从高通量评估到湿实验室研究:通过提取增强模型推进突变效应预测.
Yang Tan1,2,3,4, Ruilin Wang2, Banghao Wu1,4
1Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China.
Bioinformatics (Oxford, England)
|July 15, 2025
概括
维纳斯REM是一种新的蛋白质语言模型,通过预测突变效应来增强酶工程. 这种深度学习工具提高了酶的稳定性和活性,通过计算和实验研究进行了验证.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 蛋白质工程是指蛋白质的工程.
背景情况:
- 酶工程对于工业和研究应用至关重要,传统上依赖于定向进化和理性设计.
- 深度学习模型通过捕捉蛋白质中的共同进化模式,提供先进的,具有成本效益的替代方案.
- 了解蛋白质序列,结构和功能关系仍然是酶工程中的一个关键挑战.
研究的目的:
- 介绍VenusREM,一种用于酶工程的提取增强蛋白质语言模型.
- 评估VenusREM在预测酶特性突变效应方面的表现.
- 通过计算和实验评估验证VenusREM的实用性.
主要方法:
- 开发了VenusREM,这是一个包含检索机制的蛋白质语言模型.
- 在217个ProteinGym基准测试中评估了VenusREM.
- 使用VHH抗体和DNA聚合酶突变体进行实验验证.
主要成果:
- 维纳斯REM在ProteinGym基准上取得了最先进的表现.
- 计算和实验分析证实了VenusREM提高VHH抗体稳定性和结合亲和力的能力.
- 设计的DNA聚合酶突变体在高温下具有增强的活性,经过实验验证.
结论:
- 维纳斯REM是一个可靠的计算工具,用于酶工程.
- 该研究为评估突变效应预测模型提供了一个全面的框架.
- 深度学习方法显著提升了蛋白质工程能力.
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