ArticleNAR genomics and bioinformatics2024
Bilingual language model for protein sequence and structure.
Article in NAR genomics and bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 187 papers, 1 of them a synthesis that pooled it.
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Who cites it
187 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
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- 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
- ESpma: A method for assessing biological/non-biological interfaces using point-clouds-based structural features and protein language models.Protein science : a publication of the Protein Society · 2026Article
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- Protein function-conditioned language models for variant effect prediction and controllable design.Biophysical journal · 2026Article
- EmmaEmb: A quantitative framework for analyzing embedding spaces in molecular biology.Patterns (New York, N.Y.) · 2026Article
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- FANTASIA suite: a reproducible and configurable framework for embedding-based functional annotation of proteins.NAR genomics and bioinformatics · 2026Article
- Phage bioinformatics tools: a review of computational approaches for bacteriophage research.Briefings in bioinformatics · 2026Review
- Contrastive learning unites sequence and structure in a global representation of protein space.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- DNAreader: accurate prediction of DNA-binding residues in structured and disordered proteins using transformers and contrastive learning.Nucleic acids research · 2026Article
- Extensive library ofMicrobiology spectrum · 2026Article
- A deep learning-based score to evaluate multiple sequence alignments.Molecular biology and evolution · 2026Article
- PETIMOT: a novel framework for inferring protein motions from sparse data using SE(3)-equivariant graph neural networks.Acta crystallographica. Section D, Structural biology · 2026Article
- Unify learns cellular evolution with universal multimodal embeddings.Nature communications · 2026Article
- A genomic catalog of the mouse gut virome reveals features associated with ageing.Nature communications · 2026Article
- EPIC: A Machine-Learning Framework for Product-Dependent Behavior in β‑Glucosidases.ACS omega · 2026Article
127 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Adapting language models to protein sequences spawned the development of powerful protein language models (pLMs). Concurrently, AlphaFold2 broke through in protein structure prediction. Now we can systematically and comprehensively explore the dual nature of proteins that act and exist as three-dimensional (3D) machines and evolve as linear strings of one-dimensional (1D) sequences. Here, we leverage pLMs to simultaneously model both modalities in a single model. We encode protein structures as token sequences using the 3Di-alphabet introduced by the 3D-alignment method
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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.