Dataset Information

Identification of potential inhibitors of three key enzymes of SARS-CoV2 using computational approach.

ABSTRACT: The recent outbreak of coronavirus disease-19 (COVID-19) continues to drastically affect healthcare throughout the world. To date, no approved treatment regimen or vaccine is available to effectively attenuate or prevent the infection. Therefore, collective and multidisciplinary efforts are needed to identify new therapeutics or to explore effectiveness of existing drugs and drug-like small molecules against SARS-CoV-2 for lead identification and repurposing prospects. This study addresses the identification of small molecules that specifically bind to any of the three essential proteins (RdRp, 3CL-protease and helicase) of SARS-CoV-2. By applying computational approaches we screened a library of 4574 compounds also containing FDA-approved drugs against these viral proteins. Shortlisted hits from initial screening were subjected to iterative docking with the respective proteins. Ranking score on the basis of binding energy, clustering score, shape complementarity and functional significance of the binding pocket was applied to identify the binding compounds. Finally, to minimize chances of false positives, we performed docking of the identified molecules with 100 irrelevant proteins of diverse classes thereby ruling out the non-specific binding. Three FDA-approved drugs showed binding to 3CL-protease either at the catalytic pocket or at an allosteric site related to functionally important dimer formation. A drug-like molecule showed binding to RdRp in its catalytic pocket blocking the key catalytic residues. Two other drug-like molecules showed specific interactions with helicase at a key domain involved in catalysis. This study provides lead drugs or drug-like molecules for further in vitro and clinical investigation for drug repurposing and new drug development prospects.

SUBMITTER: Iftikhar H

PROVIDER: S-EPMC7282781 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of potential inhibitors of three key enzymes of SARS-CoV2 using computational approach.

Iftikhar Hafsa H Ali Hafiza Nayyer HN Farooq Sadia S Naveed Hammad H Shahzad-Ul-Hussan Syed S

Computers in biology and medicine 20200609

The recent outbreak of coronavirus disease-19 (COVID-19) continues to drastically affect healthcare throughout the world. To date, no approved treatment regimen or vaccine is available to effectively attenuate or prevent the infection. Therefore, collective and multidisciplinary efforts are needed to identify new therapeutics or to explore effectiveness of existing drugs and drug-like small molecules against SARS-CoV-2 for lead identification and repurposing prospects. This study addresses the i ...[more]

PMID: 32658735

Similar Datasets

Project description:Background: The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the only zoonotic-origin CoV to reach the pandemic stage, to which neither an effective vaccine nor a specific therapy is available. The spike glycoprotein harbors the receptor-binding domain (RBD) that mediates the virus's entry to host cells. This study aimed to identify novel inhibitors that target the spike protein's RBD domain through computational screening of chemical and natural compounds. Method: The spike protein was modeled from the recently reported electron microscopy protein structure (PDB ID: 6VSB) and the previously described SARS-CoV protein structure (PDB ID: 6ACD and 6ACJ). Virtual lab bench CLC Drug Discovery was used to computationally screen for potential inhibitory effects of currently prescribed drugs (n = 22), natural antiviral drugs (n = 100), and natural compounds (n = 35032). Quantitative Structure-Activity Relationship (QSAR) studies were also performed to determine the leading binders known for their antiviral activity. Results: Among the drugs currently used to treat SARS-CoV2, hydroxychloroquine and favipiravir were identified as the best binders with an average of four H-bonds, with a binding affinity of - 36.66 kcal/mol and a minimum interaction energy of - 6.63 kcal/mol. In an evaluation of antiviral compounds, fosamprenavir and abacavir showed effective binding of five H-bonds, with an average binding affinity of - 18.75 kcal.mol- 1 and minimum interaction energy of - 3.57 kcal/mol. Furthermore, screening of 100 natural antiviral compounds predicted potential binding modes of glycyrrhizin, nepritin, punicalagin, epigallocatechin gallate, and theaflavin (average binding affinity of - 49.88 kcal/mol and minimum interaction energy of - 4.35 kcal/mol). Additionally, the study reports a list of 25 natural compounds that showed effective binding with an improved average binding affinity of - 51.46 kcal/mol. Conclusions: Using computational screening, we identified potential SARS-CoV-2 S glycoprotein inhibitors that bind to the RBD region. Using structure-based design and combination-based drug therapy, the identified molecules could be used to generate anti-SARS-CoV-2 drug candidates.

Project description:The rapid outbreak of the COVID-19 also known as SARS-CoV2 has been declared pandemic with serious global concern. As there is no effective therapeutic against COVID-19, there is an urgent need for explicit treatment against it. The focused objective of the current study is to propose promising drug candidates against the newly identified potential therapeutic target (endonuclease, NSP15) of SARS-CoV2. NSP15 is an attractive druggable target due to its critical role in SARS-CoV2 replication and virulence in addition to interference with the host immune system. Here in the present study, we integrated the high throughput computational screening and dynamic simulation approach to identify the most promising candidate lead compound against NSP15.5-fluoro-2-oxo-1H-pyrazine-3-carboxamide (favipiravir), (3R,4R, 5R)-3,4-Bis(benzyloxy)-5-((benzyloxy) methyl) dihydrofuran-2(3H)-one) remedesivir, 1,3-thiazol-5-ylmethyl N-[(2S,3S, 5S)-3-hydroxy-5-[[(2 S)-3-methyl-2-[[methyl-[(2-propan-2-yl-1,3-thiazol-4-yl)methyl]carbamoyl]amino]butanoyl]amino]-1,6-diphenylhexan-2-yl]carbamate (ritonavir), ethyl (3R,4R, 5S)-4-acetamido-5-amino-3-pentan-3-yloxycyclohexene-1-carboxylate (oseltamivir), and (2 S)-N-[(2S,4S, 5S)-5-[[2-(2,6-dimethylphenoxy)acetyl]amino]-4-hydroxy-1,6-diphenylhexan-2-yl]-3-methyl-2-(2-oxo-1,3-diazinan-1-yl)butanamide (lopinavir) were chosen as a training set to generate the pharmacophore model. A dataset of ~140,000 compounds library was screened against the designed pharmacophore model and 10 unique compounds were selected that passed successfully through geometry constraints, Lipinski Rule of 5, and ADME/Tox filters along with a strong binding affinity for NSP15 binding cavity. The best fit compound was selected for dynamic simulation to have detailed structural features critical for binding with the NSP15 protein. Given our detailed integrative computational analysis, a Small molecule (3,3-Dimethyl-N-[4-(1-piperidinylcarbonyl) phenyl] butanamide) with drug-like properties and high binding affinity with the NSP15 is proposed as a most promising potential drug against COVID-19. The current computational integrative approach may complement high-throughput screening and the shortlisted small molecule may contribute to selective targeting of NSP15 to stop the replication of SARS-CoV2.

Dataset Information

Identification of potential inhibitors of three key enzymes of SARS-CoV2 using computational approach.

Publications

Identification of potential inhibitors of three key enzymes of SARS-CoV2 using computational approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets