Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

In silico identification of drug candidates against COVID-19.

ABSTRACT: The COVID-19 pandemic has caused unprecedented health and economic crisis throughout the world. However, there is no effective medication or therapeutic strategy for treatment of this disease currently. Here, to elucidate the inhibitory effects, we first tested binding affinities of 11 HIV-1 protease inhibitors or their pharmacoenhancers docked onto SARS-CoV-2 main protease (M ^pro ), and 12 nucleotide-analog inhibitors docked onto RNA dependent RNA polymerase (RdRp). To further obtain the effective drug candidates, we screened 728 approved drugs via virtual screening on SARS-CoV-2 M ^pro . Our results demonstrate that remdesivir shows the best binding energy on RdRp and saquinvir is the best inhibitor of M ^pro . Based on the binding energies, we also list 10 top-ranked approved drugs which can be potential inhibitors for M ^pro . Overall, our results do not only propose drug candidates for further experiments and clinical trials but also pave the way for future lead optimization and drug design.

SUBMITTER: Wu Y

PROVIDER: S-EPMC7574721 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

In silico identification of drug candidates against COVID-19.

Wu Yifei Y Chang Kuan Y KY Lou Lei L Edwards Lorette G LG Doma Bly K BK Xie Zhong-Ru ZR

Informatics in medicine unlocked 20201020

The COVID-19 pandemic has caused unprecedented health and economic crisis throughout the world. However, there is no effective medication or therapeutic strategy for treatment of this disease currently. Here, to elucidate the inhibitory effects, we first tested binding affinities of 11 HIV-1 protease inhibitors or their pharmacoenhancers docked onto SARS-CoV-2 main protease (M pro ), and 12 nucleotide-analog inhibitors docked onto RNA dependent RNA polymerase (RdRp). To further ...[more]

PMID: 33102688

Similar Datasets

Indole alkaloids as potential candidates against COVID-19: an in silico study.

Project description:COVID-19 has recently grown to be pandemic all around the world. Therefore, efforts to find effective drugs for the treatment of COVID-19 are needed to improve humans' life quality and survival. Since the main protease (Mpro) of SARS-CoV-2 plays a crucial role in viral replication and transcription, the inhibition of this enzyme could be a promising and challenging therapeutic target to fight COVID-19. The present study aims to identify alkaloid compounds as new potential inhibitors for SARS-CoV-2 Mpro by the hybrid modeling analyses. The docking-based virtual screening method assessed a collection of alkaloids extracted from over 500 medicinal plants and sponges. In order to validate the docking process, classical molecular dynamic simulations were applied on selected ligands, and the calculation of binding free energy was performed. Based on the proper interactions with the active site of the SARS-CoV-2 Mpro, low binding energy, few side effects, and the availability in the medicinal market, two indole alkaloids were found to be potential lead compounds that may serve as therapeutic options to treat COVID-19. This study paves the way for developing natural alkaloids as stronger potent antiviral agents against the SARS-CoV-2.

| S-EPMC9094126 | biostudies-literature

In silico drug repurposing against SARS-CoV-2 using an integrative transcriptomic profiling approach: Hydrocortisone and Benzhydrocodone as potential drug candidates against COVID-19.

Project description:COVID-19 pathogenesis is mainly attributed to dysregulated antiviral immune response, the prominent hallmark of COVID-19. As no established drugs are available against SARS-CoV-2 and developing new ones would be a big challenge, repurposing of existing drugs holds promise against COVID-19. Here, we used a signature-based strategy to delve into cellular responses to SARS-CoV-2 infection in order to identify potential host contributors in COVID-19 pathogenesis and to find repurposable drugs using in silico approaches. We scrutinized transcriptomic profile of various human alveolar cell sources infected with SARS-CoV-2 to determine up-regulated genes specific to COVID-19. Enrichment analysis revealed that the up-regulated genes were involved mainly in viral infectious disease, immune system, and signal transduction pathways. Analysis of protein-protein interaction network and COVID-19 molecular pathway resulted in identifying several anti-viral proteins as well as 11 host pro-viral proteins, ADAR, HBEGF, MMP9, USP18, JUN, FOS, IRF2, ICAM1, IFI35, CASP1, and STAT3. Finally, molecular docking of up-regulated proteins and all FDA-approved drugs revealed that both Hydrocortisone and Benzhydrocodone possess high binding affinity for all pro-viral proteins. The suggested repurposed drugs should be subject to complementary in vitro and in vivo experiments in order to be evaluated in detail prior to clinical studies in potential management of COVID-19.

| S-EPMC9212771 | biostudies-literature

Identification of Lead Drug Candidates for COVID-19 based on Drug Screening Using Human Pluripotent Stem Cell-Derived Cells/Organoids

Project description:We systematically probed which cell types are permissive to SARS-CoV-2 infection. Transcriptomic analysis following SARS-CoV-2 infection of hPSC-derived lung organoids revealed upregulation of chemokines but not type I/III interferon signaling, similar to what was seen in primary human COVID-19 pulmonary infection.

2021-01-01 | GSE148697 | GEO

Identification of Lead Drug Candidates for COVID-19 based on Drug Screening Using Human Pluripotent Stem Cell-Derived Cells/Organoids

Project description:We systematically probed which cell types are permissive to SARS-CoV-2 infection. Transcriptomic analysis following SARS-CoV-2 infection of hPSC-derived pancreatic and lung organoids revealed upregulation of chemokines but not type I/III interferon signaling, similar to what was seen in primary human COVID-19 pulmonary infection.

