ArticleeLife2024
Sensitive remote homology search by local alignment of small positional embeddings from protein language models.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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.
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.
Who cites it
16 citing papers in PubMed.
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- Inferring Local Protein Structural Similarity from Sequence Alone.Journal of chemical information and modeling · 2026Article
- pLM-Repeat: Exploiting the sequence representations of protein language models for sensitive repeat detection.Protein science : a publication of the Protein Society · 2026Article
- Accelerating natural product discovery with linked MS-genomics and language/transformer-based models.npj antimicrobials and resistance · 2026Article
- Protein embeddings and local alignments.Computational and structural biotechnology journal · 2026Article
- An interpretable alphabet for local protein structure search based on amino acid neighborhoods.Bioinformatics (Oxford, England) · 2025Article
- Inference of Cytochrome P450 Evolutionary History Using Structural and Physicochemical Metrics.Genome biology and evolution · 2025Article
- Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics.Briefings in bioinformatics · 2025Review
- Major advances in protein function assignment by remote homolog detection with protein language models - A review.Current opinion in structural biology · 2025Review
- Predicting phage-host interaction via hyperbolic Poincaré graph embedding and large-scale protein language technique.iScience · 2025Article
- Domainator, a flexible software suite for domain-based annotation and neighborhood analysis, identifies proteins involved in antiviral systems.Nucleic acids research · 2025Article
- CATHe2: Enhanced CATH superfamily detection using ProstT5 and structural alphabets.Biology methods & protocols · 2025Article
- High fitness paths can connect proteins with low sequence overlap.bioRxiv : the preprint server for biology · 2024Article
- Article
- Article
- In the twilight zone of protein sequence homology: do protein language models learn protein structure?Bioinformatics advances · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately detecting distant evolutionary relationships between proteins remains an ongoing challenge in bioinformatics. Search methods based on primary sequence struggle to accurately detect homology between sequences with less than 20% amino acid identity. Profile- and structure-based strategies extend sensitive search capabilities into this twilight zone of sequence similarity but require slow pre-processing steps. Recently, whole-protein and positional embeddings from deep neural networks have shown promise for providing sensitive sequence comparison and annotation at long evolutionary distances. Embeddings are generally faster to compute than profiles and predicted structures but still suffer several drawbacks related to the ability of whole-protein embeddings to discriminate domain-level homology, and the database size and search speed of methods using positional embeddings. In this work, we show that low-dimensionality positional embeddings can be used directly in speed-optimized local search algorithms. As a proof of concept, we use the ESM2 3B model to convert primary sequences directly into the 3D interaction (3Di) alphabet or amino acid profiles and use these embeddings as input to the highly optimized Foldseek, HMMER3, and HH-suite search algorithms. Our results suggest that positional embeddings as small as a single byte can provide sufficient information for dramatically improved sensitivity over amino acid sequence searches without sacrificing search speed.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.