ArticleNature biotechnology2022
Single-sequence protein structure prediction using a language model and deep learning.
Article in Nature biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 203 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
203 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- A structure-aware generative AI framework for revealing functional relationships in protein families.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Functional alignment of protein language models via reinforcement learning.Nature communications · 2026Article
- PlantCAD2: A DNA foundation model for interpreting genomes across flowering plants.Cell genomics · 2026Article
- Potential of inclusion bodies for disease control in aquaculture and livestock.Veterinary and animal science · 2026Review
- DeepPANB: integrating protein language model with PaiNN equivariant graph neural networks for prediction of protein-nucleic acid binding sites.Briefings in bioinformatics · 2026Article
- Mind the gap: An embedding guide to safely travel in sequence space.PLoS computational biology · 2026Article
- Review
- DeepPath: overcoming data scarcity for protein transition pathway prediction using physics-based deep learning.Chemical science · 2026Article
- Heavy-chain immune repertoire sequencing enables language-model prediction of antigen-specific antibodies.Research square · 2026Article
- Integrating AlphaFold2, RoseTTAFold2, and HADDOCK to refine Protein-Protein Interaction (PPI) candidate selection: A case study with ZWINT.Biochemistry and biophysics reports · 2026Article
- Inferring Local Protein Structural Similarity from Sequence Alone.Journal of chemical information and modeling · 2026Article
- A tri-modal contrastive learning framework for protein representation learning.Cell reports methods · 2026Article
- Phenotypic prediction of missense variants via deep contrastive learning.Nature biomedical engineering · 2026Article
- Identifying microbial protease allergens through protein language model-guided homology.Cell systems · 2026Article
- A paired sequence language model for protein-protein interaction modeling.Nature communications · 2026Article
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- PRIMED: predicting DNA binding residues by leveraging pre-trained protein language models.Frontiers in artificial intelligence · 2026Article
143 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
12 authors.
Funding
Abstract
AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain in (1) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated; (2) rapid exploration of designed structures; and (3) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) that uses a protein language model (AminoBERT) to learn latent structural information from unaligned proteins. A linked geometric module compactly represents C
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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.