Mechanistic Studies of Viral Host Cell Recognition and Entry and their Implication for Protein Design of Molecular Delivery Devices

总结 在缺乏选择性递送的情况下，许多有前途的药物不能到达靶细胞，而是通过药物递送系统到达靶细胞。 会产生毒副作用。另一方面，许多病毒已经掌握了识别 微环境的线索，并选择性地找到并感染特定的细胞。例如，它们依赖于“本地” 蛋白酶，有时是两种，以激活它们的融合蛋白。为了开发更好的瞄准装置，我们的目标是 剖析病毒表面蛋白的分子特性和功能，特别关注 受体相互作用，稳定性和融合触发（目标I），我们将采用表面展示 和感染性测定。我们将集中在副粘病毒（PMVs）的病毒表面机制，特别是 副流感病毒5（PIV 5）。大多数PMV有一个分工，保持宿主受体结合和细胞进入 作为两个独立的功能分开编码成两个分子：一个四聚体蛋白质负责分子 识别和三聚体融合蛋白负责宿主和病毒膜的合并。这 区室化使得PMV成为用于再利用的优良模型系统，因为融合蛋白可以 保持不变，而识别过程可以重新设计。 我们将利用深度突变扫描，这使我们能够评估所有可能的氨基酸 任何给定的遗传选择的替代。通过获取每个人的不同适应度景观， 上述分子特性，我们将能够解决有关生物学的有趣问题， 病毒，如在其蛋白质化学背景下的突变耐受性。重要的是，健身景观将 对输送装置的工程设计产生直接影响，因为它们将提供 这些复杂机器的分子结构。我们将使用获得的序列-功能-结构图 用于开发一种新的、适应性强的靶向递送平台， 机械与抗体片段（目的II）。关键是开发一种衔接分子， 整合抗体片段，同时维持病毒识别机制 通常表现出，这涉及构象变化的控制。以前的努力没有成功， 建立一个有效的、全面的交付系统。在这里，我们将获得并利用一个宝贵的数据库， 病毒蛋白质结构以及我们新获得的序列功能知识，我们将其与联合收割机结合起来， 新技术-蛋白质设计-推进这个看似简单但雄心勃勃的工程项目。 我们的目标是提供一个普遍适用的平台，一个新的目标机制，结合这些 分子机制，同时也利用了大量的鉴定和工程抗体。 通过将病毒感染机制的一部分与抗体片段和接头蛋白结合， 预计我们将能够大大推进药物和基因递送系统的发展， 从而也为基因组工程提供了新的急需的精确靶向技术。

Summary In the absence of selective delivery, many promising drugs do not reach the targeted cells, but rather cause toxic side effects. Many viruses, on the other hand, have mastered the art of identifying microenvironmental clues and selectively find and infect a specific cell. For instance, they depend on “local” proteases, sometimes two, to activate their fusion proteins. To develop better targeting devices, we aim to dissect molecular properties and functions embedded in viral surface proteins, specifically focusing on receptor interactions, stability, and fusion triggering (Aim I) for which we will employ surface display and infectivity assays. We will focus on the viral surface machinery of the paramyxoviruses (PMVs), specifically Parainfluenza virus 5 (PIV5). Most PMVs have a division of labor keeping host receptor binding and cell entry apart as two separate functions encoded into two molecules: one tetrameric protein responsible for molecular recognition and a trimeric fusion protein responsible for the merging of host and viral membranes. This compartmentalization makes the PMVs an excellent model system for repurposing as the fusion protein can remain untouched, while the recognition process can be re-engineered. We will take advantage of deep mutational scanning which allows us to evaluate all possible amino acid substitutions for any given genetic selection. By acquiring differential fitness landscapes for each of the aforementioned molecular properties, we will be able to address interesting questions about the biology of viruses, such as mutational tolerance in context of their protein chemistry. Importantly, fitness landscapes will have an immediate impact on engineering of delivery devices as they will provide rough blueprints of the molecular architecture of these complex machineries. We will use obtained sequence-function-structure maps for the development of a new, adaptable targeted delivery platform that will integrate viral surface machinery with antibody fragments (Aim II). The key point will be to develop an adapter molecule that integrates the antibody fragment while maintaining all regulatory function that the viral recognition machinery normally exhibits, which involves control of conformational changes. Previous efforts have not succeeded in developing an efficient, general delivery system. Here, we will obtain and leverage an invaluable database of virus protein structures together with our newly obtained sequence-function knowledge, which we combine with new technology – protein design – to advance this seemingly simple but ambitious engineering project. We aim to provide a generally applicable platform for a new targeting machinery that incorporates these molecular mechanisms while also taking advantage of the vast amount of identified and engineered antibodies. Through combining parts of the viral infection machinery with antibody fragments and adapter proteins, we anticipate that we will be able to significantly advance the development of drug and gene delivery systems and thereby also provide new and much needed precision targeting technology for genome engineering.