Evidence map›Paper›PMID 39283314›Full record

ArticleeLife2024

PPI-hotspot

Yao Chi Chen, Karen Sargsyan, Jon D Wright, Yu-Hsien Chen, Yi-Shuian Huang, Carmay Lim

Abstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. In Silico Druggability Assessment ofAntibiotics (Basel, Switzerland) · 2026
    Article
  3. Article
  4. Review
  5. Predicting interacting hotspots for nanobodies' binding using triplets of residues.Protein science : a publication of the Protein Society · 2025
    Article
  6. Article
  7. 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.

Yao Chi ChenInstitute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.ORCID https://orcid.org/0000-0003-0205-6078
Karen SargsyanInstitute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
Jon D WrightInstitute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.
Yu-Hsien ChenInstitute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.ORCID https://orcid.org/0000-0002-0475-1042
Yi-Shuian HuangInstitute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.ORCID https://orcid.org/0000-0001-9000-7748
Carmay LimInstitute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.ORCID https://orcid.org/0000-0001-9077-7769

Funding

Academia Sinica AS-IA-107-L03Ministry of Education MOST-98-2113-M-001-011
6 · The paper itself

Abstract

Experimental detection of residues critical for protein-protein interactions (PPI) is a time-consuming, costly, and labor-intensive process. Hence, high-throughput PPI-hot spot prediction methods have been developed, but they have been validated using relatively small datasets, which may compromise their predictive reliability. Here, we introduce PPI-hotspot

Indexed as

Protein Interaction MappingComputational BiologyProtein BindingProtein ConformationProteinsSoftwareProteinsAlphaFold-MultimerAutoGluonfree protein structuremachine learningmolecular biophysicsnonePPI-hot spotsprotein bindingstructural biology

Identifiers

PMID39283314
PMCPMC11405013

What Socratic 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 Socratic graph.