对于多目标蛋白质序列设计的帕雷托最佳采样
Jiaqi Luo1, Kerr Ding1, Yunan Luo1
1School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA.
iScience
|March 31, 2025
概括
使用机器学习,MosPro高效地设计具有所需属性的蛋白质序列. 这种生成方法可以在广的搜索空间中寻找新的功能性蛋白质设计.
科学领域:
- 计算生物学是一种计算生物学.
- 蛋白质工程是一种蛋白质工程.
- 机器学习是机器学习.
背景情况:
- 监督机器学习 (ML) 在从序列中预测蛋白质特性方面表现出色.
- 设计具有特定属性的蛋白质序列 (反向问题) 是由于大搜索空间和复杂的健身景观而具有挑战性的.
研究的目的:
- 介绍MosPro,一种高效的ML算法,用于属性引导的蛋白质序列设计.
- 为了解决蛋白质设计中未被充分探索的反向问题.
主要方法:
- 框架序列设计作为离散采样问题.
- 使用预训练的可微分ML模型来预测序列属性.
- 向高属性序列塑造一个概率分布.
- 在多属性序列设计中使用帕雷托优化.
主要成果:
- 莫斯普罗有效地从构建的分布中取样序列.
- 巴雷托优化成功地提出了针对多个属性的优化序列.
- 对实验性健身场景的评估证实了MosPro能够平衡多个设计需求的能力.
结论:
- 莫斯普罗证明了有效和可控制的功能性蛋白质设计的巨大潜力.
- 生成式机器学习为解决复杂的蛋白质工程挑战提供了强大的工具.
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