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[Preprint]. 2020 Nov 5:2020.11.04.369041.
doi: 10.1101/2020.11.04.369041.

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

Gergely Zahoránszky-Kőhalmi  1 , Vishal B Siramshetty  1 , Praveen Kumar  2   3 , Manideep Gurumurthy  1 , Busola Grillo  1 , Biju Mathew  1 , Dimitrios Metaxatos  1 , Mark Backus  1 , Tim Mierzwa  1 , Reid Simon  1 , Ivan Grishagin  1   4 , Laura Brovold  4 , Ewy A Mathé  1 , Matthew D Hall  1 , Samuel G Michael  1 , Alexander G Godfrey  1 , Jordi Mestres  5 , Lars J Jensen  6 , Tudor I Oprea  2   6   7   8
Affiliations

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research

Gergely Zahoránszky-Kőhalmi et al. bioRxiv. .

Update in

  • A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research.
    Zahoránszky-Kőhalmi G, Siramshetty VB, Kumar P, Gurumurthy M, Grillo B, Mathew B, Metaxatos D, Backus M, Mierzwa T, Simon R, Grishagin I, Brovold L, Mathé EA, Hall MD, Michael SG, Godfrey AG, Mestres J, Jensen LJ, Oprea TI. Zahoránszky-Kőhalmi G, et al. J Chem Inf Model. 2022 Feb 14;62(3):718-729. doi: 10.1021/acs.jcim.1c00431. Epub 2022 Jan 20. J Chem Inf Model. 2022. PMID: 35057621 Free PMC article.

Abstract

Motivation: In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy.

Results: Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.

Availability: https://neo4covid19.ncats.io.

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Conflict of interest statement

Competing Interests

LJJ is co-founder and scientific advisory board member of Intomics A/S. All other authors have no competing interests to declare.

Figures

Figure 1.
Figure 1.. Resource integration logic.
The schema highlights the most important steps of data processing. Individual inputs are labeled with letters. Orange letters indicate points of the workflow where new data sources are introduced. Arrowheads and letters aid to track the flow of information when it is not obvious. Purple, green and blue colors distinguish types of resources that utilize experimental, predicted and both types of data, respectively. HHIs: host–host protein interactions, HPIs: host–pathogen (here: SARS-CoV-2) protein interactions, DTIs: drug–target interactions, TDLs: target development levels.
Figure 2.
Figure 2.. Bipartite network of HPIs.
Human and virus proteins are depicted by circles and “v-like” shapes, respectively. The larger the node size, the higher the degree of the node connectivity. Color of the human proteins encode their TDL annotation: blue: Tclin, orange: Tchem, yellow: Tbio, dark gray: Tdark, light gray: unknown. A: The complete HPI bipartite network. B: The subnetwork centered around the virus hub YWHAQ. The network was visualized with the help of Cytoscape v3.6.0 [86].
Figure 3.
Figure 3.. Molecular structures of metformin and moroxydin.
Molecules were depicted with the help of ChemAxon’s MarvinSketch v17.15.0 [87].
See this image and copyright information in PMC

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