BERTAVP: an interpretable multi-task learning model for identification and functional prediction of antiviral peptides

Antiviral peptides (AVPs) have potential to enhance our response to various viral diseases. One of the key determinants of their diverse antiviral properties lies in specific functional motifs, making them a valuable addition to therapeutic options. While most studies do not analyze the focus of their models or lack predictions for subclasses. Therefore, it is necessary to have a functional prediction of AVPs with a motif-based interpretable model. However, this remains a challenge because of restricted peptides representation and data imbalance. Here, we introduce BERTAVP, an interpretable deep learning framework that employs multi-task learning to identify AVPs and simultaneously characterize their functional activities (eight species and six families). First, it utilizes the BERT branch to extract peptide features and CNN branch to extract amino acid and physicochemical features. It then applies the focal loss to mitigate the negative impact caused by imbalanced datasets. Finally, we further analyze key motifs learned by BERTAVP, finding ELDKWA and SLWNWF motifs may serve as critical structural and functional modules that define the overall physicochemical character of AVPs. Furthermore, within the tryptophan-rich motif (TRM), WMEWDREI motif exhibits the most potent anti-HIV activity, while the H in KxHxx motif exhibits the most distinctive coronavirus-specific characteristics. Experimental results on public datasets show our model achieves superior performance in identification and functional prediction of AVPs. Source codes are available at https://github.com/755a755/BERTAVP.