PPsAMP: A Novel Computational Framework for Short Antimicrobial Peptide Identification by Fusing Fine-Tuned Semantic and Physicochemical Features via Cross-Attention
The widespread misuse of antibiotics has led to a global antimicrobial resistance crisis, highlighting the urgent need for novel antibacterial strategies. Short antimicrobial peptides (sAMPs), while maintaining strong antimicrobial activity, offer superior bioavailability and synthetic feasibility, thus holding great promise in the development of next-generation antibiotics. In recent years, AI-based approaches have achieved notable progress in AMP prediction; however, most existing models are trained primarily on medium and long peptides, resulting in limited accuracy and representation capability when applied to identify sAMPs. To address this problem, a novel prediction model, PPsAMP, is proposed in this paper. First, the protein language model ProtBert-BFD is fine-tuned by sAMPs and non-sAMPs to extract more discriminative representations, which are then integrated with physicochemical features through a cross-attention mechanism. The fused representation is further processed by a feature learning module to achieve the identification of sAMP. The feature learning module consists of a multihead self-attention mechanism and feedforward layers, with residual connections added to enhance generalization ability. Experimental results demonstrate that PPsAMP significantly outperforms state-of-the-art models for identifying sAMPs. Moreover, PPsAMP has identified 14,839 candidate sAMPs from environmental metagenomes, most of which have not been previously reported. The predicted MIC values indicate that they possess potential antibacterial activity. PPsAMP is freely available at https://github.com/shengxiliu/PPsAMP.