Kavli Affiliate: Jia Liu
| First 5 Authors: Fan Jiang, Mingchen Li, Jiajun Dong, Yuanxi Yu, Xinyu Sun
| Summary:
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.
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