A quantitative high-throughput screening pipeline to identify small molecule inhibitors of Chikungunya nsP2 protease

Chikungunya virus (CHIKV) is a mosquito-borne RNA virus that has emerged as one of the most important global arboviral threats in the last decade. Although the first CHIKV vaccine has recently been FDA approved for use in healthy adults at increased risk, to date, there are no available antiviral drugs for CHIKV infection. CHIKV nsP2 protease plays a crucial role in the processing of the viral polypeptide precursor to release enzymes required for viral replication, thus making it a promising drug target for antiviral discovery. Here, we established a high-throughput pipeline to identify small molecule inhibitors of nsP2 proteolytic activity. The pipeline is composed of a suite of 1,536-well in vitro assays to support quantitative high-throughput (qHTS) screening campaigns. Specifically, we developed a fluorescence resonance energy transfer (FRET)-based assay using a fluorogenic peptide substrate encompassing an endogenous cleavage site and purified recombinant protease domain (nsP2pro). Using this assay, we interrogated ~ 31,000 unique small molecules, including those in drug repurposing libraries as well as chemically diverse and medicinal chemistry-friendly compounds. Hits were selected for follow-up validation against full-length nsP2 and an additional peptide. FRET-based 1,536-well assays for Papain, hepatitis C virus NS3-4A, and human Furin proteases were implemented to characterize compound selectivity. Notably, we developed a high-throughput cell-based proteolytic assay using a split nanoluciferase reporter to identify cell-active hits. Novel compounds were found to be potential nsP2 inhibitors and molecular docking analyses were performed to explain the binding mode of selected hits. In vitro antiviral activity was evaluated for a subset of compounds using a high-throughput CHIKV infection assay. To our knowledge, the pipeline presented here is unprecedented for CHIKV antiviral discovery research.