ReviewCurrent opinion in structural biology2025
Major advances in protein function assignment by remote homolog detection with protein language models - A review.
Review in Current opinion in structural biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- Protein Large Language Models Can PredictACS omega · 2026Article
- Microbial enzymes for plastic degradation: a comprehensive review of current status and emerging trends.Biodegradation · 2026Review
- Exploring the protein universe with distant similarity detection methods.Protein science : a publication of the Protein Society · 2026Review
- Large Numbers of New Human Paralogs Discovered.bioRxiv : the preprint server for biology · 2025Article
- CATHe2: Enhanced CATH superfamily detection using ProstT5 and structural alphabets.Biology methods & protocols · 2025Article
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
Abstract
There is an ever-increasing need for accurate and efficient methods to identify protein homologs. Traditionally, sequence similarity-based methods have dominated protein homolog identification for function identification, but these struggle when the sequence identity between the pairs is low. Recently, transformer architecture-based deep learning methods have achieved breakthrough performances in many fields. One type of model that uses transformer architecture is the protein language model (pLM). Here, we describe methods that use pLMs for protein homolog identification intended for function identification and describe their strengths and weaknesses. Several important ideas emerge, such as filtering the substitution matrix generated from embeddings, selecting specific pLM layers for specific purposes, compressing the embeddings, and dividing proteins into domains before searching for homologs that improve remote homolog detection accuracy considerably. All of these approaches produce huge numbers of new homologs that can reliably extend the reach of protein relationships for a deeper understanding of evolution and many other problems.
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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 OpenQuestion graph.