Govindarajan Sridhar, Mannervik Bengt, Silverman Joshua A, Wright Kathy, Regitsky Drew, Hegazy Usama, Purcell Thomas J, Welch Mark, Minshull Jeremy, Gustafsson Claes
†DNA2.0, Inc., 1140 O'Brien Drive, Suite A, Menlo Park, California 94025, United States.
‡Department of Neurochemistry, Stockholm University, SE-10691 Stockholm, Sweden.
ACS Synth Biol. 2015 Mar 20;4(3):221-7. doi: 10.1021/sb500242x. Epub 2014 Jun 13.
We have used design of experiments (DOE) and systematic variance to efficiently explore glutathione transferase substrate specificities caused by amino acid substitutions. Amino acid substitutions selected using phylogenetic analysis were synthetically combined using a DOE design to create an information-rich set of gene variants, termed infologs. We used machine learning to identify and quantify protein sequence-function relationships against 14 different substrates. The resulting models were quantitative and predictive, serving as a guide for engineering of glutathione transferase activity toward a diverse set of herbicides. Predictive quantitative models like those presented here have broad applicability for bioengineering.
我们利用实验设计（DOE）和系统方差来有效探索由氨基酸取代引起的谷胱甘肽转移酶底物特异性。使用系统发育分析选择的氨基酸取代通过DOE设计进行综合组合，以创建一组信息丰富的基因变体，称为信息日志（infologs）。我们使用机器学习来识别和量化针对14种不同底物的蛋白质序列-功能关系。所得模型具有定量性和预测性，可作为工程改造谷胱甘肽转移酶对多种除草剂活性的指导。像本文中提出的这种预测性定量模型在生物工程中具有广泛的适用性。
