CSEL-BGC:一个生物信息学框架,集成机器学习,用于定义未表征抗菌天然产品的生物合成进化景观
Minghui Du1, Yuxiang Ren1, Yang Zhang1
1School of Life Science and Bio-Pharmaceutics, Shenyang Pharmaceutical University, Shenyang, 110016, China.
Interdisciplinary sciences, computational life sciences
|September 30, 2024
概括
这项研究引入了一种机器学习框架,以加速从微生物天然产品中发现新的抗菌药物. 这种方法预测了具有抗生素潜力的细菌生物合成基因集群,有助于打击抗菌素耐药性.
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:
- 抗菌素耐药性 (AMR) 构成严重的全球健康威胁,因新抗菌药物发展缓慢而加剧.
- 微生物天然产品 (NPs) 代表了对新型抗生素发现的巨大化学资源.
- 从它们的生物合成基因集群 (BGC) 中有效地识别生物活性NP对于加速药物开发至关重要.
研究的目的:
- 开发一个计算框架,直接从BGCs预测抗菌活性.
- 为了加快从微生物基因组中识别新型抗菌剂.
- 探索抗菌NP的生物合成和进化格局.
主要方法:
- 开发一种新的级联堆叠集体学习 (CSEL) 模型.
- 在CSEL-BGC框架内整合机器学习 (ML) 技术.
- 建立一个强大的模型评估系统,用于BGC活动预测.
主要成果:
- 6,666个具有潜在抗菌活性的BGCs的预测.
- 分析了3468个完整的细菌基因组.
- 阐明生物合成进化格局,以了解NP的潜力.
结论:
- 该CSEL-BGC框架显著提高了抗菌BGCs的预测.
- 这种方法加速了下一代抗生素的发现管道.
- 为未来的药物开发提供了对NP生物合成和分泌机制的见解.
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