Multiscale modeling of mechanisms for viral capsid assembly and polymorphism

描述（由申请人提供）：在许多病毒的复制过程中，成百上千个蛋白质亚基在病毒核酸周围组装，形成称为衣壳的蛋白质外壳。在宿主生物体内，大多数病毒都会以惊人的保真度形成一种特定的结构。然而，最近的实验表明，衣壳可以组装成不同的尺寸和形态，以容纳不同尺寸的核酸、无机纳米颗粒和聚阴离子。该项目将使用计算模型来确定病毒蛋白及其货物的特征，从而使组装变得如此精确且适应性强。我们开发了病毒蛋白和货物的简化表示，范围从刚性球体到波动聚合物再到模型核酸。通过这些模型，我们将对病毒蛋白动态包裹这些物体的机制进行可实验测试的预测，以及哪些因素将组装过程引导至特定的尺寸和形态。这些整个组装过程的粗粒度模型将通过检查病毒蛋白动态构象的原子分辨率模拟进行验证和指导。公共卫生相关性：病毒性疾病和病毒获得性耐药性是主要的生物医学挑战。最有效的抗病毒治疗通过使用多种药物来针对感染过程中的几个步骤来对抗获得性耐药性，但针对病毒组装的治疗相对较少。通过识别使病毒组装成功的特征，我们可以学习阻止它，从而创造新的抗病毒疗法。

DESCRIPTION (provided by applicant): During the replication of many viruses, hundreds to thousands of protein subunits assemble around the viral nucleic acid to form a protein shell called a capsid. Within their host organism, most viruses form one particular structure with astonishing fidelity; yet, recent experiments demonstrate that capsids can assemble with different sizes and morphologies to accommodate nucleic acids, inorganic nanoparticles, and polyanions with different sizes. This project will use computational models to determine the features of viral proteins and their cargoes that enable assembly to be so precise and yet so adaptable. We develop simplified representations of viral proteins and cargoes that range from rigid spheres to fluctuating polymers to model nucleic acids. With these models we will develop experimentally testable predictions for the mechanisms by which viral proteins dynamically encapsidate these objects, and which factors direct the assembly process towards a particular size and morphology. These coarse-grained models of the overall assembly process will be validated and guided by atomic-resolution simulations that examine the dynamic conformations of viral proteins. PUBLIC HEALTH RELEVANCE: Viral diseases and acquired drug resistance by viruses are major biomedical challenges. The most effective antiviral treatments fight acquired resistance by using use multiple drugs to target several steps in the infection process, but relatively few treatments target viral assembly. By identifying the features that make viral assembly successful, we can learn to block it and thereby create novel antivirus therapies.