使用RealKcat对酶变异动力学的可靠预测
Karuna Anna Sajeevan1,2, Abraham Osinuga3, Arunraj B1
1Department of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA.
bioRxiv : the preprint server for biology
|February 24, 2025
概括
研究人员开发了RealKcat,这是一个用于预测酶动态的新型计算模型. 这种工具准确地预测了对酶活性的突变影响,推进了酶设计和生物催化剂应用.
科学领域:
- 生物催化和酶工程 生物催化和酶工程
- 计算生物学和生物信息学
- 蛋白质科学 蛋白质科学
背景情况:
- 精确预测酶动力学参数 (催化周转率,基质亲和力) 对于理解酶功能和设计新型生物催化剂至关重要.
- 现有的计算模型往往难以准确预测突变对催化必需残留物的影响,这阻碍了它们在酶设计中的应用.
- 开发复杂的预测模型是必要的,以克服这些局限性,并使精确的酶工程.
研究的目的:
- 开发和验证一个新的计算框架,RealKcat,用于准确预测酶动力学参数,特别是催化周转率 (kcat) 和基质亲和力 (Km).
- 解决当前模型在捕获对催化必需残留物突变效应方面的局限性.
- 为预测由遗传修饰引起的酶活性变化建立一个新的基准.
主要方法:
- 在10个模型架构和25671个超参数组合中进行了广泛的网格搜索.
- 开发了一个基于梯度的增材框架,名为RealKcat.
- 在手工策划的数据集 (KinHub-27k) 上训练模型,该数据集包括来自2,158篇科学文章的27,176个实验条目.
- 将动力参数 (kcat,Km) 按合理数量级进行集群,以便进行可靠的分析.
主要成果:
- 在预测动力参数方面,RealKcat实现了>85%的测试准确度.
- 与现有方法相比,该模型对突变诱导的变异性表现出更高的灵敏度.
- RealKcat是第一个准确预测催化残留物删除后酶活性完全丧失的模型.
- 在工业性酸酶 (PafA) 突变数据集上实现了最先进的96%的验证准确性,证实了概括性.
结论:
- RealKcat在预测酶动力学和突变影响方面取得了重大进展.
- 该模型能够准确地捕获每残留的催化相关性,这提高了其在酶设计和定向进化的实用性.
- RealKcat在工业数据集上的表现验证了其在生物催化和酶工程中的实际应用潜力.
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