LevSeq:用于定向进化和机器学习的快速生成序列函数数据
Yueming Long1, Ariane Mora1, Francesca-Zhoufan Li2
1Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California91125, United States.
ACS synthetic biology
|December 24, 2024
概括
我们开发了Long-read every variant Sequencing (LevSeq) 技术,以快速生成蛋白质序列功能数据. 这种方法通过改善图书馆质量控制来帮助数据驱动的蛋白质工程和机器学习引导的蛋白质工程 (MLPE).
科学领域:
- 生物化学 生物化学
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 序列功能数据对于理解蛋白质格局至关重要,但在定向进化过程中很少产生.
- 获取综合序列函数数据的现有方法通常是耗时和资源密集的.
研究的目的:
- 引入长读每种变异测序 (LevSeq),这是一个用于快速生成蛋白质序列功能数据的新管道.
- 为了实现数据驱动的蛋白质工程和机器学习引导的蛋白质工程 (MLPE).
主要方法:
- LevSeq使用双条码策略与纳米孔测序相结合.
- 该管道无地集成到现有的蛋白质工程工作流程中.
- 为数据分析和可视化提供开源软件.
主要成果:
- LevSeq可以快速生成整个蛋白质编码基因的序列功能数据.
- 该方法有助于对突变发生库的质量控制,降低成本和时间.
- 模拟研究证实了LevSeq在各种条件下对变种检测的准确性.
- LevSeq在新化学工程中证明了原蛋白质的实用性.
结论:
- LevSeq通过提供必要的序列功能数据,使数据驱动的蛋白质工程成为可能.
- 广泛采用和数据共享将促进对蛋白质序列功能关系的理解.
- 该管道提高了效率,并减少了蛋白质工程工作中的资源支出.
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