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病毒蛋白的分子生物学特性至关重要。 病毒感染的潜在过程。活细胞成像长期以来一直使用 荧光蛋白融合标签如GFP。然而，这些标签可以改变大小， 目标蛋白质的功能。此外，缓慢的成熟，降解和光漂白， 标签导致信号丢失，使得难以跟踪早期生活和最终的命运。 许多蛋白质。特别地，病毒多聚蛋白由于它们的结构而在体内仍然难以成像。 对标签的超敏反应以及它们在病毒传播过程中所经历的大量加工和组装， 生物起源。使用由遗传编码的活细胞可逆标记的线性表位标签 探测器可以解决许多这些问题。不幸的是，工程功能探针活细胞 表位的成像是昂贵和耗时的。在这项研究中，我们 联合收割机在蛋白质工程、单分子显微镜和生物化学方面的专业知识， 优化和加速正交表位/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， 我们的初步数据通过创建结合常用HA和Flag的新型scFv 在各种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 使用我们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。为了鉴定在细胞内可溶且有活性的嵌合scFv， 细胞环境，我们将已知的表位特异性CDR环移植到一组独特的稳定的 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， 设计方法以开发scFv：病毒-表位复合物的预测结合模型，验证 scFv设计流水线，设计编码多种新肽结合溶液的scFv库， 并使用创新的高通量、高含量的体内方法进行筛选。在目标3中，我们 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。具体地说，我们将使用我们的工程scFv来可视化 并量化黄病毒跨膜多蛋白的翻译动力学， 甲病毒颗粒组装动力学。总体而言，该项目将提供一个强大的新管道， 用于产生可以跟踪活细胞中病毒蛋白的scFv蛋白。试剂我们 generate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。