Inferring in vivo Capsid Assembly Kinetics from in vitro by Stochastic Simulation

描述（由申请人提供）：通过随机模拟从体外推断体内衣壳组装动力学拟议的工作将使用计算机模拟来预测体外模型和体内细胞环境之间的差异如何影响病毒衣壳的组装途径。病毒衣壳是理解生物学中复杂自组装的关键模型系统之一。关于它们的组装过程的详细信息主要来自体外模型，因为在活细胞中检查组装过程是不可行的。然而，细胞的化学环境与这些体外模型系统的化学环境在几个方面不同，这些方面已知对大分子组装反应特别重要，包括高得多的局部浓度、密集的大分子拥挤、病毒基因组的存在以及由于小的反应物拷贝数引起的随机效应。实验和理论结果表明，这种变化可以显着影响组装速率，甚至组装途径的基本选择，提出了体外组装数据的可靠性以及我们是否可以更好地解释它们以预测体内可能的组装行为的问题。拟议的工作将使用随机模拟，旨在模拟生物化学在细胞尺度上测试的假设，从体外到体内环境的变化将改变有利的衣壳组装途径。这项工作将检查三个模型系统，在体外组装数据可用：B肝炎病毒（HBV），豇豆褪绿斑驳病毒（CCMV）和人乳头瘤病毒（HPV）。然后将这些模拟从体外条件通过计算机翻译为更类似于体内环境的条件，包括高病毒蛋白浓度、密集拥挤的培养基和核酸的存在。对于每种情况，将评估一系列可能的组装途径，以确定环境变化是否足以改变整体组装机制。这项工作将对人类健康以及基础生物物理研究产生若干影响。衣壳组装已成为抗病毒药物的潜在靶点，需要鉴定组装过程中最脆弱的关键点。这项工作将导致更好地从体外数据推断体内反应机制的一般策略，并将为两种人类病原体：HBV和HPV提供具体指导。它将进一步提供指导如何模拟方法和体外组装模型可能会得到改善，以更好地代表生物化学在体内。 公共卫生相关性：通过随机模拟从体外推断体内衣壳组装动力学所提出的工作将通过确定与人类疾病有关的两种病毒组装中可能的关键步骤来影响人类健康：肝炎B病毒和乳头瘤病毒。这些信息对于设计靶向衣壳组装过程的抗病毒剂是有价值的。这项工作也将有更广泛的相关性，以了解病毒组装一般和开发计算机模型有用的广泛的生物医学建模应用。

DESCRIPTION (provided by applicant): Inferring in Vivo Capsid Assembly Kinetics from in Vitro by Stochastic Simulation The proposed work will use computer simulations to predict how differences between in vitro models and the in vivo cellular environment can be expected to affect assembly pathways of viral capsids. Viral capsids are one of the key model systems for understanding complex self- assembly in biology. Detailed information about their assembly process derives predominantly from in vitro models because of the infeasibility of examining the assembly process in living cells. Yet the chemical environment of a cell differs from that of these in vitro model systems in several respects known to be particularly important for macromolecular assembly reactions, including much higher local concentrations, dense macromolecular crowding, presence of viral genomes, and stochastic effects due to small reactant copy numbers. Experimental and theoretical results suggest that such changes can dramatically affect assembly rates and even basic choices of assembly pathways, raising the question of how reliable in vitro assembly data are and whether we can better interpret them to predict likely assembly behavior in vivo. The proposed work will use stochastic simulations designed to model biochemistry at cellular scales to test the hypothesis that changes from an in vitro to an in vivo environment will alter favored capsid assembly pathways. The work will examine three model systems for which in vitro assembly data are available: hepatitis B virus (HBV), cowpea chlorotic mottle virus (CCMV), and human papillomavirus (HPV). These simulations will then be translated in silico from in vitro conditions to conditions better resembling the in vivo environment, including high viral protein concentrations, densely crowded media, and the presence of nucleic acid. For each condition, a range of likely assembly pathways will be assessed to determine whether the change in environment is sufficient to alter the overall assembly mechanism. The work will have several implications for human health, as well as basic biophysical research. Capsid assembly has become a potential target of anti-viral drugs, creating a need for identification of critical points at which the assembly process is most vulnerable. The work will lead to general strategies for better inferring in vivo reaction mechanisms from in vitro data and will provide specific guidance for two human pathogens: HBV and HPV. It will further provide guidance on how both simulation methods and in vitro assembly models might be improved to better represent biochemistry in vivo. PUBLIC HEALTH RELEVANCE: Inferring in Vivo Capsid Assembly Kinetics from in Vitro by Stochastic Simulation The proposed work will affect human health by identifying likely key steps in assembly of two viruses implicated in human disease: hepatitis B virus and papillomavirus. This information is valuable for designing anti-viral agents targeted to the capsid assembly process. The work will also have broader relevance to understanding virus assembly in general and to developing computer models useful in a broad range of biomedical modeling applications.