Deep learning-guided dual-fitness evolution of T7 RNA polymerase for enhanced stability and activity

TL;DR

This paper introduces a deep learning-guided method to evolve T7 RNA polymerase for better stability and activity at high temperatures.

Contribution

A novel data-driven evolutionary strategy combining deep learning and multi-objective wet-lab selection for dual-fitness protein engineering.

Findings

T7 RNAP mutants showed a melting temperature increase of over 10°C.

High-temperature activity improved 60-fold with a 70% reduction in by-products.

Mutants validated for high-quality mRNA production in cell transfection.

Abstract

In protein engineering, simultaneously improving multiple fitness attributes is a critical yet challenging goal, largely due to the vastness of sequence space, the multifaceted interplay among different traits, and the complexity of non-linear mutational effects (epistasis). To address this, we developed a data-driven evolutionary strategy that couples in silico deep learning with a wet-lab multi-objective selection workflow. By employing independent model fine-tuning for distinct traits, our approach facilitates navigating the fitness landscape to identify beneficial mutation combinations. We applied this strategy to T7 RNA polymerase (T7 RNAP), performing dual-fitness evolution to simultaneously enhance thermostability and activity at elevated temperatures. After five rounds of iterative evolution, we obtained T7 RNAP mutants exhibiting a melting temperature (Tm) increase of >10°C, a 60-fold enhancement in high-temperature activity, and a 70% reduction in by-product content. Validation in cell transfection demonstrated their potential for producing high-quality mRNA for industrial applications.

Graphical Abstract

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