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.

对体内病毒蛋白的整个生命周期进行成像对于了解分子生物学至关重要 处理潜在的病毒感染。活细胞成像长期以来一直使用 荧光蛋白融合标签，例如 GFP。然而，这些标签可以改变大小和 目标蛋白的功能。此外，缓慢的成熟、降解和光漂白 标签会导致信号丢失，从而很难追踪其早期生活和最终命运 许多蛋白质。尤其是病毒多蛋白，由于其 对标签及其在病毒传播过程中经历的广泛加工和组装过敏 生物发生。使用由基因编码的活细胞可逆标记的线性表位标签 探针可以解决许多此类问题。不幸的是，活细胞的工程功能探针 表位成像既昂贵又耗时。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 完善并加速高度复用的正交表位/探针对的合理设计 活细胞中完整病毒蛋白生命周期的成像。我们展示了我们战略的力量 通过创建结合常用 HA 和 Flag 的新颖 scFv 来获得我们的初步数据 在各种要求苛刻的活细胞成像场景中具有高亲和力的表位。在目标 1 中，我们将 使用我们经过测试的策略来开发针对其他病毒表位标签的 scFv 并验证其 在成像实验中的实用性。鉴定可溶且具有活性的嵌合 scFv 在细胞环境中，我们将把已知的表位特异性 CDR 环移植到独特的稳定面板上 scFv 支架。在目标 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：病毒表位复合物，验证 scFv 设计流程，设计编码多种新肽结合解决方案的 scFv 库， 并使用创新的高通量、高内涵体内方法进行筛选。在目标 3 中，我们将 通过探测证明我们新开发的 scFv 在活细胞成像实验中的实用性 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化 scFv 来可视化 量化黄病毒跨膜多蛋白的翻译动态，并监测 甲病毒颗粒组装动力学。总体而言，该项目将提供强大的新管道 用于生成可追踪活细胞中病毒蛋白的 scFv 蛋白。我们的试剂 generate 将为病毒分子生物学界提供新的、多功能的成像工具 更好地阐明许多重要的生物过程。