Pro-PRIME: A general Temperature-Guided Language model to engineer enhanced Stability and Activity in Proteins

TL;DR

Pro-PRIME is a temperature-aware deep learning model that predicts protein mutants with enhanced stability and activity, outperforming existing models and validated across multiple proteins and properties, advancing protein engineering capabilities.

Contribution

Introduces PRIME, a novel temperature-guided language model capable of predicting improved protein mutants without prior experimental data, demonstrating broad applicability.

Findings

PRIME outperforms current state-of-the-art models on mutagenesis datasets.

Over 30% of AI-recommended mutants show improved properties.

PRIME enables rapid design of multi-site mutants with enhanced stability and activity.

Abstract

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.