high-throughput-antiviral-assays-to-screen-for-inhibitors-zika-virus

High-throughput Antiviral Assays to Screen for Inhibitors of Zika Virus Replication

Antiviral drug discovery requires the development of reliable biochemical and cellular assays that can be performed in high-throughput screening (HTS) ...

University of Sao Paulo

Peroxisome proliferator-activated receptors (PPARs) are members of a superfamily of nuclear transcription factors. They are involved in mediating numerous physiological effects in humans, including glucose and lipid metabolism. PPARα ligands effectively treat dyslipidemia and have significant antiinflammatory and anti-atherosclerotic activities. These effects and their ligand-dependent activity make nuclear receptors obvious targets for drug design. Here, we present the structure of the human PPARα in complex with WY14643, a member of fibrate class of drug, and a widely used PPAR activator. The crystal structure of this complex suggests that WY14643 induces activation of PPARα in an unusual bipartite mechanism involving conventional direct helix 12 stabilization and an alternative mode that involves a second ligand in the pocket. We present structural observations, molecular dynamics and activity assays that support the importance of the second site in WY14643 action. The unique binding mode of WY14643 reveals a new pattern of nuclear receptor ligand recognition and suggests a novel basis for ligand design, offering clues for improving the binding affinity and selectivity of ligand. We show that binding of WY14643 to PPARα was associated with antiinflammatory disease in a human corneal cell model, suggesting possible applications for PPARα ligands.

Molecular mechanism of peroxisome proliferator-activated receptor α activation by WY14643: a new mode of ligand recognition and receptor stabilization.

The current Zika virus (ZIKV) outbreak became a global health threat of complex epidemiology and devastating neurological impacts, therefore requiring urgent efforts towards the development of novel efficacious and safe antiviral drugs. Due to its central role in RNA viral replication, the non-structural protein 5 (NS5) RNA-dependent RNA-polymerase (RdRp) is a prime target for drug discovery. Here we describe the crystal structure of the recombinant ZIKV NS5 RdRp domain at 1.9 Å resolution as a platform for structure-based drug design strategy. The overall structure is similar to other flaviviral homologues. However, the priming loop target site, which is suitable for non-nucleoside polymerase inhibitor design, shows significant differences in comparison with the dengue virus structures, including a tighter pocket and a modified local charge distribution.

Crystal structure of Zika virus NS5 RNA-dependent RNA polymerase.

The glycoside hydrolase family 45 (GH45) of carbohydrate modifying enzymes is mostly comprised of β-1,4-endoglucanases. Significant diversity between the GH45 members has prompted the division of this family into three subfamilies: A, B and C, which may differ in terms of the mechanism, general architecture, substrate binding and cleavage. Here, we use a combination of X-ray crystallography, bioinformatics, enzymatic assays, molecular dynamics simulations and site-directed mutagenesis experiments to characterize the structure, substrate binding and enzymatic specificity of the GH45 subfamily C endoglucanase from Phanerochaete chrysosporium (PcCel45A). We investigated the role played by different residues in the binding of the enzyme to cellulose oligomers of different lengths and examined the structural characteristics and dynamics of PcCel45A that make subfamily C so dissimilar to other members of the GH45 family. Due to the structural similarity shared between PcCel45A and domain I of expansins, comparative analysis of their substrate binding was also carried out. Our bioinformatics sequence analyses revealed that the hydrolysis mechanisms in GH45 subfamily C is not restricted to use of the imidic asparagine as a general base in the "Newton's cradle" catalytic mechanism recently proposed for this subfamily.

Structure, computational and biochemical analysis of PcCel45A endoglucanase from Phanerochaete chrysosporium and catalytic mechanisms of GH45 subfamily C members.

The Yellow Fever virus (YFV) is transmitted by mosquitos and causes an infection with symptoms including fever, headaches and nausea. In 20-50% of the cases, the disease may evolve to a visceral stage, reaching high mortality rates. YFV NS2B-NS3 protease has been identified as an important drug target.

Structural characterization and polymorphism analysis of the NS2B-NS3 protease from the 2017 Brazilian circulating strain of Yellow Fever virus.

Calcium and integrin binding protein 1 (CIB1) is an EF-hand-containing, small intracellular protein that has recently been implicated in cancer cell survival and proliferation. In particular, CIB1 depletion significantly impairs tumor growth in triple-negative breast cancer (TNBC). Thus, CIB1 is a potentially attractive target for cancer chemotherapy that has yet to be validated by a chemical probe. To produce a probe molecule to the CIB1 helix 10 (H10) pocket and demonstrate that it is a viable target for molecular intervention, we employed random peptide phage display to screen and select CIB1-binding peptides. The top peptide sequence selected, UNC10245092, was produced synthetically, and binding to CIB1 was confirmed by isothermal titration calorimetry (ITC) and a time-resolved fluorescence resonance energy transfer (TR-FRET) assay. Both assays showed that the peptide bound to CIB1 with low nanomolar affinity. CIB1 was cocrystallized with UNC10245092, and the 2.1 Å resolution structure revealed that the peptide binds as an α-helix in the H10 pocket, displacing the CIB1 C-terminal H10 helix and causing conformational changes in H7 and H8. UNC10245092 was further derivatized with a C-terminal Tat-derived cell penetrating peptide (CPP) to demonstrate its effects on TNBC cells in culture, which are consistent with results of CIB1 depletion. These studies provide a first-in-class chemical tool for CIB1 inhibition in cell culture and validate the CIB1 H10 pocket for future probe and drug discovery efforts.

Discovery and Characterization of Peptide Inhibitors for Calcium and Integrin Binding Protein 1.

