Towards generalized structure determination of in-cell NMR using the fully automated structure analysis

研究蛋白质在生活环境中的“工作”是分子生物学的一个主要目标。新的核磁共振方法学使得能够测量活细胞中蛋白质的多维核磁共振谱。与纯化蛋白质的体外光谱相比，细胞内NMR光谱通常显示大量的背景信号、低信噪比和加宽的信号，这导致高得多的分配模糊性，这使得手动检查所有可能性变得困难和繁琐。在这个项目中，我们开发了一个全自动的结构测定算法，NOESY-FLYA方法，并将其优化为细胞内NMR数据。

Investigating proteins "at work" in a living environment is a major goal of molecular biology. New NMR methodology has enabled the measurement of multi-dimensional NMR spectra of proteins in living cells. Compared to in vitro spectra of purified proteins in-cell NMR spectra typically show a significant number of background signals, low signal-noise ratio, and broadened signals that lead to a much higher assignment ambiguity, which makes it difficult and cumbersome to check all possibilities manually. In this project, we developed a fully automated structure determination algorithm, NOESY-FLYA method, and optimized it to the in-cell NMR data.