  Designing protein mutants of both high stability and activity is a critical
yet challenging task in protein engineering. Here, we introduce Pro-PRIME, a
deep learning zero-shot model, which can suggest protein mutants of improved
stability and activity without any prior experimental mutagenesis data. By
leveraging temperature-guided language modelling, Pro-PRIME demonstrated
superior predictive power compared to current state-of-the-art models on the
public mutagenesis dataset over 33 proteins. Furthermore, we carried out wet
experiments to test Pro-PRIME on five distinct proteins to engineer certain
physicochemical properties, including thermal stability, rates of RNA
polymerization and DNA cleavage, hydrolase activity, antigen-antibody binding
affinity, or even the nonnatural properties, e.g., the ability to polymerize
non-natural nucleic acid or resilience to extreme alkaline conditions.
Surprisingly, about 40% AI-designed mutants show better performance than the
one before mutation for all five proteins studied and for all properties
targeted for engineering. Hence, Pro-PRIME demonstrates the general
applicability in protein engineering.
