饱和突变
蛋白质工程
突变
计算生物学
点突变
理论(学习稳定性)
蛋白质稳定性
蛋白质结构
计算机科学
遗传学
蛋白质折叠
突变
生物
突变体
化学
生物化学
基因
机器学习
酶
作者
David F. Thieker,Jack Maguire,Stephan T. Kudlacek,Andrew Leaver-Fay,Sergey Lyskov,Brian Kuhlman
摘要
Many proteins have low thermodynamic stability, which can lead to low expression yields and limit functionality in research, industrial and clinical settings. This article introduces two, web-based tools that use the high-resolution structure of a protein along with the Rosetta molecular modeling program to predict stabilizing mutations. The protocols were recently applied to three genetically and structurally distinct proteins and successfully predicted mutations that improved thermal stability and/or protein yield. In all three cases, combining the stabilizing mutations raised the protein unfolding temperatures by more than 20°C. The first protocol evaluates point mutations and can generate a site saturation mutagenesis heatmap. The second identifies mutation clusters around user-defined positions. Both applications only require a protein structure and are particularly valuable when a deep multiple sequence alignment is not available. These tools were created to simplify protein engineering and enable research that would otherwise be infeasible due to poor expression and stability of the native molecule.
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