Discovery of New Zika Protease and Polymerase Inhibitors through the Open Science Collaboration Project OpenZika

Abstract

The Zika virus (ZIKV) is a neurotropic arbovirus considered a global threat to public health. Although there have been several efforts in drug discovery projects for ZIKV in recent years, there are still no antiviral drugs approved to date. Here, we describe the results of a global collaborative crowdsourced open science project, the OpenZika project, from IBM's World Community Grid (WCG), which integrates different computational and experimental strategies for advancing a drug candidate for ZIKV. Initially, molecular docking protocols were developed to identify potential inhibitors of ZIKV NS5 RNA-dependent RNA polymerase (NS5 RdRp), NS3 protease (NS2B-NS3pro), and NS3 helicase (NS3hel). Then, a machine learning (ML) model was built to distinguish active vs inactive compounds for the cytoprotective effect against ZIKV infection. We performed three independent target-based virtual screening campaigns (NS5 RdRp, NS2B-NS3pro, and NS3hel), followed by predictions by the ML model and other filters, and prioritized a total of 61 compounds for further testing in enzymatic and phenotypic assays. This yielded five non-nucleoside compounds which showed inhibitory activity against ZIKV NS5 RdRp in enzymatic assays (IC₅₀ range from 0.61 to 17 μM). Two compounds thermally destabilized NS3hel and showed binding affinity in the micromolar range (K_d range from 9 to 35 μM). Moreover, the compounds LabMol-301 inhibited both NS5 RdRp and NS2B-NS3pro (IC₅₀ of 0.8 and 7.4 μM, respectively) and LabMol-212 thermally destabilized the ZIKV NS3hel (K_d of 35 μM). Both also protected cells from death induced by ZIKV infection in in vitro cell-based assays. However, while eight compounds (including LabMol-301 and LabMol-212) showed a cytoprotective effect and prevented ZIKV-induced cell death, agreeing with our ML model for prediction of this cytoprotective effect, no compound showed a direct antiviral effect against ZIKV. Thus, the new scaffolds discovered here are promising hits for future structural optimization and for advancing the discovery of further drug candidates for ZIKV. Furthermore, this work has demonstrated the importance of the integration of computational and experimental approaches, as well as the potential of large-scale collaborative networks to advance drug discovery projects for neglected diseases and emerging viruses, despite the lack of available direct antiviral activity and cytoprotective effect data, that reflects on the assertiveness of the computational predictions. The importance of these efforts rests with the need to be prepared for future viral epidemic and pandemic outbreaks.

Figure 1.

General workflow applied in this work to identify ZIKV NS5 polymerase, NS3 helicase and NS2B-NS3 protease inhibitors. First the ZINC database and ZIKV targets were prepared for molecular docking. In parallel, were developed a machine learning model for prediction of cytoprotective effect against ZIKV. Then, were performed VS campaigns using the computational filters: (i) molecular docking at OpenZika, (ii) ML model for cytoprotective effect against ZIKV, (iii) Bayesian models for BBB permeability, (iv) filters for LogP, PAINS and aggregators, (v) MedChem-based inspection. The prioritized virtual hits of each ZIKV target were validated through enzymatic assays, and for the most promising compounds we performed molecular dynamics simulations to characterize the molecular mechanism of action. Finally, cell-based assays to estimate the cytoprotection and potential cytotoxicity of the hits were performed.

Figure 2.. Virtual screening workflow used to identify inhibitors of ZIKV NS5 polymerase, NS2B-NS3 protease and NS3 helicase.

The independent virtual screenings of ZINC15 database were filtered through: (i) molecular docking, (ii) machine learning model for cytoprotective effect against ZIKV, (iii) Bayesian models for BBB permeability, (iv) LogP, PAINS and aggregators, (v) MedChem based inspection. At the end, we selected 61 compounds for experimental validation.

Figure 3.. ZIKV NS5 RdRp assays.

Concentration-response curves adjusted with Hill to determine IC₅₀ ± Δ IC₅₀ values for compounds A) LabMol-202; B) LabMol-204; C) LabMol-319; D) LabMol-301 and E) LabMol-309.

Figure 4.. Binding modes of LabMol-301 during MD simulations with ZIKV NS5 RdRp.

A) Binding mode 1 convergence RMSD of the two replicates from docking pose 8 (regular and light orange) and the first one from pose 19 (blue) towards the last frame of pose 8 replicate 1 MD. B) Binding mode 2 convergence RMSD of the first replicates from poses 6 (green), 17 (dark blue), and 59 (brown) regarding the last frame of pose 6 replicate 1 MD. C) LabMol-301 (sticks and surface) interacting with multiple RdRp subdomains (color-coded ribbons: palm in grey, thumb in blue, fingers in light yellow, and N- terminal extension in pink) in the two binding modes identified. Detailed representation of final MD frames from the converged simulations of LabMol-301 D) Binding mode 1 and E) Binding mode 2 and its respective poses and replicates.

Figure 5.. Radial plot of LabMol-301 and the NS5 RdRp NNIs reported in the literature.

This analysis was performed with MACCS descriptors and the Tanimoto coefficient (Tc) between LabMol-301 and (A) TBP (Tc = 0.5); (B) lycorine, (Tc = 0.4); (C) 10-undecenoic acid zinc salt, (Tc = 0.37); (D) 2-morpholino-5-((pentyloxy)methyl)-N-(2-phenoxyethyl)-6-((4-sulfamoylphenyl)amino)pyrimidine-4-carboxamide, (Tc = 0.36); (E) xanthoangelol, (Tc = 0.16); and (F) emetine, (Tc = 0.03).

Figure 6.. ZIKV NS2B-NS3pro enzymatic assays.

A) Concentration-response curve adjusted with Hill to determine IC₅₀ ± Δ IC₅₀ for compound LabMol-301. B) Kinetic curves adjusted with Michaelis-Menten model, showing a non-competitive mechanism of action. C) Predicted pose by molecular docking of LabMol-301 at allosteric site of ZIKV NS2B-NS3pro.

Figure 7.. MD convergence and interactions of LabMol-301 at ZIKV NS2B-NS3pro allosteric pocket.

A) Tight coupling of LabMol-301 (green sticks and transparent surface) at the interaction site of NS2B-NS3pro (grey and yellow ribbons, transparent surface). The arrow highlights the subpocket B present exclusively on NS2B-NS3pro allosteric conformation. B) Ligand’s binding mode (green sticks) with NS2B-NS3pro (white ribbons and sticks) after simulation convergence. Blue sticks represent the interacting residues of the hydrophobic core. C) LabMol-301 converged into a stable conformation with low RMSD variation compared to the final replicate 1 structure.

Figure 8.. ZIKV NS2B-NS3pro inhibitors and their respective IC₅₀s at NS2B-NS3pro.

Figure 9.. ZIKV NS3hel activity

(A) Thermal stability assessment of NS3hel in the presence of the ligand candidates. Kd values determination by microscale thermophoresis (MST) (B) of LabMol-212 and (C) LabMol-307. Docking poses of (D) LabMol-307 and LabMol-212 at the RNA biding site of ZIKV NS3hel. LabMol-307 (carbon atoms in yellow sticks representation) interacting with the adenine nucleobase of viral RNA and with the protein residues of NS3hel and (E) LabMol-212 (carbon atoms in cyan sticks representation) interacting with the guanine of RNA and NS3hel residues. Hydrogen bonds are represented in green doted lines and hydrophobic interactions are in transparent surface.