Evidence map›Paper›PMID 36466147›Full record

ArticleFrontiers in bioinformatics2022

Nearest neighbor search on embeddings rapidly identifies distant protein relations.

Konstantin Schütze, Michael Heinzinger, Martin Steinegger, Burkhard Rost

Open access · goldAbstract read
In one paragraph

Article in Frontiers in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
3.3field-weighted citation impact, top 6% 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

24 citing papers in PubMed, 41 citations in OpenAlex.

  1. Article
  2. Advances in Protein Function Prediction from the Fifth CAFA Challenge.bioRxiv : the preprint server for biology · 2026
    Article
  3. Article
  4. Protein embeddings and local alignments.Computational and structural biotechnology journal · 2026
    Article
  5. Article
  6. NEAR: neural embeddings for amino acid relationships.Bioinformatics (Oxford, England) · 2025
    Article
  7. NEAR: Neural Embeddings for Amino acid Relationships.bioRxiv : the preprint server for biology · 2025
    Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Artificial Intelligence Learns Protein Prediction.Cold Spring Harbor perspectives in biology · 2024
    Review
  15. Article
  16. Article
  17. Article
  18. Protein embedding based alignment.BMC bioinformatics · 2024
    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 at 2 institutions in 2 countries.

Konstantin SchützeTUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology-i12, Munich, Germany.
Michael HeinzingerTUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology-i12, Munich, Germany.
Martin SteineggerSchool of Biological Sciences, Seoul National University, Seoul, South Korea.
Burkhard RostTUM (Technical University of Munich) Department of Informatics, Bioinformatics & Computational Biology-i12, Munich, Germany.
Technical University of Munich · DESeoul National University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since 1992, all state-of-the-art methods for fast and sensitive identification of evolutionary, structural, and functional relations between proteins (also referred to as "homology detection") use sequences and sequence-profiles (PSSMs). Protein Language Models (pLMs) generalize sequences, possibly capturing the same constraints as PSSMs, e.g., through embeddings. Here, we explored how to use such embeddings for nearest neighbor searches to identify relations between protein pairs with diverged sequences (remote homology detection for levels of <20% pairwise sequence identity, PIDE). While this approach excelled for proteins with single domains, we demonstrated the current challenges applying this to multi-domain proteins and presented some ideas how to overcome existing limitations, in principle. We observed that sufficiently challenging data set separations were crucial to provide deeply relevant insights into the behavior of nearest neighbor search when applied to the protein embedding space, and made all our methods readily available for others.

Indexed as

homology searchlanguage modelsnearest neighbor searchprotein embeddingsremote homology detection

Identifiers

PMID36466147
PMCPMC9714024
OpenAlexW4309609816

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.