Computational modeling of viral capsid and bacterial microcompartment assembly

点击翻译按钮获取中文摘要

In many virus families, replication requires that hundreds to thousands of proteins assemble around the viral nucleic acid to form a protein shell called a capsid. Understanding the assembly pathways for capsid formation and learning how antiviral drugs can block or alter these pathways would provide information to develop new antiviral strategies and improve existing ones. Similarly, at least 20% of bacterial species have protein-based organelles called bacterial microcompartments, which are protein shells that assemble around a group of enzymes. Since microcompartments are essential for bacterial growth and pathogenesis, understanding the mechanisms that control their assembly would provide information for developing novel antibiotics that work by inhibiting microcompartment formation. Assembly mechanisms inferred from experiments alone are incomplete because intermediates are transient and thus not readily observed. Therefore, this project develops and applies computational models for capsid proteins, nucleic acids (NAs), putative antiviral agents, and microcompartment components that reveal details of assembly not accessible to experiments. The first aim will study how NAs guide assembly pathways toward particular capsid structures. Goals will include understanding experiments in which capsid proteins form different icosahedral morphologies to accommodate NAs with different physical properties (e.g. sequence length and base-pairing interactions), and testing simulated pathways against experiments from collaborators. The latter effort will include developing a computational tool to predict small angle x-ray scattering profiles from simulation trajectories as well as developing models that interpret novel light scattering experiments that monitor assembly of individual capsids. The second aim will examine how small molecules that perturb protein-protein or protein- NA interactions redirect assembly pathways. The goal is to learn how to design optimal antiviral agents. We will develop coarse-grained computational models that are informed by atomistic simulations and predict assembly pathways and products as a function of the amount and type of putative antiviral agent. Predictions will be compared against extensive data from our experimental collaborator on assembly of hepatitis B virus (HBV) proteins in the presence of potential antiviral agents. Finally, the third aim will study how bacterial microcompartments assemble around their enzyme cargos. The simulations will identify assembly pathways and critical control parameters for microcompartment assembly, while learning how protein shell assembly can promote and regulate liquid-liquid phase separation within cells. To enable simulating the length and time scales of assembly, our simulations employ advanced GPU computing and an approach to apply Markov state modeling to assembly reactions developed by our group. Furthermore, we use coarse-grained models that are informed by experiments and atomistic simulations.