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利用精心设计的组合简并引物构建“小型智能”聚焦诱变文库。

Construction of "small-intelligent" focused mutagenesis libraries using well-designed combinatorial degenerate primers.

机构信息

School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.

出版信息

Biotechniques. 2012 Mar;52(3):149-58. doi: 10.2144/000113820.

Abstract

Site-saturation mutagenesis is a powerful tool for protein optimization due to its efficiency and simplicity. A degenerate codon NNN or NNS (K) is often used to encode the 20 standard amino acids, but this will produce redundant codons and cause uneven distribution of amino acids in the constructed library. Here we present a novel "small-intelligent" strategy to construct mutagenesis libraries that have a minimal gene library size without inherent amino acid biases, stop codons, or rare codons of Escherichia coli by coupling well-designed combinatorial degenerate primers with suitable PCR-based mutagenesis methods. The designed primer mixture contains exactly one codon per amino acid and thus allows the construction of small-intelligent mutagenesis libraries with one gene per protein. In addition, the software tool DC-Analyzer was developed to assist in primer design according to the user-defined randomization scheme for library construction. This small-intelligent strategy was successfully applied to the randomization of halohydrin dehalogenases with one or two randomized sites. With the help of DC-Analyzer, the strategy was proven to be as simple as NNS randomization and could serve as a general tool to efficiently randomize target genes at positions of interest.

摘要

定点饱和突变是一种强大的蛋白质优化工具,因为它高效且简单。通常使用简并密码子 NNN 或 NNS (K) 来编码 20 种标准氨基酸,但这会产生冗余密码子,并导致构建文库中氨基酸的不均匀分布。在这里,我们提出了一种新颖的“小智能”策略,通过将精心设计的组合简并引物与合适的基于 PCR 的突变方法相结合,构建具有最小基因文库大小、无固有氨基酸偏倚、无终止密码子或大肠杆菌稀有密码子的突变文库。设计的引物混合物每个氨基酸恰好包含一个密码子,因此可以构建每个蛋白质一个基因的小智能突变文库。此外,还开发了软件工具 DC-Analyzer,根据用户定义的文库构建随机化方案辅助引物设计。该小智能策略已成功应用于卤代醇脱卤酶的一个或两个随机化位点的随机化。在 DC-Analyzer 的帮助下,该策略被证明与 NNS 随机化一样简单,可作为一种通用工具,有效地在感兴趣的位置随机化目标基因。

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