Multiplexed imaging of viral protein processing and assembly in live cells

活细胞中病毒蛋白加工和组装的多重成像

• 批准号: 10708987
• 负责人: Christopher Davis Snow
• 金额: $ 47.96万
• 依托单位: COLORADO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-21 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10708987
• 关键词: Acceleration; Address; Affinity; Alphavirus; Antibodies; Binding; Biochemistry; Biogenesis; Biological Process; Biology; Capsid; Capsid Proteins; Cells; Chimera organism; Chimeric Proteins; Color; Communities; Complex; Computers; Consumption; Darkness; Data; Development; Directed Molecular Evolution; Engineering; Ensure; Epitopes; Eukaryotic Cell; Flavivirus; Genetic Materials; Genetic Recombination; Genome; HIV-1; Hand; Homo; Hypersensitivity; Image; Imaging Device; Imaging technology; Immunoglobulin Fragments; Infection; Investigation; Kinetics; Label; Lead; Libraries; Life; Life Cycle Stages; Light; Machine Learning; Mechanics; Membrane; Methods; Microscopy; Modeling; Molecular; Molecular Biology; Molecular Virology; Monitor; Peptides; Photobleaching; Polyproteins; Positioning Attribute; Predisposition; Process; Protein Dynamics; Protein Engineering; Protein Fragment; Proteins; Reagent; Recombinant Proteins; Refractory; Replication-Associated Process; Research; Research Personnel; Ribosomes; Scientist; Signal Transduction; Specificity; Technology; Testing; Time; Translations; Validation; Variant; Viral; Viral Proteins; Virus; Virus Diseases; Virus Replication; Visualization; antibody mimetics; cost; design; experimental study; in vivo; in vivo imaging; innovation; live cell imaging; live cell microscopy; molecular dynamics; molecular imaging; multiplexed imaging; next generation; non-invasive imaging; novel; particle; prediction algorithm; protein structure; protein structure prediction; rational design; real-time images; scaffold; single molecule; spatiotemporal; success; temporal measurement; virology

Imaging the full lifecycle of viral proteins in vivo is essential for understanding the molecular processes underlying viral infection. Live-cell imaging has long been performed using fluorescent protein fusion tags such as GFP. However, these tags can alter the size and function of targeted proteins. Furthermore, slow maturation, degradation, and photobleaching of tags results in the loss of signal, making it difficult to track the early life and ultimate fate of many proteins. Viral polyproteins, in particular, remain refractory to imaging in vivo due to their hypersensitivity to tags and the extensive processing and assembly they undergo during viral biogenesis. The use of linear epitope tags reversibly labeled by genetically encoded live-cell probes can solve many of these issues. Unfortunately, engineering functional probes for live-cell imaging of epitopes has been costly and time-consuming. In the proposed research, we combine expertise in protein engineering, single-molecule microscopy, and biochemistry to refine and accelerate the rational design of orthogonal epitope/probe pairs for highly multiplexed imaging of full viral protein lifecycles in living cells. We demonstrate the power of our strategy in our Preliminary Data by creating novel scFvs that bind the commonly used HA and Flag epitopes with high affinity in a variety of demanding live-cell imaging scenarios. In Aim 1, we will use our tested strategy to develop scFv against additional viral epitope tags and validate their utility in imaging experiments. To identify chimeric scFv that are both soluble and active within the cellular milieu, we will graft known epitope-specific CDR loops onto a unique panel of stable scFv scaffolds. In Aim 2, we will use state-of-the-art machine learning protein modeling and design methods to develop predictive binding models for scFv:viral-epitope complexes, validate a scFv design pipeline, engineer scFv libraries encoding multiple new peptide-binding solutions, and screen using innovative high-throughput, high-content in vivo methods. In Aim 3, we will demonstrate the utility of our newly developed scFv in live-cell imaging experiments by probing several critical aspects of viral biology. Specifically, we will use our engineered scFv to visualize and quantify the translation dynamics of flavivirus transmembrane polyproteins, and to monitor alphavirus particle assembly kinetics. Overall, this project will provide a powerful new pipeline for generating scFv proteins that can track viral proteins in living cells. The reagents we generate will provide the virus molecular biology community with new, versatile imaging tools to better illuminate many important biological processes.

成像体内病毒蛋白的完整生命周期对于理解分子至关重要 病毒感染的过程。长期以来，使用 荧光蛋白融合标签，例如GFP。但是，这些标签可以改变大小，并且 靶向蛋白的功能。此外，缓慢的成熟，退化和光漂白 标签导致信号丢失，因此很难跟踪早期生活和最终命运 许多蛋白质。尤其是病毒多蛋白，由于它们 对标签的高敏性以及它们在病毒期间经历的广泛处理和组装 生物发生。使用遗传编码的Live细胞可逆标记的线性表位标签的使用 探针可以解决许多此类问题。不幸的是，实时电池的工程功能探针 表位的成像是昂贵且耗时的。在拟议的研究中，我们 结合蛋白质工程，单分子显微镜和生物化学方面的专业知识 改进并加速了高度多路复用的正交表位/探针对的合理设计 活细胞中全病毒蛋白生命周期的成像。我们证明了我们战略的力量 我们的初步数据通过创建结合常用的HA和标志的新型SCFV 在各种苛刻的活电池成像场景中具有高亲和力的表位。在AIM 1中，我们将 使用我们的测试策略来开发SCFV针对其他病毒表位标签并验证其 成像实验中的实用程序。识别既溶于又活跃的嵌合SCFV 细胞环境，我们将把已知表位特异性的CDR循环移植到独特的稳定面板上 SCFV脚手架。在AIM 2中，我们将使用最先进的机器学习蛋白质建模和 开发SCFV预测结合模型的设计方法：病毒 - 远足络合物，验证 SCFV设计管道，工程师SCFV库编码多个新的肽结合解决方案， 和屏幕使用创新的高通量，高含量的体内方法。在AIM 3中，我们将 通过探测，在活细胞成像实验中演示了我们新开发的SCFV的实用性 病毒生物学的几个关键方面。具体来说，我们将使用我们的工程SCFV可视化 并量化黄素跨膜多蛋白的翻译动力学，并监测 α病毒颗粒组件动力学。总体而言，该项目将提供强大的新管道 用于生成可以跟踪活细胞中病毒蛋白的SCFV蛋白。试剂我们 生成将为病毒分子生物学社区提供新的多功能成像工具 更好地阐明了许多重要的生物过程。