ACP-ESM2: Enhancing Anticancer Peptide Prediction With Pre-Trained Protein Language Models

Anticancer peptide (ACP) are short peptides with anti-cancer properties that have generated increasing attention in recent years due to their low toxicity, minimal side effects, and their ability to precisely target and kill cancer cells. Traditionally, identifying ACP has relied on experimental methods, which are time-consuming and labor-intensive. While deep learning-based prediction methods have made significant progress, there is still room for improvement in achieving optimal performance. In this study, we present ACP-ESM2, a deep learning framework based on the Evolutionary Scale Modeling 2 (ESM2) pre-trained model, which captures rich evolutionary information from protein sequences. By combining ESM2 with convolutional neural network (CNN) that excels at detecting local patterns, ACP-ESM2 offers a highly accurate tool for ACP prediction. The experimental results indicate that ACP-ESM2 shows significant improvements over best-existing recognition techniques on the Test1 set, with enhancements of 2.3%, 7.2%, 12.6%, and 5% in ACC, SN, SP, and MCC, respectively. Notably, on the Test2 set, ACP-ESM2 achieves an accuracy of 97.6%, showcasing its exceptional robustness. This establishes ACP-ESM2 as an efficient and precise tool for predicting anticancer peptides.