Text-To-AMP: Antimicrobial Peptide Design Guided by Natural Text

Antimicrobial peptides (AMPs) are vital components of the natural immune system. With their broad-spectrum antibacterial properties, low resistance, and multifunctionality, they are considered important molecules for addressing the problem of bacterial resistance. At the same time, with the rapid development of large language models, their powerful intelligence has made natural language an increasingly important bridge for human-machine interaction, bringing new perspectives to AMP design. By integrating natural language with protein language, we can significantly reduce the complexity of tasks, enhance intuitiveness and operational convenience, and deeply incorporate expert knowledge, thus providing new insights for exploring and understanding peptide design. Based on these benefits, we propose Text-to-AMP, an innovative framework that uses natural language descriptions to directly guide AMP sequence generation. Through the integration of multimodal implicit alignment, explicit alignment, and autoregressive sequence generation, Text-to-AMP establishes a semantic connection between natural language and peptide sequences, enabling precise and interpretable peptide design. Comprehensive experimental evaluations demonstrate that our model outperforms baseline methods on most metrics in AMP activity prediction tasks. In zero-shot text-guided sequence generation tasks, the model surpasses benchmarks, including the GPT series, by generating novel peptide sequences with enhanced antimicrobial activity and reduced toxicity. Furthermore, case studies validate the model’s capability to meet fine-grained structural and functional design criteria. In conclusion, Text-to-AMP introduces a advanced paradigm for AMP design by significantly lowering technical barriers and accelerating the discovery of antimicrobial peptides.