Predictive modeling of viral RNA cellular behavior

PROJECT SUMMARY/ABSTRACT Modern biomedical science enjoys an unprecedented ability to identify and describe viral pathogenic mechanisms, as well as characterize the biomolecular components that constitute them. However, we have not yet achieved a fully quantitative biophysical understanding in which modeling of component molecules is accurately predictive of viral functions in cells. Characterization of biomolecular structural dynamics, as opposed to their static structures alone, is creating to new inroads to quantitative modeling of cellular function as well as novel drug-targeting strategies. My recent work examining an RNA-protein interaction critical to HIV genome transcription suggests that highly quantitative measurements and systematic mutant design, which specifically perturbs dynamic properties, is a viable strategy for predicting viral RNA function in cells. In this proposal I plan to use a multi-modal integrative approach to quantitatively measuring RNA dynamics for the purposes of building a predictive model of RNA function and developing a novel RNA-targeting strategy. I will use the interaction of the HIV 5’-leader RNA with the Gag polyprotein as the model system for these studies as it is a well-studied yet complex interaction that involves critical interaction with the lipid bilayer and is also essential for the process of viral genome packaging, making it a relevant drug target. In Aim 1 I will develop integrative high-throughput (HTP) technologies that combine three-dimensional structural ensembles of component biomolecules with quantitative measurements of in vitro and cellular functions to build predictive models of viral activity that can be applied broadly. Specifically, I will construct a library of thousands of 5’-leader RNA mutants designed to systematically perturb its structural dynamics. I will create a plasmid library of these sequences to develop an RNA-Seq-based methodology to quantitatively measure the cellular activity of RNA mutants in HTP. I will then use existing HTP methods such as RNA-MaP to evaluate the in vitro binding affinity of the same library of sequences. Lastly, I will interpret this experimental data using structural dynamic ensembles of each RNA mutant, determined from nuclear magnetic resonance (NMR)-informed structure prediction programs, to build a predictive model of the 5’-leader:Gag interaction. By evaluating RNA mutants in HTP, I simultaneously screen for non-functional, low abundance RNA conformations in cells that could represent both attractive drug targets and tools for applications in synthetic biology. In Aim 2 I will identify and obtain ensemble descriptions of these non-functional conformations using NMR and computational modeling and target them for antiviral drug development using ensemble-based virtual screening (EBVS). I will also develop a fluorescence-based in vitro screening assay involving the lipid bilayer with which to test hits from virtual screening as well as in-house small molecule libraries. Lastly, I will use orthogonal biophysical methods to further validate hits, as well as use them to test the model constructed in Aim 1.