2023/07/24 by Fan Jiang, Mingchen Li, Jiang, Fan +39
Biochemistry, Genetics and Molecular Biology · Computer Science · #Biomolecules (q-bio.BM) #FOS: Biological sciences #Machine Learning in Bioinformatics #Protein Structure and Dynamics #Software Engineering Research
paper · pdf · doi:10.48550/arxiv.2307.12682
openalex publication_date 2023/07/24 · openalex created_date 2023/07/26 · openalex updated_date 2026/07/28
Designing protein mutants of 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 of improved stability and activity without any prior experimental mutagenesis data of the specified protein. Leveraging temperature-aware language modeling, PRIME demonstrated superior predictive power compared to current state-of-the-art models on the public mutagenesis dataset over 283 protein assays. Furthermore, we validated PRIME's predictions on five proteins, examining the top 30-45 single-site mutations' impact on various protein properties, including thermal stability, antigen-antibody binding affinity, and the ability to polymerize non-natural nucleic acid or resilience to extreme alkaline conditions. Remarkably, over 30% of the AI-recommended mutants exhibited superior performance compared to their pre-mutation counterparts across all proteins and desired properties. Moreover, we have developed an efficient, and successful method based on PRIME to rapidly obtain multi-site mutants with enhanced activity and stability. Hence, PRIME demonstrates the general applicability in protein engineering.