DeepBindPPI: Epitope-Paratope Prediction Using Attention Based Graph Convolutional Network

Due to the importance of antigen-antibody interactions in defence mechanism of living body, reasoned attempts were made to investigate its attributes, including, but not limited to, binding affinity, and binding region. Contemporary strategies for binding site prediction largely resort to deep learning techniques but turned out to be low precision models. As laboratory experiments for drug discovery tasks utilize this informaiton, increased false positives devalue the computational methods. This emphasize the need to develop enhanced strategies. DeepBindPPI employs deep learning technique to predict the binding regions of proteins, particularly antigen-antibody interaction sites. The results obtained are applied in a docking environment to confirm their correctness. An integration of graph convolutional network with attention mechanism predicts interacting amino acids with improved precision. The model learns the determining factors in interaction from a general pool of proteins and is then fine-tuned using antigen-antibody data. Comparison of the proposed method with existing techniques shows that the developed model has comparable performance. DeepBindPPI improved the precision for epitope prediction from 0.216 to 0.315 and paratope prediction from 0.48 to 0.60. An attempt to utilize the interface information for docking using the HDOCK server gives promising results, with high-quality structures appearing in the top10 ranks. The application of self-supervision may contribute to the improvement of the deep learning model's performance.