Advances in Protein Structure Prediction Highlight Unexpected Commonalities Between Gram-positive and Gram-negative Conjugative T4SSs

IF 4.7 2区生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY

Journal of Molecular Biology Pub Date : 2024-12-31 DOI:10.1016/j.jmb.2024.168924

Annika Breidenstein, Dennis Svedberg, Josy ter Beek, Ronnie P.-A. Berntsson

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Abstract

Despite recent advances in our understanding of the structure and function of conjugative Type 4 Secretion Systems (T4SSs), there is still only very scarce data available for the ones from Gram-positive (G⁺) bacteria. This is a problem, as conjugative T4SSs are main drivers for the spread of antibiotic resistance genes and virulence factors. Here, we aim to increase our understanding of G⁺ systems, by using bioinformatic approaches to identify proteins that are conserved in all conjugative T4SS machineries and reviewing the current knowledge available for these components. We then combine this information with the most recent advances in structure prediction technologies to propose a structural model for a G⁺ T4SS from the model system encoded on pCF10. By doing so, we show that conjugative G⁺ T4SSs likely have more in common with their Gram-negative counterparts than previously expected, and we highlight the potential of predicted structural models to serve as a starting point for experimental design.

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蛋白质结构预测的进展突出了革兰氏阳性和革兰氏阴性结合t4ss之间意想不到的共性。

尽管最近我们对共轭4型分泌系统（t4ss）的结构和功能的了解取得了进展，但关于革兰氏阳性（G+）细菌的结构和功能的数据仍然非常缺乏。这是一个问题，因为共轭t4ss是抗生素耐药基因和毒力因子传播的主要驱动因素。在这里，我们的目标是增加我们对G+系统的理解，通过使用生物信息学方法来识别所有共轭T4SS机制中保守的蛋白质，并回顾这些成分的现有知识。然后，我们将这些信息与结构预测技术的最新进展相结合，从pCF10上编码的模型系统中提出了G+ T4SS的结构模型。通过这样做，我们表明共轭G+ t4ss与G-对应物可能比之前预期的有更多的共同点，并且我们强调了预测结构模型作为实验设计起点的潜力。

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 Molecular Biology 生物-生化与分子生物学

CiteScore

11.30

自引率

1.80%

发文量

412

审稿时长

28 days

期刊介绍： Journal of Molecular Biology (JMB) provides high quality, comprehensive and broad coverage in all areas of molecular biology. The journal publishes original scientific research papers that provide mechanistic and functional insights and report a significant advance to the field. The journal encourages the submission of multidisciplinary studies that use complementary experimental and computational approaches to address challenging biological questions. Research areas include but are not limited to: Biomolecular interactions, signaling networks, systems biology; Cell cycle, cell growth, cell differentiation; Cell death, autophagy; Cell signaling and regulation; Chemical biology; Computational biology, in combination with experimental studies; DNA replication, repair, and recombination; Development, regenerative biology, mechanistic and functional studies of stem cells; Epigenetics, chromatin structure and function; Gene expression; Membrane processes, cell surface proteins and cell-cell interactions; Methodological advances, both experimental and theoretical, including databases; Microbiology, virology, and interactions with the host or environment; Microbiota mechanistic and functional studies; Nuclear organization; Post-translational modifications, proteomics; Processing and function of biologically important macromolecules and complexes; Molecular basis of disease; RNA processing, structure and functions of non-coding RNAs, transcription; Sorting, spatiotemporal organization, trafficking; Structural biology; Synthetic biology; Translation, protein folding, chaperones, protein degradation and quality control.