Generative Artificial Intelligence-Empowered Virtual Evolution of Enzyme with the VERnet Model

Large language models
(LLMs) have demonstrated their limitations
in addressing the design of active proteins that rely on intricate
intramolecular interactions, particularly in the engineering of biocatalysts.
Conducting real-world studies from targeted laboratory assays has
become the de facto standard for artificial intelligence (AI) research
in complex biological tasks. In this study, we present a standardized
strategy using function-targeted models to decode the subtle effect
of sequence variations on the function. Unlike affinity-oriented protein–protein
interaction studies using LLMs, our model targets the specific functional
interpretation, thereby guiding enzyme evolution. We established the
VERnet model using deep mutation scanning data that underwent self-distillation,
achieving an optimal accuracy of 93.5% for interpreting CYP2C9 variants.
Through directed evolution at conserved positions enhanced by generative
AI, we identified multiple CYP2C9 variants exhibiting a broad range
of functional alterations. Additionally, a fine-tuned model optimized
by AlphaFold3 significantly improved the prediction of variants involving
the substitution of two amino acids. Molecular dynamics simulations
revealed the structural and dynamic features of the catalytic alterations
in evolved variants. The in vitro validation of metabolic
activity strongly corroborated the in silico predictions,
highlighting the substantial potential of AI models in predicting
functional evolution.