A(C)VPpred: Transfer Learning-Enhanced Prediction of Antiviral and Anticoronavirus Peptides from Sequence Data

Viral infections pose an ongoing public health challenge and create a need for innovative therapies. Antiviral peptides (AVPs) offer a promising solution due to their high specificity, low toxicity, and reduced risk of inducing drug resistance. The COVID-19 pandemic has heightened the need for targeted anticoronavirus peptides (ACVPs). We introduce the term A(C)VPs (where "(C)" indicates the optional coronavirus specificity) to encompass both broad-spectrum AVPs and ACVPs. However, experimental screening of effective A(C)VPs remains slow and costly. To overcome these limitations, we propose A(C)VPpred, a computational pipeline integrating transfer learning via a fine-tuned BERT model, for the simultaneous prediction of AVPs and ACVPs. This framework first trains a general AVP predictor and then fine-tunes it for ACVP specificity, enabling the precise identification of peptides effective against diverse viruses, including emerging coronaviruses. Comparative analysis reveals that our A(C)VPpred model outperforms the state-of-the-art models on the test data set. Specifically, the AVP module attains peak performance at 98.6% accuracy and 97.2% Matthews correlation coefficient (MCC), whereas the ACVP module achieves 97% accuracy with 94.1% MCC. Additionally, analysis of BERT's attention mechanism revealed that A(C)VPpred captures structural features directly from sequence data. A(C)VPpred is expected to contribute to computer-aided screening and the design of AVP antiviral drugs. To further enhance accessibility for researchers, we have developed a user-friendly web interface, available at https://fond-defensive-day.anvil.app/.