The invention discloses a universal HLA
antigen presentation prediction method based on a
protein language model and a multi-
modal neural network, which comprises the following steps: firstly, extracting verified HLA
binding peptide fragment sequences from an immune
epitope database, generating equivalent non-
epitope peptide fragment sequences in combination with a
protein database, and constructing an HLA-I class and HLA-II class balanced
data set; then, extracting sequence embedding characteristics and contact graph structure information of the
peptide fragment sequence by utilizing a
protein language model; the method comprises the following steps:
processing a protein map constructed by a contact map through a map neural network to obtain
global structure features, and meanwhile, carrying out regional
convolution and residual
convolution processing on sequence embedding by adopting a one-dimensional
convolutional neural network (1DCNN) to extract local context sequence features; the method effectively fuses sequence
semantics and space structure information, improves the accuracy and generalization ability of HLA
antigen presentation prediction, and is suitable for
immune recognition modeling tasks under various HLA subtypes.