Transfer Learning-Based Attention Gated Siamese Network for Human and SARS-CoV-2 Protein Interactions

Abstract

For the past year SARS-CoV-2 has affected the lives of people around the globe. Therefore, research community is continuously putting in their best efforts to find a solution to curb and cure the disease. SARS-CoV-2 is a 29.9k bp long sequence genome comprising of 25 different proteins among which spike glycoprotein plays a vital role in interaction with the host cells. Hence, majority of the scientific studies were focused towards targeting the spike region for the vaccine design against the contagious virus. Thorough study of protein-protein interaction between human and virus can help us in better understanding and management of this disease. For this purpose, an Attention gated Siamese framework is utilized from which a consensus of prominent features and contextual information is taken into account to identify the influence of protein sequences. Moreover, to obtain the pattern of interacting pairs of human and SARS-CoV-2 proteins, a transfer learning-based approach is opted from the proposed network through which we obtained an accuracy of 85%. Additionally, by using this model, we identified that there were 30, 13 and 17 human proteins interacting with spike glycoprotein, nucleocapsid and membrane respectively, having predictive interaction of above 90% for each of the interactions.