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

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 (Kd 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 (Kd 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.