FusPB-ESM2: Fusion model of ProtBERT and ESM-2 for cell-penetrating peptide prediction

Abstract

Cell-penetrating peptides have attracted much attention for their ability to break through cell membrane barriers, which can improve drug bioavailability, reduce side effects, and promote the development of gene therapy. Traditional wet-lab prediction methods are time-consuming and costly, and computational methods provide a short-time and low-cost alternative. Still, the accuracy and reliability need to be further improved. To solve this problem, this study proposes a feature fusion-based prediction model, where the protein pre-trained language models ProtBERT and ESM-2 are used as feature extractors, and the extracted features from both are fused to obtain a more comprehensive and effective feature representation, which is then predicted by linear mapping. Validated by many experiments on public datasets, the method has an AUC value as high as 0.983 and shows high accuracy and reliability in cell-penetrating peptide prediction.