突变体
理论(学习稳定性)
协议(科学)
二硫键
计算生物学
计算机科学
蛋白质稳定性
酶
点突变
虚拟筛选
化学
生物
药物发现
生物化学
基因
医学
机器学习
病理
替代医学
作者
Hein J. Wijma,Maximilian J. L. J. Fürst,Dick B. Janssen
标识
DOI:10.1007/978-1-4939-7366-8_5
摘要
The ability to stabilize enzymes and other proteins has wide-ranging applications. Most protocols for enhancing enzyme stability require multiple rounds of high-throughput screening of mutant libraries and provide only modest improvements of stability. Here, we describe a computational library design protocol that can increase enzyme stability by 20-35 °C with little experimental screening, typically fewer than 200 variants. This protocol, termed FRESCO, scans the entire protein structure to identify stabilizing disulfide bonds and point mutations, explores their effect by molecular dynamics simulations, and provides mutant libraries with variants that have a good chance (>10%) to exhibit enhanced stability. After experimental verification, the most effective mutations are combined to produce highly robust enzymes.
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