Accelerating Zika Virus Drug Discovery Using Chemogenomic Approaches

Overview

BACKGROUND: The Zika virus (ZIKV) is a rapidly spreading mosquito-borne viral illness that is causing global concern due to severe complications such as congenital malformations (microcephaly, i.e. small head size) and neurological syndromes such as Guillian Barre Syndrome. Currently, due to lack of specific anti-ZIKV therapies or vaccines, ZIKV complications cannot be prevented. Hence, given the global health importance of ZIKV, there is an urgent need for rapid and cost-effective identification of anti-ZIKV drugs. In this project, I hypothesized that drugs that target host (human) proteins that directly interact with ZIKV can represent potential anti-ZIKV drugs METHODS: A step-wise computer-aided bioinformatics approach was undertaken to test the hypothesis. Step 1: A list of human proteins that interact with ZIKV were compiled using “DeNovo”, a novel virus-host sequence based protein-protein interaction prediction software (Eid F et al, 2016); Step 2: The FASTA sequence of each human interacting protein emerging from Step 1 was then examined in the Therapeutic Target Database (TTD) using BLAST function to identify potential targets for each protein. Proteins that reached statistical significance with an of E-value of <10-10 were chosen as putative anti-ZIKV targets; Step 3: Drugs that act on these significant protein targets were then examined in both the TTD and Drugbank databases which house currently available drugs that can be repurposed for novel indications. Step 4: A network analysis was then performed on the protein/drug targets using the Genets platform (Li et al, 2017) to obtain insights into the molecular circuity that is critical for ZIKV-host interactions. RESULTS: A total of 40 human proteins interacting with ZIKV was identified through the DeNovo database of which 13 proteins reached statistical significance as potential anti-ZIKV targets. Of these 13 proteins, 8 proteins could be targeted by 51 existing drug candidates from the TTD and Drugbank databases. Drugs targeting the tubulin folding pathway emerged as the significantly enriched pathway among the putative anti-ZIKV drugs. CONCLUSIONS: These results confirm the hypothesis that computational analysis of novel viral-host protein-protein interactions can yield several putative anti-ZIKV drugs in a rapid and cost-effective manner. Pathway enrichment analysis of targets/drugs showed enrichment of the tubulin folding pathway as one of the prime anti-ZIKV targets. These in silico results now require confirmation using in vitro screens. If promising findings are discovered from such cellular studies, they could constitute important starting points for in vivo studies in model systems and eventually for clinical trials in humans. REFERENCES: Eid F, et al. DeNovo: virus host sequence based protein-protein intereaction prediction (2016). Bioinformatics. 32; 1144-50. Li T, et al. A scored human protein-protein interaction network to catalyze genomic interpretation (2017). Nat Methods. Jan;14(1):61-64.

Competition history

• AJAS 2019 Category not listed

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