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通过使用BoostMut的分子动力学模拟的自动化分析来丰富稳定突变

Kerlen T Korbeld¹, Maximilian J L J Fürst¹

¹Molecular Enzymology, University of Groningen, Groningen, The Netherlands.

Protein science : a publication of the Protein Society

|October 11, 2025

概括

蛋白质工程旨在提高生物催化剂和生物药物的蛋白质热稳定性. 一个新的计算工具BoostMut使用动态结构特征自动化突变过,提高预测准确性和成功率.

科学领域:

蛋白质工程是指蛋白质的工程.
计算生物学计算生物学
生物物理学的生物物理.

背景情况:

热稳定性对于蛋白质应用至关重要,但识别稳定突变是具有挑战性的.
目前的方法,如分子动力学 (MD) 模拟和突变过的视觉检查是缓慢和主观的.
需要对稳定突变进行自动预测,以改善蛋白质工程工作流程.

研究的目的:

介绍最佳稳定突变的生物物理概述 (BoostMut),用于标准化和自动化突变过的计算工具.
从MD模拟中对蛋白质稳定性评估的动态结构特征的分析进行正式化和自动化.
提高在蛋白质工程中识别稳定突变的成功率.

主要方法:

开发BoostMut,这是一个计算工具,从MD模拟中分析动态结构特征.
标准化和自动化以前用于手动验证的突变过原理.
在多个数据集中对BoostMut进行基准测试,并将其与机器学习模型集成,以提高性能.

主要成果:

BoostMut提供一致和可重复的蛋白质稳定性评估.
整合BoostMut的生物物理分析改善了各种热稳定性预测指标的预测率.
在烯环氧化酶的实验验证中,发现了新的稳定突变,从而提高了整体成功率.

结论:

BoostMut提供了一种标准化,自动化和有效的方法来过稳定突变.
该工具增强了现有的热稳定性预测工作流程,并可以帮助数据标签用于未来的预测器培训.
BoostMut促进了被忽视的稳定突变的发现,推进了生物催化剂和生物医学的蛋白质工程.

关键词:

模拟MDMD的模拟能源计算能源计算机器学习是机器学习.蛋白质工程工程蛋白质工程热稳定性热稳定性

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