This invention belongs to the field of
bioinformatics and relates to a method for predicting
protein post-translational modifications based on multimodal
deep learning. First, multimodal
feature extraction is performed on the input
protein sequence and 3D structural data to obtain
sequence feature vectors and structural feature vectors. Second, a cross-
modal attention mechanism and an adaptive gating network are used for
feature fusion. Then, the fused features are combined with
disease type information, and the predicted probabilities are fine-tuned using a
disease-specific coding network. Next, a multi-
task learning framework is used to predict the site probabilities of various
protein post-translational modification types in parallel. Finally, gradient
backpropagation is used to calculate feature importance, and a comprehensive report is output based on variation
impact analysis. This invention achieves high-precision, interpretable, and
disease-aware protein post-translational modification prediction, providing an important
computational analysis tool for revealing the molecular mechanisms of diseases and discovering precise
drug targets.