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朝着自动化甲基分配方向的进展,以应用于甲基-TROSY 实验。

Progress toward automated methyl assignments for methyl-TROSY applications.

机构信息

Department of Structural Biology, St Jude Children's Research Hospital, Memphis, TN, USA.

Department of Structural Biology, St Jude Children's Research Hospital, Memphis, TN, USA.

出版信息

Structure. 2022 Jan 6;30(1):69-79.e2. doi: 10.1016/j.str.2021.11.009. Epub 2021 Dec 15.

Abstract

Methyl-TROSY spectroscopy has extended the reach of solution-state NMR to supra-molecular machineries over 100 kDa in size. Methyl groups are ideal probes for studying structure, dynamics, and protein-protein interactions in quasi-physiological conditions with atomic resolution. Successful implementation of the methodology requires accurate methyl chemical shift assignment, and the task still poses a significant challenge in the field. In this work, we outline the current state of technology for methyl labeling, data collection, data analysis, and nuclear Overhauser effect (NOE)-based automated methyl assignment approaches. We present MAGIC-Act and MAGIC-View, two Python extensions developed as part of the popular NMRFAM-Sparky package, and MAGIC-Net a standalone structure-based network analysis program. MAGIC-Act conducts statistically driven amino acid typing, Leu/Val pairing guided by 3D HMBC-HMQC, and NOESY cross-peak symmetry checking. MAGIC-Net provides model-based NOE statistics to aid in selection of a methyl labeling scheme. The programs provide a versatile, semi-automated framework for rapid methyl assignment.

摘要

Methyl-TROSY 光谱学将溶液态 NMR 的应用范围扩展到了超过 100 kDa 的超分子机器,甲基基团是在接近生理条件下研究结构、动态和蛋白质-蛋白质相互作用的理想探针,具有原子分辨率。该方法的成功实施需要准确的甲基化学位移赋值,而这在该领域仍然是一个重大挑战。在这项工作中,我们概述了目前用于甲基标记、数据收集、数据分析和基于核 Overhauser 效应 (NOE) 的自动甲基赋值方法的技术现状。我们介绍了 MAGIC-Act 和 MAGIC-View,这两个 Python 扩展是作为流行的 NMRFAM-Sparky 包的一部分开发的,以及 MAGIC-Net 一个独立的基于结构的网络分析程序。MAGIC-Act 进行基于统计的氨基酸类型推断,3D HMBC-HMQC 引导的 Leu/Val 配对,以及 NOESY 交叉峰对称性检查。MAGIC-Net 提供基于模型的 NOE 统计数据,以帮助选择甲基标记方案。这些程序提供了一个通用的、半自动的快速甲基赋值框架。

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