The application relates to the technical field of the cross of
protein engineering and
computational biology, and discloses a
uracil-
DNA glycosylase de novo design method based on physical priori and
deep learning. A
fixed function motif containing a catalytic core layer,
a DNA binding
interface layer and a structural support layer is extracted, and the
fixed function motif and a
substrate analog are used as geometric constraints; a full-atom
diffusion model is used to generate a new
protein topological skeleton; iteration optimization is carried out through
deep learning sequence generation and physical force field refinement; sub-Angstrom level precision control of an active pocket and sequence-structure self-consistency are realized; finally, multi-dimensional screening is carried out by using an orthogonal prediction model and a
Pareto optimal strategy. The application can break through the natural evolution limit, obtain a de novo designed UNG which is unique in sequence, has a sub-Angstrom level precision catalytic pocket and is compact in structure, and provide a new
computational design strategy for the development of programmable
gene editing tool enzymes.