从反动物胃肠道微生物组中揭开新型抗菌:以深度学习为驱动的方法产生了一种抗MRSA候选药物
Hong Shen1, Yanru Li2, Qingjie Pi3
1Bioinformatics Center, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China.
Journal of advanced research
|January 5, 2025
概括
研究人员使用深度学习从反动物微生物组中发现了新型抗菌 (AMP). 4对具有低抗性潜力的MRSA感染表现出强烈的活性,提供了一种新的治疗途径.
科学领域:
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
- 药物发现 药物发现 药物发现
背景情况:
- 抗微生物 (AMP) 在打击抗生素耐药性方面至关重要.
- 食性胃肠道微生物组是新型AMP的丰富,尚未探索的来源.
研究的目的:
- 开发一种深度学习方法,用于从反动物微生物组中识别AMP.
- 评估已识别的AMP候选药物的抗菌疗效和机制.
主要方法:
- 一个深度学习模型被应用于120个元基因组和10,373个来自反动物微生物组的基因组.
- 生物信息分析通过体外和体内实验选择了化学合成和验证的候选者.
- 分子动力学模拟阐明了强大的AMP候选者的作用机制.
主要成果:
- 该模型确定了27,192个潜在的AMP;39个被合成和测试.
- 所有合成的都对金黄色葡萄球菌表现出活性; 79.5%对多种病原体有效.
- 4在体外和小鼠伤口感染模型中显示出对抗抗甲素耐药黄金葡萄球菌 (MRSA) 的高疗效,具有较低的耐药性和细胞毒性.
结论:
- 深度学习与微生物组研究相结合,有效地识别出新型抗菌疗法.
- 4代表了一种有希望的下一代抗微生物药物,用于对抗MRSA感染.
- 动物微生物资源为发现新的治疗剂提供了巨大的潜力.
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