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分区辅助相互作用筛选揭示了 SARS-CoV-2 蛋白质组中广泛的高阶复合物。

Compartmentalization-aided interaction screening reveals extensive high-order complexes within the SARS-CoV-2 proteome.

机构信息

Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Beijing, China; Tsinghua-Peking Center for Life Sciences, Beijing, China; School of Life Sciences, Tsinghua University, Beijing, China.

School of Life Sciences, Tsinghua University, Beijing, China.

出版信息

Cell Rep. 2021 Aug 3;36(5):109482. doi: 10.1016/j.celrep.2021.109482. Epub 2021 Jul 17.

Abstract

Bearing a relatively large single-stranded RNA genome in nature, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) utilizes sophisticated replication/transcription complexes (RTCs), mainly composed of a network of nonstructural proteins and nucleocapsid protein, to establish efficient infection. In this study, we develop an innovative interaction screening strategy based on phase separation in cellulo, namely compartmentalization of protein-protein interactions in cells (CoPIC). Utilizing CoPIC screening, we map the interaction network among RTC-related viral proteins. We identify a total of 47 binary interactions among 14 proteins governing replication, discontinuous transcription, and translation of coronaviruses. Further exploration via CoPIC leads to the discovery of extensive ternary complexes composed of these components, which infer potential higher-order complexes. Taken together, our results present an efficient and robust interaction screening strategy, and they indicate the existence of a complex interaction network among RTC-related factors, thus opening up opportunities to understand SARS-CoV-2 biology and develop therapeutic interventions for COVID-19.

摘要

在自然界中,严重急性呼吸综合征冠状病毒 2(SARS-CoV-2)携带相对较大的单链 RNA 基因组,利用复杂的复制/转录复合物(RTC),主要由非结构蛋白和核衣壳蛋白网络组成,建立有效的感染。在这项研究中,我们开发了一种基于细胞内相分离的创新相互作用筛选策略,即细胞内蛋白质-蛋白质相互作用的区室化(CoPIC)。利用 CoPIC 筛选,我们绘制了与 RTC 相关的病毒蛋白之间的相互作用网络。我们总共鉴定出 14 种调控冠状病毒复制、不连续转录和翻译的蛋白质之间的 47 种二元相互作用。通过 CoPIC 的进一步探索发现了由这些成分组成的广泛的三元复合物,推断出潜在的更高阶复合物。总之,我们的结果提出了一种高效、稳健的相互作用筛选策略,并表明 RTC 相关因子之间存在复杂的相互作用网络,从而为理解 SARS-CoV-2 生物学和开发 COVID-19 的治疗干预措施提供了机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c829/8285250/846252160fe3/fx1_lrg.jpg

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