Suppr超能文献

用于高保真度和高复杂度 Golden Gate 组装的融合位点突出端集的选择。

Selection of Fusion-Site Overhang Sets for High-Fidelity and High-Complexity Golden Gate Assembly.

机构信息

Research Department, New England Biolabs, Ipswich, MA, USA.

出版信息

Methods Mol Biol. 2025;2850:41-60. doi: 10.1007/978-1-0716-4220-7_3.

Abstract

Golden Gate Assembly depends on the accurate ligation of overhangs at fragment fusion sites to generate full-length products with all parts in the desired order. Traditionally, fusion-site sequences are selected by using validated sets of overhang sequences or by applying a handful of semi-empirical rules to guide overhang choice. While these approaches allow dependable assembly of 6-8 fragments in one pot, recent work has demonstrated that comprehensive measurement of ligase fidelity allows prediction of high-fidelity junction sets that enable much more complex assemblies of 12, 24, or even 36+ fragments in a single reaction that will join with high accuracy and efficiency. In this chapter, we outline the application of a set of online tools that apply these comprehensive datasets to the analysis of existing junction sets, the de novo selection of new high-fidelity overhang sets, the modification and expansion of existing sets, and the principles for dividing known sequences at an arbitrary number of high-fidelity breakpoints.

摘要

金门装配依赖于片段融合位点的突出端的精确连接,以生成具有所需顺序的所有部分的全长产物。传统上,通过使用经过验证的突出端序列集或应用少量半经验规则来指导突出端选择来选择融合位点序列。虽然这些方法允许在一个锅中可靠地组装 6-8 个片段,但最近的工作表明,全面测量连接酶保真度可以预测高保真性连接组,从而能够在单个反应中更复杂地组装 12、24 甚至 36 个以上的片段,并且具有高精度和高效率。在本章中,我们概述了一组在线工具的应用,这些工具将这些全面的数据集应用于现有连接组的分析、新的高保真突出端集的从头选择、现有集的修改和扩展,以及在任意数量的高保真断点处划分已知序列的原则。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验