ArticleNature communications2024
Context-aware geometric deep learning for protein sequence design.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- ModelCIF Update: Supporting Emerging Classes of Computational Macromolecular Models.Journal of molecular biology · 2026Article
- A Generative Neuro-Symbolic AI for Protein Sequence Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Optimization of Expression and Thermostability of Terminal Deoxynucleotidyl Transferase Through Iterative Mutagenesis and Computational Design.Applied biochemistry and biotechnology · 2026Article
- Validation and analysis of 12,000 AI-driven CAR-T designs in thebioRxiv : the preprint server for biology · 2026Article
- Hybrid deep learning framework MedFusionNet assists multilabel biomedical risk stratification from imaging and tabular data.Communications medicine · 2026Article
- Compounding asymmetries in nucleic acid synthesis screening: policy pathways for strengthening global biosecurity governance.Frontiers in bioengineering and biotechnology · 2026Article
- Advancing virulence factor prediction using protein language models.BMC biology · 2025Article
- The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe.Biology · 2025Review
- Decoding Protein Stabilization: Impact on Aggregation, Solubility, and Unfolding Mechanisms.Journal of chemical information and modeling · 2025Article
- ProDualNet: dual-target protein sequence design method based on protein language model and structure model.Briefings in bioinformatics · 2025Article
- AlphaFold distillation for inverse protein design.Scientific reports · 2025Article
- G-Computational and structural biotechnology journal · 2024Article
- Re-engineering of a carotenoid-binding protein based on NMR structure.Protein science : a publication of the Protein Society · 2024Article
- The Nobel Prize in Chemistry: past, present, and future of AI in biology.Communications biology · 2024Article
- Exploring the potential of structure-based deep learning approaches for T cell receptor design.PLoS computational biology · 2024Article
- Reengineering of a flavin-binding fluorescent protein using ProteinMPNN.Protein science : a publication of the Protein Society · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning approach based solely on a geometric transformer of atomic coordinates and element names that predicts protein sequences from backbone scaffolds aware of the restraints imposed by diverse molecular environments. To validate the method, we show that it can produce highly thermostable, catalytically active enzymes with high success rates. This concept is anticipated to improve the versatility of protein design pipelines for crafting desired functions.
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Registered trials
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