AMIGO - Guided assignment of 13C-methyl labelled proteins

IF 1.9 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Thorben Maass, Lorena Rudolph, Thomas Peters, Alvaro Mallagaray
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引用次数: 0

Abstract

Over the last 20 years, the number of large proteins accessible to protein-NMR analysis has increased significantly due to the development of selective [1H,13C]-methyl labelling techniques in combination with methyl-TROSY based NMR experiments. Structure-based strategies for the assignment of [¹H,¹³C]-methyl groups rely on comparing spatial constraints derived from methyl–methyl NOEs to a known three-dimensional structure of the protein of interest. Cross peaks in methyl-TROSY spectra are assigned to specific methyl groups by matching methyl–methyl NOEs, as observed for example in 4D HMQC-NOESY-HMQC spectra, with distances derived from a structural model. This process is commonly referred to as a “methyl walk”. Here, we present AMIGO (Automated Methyl assignment via Iterative Graph Optimization), a novel assignment algorithm that formalises the intuitive methyl walk procedure by constructing graphs with nodes representing specific methyl groups and edges reflecting methyl-methyl NOEs or short methyl-methyl distances in a model. “Building blocks” consisting of nodes and edges are then generated to reconcile structure-based and NOE-based graphs. Assignments are achieved through permutation and concatenation of individual “building blocks” in a modular fashion, enabling efficient computation even for large proteins. Additional experimental restraints, such as paramagnetic relaxation enhancements (PREs) or pseudocontact shifts (PCSs), can be integrated to validate and extend the assignments. The performance of AMIGO was validated using 11 proteins that had previously been assigned and 32 NOE networks that had been generated synthetically.

AMIGO - 13c -甲基标记蛋白的指导分配。
在过去的20年里,由于选择性[1H,13C]-甲基标记技术的发展以及基于甲基- trosy的核磁共振实验,可用于蛋白质核磁共振分析的大蛋白质的数量显着增加。基于结构的[¹H,¹³C]-甲基分配策略依赖于将甲基NOEs的空间约束与已知的感兴趣蛋白质的三维结构进行比较。甲基- trosy光谱中的交叉峰通过匹配甲基-甲基NOEs来分配给特定的甲基,例如在4D hmqc - nosy - hmqc光谱中观察到,其距离来自结构模型。这个过程通常被称为“甲基游走”。在这里,我们提出了AMIGO(通过迭代图优化自动甲基分配),这是一种新的分配算法,它通过构建图来形式化直观的甲基行走过程,图中节点代表特定的甲基,边缘反映模型中的甲基-甲基NOEs或短甲基-甲基距离。然后生成由节点和边组成的“构建块”,以协调基于结构和基于nos的图。分配是通过以模块化方式排列和连接单个“构建块”来实现的,即使对大型蛋白质也能进行高效计算。额外的实验约束,如顺磁松弛增强(PREs)或伪接触位移(PCSs),可以集成来验证和扩展分配。AMIGO的性能使用先前已分配的11个蛋白质和合成的32个NOE网络进行验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Biomolecular NMR
Journal of Biomolecular NMR 生物-光谱学
CiteScore
6.00
自引率
3.70%
发文量
19
审稿时长
6-12 weeks
期刊介绍: The Journal of Biomolecular NMR provides a forum for publishing research on technical developments and innovative applications of nuclear magnetic resonance spectroscopy for the study of structure and dynamic properties of biopolymers in solution, liquid crystals, solids and mixed environments, e.g., attached to membranes. This may include: Three-dimensional structure determination of biological macromolecules (polypeptides/proteins, DNA, RNA, oligosaccharides) by NMR. New NMR techniques for studies of biological macromolecules. Novel approaches to computer-aided automated analysis of multidimensional NMR spectra. Computational methods for the structural interpretation of NMR data, including structure refinement. Comparisons of structures determined by NMR with those obtained by other methods, e.g. by diffraction techniques with protein single crystals. New techniques of sample preparation for NMR experiments (biosynthetic and chemical methods for isotope labeling, preparation of nutrients for biosynthetic isotope labeling, etc.). An NMR characterization of the products must be included.
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