The invention discloses a
protein optimization design and
screening method and device based on an
artificial intelligence algorithm, and the method takes fungal
luciferase as an example, and integrates multi-dimensional
bioinformatics analysis and
deep learning technology to realize efficient
protein engineering transformation and optimization. The method comprises the following steps: firstly, identifying a
binding domain of
luciferase and
fluorescein by utilizing an AI-driven molecular docking
simulation method, and determining a key conservative site by combining literature, evolutionary analysis and structural prediction; then, generating a
protein functional domain skeleton under the constraint of a fixed site by adopting a
diffusion model, and performing protein
sequence prediction by utilizing a graph neural
network model; and finally, scoring and screening the generated sequences to obtain high-stability candidate variants. According to the method, a conservative site dynamic fusion strategy is innovatively constructed, a fixed region is optimized through logic of structure prediction, evolution site intersection priority and literature site union
set expansion, the diversity and adaptability of a
protein design sequence are met, and the efficiency
bottleneck of a traditional scheme is broken through.