AssignSLP_GUI, a software tool exploiting AI for NMR resonance assignment of sparsely labeled proteins.
AssignSLP_GUI, a software tool exploiting AI for NMR resonance assignment of sparsely labeled proteins.
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DOI：
10.1016/j.jmr.2022.107336
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发表时间：
2022-12
影响因子：
2.2
通讯作者：
Prestegard, James H.
中科院分区：
文献类型：
--
作者：
V. Williams, Robert;Rogals, Monique J.;Eletsky, Alexander;Huang, Chin;Morris, Laura C.;Moremen, Kelley W.;Prestegard, James H.
并不是所有的蛋白质都能用13 C和15 N进行统一的同位素标记，这是广泛使用的，并且在很大程度上是演绎的三重共振分配过程所需要的。其中包括在哺乳动物细胞培养物中表达的蛋白质，其中可以维持天然糖基化，并促进二硫键的适当形成。在哺乳动物细胞中进行均匀标记是非常昂贵的，但是用一种或几种同位素富集的氨基酸类型进行稀疏标记是这些蛋白质的一种选择。然而，分配依赖于访问各种测量的NMR参数和基于3D结构的预测之间的最佳匹配，通常来自X射线晶体学。找到这种匹配是一个具有挑战性的过程，它受益于许多计算工具，包括用于化学位移预测的训练神经网络，用于搜索无数分配可能性的遗传算法，以及现在基于人工智能的蛋白质靶点高质量结构预测。AssignSLP_GUI是一个新版本的软件包，用于分配稀疏标记蛋白质的共振，使用了许多这些工具。这些工具和新增加的软件包中突出显示的应用程序稀疏标记的结构域从糖蛋白，CEACAM1。
Not all proteins are amenable to uniform isotopic labeling with 13C and 15N, something needed for the widely used, and largely deductive, triple resonance assignment process. Among them are proteins expressed in mammalian cell culture where native glycosylation can be maintained, and proper formation of disulfide bonds facilitated. Uniform labeling in mammalian cells is prohibitively expensive, but sparse labeling with one or a few isotopically enriched amino acid types is an option for these proteins. However, assignment then relies on accessing the best match between a variety of measured NMR parameters and predictions based on 3D structure, often from X-ray crystallography. Finding this match is a challenging process that has benefitted from many computational tools, including trained neural nets for chemical shift prediction, genetic algorithms for searches through a myriad of assignment possibilities, and now AI-based prediction of high-quality structures for protein targets. AssignSLP_GUI, a new version of a software package for assignment of resonances from sparsely-labeled proteins, uses many of these tools. These tools and new additions to the package are highlighted in an application to a sparsely-labeled domain from a glycoprotein, CEACAM1.
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影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
5.6
作者：
Chalmers, Gordon R.;Eletsky, Alexander;Prestegard, James H.
通讯作者：
Prestegard, James H.
影响因子：
2.2
作者：
Prestegard, James H.;Agard, David A.;Moremen, Kelley W.;Lavery, Laura A.;Morris, Laura C.;Pederson, Kari
通讯作者：
Pederson, Kari
DOI：
10.1007/978-1-4939-7386-6_13
发表时间：
2018-01-01
期刊：
PROTEIN NMR: METHODS AND PROTOCOLS
影响因子：
--
作者：
Johnson, Bruce A.
通讯作者：
Johnson, Bruce A.
影响因子：
2.2
作者：
Masse, JE;Keller, R
通讯作者：
Keller, R