Machine and deep learning to predict viral fusion peptides

Abstract

Viral fusion proteins, located on the surface of enveloped viruses like SARS-CoV-2, Influenza, and HIV, play a vital role in fusing the virus envelope with the host cell membrane. Fusion peptides, conserved segments within these proteins, are crucial for the fusion process and are potential targets for therapy. Experimental identification of fusion peptides is time-consuming and costly, which creates the need for bioinformatics tools that can predict the segment within the fusion protein sequence that corresponds to the FP. Although homology-based methods have been used towards this end, they fail to identify fusion peptides lacking overall sequence similarity to known counterparts. Therefore, alternative methods are needed to discover new putative fusion peptides, namely those based on machine learning. In this study, we explore various ML-based approaches to identify fusion peptides within a fusion protein sequence. We employ token classification methods and sliding window approaches coupled with machine and deep learning models. We evaluate different protein sequence representations, including one-hot encoding, physicochemical features, as well as representations from Natural Language Processing, such as word embeddings and transformers. Through the examination of over 50 combinations of models and features, we achieve promising results, particularly with models based on a state-of-the-art transformer for amino acid token classification. Furthermore, we utilize the best models to predict hypothetical fusion peptides for SARS-CoV-2, and critically analyse annotated peptides from existing research. Overall, our models effectively predict the location of fusion peptides, even in viruses for which limited experimental data is available.