Single-stranded positive RNA ((+) ssRNA) viruses include several important human pathogens. Some members are responsible for large outbreaks, such as Zika virus, West Nile virus, SARS-CoV, and SARS-CoV-2, while others are endemic, causing an enormous global health burden. Since vaccines or specific treatments are not available for most viral infections, the discovery of direct-acting antivirals (DAA) is an urgent need. Still, the low-throughput nature of and biosafety concerns related to traditional antiviral assays hinders the discovery of new inhibitors. With the advances of reverse genetics, reporter replicon systems have become an alternative tool for the screening of DAAs. Herein, we review decades of the use of (+) ssRNA viruses replicon systems for the discovery of antiviral agents. We summarize different strategies used to develop those systems, as well as highlight some of the most promising inhibitors identified by the method. Despite the genetic alterations introduced, reporter replicons have been shown to be reliable systems for screening and identification of viral replication inhibitors and, therefore, an important tool for the discovery of new DAAs.

Reporter Replicons for Antiviral Drug Discovery against Positive Single-Stranded RNA Viruses.

SARS-CoV-2 is the causative agent of COVID-19. The dimeric form of the viral M is responsible for the cleavage of the viral polyprotein in 11 sites, including its own N and C-terminus. The lack of structural information for intermediary forms of M is a setback for the understanding its self-maturation process. Herein, we used X-ray crystallography combined with biochemical data to characterize multiple forms of SARS-CoV-2 M. For the immature form, we show that extra N-terminal residues caused conformational changes in the positioning of domain-three over the active site, hampering the dimerization and diminishing its activity. We propose that this form preludes the cis and trans-cleavage of N-terminal residues. Using fragment screening, we probe new cavities in this form which can be used to guide therapeutic development. Furthermore, we characterized a serine site-directed mutant of the M bound to its endogenous N and C-terminal residues during dimeric association stage of the maturation process. We suggest this form is a transitional state during the C-terminal trans-cleavage. This data sheds light in the structural modifications of the SARS-CoV-2 main protease during its self-maturation process.

A Crystallographic Snapshot of SARS-CoV-2 Main Protease Maturation Process.

Yellow fever (YF) is an acute viral hemorrhagic disease transmitted by infected mosquitoes. Large epidemics of YF occur when the virus is introduced into heavily populated areas with high mosquito density and low vaccination coverage. The lack of a specific small molecule drug treatment against YF as well as for homologous infections, such as zika and dengue, highlights the importance of these flaviviruses as a public health concern. With the advancement in computer hardware and bioactivity data availability, new tools based on machine learning methods have been introduced into drug discovery, as a means to utilize the growing high throughput screening (HTS) data generated to reduce costs and increase the speed of drug development. The use of predictive machine learning models using previously published data from HTS campaigns or data available in public databases, can enable the selection of compounds with desirable bioactivity and absorption, distribution, metabolism, and excretion profiles. In this study, we have collated cell-based assay data for yellow fever virus from the literature and public databases. The data were used to build predictive models with several machine learning methods that could prioritize compounds for in vitro testing. Five molecules were prioritized and tested in vitro from which we have identified a new pyrazolesulfonamide derivative with EC 3.2 μM and CC 24 μM, which represents a new scaffold suitable for hit-to-lead optimization that can expand the available drug discovery candidates for YF.

Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus.

With the rapidly evolving SARS-CoV-2 variants of concern, there is an urgent need for the discovery of further treatments for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need, and numerous compounds have already been selected for  testing by several groups. These have led to a growing database of molecules with  activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein, we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2  inhibition data and used them to prioritize additional FDA-approved compounds for  testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, lumefantrine, an antimalarial was selected for testing and showed limited antiviral activity in cell-based assays while demonstrating binding ( 259 nM) to the spike protein using microscale thermophoresis. Several other compounds which we prioritized have since been tested by others and were also found to be active . This combined machine learning and  testing approach can be expanded to virtually screen available molecules with predicted activity against SARS-CoV-2 reference WIV04 strain and circulating variants of concern. In the process of this work, we have created multiple iterations of machine learning models that can be used as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 with over 500 compounds is now freely available at www.assaycentral.org.

Machine Learning Models Identify Inhibitors of SARS-CoV-2.

The COVID-19 outbreak has rapidly spread on a global scale, affecting the economy and public health systems throughout the world. In recent years, peptide-based therapeutics have been widely studied and developed to treat infectious diseases, including viral infections. Herein, the antiviral effects of the lysine linked dimer des-Cys, Lys,Lys-(pBthTX-I)K () and derivatives against SARS-CoV-2 are reported. The lead peptide  and derivatives showed attractive inhibitory activities against SARS-CoV-2 (EC = 28-65 µM) and mostly low cytotoxic effect (CC > 100 µM). To shed light on the mechanism of action underlying the peptides' antiviral activity, the Main Protease (M) and Papain-Like protease (PL) inhibitory activities of the peptides were assessed. The synthetic peptides showed PL inhibition potencies (ICs = 1.0-3.5 µM) and binding affinities ( = 0.9-7 µM) at the low micromolar range but poor inhibitory activity against M (IC > 10 µM). The modeled binding mode of a representative peptide of the series indicated that the compound blocked the entry of the PL substrate toward the protease catalytic cleft. Our findings indicated that non-toxic dimeric peptides derived from the Bothropstoxin-I have attractive cellular and enzymatic inhibitory activities, thereby suggesting that they are promising prototypes for the discovery and development of new drugs against SARS-CoV-2 infection.

Andre S. Godoy

São Carlos Institute of Physics

University of Sao Paulo

High-throughput Antiviral Assays to Screen for Inhibitors of Zika Virus Replication