Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks
Fig 2
(A) Each protein is first converted into a structural representation derived from its contact map and covalent connectivity.
These structural features are encoded into compact protein embeddings using a pre-trained graph neural network autoencoder. In simple terms, this step transforms complex structural information into a learnable numerical representation of each protein. (B) The adaptive fusion block integrates different sources of structural information using a gating mechanism, which automatically determines how much weight to assign to each feature type depending on the context. (C) The core of DMG-PPI models two complementary principles of protein interactions. AMP captures similarity-based relationships, where proteins with similar structural patterns tend to interact. DMP captures complementarity-based relationships, where structurally distinct but functionally compatible proteins interact. The outputs of these channels are combined to form refined protein representations. Finally, the interaction between two proteins is quantified by combining their representations into a pair-level descriptor, which is used to predict whether the proteins interact.