Voigt C A, Mayo S L, Arnold F H, Wang Z G
Biochemistry Option, Divisions of Biology and Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
Proc Natl Acad Sci U S A. 2001 Mar 27;98(7):3778-83. doi: 10.1073/pnas.051614498.
We introduce a computational method to optimize the in vitro evolution of proteins. Simulating evolution with a simple model that statistically describes the fitness landscape, we find that beneficial mutations tend to occur at amino acid positions that are tolerant to substitutions, in the limit of small libraries and low mutation rates. We transform this observation into a design strategy by applying mean-field theory to a structure-based computational model to calculate each residue's structural tolerance. Thermostabilizing and activity-increasing mutations accumulated during the experimental directed evolution of subtilisin E and T4 lysozyme are strongly directed to sites identified by using this computational approach. This method can be used to predict positions where mutations are likely to lead to improvement of specific protein properties.
我们介绍了一种用于优化蛋白质体外进化的计算方法。通过使用一个简单模型模拟进化，该模型从统计学角度描述了适应度景观，我们发现在小文库和低突变率的情况下，有益突变倾向于发生在对替换具有耐受性的氨基酸位置。我们将这一观察结果转化为一种设计策略，即将平均场理论应用于基于结构的计算模型，以计算每个残基的结构耐受性。在枯草杆菌蛋白酶E和T4溶菌酶的实验定向进化过程中积累的热稳定和活性增强突变，强烈指向使用这种计算方法确定的位点。该方法可用于预测突变可能导致特定蛋白质特性改善的位置。
