Screening Antimicrobial Peptides from Metagenomes Based on Deep Learning and Molecular Simulation(深度学习结合分子模拟高效筛选宏基因组数据中的抗菌肽)
摘要
Antimicrobial peptides are a type of peptide capable of exerting antibacterial functions by interacting with bacterial cell membranes or intracellular biomolecules, thereby disrupting bacterial physiological processes and ultimately leading to bacterial death. A novel deep learning model was constructed to screen antimicrobial peptides from soil metagenomic data and validated the screened peptides using techniques such as molecular docking and molecular dynamics simulations. The model demonstrated an outstanding performance with a precision of 98.7%, an accuracy of 96.5%, a recall rate of 91.9%, an F1-score of 95.2%, and a specificity of 99.2%, showcasing excellent efficiency, interpretability, and practical application value alongside robust generalization capabilities. After training, the model successfully identified several short peptides with significant antimicrobial potential, with a subset chosen for further investigation. The findings revealed that the screened peptide Gly-Thr-Ala-Trp-Arg-Trp-His-Tyr-Arg-Ala-Arg-Ser could effectively attach to the bacterial transcription regulator protein MrkH, exhibiting inhibitory effects on </i>Klebsiella pneumoniae<i>, </i>Escherichia coli<i>, and </i>Staphylococcus aureus<i>. This study aimed to provide a theoretical basis for the development and application of new antimicrobials in the food industry by integrating deep learning with molecular simulation technologies.(抗菌肽是一种可以通过与细菌细胞膜或细胞内生物分子相互作用,破坏细菌生理过程,最终导致细菌死亡,发挥抗菌功能的多肽。通过构建全新的深度学习模型,从土壤宏基因组数据中筛选抗菌肽,并使用分子对接和分子动力学模拟等技术对筛选出的肽进行验证。模型的精准度为98.7%、准确率为96.5%、召回率为91.9%、F1-score为95.2%、特异性为99.2%,该模型展现了出色的筛选性能和强大的泛化能力,同时在效率、可解释性和实际应用价值方面也体现出了显著的优势。经过训练后,模型成功地识别出了若干极具抗菌潜力的短肽,并选择了部分样本进一步研究。结果表明,筛选出的短肽Gly-Thr-Ala-Trp-Arg-Trp-His-Tyr-Arg-Ala-Arg-Ser能有效地附着在细菌的转录调节因子MrkH蛋白上,对肺炎克雷伯氏菌、大肠杆菌和金黄色葡萄球菌产生抑制作用。研究旨在结合深度学习与分子模拟技术等,为开发食品行业新型抗菌剂的开发和应用提供一定的理论依据。)