Prediction of the Trimer Protein Interface Residue Pair by CNN-GRU Model Based on Multi-Feature Map

Most life activities of organisms are realized through protein–protein interactions, and these interactions are mainly achieved through residue–residue contact between monomer proteins. Consequently, studying residue–residue contact at the protein interaction interface can contribute to a deeper understanding of the protein–protein interaction mechanism. In this paper, we focus on the research of the trimer protein interface residue pair. Firstly, we utilize the amino acid k-interval product factor descriptor () to integrate the positional information and physicochemical properties of amino acids, combined with the electric properties and geometric shape features of residues, to construct an 8 × 16 multi-feature map. This multi-feature map represents a sample composed of two residues on a trimer protein. Secondly, we construct a CNN-GRU deep learning framework to predict the trimer protein interface residue pair. The results show that when each dimer protein provides 10 prediction results and two protein–protein interaction interfaces of a trimer protein needed to be accurately predicted, the accuracy of our proposed method is 60%. When each dimer protein provides 10 prediction results and one protein–protein interaction interface of a trimer protein needs to be accurately predicted, the accuracy of our proposed method is 93%. Our results can provide experimental researchers with a limited yet precise dataset containing correct trimer protein interface residue pairs, which is of great significance in guiding the experimental resolution of the trimer protein three-dimensional structure. Furthermore, compared to other computational methods, our proposed approach exhibits superior performance in predicting residue–residue contact at the trimer protein interface.