ArticleNature biotechnology2025
Democratizing protein language model training, sharing and collaboration.
Article in Nature biotechnology, 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.
- Mechanistic interpretability of fine-tuned protein language models for nanobody thermostability prediction.Bioinformatics (Oxford, England) · 2026Article
- Article
- Incorporating AI-optimized zinc finger proteins enhances the efficiencies and targeting ranges of miniature base editors.Nature communications · 2026Article
- Decoding viral protein sequences by large language models.Briefings in bioinformatics · 2026Review
- Article
- Sequence Display enables large-scale sequence-activity datasets for rapid protein evolution.Nature biotechnology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Training and deploying large-scale protein language models typically requires deep machine learning expertise-a barrier for researchers outside this field. SaprotHub overcomes this challenge by offering an intuitive platform that facilitates training and prediction as well as storage and sharing of models. Here we provide the ColabSaprot framework built on Google Colab, which potentially powers hundreds of protein training and prediction applications, enabling researchers to collaboratively build and share customized models.
Identifiers
41136773What OpenQuestion 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 OpenQuestion graph.