A computational framework of host-based drug repositioning for broad-spectrum antivirals against RNA viruses

Abstract

Summary RNA viruses are responsible for many zoonotic diseases that post great challenges for public health. Effective therapeutics against these viral infections remain limited. Here, we deployed a computational framework for host-based drug repositioning to predict potential antiviral drugs from 2,352 approved drugs and 1,062 natural compounds embedded in herbs of traditional Chinese medicine. By systematically interrogating public genetic screening data, we comprehensively cataloged host dependency genes (HDGs) that are indispensable for successful viral infection corresponding to 10 families and 29 species of RNA viruses. We then utilized these HDGs as potential drug targets and interrogated extensive drug-target interactions through database retrieval, literature mining, and de novo prediction using artificial intelligence-based algorithms. Repurposed drugs or natural compounds were proposed against many viral pathogens such as coronaviruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), flaviviruses, and influenza viruses. This study helps to prioritize promising drug candidates for in-depth evaluation against these virus-related diseases.

An in silico drug repositioning workflow for host-based antivirals

“… Note: In addition to PubMed, public integrated database such as “CRISP-view” ( http://crispview.weililab.org/ ) can also be used to search high throughput genetic screen studies or datasets ( Cui et al., 2021 ). In addition, virus-specific HDGs for 10 families and 29 species of RNA viruses can be downloaded from ( Li et al., 2021 ).

Figure 1 Prepare DeepCPI input file (A) Gene symbol list of target gene set exemplified by 165 high confidence HDGs for Coronaviridae viruses.

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“…Public datasets derived from functional genetic screens using techniques such as gene-trap, RNA interference (RNAi) and clustered regularly interspaced palindromic repeats (CRISPR) have provided a wealth of resource about virus-specific HDGs. We have collected Coronaviridae -specific HDGs in our previous study ( Li et al., 2021 ) and use them as target gene set in this protocol. HDGs for a broader range of RNA viruses can also be found in Li et al., 2021 .…”

“…We have collected Coronaviridae -specific HDGs in our previous study ( Li et al., 2021 ) and use them as target gene set in this protocol. HDGs for a broader range of RNA viruses can also be found in Li et al., 2021 . For the interrogated drug candidates, we build a chemical cohort by collecting 2457 Food and Drug Administration (FDA) approved drugs (Database: DrugBank, version 5.1.7, released 2020-07-02; https://www.drugbank.ca ) and 1062 selected natural compounds embedded in herbs of traditional Chinese medicine with favorable druggability ( Li et al., 2021 ).…”

“…HDGs for a broader range of RNA viruses can also be found in Li et al., 2021 . For the interrogated drug candidates, we build a chemical cohort by collecting 2457 Food and Drug Administration (FDA) approved drugs (Database: DrugBank, version 5.1.7, released 2020-07-02; https://www.drugbank.ca ) and 1062 selected natural compounds embedded in herbs of traditional Chinese medicine with favorable druggability ( Li et al., 2021 ). This refined drug candidate list does not include experimental and investigational chemicals.…”

“…The workflow described here can also apply to more general purposes, especially when given a user-defined druggable target gene set. For complete details on the use and execution of this protocol, please refer to Li et al. (2021) .…”