Integrating Evolutionary and Structural Properties for Protein Interaction Site Prediction Using Graph and Temporal Convolutions

Predicting protein interaction sites is crucial for tasks such as constructing protein interaction networks, analyzing protein functions, studying molecular-level pathology, and designing novel drugs. However, the restricted predictive performance of sequence-based computational approaches has led to the rise of structure-oriented approaches. Existing cutting-edge methods mostly focus on the secondary structural features, leaving significant scope for further performance improvement. This study incorporates additional structural features from a tertiary-level perspective to derive composite features using graph and temporal convolutions. A hybrid weighted loss function efficiently handles the class imbalance. A fully connected neural network generates the final predictions. The outlined model is tested on various publicly accessible datasets, showing a substantial improvement in performance over leading models. Comparative analysis with the best models from the literature reports enhancement in the Matthews Correlation Coefficient(MCC) and Area under the precision-recall curve (AUPRC) by 4.8% and 4.1% on the Test_60 dataset, 9.8% and 11.2% on the Test_315 dataset, 10.4% and 11.5% on the Dtestset72 dataset, 12.6% and 13.9% on the PDBtestset164 dataset and 10% and 13.1% on the Test_84 dataset. Finally, the statistical t-test showcases the significance of the proposed model in the protein interaction site prediction task.