ArticleScience advances2024
A general temperature-guided language model to design proteins of enhanced stability and activity.
Article in Science advances, 2024. 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 26 papers.
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Who cites it
26 citing papers in PubMed.
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- LoMuS: low-rank adaptation with sequence multi-representation improves protein stability prediction.Bioinformatics (Oxford, England) · 2026Article
- Long-stranded XNA-cssDNA hybrids for robust data storage.Science advances · 2026Article
- Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Engineering ω-transaminase for efficient dihydroxyacetone transamination in serinol biosynthesis starting from methanol.Synthetic and systems biotechnology · 2026Article
- Accurate protein stability prediction for small domains using mega-scale experiments.bioRxiv : the preprint server for biology · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Enzymatic degradation of biopolymers in amorphous and molten states: mechanisms and applications.FEBS open bio · 2026Review
- Deep learning-guided dual-fitness evolution of T7 RNA polymerase for enhanced stability and activity.Nucleic acids research · 2026Article
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
- Advances and challenges in non-canonical nucleic acids data storage.Nature communications · 2026Review
- JanusDDG: a physics-informed neural network for sequence-based protein stability via two-fronts attention.Communications biology · 2026Article
- Harnessing deep learning to accelerate the development of antibodies and aptamers.Acta pharmaceutica Sinica. B · 2026Review
- Computational redesign of a thermostable T7 RNA polymerase.Protein engineering, design & selection : PEDS · 2026Article
- FireProtDB 2.0: large-scale manually curated database of the protein stability data.Nucleic acids research · 2026Article
- Computational redesign of a thermostable T7 RNA polymerase.bioRxiv : the preprint server for biology · 2025Article
- Enriching stabilizing mutations through automated analysis of molecular dynamics simulations using BoostMut.Protein science : a publication of the Protein Society · 2025Article
- GeoEvoBuilder: A deep learning framework for efficient functional and thermostable protein design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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Corrections and comments
- Erratum issued
Authors and funding
26 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Designing protein mutants with both high stability and activity is a critical yet challenging task in protein engineering. Here, we introduce PRIME, a deep learning model, which can suggest protein mutants with improved stability and activity without any prior experimental mutagenesis data for the specified protein. Leveraging temperature-aware language modeling, PRIME demonstrated superior predictive ability compared to current state-of-the-art models on the public mutagenesis dataset across 283 protein assays. Furthermore, we validated PRIME's predictions on five proteins, examining the impact of the top 30 to 45 single-site mutations on various protein properties, including thermal stability, antigen-antibody binding affinity, and the ability to polymerize nonnatural nucleic acid or resilience to extreme alkaline conditions. More than 30% of PRIME-recommended mutants exhibited superior performance compared to their premutation counterparts across all proteins and desired properties. We developed an efficient and effective method based on PRIME to rapidly obtain multisite mutants with enhanced activity and stability. Hence, PRIME demonstrates broad applicability in protein engineering.
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