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计算评估细菌蛋白结构表明存在对聚集的选择。

Computational Assessment of Bacterial Protein Structures Indicates a Selection Against Aggregation.

机构信息

Institut de Biotecnologia i Biomedicina and Departament de Bioquímica i Biologia Molecular, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain.

出版信息

Cells. 2019 Aug 8;8(8):856. doi: 10.3390/cells8080856.

DOI:10.3390/cells8080856
PMID:31398930
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6721704/
Abstract

The aggregation of proteins compromises cell fitness, either because it titrates functional proteins into non-productive inclusions or because it results in the formation of toxic assemblies. Accordingly, computational proteome-wide analyses suggest that prevention of aggregation upon misfolding plays a key role in sequence evolution. Most proteins spend their lifetimes in a folded state; therefore, it is conceivable that, in addition to sequences, protein structures would have also evolved to minimize the risk of aggregation in their natural environments. By exploiting the AGGRESCAN3D structure-based approach to predict the aggregation propensity of >600 Escherichia coli proteins, we show that the structural aggregation propensity of globular proteins is connected with their abundance, length, essentiality, subcellular location and quaternary structure. These data suggest that the avoidance of protein aggregation has contributed to shape the structural properties of proteins in bacterial cells.

摘要

蛋白质的聚集损害了细胞的适应性,这要么是因为它将功能性蛋白质滴定成非生产性的包含物,要么是因为它导致了有毒聚集体的形成。因此,计算蛋白质组范围内的分析表明,防止错误折叠时的聚集在序列进化中起着关键作用。大多数蛋白质在折叠状态下度过它们的一生;因此,可以想象,除了序列之外,蛋白质结构也已经进化到最小化它们在自然环境中聚集的风险。通过利用 AGGRESCAN3D 基于结构的方法来预测 >600 种大肠杆菌蛋白质的聚集倾向,我们表明球状蛋白质的结构聚集倾向与其丰度、长度、必需性、亚细胞位置和四级结构有关。这些数据表明,避免蛋白质聚集有助于塑造细菌细胞中蛋白质的结构特性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/033bd46b32d9/cells-08-00856-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/640e670a76a7/cells-08-00856-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/ce1712c9d3a1/cells-08-00856-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/5f318f1ea87b/cells-08-00856-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/f8574b5a2e29/cells-08-00856-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/405b2347acf3/cells-08-00856-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/844ee842a6d6/cells-08-00856-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/e4661f3fe295/cells-08-00856-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/d439280a2423/cells-08-00856-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/b539f26eeef9/cells-08-00856-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/dfbcacb55cab/cells-08-00856-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/a3c760153faf/cells-08-00856-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/f5e659c99e8a/cells-08-00856-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/033bd46b32d9/cells-08-00856-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/640e670a76a7/cells-08-00856-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/ce1712c9d3a1/cells-08-00856-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/5f318f1ea87b/cells-08-00856-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/f8574b5a2e29/cells-08-00856-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/405b2347acf3/cells-08-00856-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/844ee842a6d6/cells-08-00856-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/e4661f3fe295/cells-08-00856-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/d439280a2423/cells-08-00856-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/b539f26eeef9/cells-08-00856-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/dfbcacb55cab/cells-08-00856-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/a3c760153faf/cells-08-00856-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/f5e659c99e8a/cells-08-00856-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d35/6721704/033bd46b32d9/cells-08-00856-g013.jpg

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2
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3
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BMC Biol. 2022 Oct 22;20(1):197. doi: 10.1186/s12915-022-01374-4.
4
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Bioinformatics. 2022 May 26;38(11):3121-3123. doi: 10.1093/bioinformatics/btac215.
5
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6
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