Structure-based discovery and design of antivirals against severe fever with thrombocytopenia syndrome virus

Abstract

Severe fever with thrombocytopenia syndrome virus (SFTSV) is a tick-borne RNA virus that causes severe disease with a mortality rate of up to about 30% in human. The clinical manifestations of SFTSV infection include high fever, thrombocytopenia, leukocytopenia, haemorrhage, and multiorgan dysfunction. SFTSV is a member of the Phenuiviridae family in the order Bunyavirales that was first discovered and isolated from a patient in Henan Province, China. Subsequently, SFTSV infection has been increasing reported in East Asia, including other parts of China, Japan, South Korea, and Vietnam. Despite the clinical importance of SFTSV, therapeutic options are limited. To accelerate the discovery of anti-SFTSV therapeutics, in this thesis, we utilized an in silico structure-based approach to identify potentially repurposable drug compounds and to design novel antiviral peptides against SFTSV through integrating different computational drug discovery methods, including ligand-protein docking, molecular dynamics simulation, and protein design to develop a rapid drug discovery platform. In Chapter3, virtual screening against the DrugBank library containing more than 8000 drug compounds was conducted to discover small molecule drug compounds that potentially interact with the nucleoprotein of SFTSV to inhibit viral replication. Through molecular docking and molecular dynamics analysis, 17 top-hit drug compounds were prioritized for downstream experimental validation. As a result, the clinically approved pyrimethamine and clofazimine were identified to inhibit viral replication in vitro with high efficiency. Importantly, pyrimethamine-treated type I interferon receptor-deficient A129 mice demonstrated significantly higher survival rate, lower body weight loss, and reduced tissue viral burden. Mechanistic study showed that pyrimethamine occupies the RNA-binding groove of the SFTSV nucleoprotein to inhibit ribonucleoprotein activity. In Chapter 4, in silico structure-based drug discovery platform was utilized to design novel antiviral peptides against SFTSV since peptides can exhibit potent antiviral activity by binding to large and flat protein surfaces to modulate protein-protein interactions. Cyclic peptides are of particular interests due to their tunable rigidity, stability, and pharmacokinetics properties. Compared to peptide library screening, in silico structure-based peptide design is more efficient and less labour-intensive. Using state-of-the-art conformation sampling algorithms and scoring functions in Rosetta suite, a series of cyclic peptides were designed to block cell entry of SFTSV by targeting the surface glycoprotein Gn. As a result, four cyclic peptides, designated as HKU-P1 to -P4, were designed based on the high-resolution crystal structure of SFTSV Gn in complex with a monoclonal antibody MAb 4-5. Among these peptides, HKU-P1 exhibits the most potent antiviral activity and negligible cytotoxicity. Enzyme-linked immunosorbent assay results confirmed the binding between HKU-P1 and the SFTSV Gn protein, and showed that the binding is dose-dependent. Importantly, combinatorial HKU-P1 and favipiravir resulted in synergistic antiviral activity against SFTSV by targeting different viral proteins. Taken together, the novel findings in this thesis demonstrated that structure-based drug discovery approach can facilitate the identification of repurposable and the design of novel drug compounds for the medically important SFTSV. The application of this drug discovery approach should be extended to other bunyaviruses and emerging viruses as there are limited treatment options for most of these important pathogens.