Leveraging protein language models for robust antimicrobial peptide detection

Antimicrobial peptides (AMPs) are promising candidates for addressing the global challenge of antibiotic resistance due to their broad-spectrum antimicrobial properties. Traditional AMP identification methods, while effective, are labor-intensive and time-consuming. Recent advancements in deep learning and large language models (LLMs), especially protein language models (PLMs) present a transformative approach for AMP prediction. In this study, we propose PLAPD, a novel framework leveraging a pre-trained ESM2 protein language model for AMP classification. Besides, PLAPD combines local feature extraction via convolutional layers and global feature extraction with a residual Transformer module. We benchmarked PLAPD against state-of-the-art AMP prediction models using a dataset comprising 8,268 peptide sequences, achieving superior performance in Accuracy (0.87), Precision (0.9359), Specificity (0.9456), MCC (0.7486), and AUC (0.9225). The results highlight the potential of PLAPD as a high-throughput and accurate tool for AMP discovery.