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Updated: Apr 13, 2026

High-throughput Screening for Broad-spectrum Chemical Inhibitors of RNA Viruses
Published on: May 5, 2014
Genotype-by-inhibitor interactions to dissect enterovirus replication
William Bakhache1,2, Walker Symonds-Orr3, Patrick T Dolan4
1Quantitative Virology and Evolution Unit, Laboratory of Viral Diseases, NIH-NIAID Division of Intramural Research, Bethesda, MD, USA. bakhache@unistra.fr.
Abstract:
Replication organelles of positive-sense RNA viruses are essential to virus biology, yet their molecular mechanisms remain poorly defined. While deep mutational scanning (DMS) measures the impact of mutations across viral proteins, it cannot resolve their effects on specific functions. Here, we present a strategy to integrate mutational scanning in the context of specific virus- and host-targeted inhibitors with structural modeling to dissect mechanistic details of Enterovirus replication. Our results reveal key insights into the function of nonstructural proteins in the context of viral replication. We use this approach to clarify the modular architecture of the viral 2 C protein, dissecting the functional partitioning of its 'virus-facing' cytoplasmic, enzymatic domain from 'host-facing' functions at the membrane-binding domain, revealing evidence for computationally-predicted structural transitions associated with host protein binding. We further show that inhibition of the 3 C protease enriches for mutations in 2 A, highlighting compensatory crosstalk between viral proteases. Finally, targeting host phospholipid synthesis triggers a dose-dependent shift in mutational tolerance in the viral 3 A protein, showing how preference for distinct interaction interfaces with either a host enzyme or its adapter protein varies across inhibitory environments. Our approach, which quantifies the impact of pharmacological probes on viral fitness by comprehensive mutational scanning, creates a virtuous cycle where DMS validates and refines structural predictions that, in turn, serve to contextualize mutational data, all toward a more complete model of positive-sense RNA virus replication.
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