DLFea4AMPGen de novo design of antimicrobial peptides by integrating features learned from deep learning models

Han Gao, Feifei Guan, Boyu Luo, Dongdong Zhang, Wei Liu, Yuying Shen, Lingxi Fan, Guoshun Xu, Yuan Wang, Tao Tu, Ningfeng Wu, Bin Yao, Huiying Luo (), Yue Teng (), Jian Tian () and Huoqing Huang ()
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Han Gao: Chinese Academy of Agricultural Sciences
Feifei Guan: Chinese Academy of Agricultural Sciences
Boyu Luo: Academy of Military Medical Sciences
Dongdong Zhang: Western Medical Branch of PLA General Hospital
Wei Liu: Laboratory Department of the Second Medical Center of the General Hospital of the PLA
Yuying Shen: Chinese Academy of Agricultural Sciences
Lingxi Fan: Chinese Academy of Agricultural Sciences
Guoshun Xu: Chinese Academy of Agricultural Sciences
Yuan Wang: Chinese Academy of Agricultural Sciences
Tao Tu: Chinese Academy of Agricultural Sciences
Ningfeng Wu: Chinese Academy of Agricultural Sciences
Bin Yao: Chinese Academy of Agricultural Sciences
Huiying Luo: Chinese Academy of Agricultural Sciences
Yue Teng: Academy of Military Medical Sciences
Jian Tian: Chinese Academy of Agricultural Sciences
Huoqing Huang: Chinese Academy of Agricultural Sciences

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Deep learning models show promise in accelerating the design and optimization of antimicrobial peptides (AMPs), but current methods face challenges, such as low success rates, or large virtual library scales. In this study, we introduce DLFea4AMPGen, a bioactive peptide design strategy that leverages deep learning models to identify and extract key features associated with antimicrobial peptide activity. This approach enables the generation of peptide sequences with potential bioactivities. Using the SHapley Additive exPlanations (SHAP) method, we quantify the contribution of each amino acid in multifunctional peptides with potential antibacterial, antifungal, and antioxidant activities. Key feature fragments (KFFs) with the highest average contributions are extracted and classified into four subfamilies based on amino acid frequency. These high-frequency amino acids are systematically arranged to generate a plausible sequence subspace for candidate peptides, from which 16 representative sequences were selected for experimental validation. The results show that 75% (12/16) of the sequences exhibited at least two types of activity. Notably, D1 exhibits broad-spectrum antimicrobial activity, including efficacy against multidrug-resistant clinical pathogenic isolates both in vitro and in vivo. This proof-of-concept study underscores the potential of the DLFea4AMPGen platform for efficient design and screening of bioactive peptides, showcasing its value in AMP research.

Date: 2025
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DOI: 10.1038/s41467-025-64378-y

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