ArticleBiodesign research2024
High-Temperature Tolerance Protein Engineering through Deep Evolution.
Article in Biodesign research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Heuristic multi-site optimization for protein sequence design using Masked Protein Language Models.PLoS computational biology · 2026Article
- Encapsulation in a Bacterial Microcompartment Shell Improves Thermal Stability of a Glycolytic Enzyme.ACS synthetic biology · 2026Article
- Heptad insertion extends theBiodesign research · 2026Article
- DeepCodon: A deep learning codon-optimization model to enhance protein expression.Biodesign research · 2025Article
- Enhancing functional proteins through multimodal inverse folding with ABACUS-T.Nature communications · 2025Article
- Article
- Recent advances on protein engineering for improved stability.Biodesign research · 2025Review
- A Review: Development of a Synthetic Lactoferrin Biological System.Biodesign research · 2024Article
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Authors and funding
10 authors.
Funding
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Abstract
Protein engineering aimed at increasing temperature tolerance through iterative mutagenesis and high-throughput screening is often labor-intensive. Here, we developed a deep evolution (DeepEvo) strategy to engineer protein high-temperature tolerance by generating and selecting functional sequences using deep learning models. Drawing inspiration from the concept of evolution, we constructed a high-temperature tolerance selector based on a protein language model, acting as selective pressure in the high-dimensional latent spaces of protein sequences to enrich those with high-temperature tolerance. Simultaneously, we developed a variant generator using a generative adversarial network to produce protein sequence variants containing the desired function. Afterward, the iterative process involving the generator and selector was executed to accumulate high-temperature tolerance traits. We experimentally tested this approach on the model protein glyceraldehyde 3-phosphate dehydrogenase, obtaining 8 variants with high-temperature tolerance from just 30 generated sequences, achieving a success rate of over 26%, demonstrating the high efficiency of DeepEvo in engineering protein high-temperature tolerance.
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Registered trials
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