Fig. 1

The framework for protein-protein affinity prediction using pre-trained models and the PPI-Graphomer module. A The workflow of this study involves: utilizing ESM2 to extract sequence representations of protein complexes and ESM-IF1 to extract structural representations, which are subsequently concatenated. The concatenated representations are then processed through the PPI-Graphomer module to obtain interface representations. These interface representations are further concatenated with the original features. Ultimately, a MLP is employed to predict affinity values quantitatively. B The architecture of PPI-Graphomer incorporates bias terms based on three different encodings into the attention matrix, which are then multiplied by distance-based weight coefficients. C Three encodings based on amino acid structural information are utilized: encoding of amino acid pair types, encoding based on intermolecular interaction forces, and a masking matrix based on interface information