MVIL6: Accurate identification of IL-6-induced peptides using multi-view feature learning

Abstract

Interleukin-6 (IL-6) is a potential therapeutic target for many diseases, and it is of great significance in accurately predicting IL-6-induced peptides for IL-6 research. However, the cost of traditional wet experiments to detect IL-6-induced peptides is huge, and the discovery and design of peptides by computer before the experimental stage have become a promising technology. In this study, we developed a deep learning model called MVIL6 for predicting IL-6-inducing peptides. Comparative results demonstrated the outstanding performance and robustness of MVIL6. Specifically, we employ a pre-trained protein language model MG-BERT and the Transformer model to process two different sequence-based descriptors and integrate them with a fusion module to improve the prediction performance. The ablation experiment demonstrated the effectiveness of our fusion strategy for the two models. In addition, to provide good interpretability of our model, we explored and visualized the amino acids considered important for IL-6-induced peptide prediction by our model. Finally, a case study presented using MVIL6 to predict IL-6-induced peptides in the SARS-CoV-2 spike protein shows that MVIL6 achieves higher performance than existing methods and can be useful for identifying potential IL-6-induced peptides in viral proteins.