proteins.

Jessica L Binder, Joel Berendzen, Amy O Stevens, Yi He, Jian Wang, Nikolay V Dokholyan, Tudor I Oprea

Abstract readReview
In one paragraph

Review in Current opinion in structural biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 1 pooled it
–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

44 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Targeting p53 pathways: mechanisms, structures, and advances in therapy.Signal transduction and targeted therapy · 2023
    Pooled it
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  4. Review
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  12. PEGASUS: Prediction of MD-derived protein flexibility from sequence.Protein science : a publication of the Protein Society · 2025
    Article
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  14. Review
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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

7 authors.

Jessica L BinderTranslational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: https://twitter.com/@jessicamaine.
Joel BerendzenTranslational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA.
Amy O StevensDepartment of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, NM 87131, USA.
Yi HeTranslational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA; Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, NM 87131, USA.
Jian WangDepartment of Pharmacology, Department of Biochemistry and Molecular Biology, Penn State University College of Medicine, Hershey, PA 17033, USA.
Nikolay V DokholyanDepartment of Pharmacology, Department of Biochemistry and Molecular Biology, Penn State University College of Medicine, Hershey, PA 17033, USA; Department of Chemistry and Department of Biomedical Engineering, Pennsylvania State University, University Park, PA 16802, United States.
Tudor I OpreaTranslational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87131, USA; UNM Comprehensive Cancer Center, Albuquerque, NM, USA; Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden; Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. Electronic address: toprea@salud.unm.edu.

Funding

Machine Learning Model Validation for AD/ADRDU24CA224370 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI EDWARDS, JEREMY S · 2018 to 2023
$6.4M
Nanoscale programming of cellular and physiological phenotypes: EquipmentR35GM134864 · NIGMS · UNIVERSITY OF VIRGINIA · PI Nikolay Dokholyan · 2020 to 2026
$5.2M
NCI NIH HHS U24 CA224370NIGMS NIH HHS R35 GM134864
6 · The paper itself

Abstract

We investigate the use of confidence scores to evaluate the accuracy of a given AlphaFold (AF2) protein model for drug discovery. Prediction of accuracy is improved by not considering confidence scores below 80 due to the effects of disorder. On a set of recent crystal structures, 95% are likely to have accurate folds. Conformational discordance in the training set has a much more significant effect on accuracy than sequence divergence. We propose criteria for models and residues that are possibly useful for virtual screening. Based on these criteria, AF2 provides models for half of understudied (dark) human proteins and two-thirds of residues in those models.

Indexed as

FurylfuramideProteinsDrug DiscoveryHumansProtein FoldingFurylfuramideProteinsArtificial intelligenceDrug discoveryModel evaluationProtein foldingUnderstudied proteinsVirtual screening

Identifiers

PMID35439658
PMCPMC10669925

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.