揭开 Rubisco 在 β-羧基体中的包装奥秘

IF 4.4 2区生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY

Structure Pub Date : 2024-08-08 DOI:10.1016/j.str.2024.07.006

引用次数: 0

摘要

在本期《结构》杂志中，Kong 等人利用冷冻电子断层扫描技术仔细研究了 Rubisco 在 β 羧聚体中的包装。他们观察到了独特的 Rubisco 包裹排列，这可能对羧酶体结构的完整性有重要影响。

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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Unraveling Rubisco packaging within β-carboxysomes

In this issue of Structure, Kong et al. utilized cryoelectron tomography to closely examine Rubisco packaging within β-carboxysomes. They observed unique Rubisco packaging arrangements that may have important implications for carboxysome structural integrity.

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来源期刊

Structure 生物-生化与分子生物学

CiteScore

8.90

自引率

1.80%

发文量

155

审稿时长

3-8 weeks

期刊介绍： Structure aims to publish papers of exceptional interest in the field of structural biology. The journal strives to be essential reading for structural biologists, as well as biologists and biochemists that are interested in macromolecular structure and function. Structure strongly encourages the submission of manuscripts that present structural and molecular insights into biological function and mechanism. Other reports that address fundamental questions in structural biology, such as structure-based examinations of protein evolution, folding, and/or design, will also be considered. We will consider the application of any method, experimental or computational, at high or low resolution, to conduct structural investigations, as long as the method is appropriate for the biological, functional, and mechanistic question(s) being addressed. Likewise, reports describing single-molecule analysis of biological mechanisms are welcome. In general, the editors encourage submission of experimental structural studies that are enriched by an analysis of structure-activity relationships and will not consider studies that solely report structural information unless the structure or analysis is of exceptional and broad interest. Studies reporting only homology models, de novo models, or molecular dynamics simulations are also discouraged unless the models are informed by or validated by novel experimental data; rationalization of a large body of existing experimental evidence and making testable predictions based on a model or simulation is often not considered sufficient.