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数据驱动的蛋白质酶工程通过DNA记录和表观意识机器学习来实现.

Lukas Huber1, Tim Kucera1,2,3, Simon Höllerer1

  • 1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.

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机器学习现在有助于蛋白质工程,但预测催化活性是困难的. 本研究介绍了一种DNA记录器和深度学习模型,用于设计具有特定功能的蛋白酶,克服数据限制.

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科学领域:

  • 生物化学 生物化学
  • 分子生物学分子生物学
  • 计算生物学 计算生物学

背景情况:

  • 机器学习 (ML) 工具有先进的蛋白质结构预测.
  • 预测蛋白质催化活性和设计具有所需功能的序列仍然是重大挑战.
  • 目前的局限性源于实验数据不足和对庞大的蛋白质序列空间的低效探索.

研究的目的:

  • 为了设计具有量身定制基质特异性的蛋白酶.
  • 克服ML大规模生成序列活动数据的局限性.
  • 开发一个数据效率高的ML模型来预测蛋白酶功能.

主要方法:

  • 开发一种DNA记录器,用于对大肠杆菌中的蛋白酶进行深度特异性分析.
  • 对29716个候选蛋白酶进行了对134个基质的并行测试.
  • 产生约60万个蛋白酶-基质对数据.
  • 应用epistasis意识到训练集设计的ML模型优化.

主要成果:

  • 确定控制蛋白酶特异性的关键序列决定因素.
  • 创建一个数据效率高的深度学习模型,能够准确预测蛋白酶序列.
  • 展示所需的目标和目标之外的活动预测.
  • 对于高效的序列空间探索而设计的经验意识训练集设计的验证.

结论:

  • 开发的DNA记录器和ML方法使蛋白酶特异性的精确工程成为可能.
  • 经验意识的训练集设计显著提高了模型的准确性和实验效率.
  • 这项工作为蛋白质工程提供了一种超出蛋白酶范围的可通用策略.