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

Abstract

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, host-pathogen, and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Here, we describe a workflow we designed for a semiautomated integration of rapidly emerging data sets 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, 63 278 host-host protein, and 1221 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is made publicly accessible via a web interface and via API calls based on the Bolt protocol. Details for accessing the database are provided on a landing page (https://neo4covid19.ncats.io/). 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.

Figure 1.. Resource integration logic.

The schema highlights the most important steps of data processing. Individual inputs are labeled with letters. PPIs: host–host protein interactions, HPIs: host–pathogen (here: SARS-CoV-2) protein interactions, DTIs: drug–target interactions, TDL: target development levels, T: True. Blue: data processing step, white: not yet aggregated data, yellow: input-independent data source and processing step, dark gray: experimental dataset, light gray: predicted/hypothesized dataset, green: final dataset. Solid triangle symbol indicates which proteins should be used as starting nodes in a SmartGraph analysis, whereas the solid diamond symbol indicates which host proteins should be used for integrating relevant PPIs from the STRING DB. The open triangle and diamond symbols indicate the destination of human proteins routed toward a specific resource, i.e. to SmartGraph as starting nodes and to STRING for PPI expension, respectively.

Figure 2.. Molecular structures of metformin and moroxydin.

Molecules were depicted with the help of ChemAxon’s MarvinSketch v17.15.0 [56].

Figure 3.. 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: T_clin, orange: T_chem, yellow: T_bio, gray: T_dark. A: The complete HPI bipartite network visualized in a “yFiles Circular” layout. B: The subnetwork of the largest connected component centered around the virus hub YWHAQ visualized in a “yFiles Radial” layout. network layouts were generated with the help of Cytoscape v3.8.2 [57] and yFiles modules [58].