dbSCI: A manually curated database of SARS-CoV-2 inhibitors for COVID-19

Introduction

The coronavirus disease 2019 (COVID-19) pandemic is caused by a novel family member of coronaviruses named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 has been threatening global public health for more than two years (Feikin et al., 2022). Until now, there are 572,239,451 confirmed cases and 6,390,401 confirmed deaths worldwide, areas, or territories by 30 July 2022 (WHO, 2022). The morbidity rate of COVID-19 is still high because of the late entry of vaccines and the lack of specific potent drugs (Angrup et al., 2020). SARS-CoV-2 is a single-stranded RNA (ssRNA) virus, and its genome is 29903 nucleotides (nt) long (Bai et al., 2022). There are six functional open reading frames (ORFs) arranged from 5′ to 3′: ORF1a/ORF1b (replicase), ORF2 (spike protein, S), ORF4 (envelope protein, E), ORF5 (membrane protein, M), ORF9 (nucleocapsid protein, N) (Finkel et al., 2021). All genes and their functions in SARS-CoV-2 are mentioned in Suppl. Table S1 (Yoshimoto, 2020). The S1 subunit of S protein promotes angiotensin-converting enzyme-2 (ACE2)-mediated virus attachment while the S2 subunit enhances membrane fusion (Ziegler et al., 2020). Besides, the N protein is formed of a serine-rich linker region sandwiched between the N terminal domain (NTD) and C terminal domain (CTD), which plays a crucial role in viral entry and its post-entry processing (Peng et al., 2020). The E protein contains an NTD, a hydrophobic domain, and CTD and forms viroporins needed for viral assembly (Jackson et al., 2022). The M protein has a hydrophilic C terminal and amphipathic N terminal; its long form facilitates spike incorporation, and the interaction with E promotes virion production. Moreover, several putative ORFs encode accessory proteins (Lu et al., 2021).

Drug development requires strict and extensive in vitro and in vivo experiments as well as multiple clinical trials before clinical application (Ng, 2000). Generally, de novo drug design via structure-based drug repurposing by in silico simulations is a promising approach often used by researchers to identify possible drugs for viral diseases (Marston et al., 2014). Repurposing the approved drugs has been achieving promising results for sudden major health emergencies because the drugs with known modes of action, biotoxicity, and side effects can be more easily translated (Schein, 2020). Through the joint efforts of scientists all over the world to fight SARS-CoV-2, more and more data are now becoming available to allow researchers to explore new potential therapies. The rationale of drug design for COVID-19 is to target viral proteins to inhibit the biological functions of the virus (Lou et al., 2014), directly or indirectly (Martinez, 2020). The potential drug targets for SARS-CoV-2 include spike protein, NSP3 (papain-like proteinase), NSP5 (3C-like proteinase), NSP12 (RNA-dependent RNA polymerase), NSP15 (endoRNAse), etc. (Ou et al., 2020; Quimque et al., 2021). Antiviral compounds for SARS-CoV-2 have also been reported (Yoshimoto, 2020), such as alprostadil, brequinar, and cepharanthine. To accelerate the drug development of COVID-19, we constructed a DataBase for SARS-CoV-2 Inhibitors called dbSCI. In this database, we have collected and curated more than 2000 published potential inhibitors/drugs against COVID-19. This elaborate database specially designed for COVID-19 is expected to facilitate researchers in designing new drugs against SARS-CoV-2 infection.

Materials and Methods

To construct dbSCI, a total of 22,521 articles were identified upon searching publications up to 16 March 2022 using keywords “((COVID-19) or (SARS-CoV-2)) AND ((drug) OR (inhibitor))” in the PubMed database (https://www.ncbi.nlm.nih.gov/pubmed/). The eligibility criteria for researches were: (1) availability of full-text; (2) study reporting inhibitors or drugs for COVID-19 or SARS-CoV-2; (3) published papers. The exclusion criteria were as follows: (1) not written in English; (2) reviews, comments, case reports, letters, and meta-analyses; (3) studies about diseases other than COVID-19; (4) studies not on inhibitors or drugs. All evaluations were independently conducted by two individuals in parallel. Any conflicts were discussed by all authors to reach a consensus. Finally, the relative information about inhibitors for SARS-CoV-2 from in vitro/in vivo studies, clinical trials, or computational simulation studies from the publications were collected.

Finally, 236 SARS-CoV-2 inhibitors from cell-based experiments, 86 drugs from COVID-19 clinical trials, and 1341 potential SARS-CoV-2 inhibitors from bioinformatics analysis were included. A total of 2777 manually curated entries were obtained, and each entry encompasses comprehensive information, including inhibitor/drug name, targeting protein, the functional mechanism of inhibitor, experimental technique, experimental sample type (cell line and/or tissue), reference information (PubMed ID, year of publication, title of paper). Thereafter, we developed dbSCI using Hypertext Markup Language (HTML5), Java Server Pages (JSP), and JavaScript. The server end is constructed by Java, and the backend database uses SQL Server to store all curated information. dbSCI is currently hosted on a windows server and adopts Apache Tomcat as the application container. dbSCI is freely available at http://bioinfo.henu.edu.cn/COVID/COVIDIH.html.

