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萜烯合酶进化中的稀疏上位模式。

Sparse Epistatic Patterns in the Evolution of Terpene Synthases.

机构信息

Department of Physics & Astronomy and Center for Quantitative Biology, Rutgers University, Piscataway, NJ.

John Innes Centre, Department of Metabolic Biology, Norwich Research Park, Norwich, United Kingdom.

出版信息

Mol Biol Evol. 2020 Jul 1;37(7):1907-1924. doi: 10.1093/molbev/msaa052.

DOI:10.1093/molbev/msaa052
PMID:32119077
Abstract

We explore sequence determinants of enzyme activity and specificity in a major enzyme family of terpene synthases. Most enzymes in this family catalyze reactions that produce cyclic terpenes-complex hydrocarbons widely used by plants and insects in diverse biological processes such as defense, communication, and symbiosis. To analyze the molecular mechanisms of emergence of terpene cyclization, we have carried out in-depth examination of mutational space around (E)-β-farnesene synthase, an Artemisia annua enzyme which catalyzes production of a linear hydrocarbon chain. Each mutant enzyme in our synthetic libraries was characterized biochemically, and the resulting reaction rate data were used as input to the Michaelis-Menten model of enzyme kinetics, in which free energies were represented as sums of one-amino-acid contributions and two-amino-acid couplings. Our model predicts measured reaction rates with high accuracy and yields free energy landscapes characterized by relatively few coupling terms. As a result, the Michaelis-Menten free energy landscapes have simple, interpretable structure and exhibit little epistasis. We have also developed biophysical fitness models based on the assumption that highly fit enzymes have evolved to maximize the output of correct products, such as cyclic products or a specific product of interest, while minimizing the output of byproducts. This approach results in nonlinear fitness landscapes that are considerably more epistatic. Overall, our experimental and computational framework provides focused characterization of evolutionary emergence of novel enzymatic functions in the context of microevolutionary exploration of sequence space around naturally occurring enzymes.

摘要

我们探索了萜烯合酶这一主要酶家族中酶活性和特异性的序列决定因素。该家族中的大多数酶催化产生环状萜烯的反应——这些复杂的碳氢化合物被植物和昆虫广泛用于各种生物过程,如防御、通讯和共生。为了分析萜烯环化出现的分子机制,我们深入研究了青蒿素(Artemisia annua)酶(E)-β-法呢烯合酶周围的突变空间,该酶催化产生线性碳氢链。我们合成文库中的每个突变酶都进行了生化特性表征,所得的反应速率数据被用作酶动力学米氏方程(Michaelis-Menten model of enzyme kinetics)的输入,其中自由能表示为一个氨基酸贡献和两个氨基酸耦合的总和。我们的模型以高精度预测了测量的反应速率,并产生了由相对较少的耦合项描述的自由能景观。结果,米氏方程自由能景观具有简单、可解释的结构,并且表现出很少的上位性。我们还根据假设开发了生物物理适应性模型,即高度适应的酶已经进化到最大限度地提高正确产物(如环状产物或特定感兴趣产物)的输出,同时最小化副产物的输出。这种方法导致了相当多的上位性非线性适应性景观。总的来说,我们的实验和计算框架提供了一种集中的方法来描述自然存在的酶序列空间的微进化探索背景下新型酶功能的进化出现。

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