Evidence map›Paper›PMID 29087945›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2017

High-throughput characterization of protein-protein interactions by reprogramming yeast mating.

David Younger, Stephanie Berger, David Baker, Eric Klavins

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing 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

51 citing papers in PubMed.

  1. Article
  2. Amplicon/Protein Bead Display enables quantitativebioRxiv : the preprint server for biology · 2026
    Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. A yeast mating platform for multiplex screening of fungal GPCR-ligand interactions.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  9. ADAPT-M: A workflow for rapid, quantitativebioRxiv : the preprint server for biology · 2025
    Article
  10. Article
  11. Article
  12. Recent progress and future challenges in structure-based protein-protein interaction prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2025
    Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

4 authors.

David YoungerBioengineering Department, University of Washington, Seattle, WA 98105.ORCID 0000-0003-4294-8302
Stephanie BergerBioengineering Department, University of Washington, Seattle, WA 98105.
David BakerInstitute for Protein Design, University of Washington, Seattle, WA 98195; dabaker@uw.edu klavins@uw.edu.
Eric KlavinsElectrical Engineering Department, University of Washington, Seattle, WA 98195 dabaker@uw.edu klavins@uw.edu.

Funding

Howard Hughes Medical Institute
6 · The paper itself

Abstract

High-throughput methods for screening protein-protein interactions enable the rapid characterization of engineered binding proteins and interaction networks. While existing approaches are powerful, none allow quantitative library-on-library characterization of protein interactions in a modifiable extracellular environment. Here, we show that sexual agglutination of

Indexed as

Gene Expression Regulation, FungalTranslocation, GeneticAgglutinationGene LibraryMating FactorProtein BindingProtein Interaction MappingSaccharomyces cerevisiaeTwo-Hybrid System TechniquesMating Factorhigh-throughput screeningprotein engineeringprotein–protein interaction networkssynthetic biologyyeast mating

Identifiers

PMID29087945
PMCPMC5699035

What OpenQuestion holds

Textmetadata
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.