Results and Discussion

dbSCI provides a very convenient interface for users to search, browse, download, and submit data (Fig. 1). On the “search” webpage, users can easily search for the inhibitor or its target using the protein target name or inhibitor/drug name; then, comprehensive information on the target/inhibitor will be presented. In addition, users can browse all inhibitors or their targets on three-tab pages, including cell-based experiments, clinical trials, and computational simulations. Users can directly obtain the PubMed reference number (PMIDs), drug name, target(s), clinical trials, and some other information from the website. Meanwhile, the related pharmacological information, including half maximal inhibitory concentration (IC50), half maximal effective concentration EC50, 50% cytotoxic concentration (CC50) (from in vitro studies), and the effectiveness (positive or negative, from clinical trials) as well as the imperative information of International Union of Pure and Applied Chemistry (IUPAC) name, an identifier from the database of chemical (CID) and simplified molecular input line entry system (SMILES) (from computational simulation studies) are also shown on the browser page. Thus, the user can easily obtain this information from different studies and compare them. By clicking “more” in the last column with column name “Detail” on the “search” and “Browse” pages, additional information will be presented. For instance, if the user wants to query a potential drug, such as chloroquine, the user can input the drug name “chloroquine” in the “Search” page. Then, the output page will list all potential entries related to your input “chloroquine,” including the PMID of the related article, the drug target for SARS-CoV-2, and IC50, EC50, and CC50 from in vitro studies. Moreover, any clinical trials or bioinformatics analysis of chloroquine will be shown on the same page as well, including the effectiveness of this drug in clinical trials and bioinformatics results. Furthermore, the user can download the complete data from the download page. Researchers can also de novo submit new data into the database through the submission page provided by dbSCI. All submissions will be quality checked by our research team before being deposited into the dbSCI database.

Figure 1: A diagram of dbSCI main interface and subpages.

Currently, there are several databases reporting drug screening for COVID-19, such as COVID19db (Zhang et al., 2022), DockCoV2 (Chen et al., 2021), and CoV-RDB (Tzou et al., 2020). Compared with these existing databases, dbSCI contains all papers reporting inhibitors for SARS-CoV-2, which can be a good supplement for other databases. In the meanwhile, dbSCI has some limitations. First, dbSCI only contains the published papers for COVID-19, while a large number of the preprints of COVID-19 have been excluded. Second, dbSCI offers limited online visualization functions, like the structure visualization of potential drugs and target proteins of SARS-CoV-2. Yu et al. (2020) found that luteolin can bind with the main protease of SARS-CoV-2 by the molecular docking function of AutoDock. Meanwhile, Oso et al. (2021) reported that some natural products (resveratrol, kaempferol, and quercetin) had higher binding affinities for the main protease of SARS-CoV-2 and lower acute oral toxicity compared with chloroquine based on ADMET analysis. In the future, we will update dbSCI to implement these functions by collecting data from PubChem and designing the model for molecular docking and virtual screening (Ferreira and Andricopulo, 2019; Trott and Olson, 2010).

Conclusions

In this study, we manually retrieved and curated the entries of inhibitors or drugs against SARS-CoV-2 for COVID-19 from published literature and developed the dbSCI database. The dbSCI website is updated monthly to offer long-term service. dbSCI provides a relatively comprehensive, credible repository for inhibitors/drugs against SARS-CoV-2 and their potential targeting mechanisms and it will be valuable for basic and clinical researchers to control COVID-19.

Availability of Data and Materials: All data generated or analyzed during this study are included in published articles (and their supplementary information files).

Author Contribution: Study concept and design: Qiang Wang, Longxiang Xie, and Xiangqian Guo. Acquisition and analysis of data: Qiang Wang, Guo Zhao, Baoping Zheng, Xuan Li, Xixi Yu, Zulipinuer Wusiman, Longxiang Xie, and Xiangqian Guo. Design and implementation of the database: Qiang Wang, Guo Zhao, Longxiang Xie, Xuan Li, Xixi Yu, and Xiangqian Guo. Design and implementation of software: Qiang Wang, Guo Zhao, Longxiang Xie, and Xiangqian Guo. Draft of the manuscript: Qiang Wang, Guo Zhao, Longxiang Xie, and Xiangqian Guo.

Ethics Approval: Not applicable.

Funding Statement: This work was supported by the Innovation and Entrepreneurship Training Program for College Students in Henan University (20221022009), the Key Scientific Research Project of Henan Province (22A310012), supporting program for Central Plain Young Top Talents (ZYQR201912176), the program from Academy for Advanced Interdisciplinary Studies of Henan University (Y21008L).

Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.

Supplementary Materials