通过计算式质谱和基因组采矿使用Seq2PKS发现I型cis-AT多基化物
Donghui Yan1, Muqing Zhou1, Abhinav Adduri1
1Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
Nature communications
|June 25, 2024
概括
一个新的机器学习工具Seq2PKS可以预测1型多基基因集群中的化学结构. 这加快了发现具有潜在治疗应用的新型天然产品的速度.
科学领域:
- 自然产品化学 自然产品化学
- 生物技术是生物技术.
- 生物信息学是一种生物信息学.
背景情况:
- 1型多基类是重要的天然产品,具有多种药物应用,包括抗病毒,抗生素和抗瘤活动.
- 在公共微生物基因组中存在超过6万个1型多基因基因集群,但只确定了它们相应的代谢物的很小一部分.
- 目前用于描述多基基的方法,如生物活性引导的净化,成本昂贵且效率低下,阻碍了新型化合物的发现.
研究的目的:
- 开发一种机器学习算法Seq2PKS,用于预测1型多基合成酶的化学结构.
- 为了提高从基因组数据中识别未知的多基化物代谢物的准确性和效率.
- 通过分析大规模的基因组数据集,促进新生物活性天然产品的发现.
主要方法:
- 机器学习算法Seq2PKS被开发用于预测与1型多基合成酶相关的化学结构.
- 该算法为每个基因集群生成多个假定结构,以提高预测的准确性.
- 使用可变质谱数据库搜索来确定预测中的正确化学结构.
主要成果:
- 与现有的方法相比,Seq2PKS在预测多基基结构方面表现优越.
- 将Seq2PKS应用于Actinobacteria数据集,导致识别了负责生产蒙纳佐米,奥索米A和2-aminobenzamide-actiphenol的基因集群.
- 该研究成功地表征了以前未知的多基化物代谢物,扩大了已知的天然产品领域.
结论:
- Seq2PKS是预测1型多基基结构的有效工具,大大促进了天然产品的发现.
- 该算法加速了从微生物基因组中识别新型代谢物,克服了传统方法的局限性.
- 这项工作为发现来自多基虫的新治疗剂铺平了道路.
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