five

Structural stability based deep sequencing of 5 protein targets

收藏
NIAID Data Ecosystem2026-05-01 收录
下载链接:
https://www.ncbi.nlm.nih.gov/sra/SRP474374
下载链接
链接失效反馈
官方服务:
资源简介:
We developed an efficient, cost-effective assay for Structural Inference By Structure-stability-based selection of deep mutation variants and high throughput sequencing (Sibs-Seq). We demonstrated that unlike naturally occurring homologous sequences evolved over billions of years, these stability-selected, artificial homologs in a single-round 24 or 36 hours of mutation selections is often sufficient for producing high-accuracy structure prediction with < 2 angstrom root-mean-squared deviation (RMSD) to the native structure. Overall design: A target protein of interest (POI) is subjected to error prone PCR (EP-PCR) to construct a mutation library (>106) that is inserted between two assisted-complementary fragments of the murine dihydrofolate reductase. A functional mDHFR will then produce tetrahydrofolate (THF) essential for Trimethoprim(TMP) resistance and allow growth of TMP-sensitive E. coli in the presence of TMP. By contrast, if the POI is in an unstable structural state, two fragments of mDHFR would be too far apart to reconstitute into a functional mDHFR. Lacking a functional mDHFR will inhibit the growth of TMP-sensitive E. coli cells when treated with TMP. Thus, when inserting a library of randomly mutated POIs, those stably folded variants will lead to TMP-resistant E. coli that can grow in the presence of TMP. The sequence counts of each mutant available from high-throughput sequencing will allow to calculate its fitness score, which provides an estimation for its stability.
创建时间:
2023-12-02
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作