AttnSeq-PPI: Enhancing protein-protein interaction network prediction using transfer learning-driven hybrid attention

Study of protein-protein interaction (PPI) network is fundamental to all cellular processes in living organisms. PPI study based on experimental techniques such as high-throughput assays, mass spectrometry is time-consuming and expensive. Computational techniques like molecular docking are restricted to availability of 3D protein structures and not suitable for large numbers of proteins. On the contrary Sequence-based PPI study is advantageous over the limitations of above techniques. Sequence-based PPI study has gained a broader scope with the help of deep learning techniques and large language model (LLM). In this study, we proposed AttnSeq-PPI, a deep learning framework based on two channels of hybrid attention mechanism. The protein sequences are embedded in high dimensional space using ProtT5 language model. In our model hybrid attention mechanism is designed by combining self-attention and cross-attention which enable the model to extract features from each protein with respect to the contextual features of both the proteins. The hybrid attention mechanism effectively captures long-range dependencies within protein sequences as well as interacting features of both proteins. The model was trained and evaluated based on 5-fold cross validation on intra-species and multi-species datasets. Additionally, four datasets of independent species and true PPI network datasets were used for validation. AttnSeq-PPI demonstrates superior generalization and outperforms existing models, achieving 99 % accuracy for both human and multi-species datasets. It can provide prediction of novel PPIs with fewer false negatives and higher precision. Additionally, we developed a web-based tool on AttnSeq-PPI accessible at https://compbiosysnbu.in/attnseqppi/ to provide PPI prediction based on protein sequences.