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.

寨卡病毒（Zika virus, ZIKV）是一种嗜神经性虫媒病毒，被视为全球公共卫生威胁。近年来尽管针对寨卡病毒的药物研发项目已开展多项工作，但目前仍无获批的抗病毒药物。本文介绍了由IBM全球社区网格（IBM’s World Community Grid, WCG）发起的全球协作众包开放科学项目——OpenZika项目的研究成果，该项目整合了多种计算与实验策略，以推进寨卡病毒候选药物的研发。最初，研究人员开发了分子对接方案，用于鉴定寨卡病毒NS5 RNA依赖型RNA聚合酶（NS5 RNA-dependent RNA polymerase, NS5 RdRp）、NS3蛋白酶（NS2B-NS3pro）以及NS3解旋酶（NS3 helicase, NS3hel）的潜在抑制剂。随后，构建了机器学习（machine learning, ML）模型，用于区分对寨卡病毒感染具有细胞保护作用的活性化合物与非活性化合物。我们开展了三项独立的基于靶点的虚拟筛选工作（针对NS5 RdRp、NS2B-NS3pro及NS3hel），随后通过机器学习模型及其他筛选条件进行预测，最终优先筛选出共计61种化合物用于酶学与表型实验验证。实验结果显示，有5种非核苷类化合物在酶学实验中对寨卡病毒NS5 RdRp表现出抑制活性，半数抑制浓度（IC50）范围为0.61至17 μM。另有2种化合物可使NS3hel发生热不稳定化，并在微摩尔级别表现出结合亲和力，解离常数（Kd）范围为9至35 μM。此外，化合物LabMol-301可同时抑制NS5 RdRp与NS2B-NS3pro，其半数抑制浓度分别为0.8 μM与7.4 μM；LabMol-212可使寨卡病毒NS3hel发生热不稳定化，解离常数为35 μM。二者在体外基于细胞的实验中，均可保护细胞免受寨卡病毒感染诱导的死亡。不过，尽管有8种化合物（包括LabMol-301与LabMol-212）表现出细胞保护作用，可阻止寨卡病毒诱导的细胞死亡，这与我们的机器学习模型对该细胞保护作用的预测结果一致，但未发现任何一种化合物对寨卡病毒具有直接的抗病毒活性。综上，本研究发现的新型骨架化合物是极具潜力的命中化合物，可用于后续的结构优化，以及推进寨卡病毒更多候选药物的研发。此外，本研究证实了计算与实验方法整合的重要性，以及大规模协作网络在推进被忽视疾病与新发病毒的药物研发项目中的潜力——尽管目前仍缺乏直接抗病毒活性与细胞保护作用的完整数据，但这也反映出计算预测的准确性。这些研究工作的重要意义在于，我们需要为未来的病毒流行病与大流行暴发做好准备。