A
protein interface prediction method based on three-track coding comprises the following steps: combining a fine-tuned
protein language model SiteT5 with evolutionary, geometric and statistical features extracted from a sequence, sending the combined features into a three-track coding network, and integrating a cyclic gating module, a multi-resolution aggregation module and a long sequence deformation module to obtain a
protein interface prediction model SiteT5; the method comprises the following steps: respectively capturing a
time sequence relation, a local mode and long-range dependence among residues, respectively mapping the three codes into different weights, carrying out point multiplication on the three codes, and carrying out aggregation through a multi-view cross attention module; then the protein residues are sent to a three-layer hierarchical
interactive learning module,
local structure and global dependency are cooperatively mined through an eight-head gating self-attention module and a position-by-position feedforward module, and finally the probability that each protein residue is an interface is obtained through a classifier. According to the invention, a protein-
DNA interface, a protein-
RNA interface, a protein-protein interface and an
antibody-
antigen interface can be effectively captured. And the robustness is ensured, and meanwhile, relatively high prediction precision is also shown.