Virus-CKB: an integrated bioinformatics platform and analysis resource for COVID-19 research

Feng, Zhiwei; Chen, Maozi; Liang, Tianjian; Shen, Mingzhe; Chen, Hui; Xie, Xiang‐Qun

doi:10.1093/bib/bbaa155

Cited by 29 publications

(25 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the basis of our established domain specific chemogenomics databases [ 32 , 35 ] and our novel computational techniques, we constructed a virus-associated disease-specific chemogenomics knowledgebase (Virus-CKB, https://www.cbligand.org/g/virus-ckb ) [ 27 ] and applied our computational systems pharmacology-target mapping (CSP-Target Mapping) to accelerate drug combinational therapy to meet the urgent demand due to the COVID-19 outbreak.…”

Section: Resultsmentioning

confidence: 99%

“…On the basis of our established domain specific chemogenomics databases [ 32–35 ] and our novel computational techniques [ 32 , 36–39 ], we constructed and reported a novel virus-associated disease-specific chemogenomics knowledgebase (Virus-CKB, https://www.cbligand.org/g/virus-ckb ) [ 27 ] and applied our computational systems pharmacology-target mapping (CSP-Target Mapping) to rapidly identify the FDA-approved drugs for repurposing into new indications by fast progress into clinical trials to meet the urgent demand due to the COVID-19 outbreak. Virus-CKB, a one-stop computing platform describes the chemical molecules, genes, proteins and signaling pathways involved in the regulation of virus-associated diseases.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, 10 additional antiviral chemicals from the CAS dataset are also predicted as promising agents for COVID-19. Finally, we also utilize our novel virus chemogenomics knowledgebase platform [ 27 ] to identify potential combinations of medications for COVID-19.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs

Feng

Chen

Xue

et al. 2020

Briefings in Bioinformatics

Self Cite

View full text Add to dashboard Cite

Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CLPro in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs

Feng

Chen

Xue

et al. 2020

Briefings in Bioinformatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since the pandemic COVID-19 began, several research groups applying this strategy was published, and lists of candidates were provided [ 43 ]. Furthermore, some databases, web servers (Virus-CKB [ 44 ], D3Targets-2019-nCoV [ 45 ], COVID-19 Docking Server [ 46 ]) and tools (MolAICal [ 47 ]) have been developed to facilitate and accelerate the process of drug discovery against COVID-19.…”

Section: Sars-cov-2 Drug Discoverymentioning

confidence: 99%

“…The platform is developed based on Shiny app framework and comprising an intuitive graphical interface and interactive visualizations with flexible parameter setting. To automatically screen and discover drug candidates for COVID-19, an integrated web server called viral-associated disease-specific chemogenomics knowledgebase (Virus-CKB) has been developed [ 44 ]. The knowledgebase could be helpful for the prediction of the relevant protein targets as well as analysis data and visualized the outputs by using HTDocking, TargetHunter, BBB predictor, NGL Viewer, Spider Plot, etc.…”

Section: Web Platforms For Target Identification Or/and Drug Repurposmentioning

confidence: 99%

Bioinformatics resources facilitate understanding and harnessing clinical research of SARS-CoV-2

Ahsan

Liu

Cong

et al. 2021

Briefings in Bioinformatics

View full text Add to dashboard Cite

The coronavirus disease 2019 (COVID-19) pandemic, caused by the coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has created an unprecedented threat to public health. The pandemic has been sweeping the globe, impacting more than 200 countries, with more outbreaks still lurking on the horizon. At the time of the writing, no approved drugs or vaccines are available to treat COVID-19 patients, prompting an urgent need to decipher mechanisms underlying the pathogenesis and develop curative treatments. To fight COVID-19, researchers around the world have provided specific tools and molecular information for SARS-CoV-2. These pieces of information can be integrated to aid computational investigations and facilitate clinical research. This paper reviews current knowledge, the current status of drug development and various resources for key steps toward effective treatment of COVID-19, including the phylogenetic characteristics, genomic conservation and interaction data. The final goal of this paper is to provide information that may be utilized in bioinformatics approaches and aid target prioritization and drug repurposing. Several SARS-CoV-2-related tools/databases were reviewed, and a web-portal named OverCOVID (http://bis.zju.edu.cn/overcovid/) is constructed to provide a detailed interpretation of SARS-CoV-2 basics and share a collection of resources that may contribute to therapeutic advances. These information could improve researchers’ understanding of SARS-CoV-2 and help to accelerate the development of new antiviral treatments.

show abstract

Analyzing Multi-Omic Data with Integrative Platforms

Zou

2022

Integrative Bioinformatics

View full text Add to dashboard Cite

Virus-CKB: an integrated bioinformatics platform and analysis resource for COVID-19 research

Cited by 29 publications

References 42 publications

MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs

MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs

Bioinformatics resources facilitate understanding and harnessing clinical research of SARS-CoV-2

Analyzing Multi-Omic Data with Integrative Platforms

Contact Info

Product

Resources

About