ArticleNature biotechnology2023
Large language models generate functional protein sequences across diverse families.
Article in Nature biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 501 papers.
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Who cites it
501 citing papers in PubMed.
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- Towards mRNA therapeutics 2.0.Nature reviews. Drug discovery · 2026Review
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- Protein function-conditioned language models for variant effect prediction and controllable design.Biophysical journal · 2026Article
- Functional alignment of protein language models via reinforcement learning.Nature communications · 2026Article
- Unsupervised discovery of functional sequence patterns from protein language model with MotifAE.Nature communications · 2026Article
- Large Language Models in Preclinical Spine Research: A Scoping Review and Expert Perspective on Evidence-Aware Experimental Workflows.JOR spine · 2026Article
- Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data.PLoS computational biology · 2026Article
- Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.Comprehensive reviews in food science and food safety · 2026Review
- GeoEPred: A multimodal structure-aware geometric deep learning framework for Gram-negative bacterial secreted effector prediction with sequence semantics.PLoS computational biology · 2026Article
- Programming biology: next-gen AI firms raise billions to design better medicines.Nature biotechnology · 2026Article
- Amylosucrase as a Versatile Biocatalyst for Next-Generation Functional Ingredients: Starch Modification and Glycoside Synthesis.Comprehensive reviews in food science and food safety · 2026Review
- From rules to foundation models: a comprehensive review of machine learning approaches for siRNA design.NAR genomics and bioinformatics · 2026Review
441 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
Abstract
Deep-learning language models have shown promise in various biotechnological applications, including protein design and engineering. Here we describe ProGen, a language model that can generate protein sequences with a predictable function across large protein families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. The model was trained on 280 million protein sequences from >19,000 families and is augmented with control tags specifying protein properties. ProGen can be further fine-tuned to curated sequences and tags to improve controllable generation performance of proteins from families with sufficient homologous samples. Artificial proteins fine-tuned to five distinct lysozyme families showed similar catalytic efficiencies as natural lysozymes, with sequence identity to natural proteins as low as 31.4%. ProGen is readily adapted to diverse protein families, as we demonstrate with chorismate mutase and malate dehydrogenase.
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