使用FolDE优化低N蛋白活性
Jacob B Roberts1,2,3, Catherine R Ji4, Isaac Donnell1,3,5
1Joint BioEnergy Institute, Emeryville, CA, USA.
ArXiv
|November 24, 2025
概括
FolDE通过智能选择用于测试的突变体来增强蛋白质优化. 这种主动学习辅助定向进化 (ALDE) 方法提高了预测准确性,更有效地发现了优质蛋白质变体.
科学领域:
- 蛋白质工程是一种蛋白质工程.
- 计算生物学是一种计算生物学.
- 生物技术是生物技术.
背景情况:
- 蛋白质优化传统上需要对突变物进行广泛的实验选.
- 积极学习辅助的定向进化 (ALDE) 通过预测有益的突变来降低成本.
- 目前的ALDE方法受到同质的训练数据的影响,这限制了以后轮的预测准确性.
研究的目的:
- 开发一种新的ALDE方法,FolDE,以最大限度地提高蛋白质优化活动的成功率.
- 提高预测有益蛋白质突变的准确性和效率.
主要方法:
- FolDE使用基于自然性的热启动,将蛋白质语言模型输出与实验数据集成在一起.
- 引入了一种恒定骗子批量选择器,以增强多变异运动的批量多样性.
- 该方法通过对20个不同的蛋白质标进行模拟来评估.
主要成果:
- 与基线方法相比,FolDE发现了23%更多的前10%突变物 (p=0.005).
- 在FolDE中,发现前1%突变的可能性提高了55%.
- 基于自然性的热启动,通过增加有限的测量,显著改善了活动预测.
结论:
- 在蛋白质工程方面,FolDE代表了ALDE的重大进步.
- 该方法增强了高性能蛋白质变体的发现.
- 作为开源软件提供的FolDE使高效的蛋白质优化实现了民主化.
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