Department of Chemical and Biological Engineering, University of Colorado, Boulder, CO 80305, USA.
Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI 48824, USA.
Protein Eng Des Sel. 2021 Feb 15;34. doi: 10.1093/protein/gzab017.
Generating combinatorial libraries of specific sets of mutations are essential for addressing protein engineering questions involving contingency in molecular evolution, epistatic relationships between mutations, as well as functional antibody and enzyme engineering. Here we present optimization of a combinatorial mutagenesis method involving template-based nicking mutagenesis, which allows for the generation of libraries with >99% coverage for tens of thousands of user-defined variants. The non-optimized method resulted in low library coverage, which could be rationalized by a model of oligonucleotide annealing bias resulting from the nucleotide mismatch free-energy difference between mutagenic oligo and template. The optimized method mitigated this thermodynamic bias using longer primer sets and faster annealing conditions. Our updated method, applied to two antibody fragments, delivered between 99.0% (32451/32768 library members) to >99.9% coverage (32757/32768) for our desired libraries in 2 days and at an approximate 140-fold sequencing depth of coverage.
生成特定突变集的组合文库对于解决涉及分子进化中的偶然性、突变之间的上位关系以及功能性抗体和酶工程的蛋白质工程问题至关重要。在这里，我们提出了一种组合诱变方法的优化，该方法涉及基于模板的缺口诱变，可用于生成数万种用户定义变体的覆盖率> 99%的文库。未经优化的方法导致文库覆盖率低，这可以通过突变寡核苷酸与模板之间无核苷酸错配自由能差异导致的寡核苷酸退火偏置的模型来合理化。优化后的方法通过使用更长的引物组和更快的退火条件来减轻这种热力学偏差。我们的更新方法应用于两个抗体片段，在两天内为我们期望的文库提供了 99.0%（32451/32768 文库成员）至> 99.9%（32757/32768）的覆盖率，并且大约有 140 倍的测序深度覆盖。
