eCodonOpt:: a systematic computational framework for optimizing codon usage in directed evolution experiments
eCodonOpt:: a systematic computational framework for optimizing codon usage in directed evolution experiments
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DOI：
10.1093/nar/30.11.2407
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发表时间：
2002-06-01
影响因子：
14.9
通讯作者：
Maranas, CD
中科院分区：
文献类型：
--
作者：
Moore, GL;Maranas, CD
我们提出了一个系统的计算框架eCodonOpt，用于通过优化密码子的使用来设计用于定向进化实验的亲本DNA序列。给定一组要在DNA水平上重组的同源亲本蛋白质，为给定的多样性目标寻找编码这些蛋白质的最佳DNA序列。我们发现，重组DNA序列之间的退火自由能比序列同一性更好地描述了交叉形成的程度。对DNA改组协议的三个不同的多样性目标进行了研究，以展示eCodonOpt框架的实用性：(I)最大化每个重组序列的平均交叉次数；(Ii)最小化家族DNA改组中的偏差，使得每个亲本序列对对文库贡献相似的交叉次数；以及(Iii)最大化特定结构区域中的交叉的相对频率。这些设计挑战中的每一个都被描述为一个约束优化问题，该问题利用0-1个二进制变量作为开/关开关来为每个残基位置选择不同的密码子选择建模。计算结果表明，在交叉频率、位置和特异性方面有可能提高许多倍，这为定向进化协议的工程提供了有价值的见解。
We present a systematic computational framework, eCodonOpt, for designing parental DNA sequences for directed evolution experiments through codon usage optimization. Given a set of homologous parental proteins to be recombined at the DNA level, the optimal DNA sequences encoding these proteins are sought for a given diversity objective. We find that the free energy of annealing between the recombining DNA sequences is a much better descriptor of the extent of crossover formation than sequence identity. Three different diversity targets are investigated for the DNA shuffling protocol to showcase the utility of the eCodonOpt framework: (i) maximizing the average number of crossovers per recombined sequence; (ii) minimizing bias in family DNA shuffling so that each of the parental sequence pair contributes a similar number of crossovers to the library; and (iii) maximizing the relative frequency of crossovers in specific structural regions. Each one of these design challenges is formulated as a constrained optimization problem that utilizes 0-1 binary variables as on/off switches to model the selection of different codon choices for each residue position. Computational results suggest that many-fold improvements in the crossover frequency, location and specificity are possible, providing valuable insights for the engineering of directed evolution protocols.