2020-06-12 | GSE151803 | GEO

Predicting novel drug candidates against Covid-19 using generative deep neural networks.

Project description:The novel Coronavirus outbreak has created a massive economic crisis, and many succumb to death, disturbing the lives of mankind all over the world. Currently, there are no viable treatment for this condition, drug development approaches are being pursued with vigor. The major treatment options are to repurpose existing drugs or to find new ones. Traditional methods for drug discovery take a longer time, so there is an urgent need to develop some alternative techniques that reduces search space for drug candidates. Towards this endeavor, we propose a novel drug discovery method that leverages on long short term memory (LSTM) model to generate novel molecules that are adept at binding with novel Coronavirus protease. Our study demonstrates that the proposed method is able to recreate novel molecules that correlate very much with the properties of trained molecules. Further, we fine-tune the model to generate novel drug-like molecules that are active towards a specific target. We consider 3CLPro, the main protease of novel Coronavirus, as a therapeutic target and demonstrated in silico screening to assess target structural binding affinities with docking simulations. We observed that 80% of generated molecules show docking free energy of less than -5.8 kcal/mol. The top generated drug candidate has the highest binding affinity with a docking score of -8.5 kcal/mol, which is very much lower when compared to approved existing commercial drugs including, Remdesivir. The low binding energy indicates that the generated molecules could be explored as potential drug candidates for Covid-19.

| S-EPMC8510991 | biostudies-literature

Molecules against Covid-19: An in silico approach for drug development

Project description: Not available

| S-EPMC7946544 | biostudies-literature

Multi-conformation representation of Mpro identifies promising candidates for drug repurposing against COVID-19.

Project description:The COVID-19 main protease (Mpro), one of the conserved proteins of the novel coronavirus is crucial for its replication and so is a very lucrative drug target. Till now, there is no drug molecule that has been convincingly identified as the inhibitor of the function of this protein. The current pandemic situation demands a shortcut to quickly reach to a lead compound or a drug, which may not be the best but might serve as an interim solution at least. Following this notion, the present investigation uses virtual screening to find a molecule which is alraedy approved as a drug for some other disease but could be repurposed to inhibit Mpro. The potential of the present method of work to identify such a molecule, which otherwise would have been missed out, lies in the fact that instead of just using the crystallographically identified conformation of the receptor's ligand binding pocket, molecular dynamics generated ensemble of conformations has been used. It implicitly included the possibilities of "induced-fit" and/or "population shift" mechanisms of ligand fitting. As a result, the investigation has not only identified antiviral drugs like ribavirin, ritonavir, etc., but it has also captured a wide variety of drugs for various other diseases like amrubicin, cangrelor, desmopressin, diosmin, etc. as the potent possibilities. Some of these ligands are versatile to form stable interactions with various different conformations of the receptor and therefore have been statistically surfaced in the investigation. Overall the investigation offers a wide range of compounds for further testing to confirm their scopes of applications to combat the COVID-19 pandemic.

| S-EPMC8052536 | biostudies-literature

Modern drug discovery applications for the identification of novel candidates for COVID-19 infections.

Project description:In early December 2019, a large pneumonia epidemic occurred in Wuhan, China. The World Health Organization is concerned about the outbreak of another coronavirus with the powerful, rapid, and contagious transmission. Anyone with minor symptoms like fever and cough or travel history to contaminated places might be suspected of having COVID-19. COVID-19 therapy focuses on treating the disease's symptoms. So far, no such therapeutic molecule has been shown effective in treating this condition. So the treatment is mostly supportive and plasma. Globally, numerous studies and researchers have recently started fighting this virus. Vaccines and chemical compounds are also being investigated against infection. COVID-19 was successfully diagnosed using RNA detection and very sensitive RT-PCR (reverse transcription-polymerase chain reaction). The evolution of particular vaccinations is required to reduce illness severity and spread. Numerous computational analyses and molecular docking have predicted various target compounds that might stop this condition. This paper examines the main characteristics of coronavirus and the computational analyses necessary to avoid infection.

| S-EPMC9273307 | biostudies-literature

In Silico Designed Axl Receptor Blocking Drug Candidates Against Zika Virus Infection.

Project description:After a large outbreak in Brazil, novel drugs against Zika virus became extremely necessary. Evaluation of virus-based pharmacological strategies concerning essential host factors brought us to the idea that targeting the Axl receptor by blocking its dimerization function could be critical for virus entry. Starting from experimentally validated compounds, such as RU-301, RU-302, warfarin, and R428, we identified a novel compound 2' (R428 derivative) to be the most potent for this task amongst a number of alternative compounds and leads. The improved affinity of compound 2' was confirmed by molecular docking as well as molecular dynamics simulation techniques using implicit solvation models. The current study summarizes a new possibility for inhibition of the Axl function as a potential target for future antiviral therapies.

| S-EPMC6044927 | biostudies-literature

Identification of daclatasvir as a repurposed drug against Nsp15 of SASR-CoV2 (COVID-19) by using in silico approaches

Project description: Not available

| S-EPMC8460215 | biostudies-literature