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Targeting SARS-CoV-2 main protease: structure based virtual screening, in silico ADMET studies and molecular dynamics simulation for identification of potential inhibitors.
Uniyal, Ankit; Mahapatra, Manoj Kumar; Tiwari, Vinod; Sandhir, Rajat; Kumar, Rajnish.
  • Uniyal A; Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, India.
  • Mahapatra MK; Kanak Manjari Institute of Pharmaceutical Sciences, Rourkela, India.
  • Tiwari V; Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, India.
  • Sandhir R; Department of Biochemistry, Panjab University, Chandigarh, India.
  • Kumar R; Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (B.H.U.), Varanasi, India.
J Biomol Struct Dyn ; 40(8): 3609-3625, 2022 May.
Article in English | MEDLINE | ID: covidwho-939480
Semantic information from SemMedBD (by NLM)
1. 2019 novel coronavirus LOCATION_OF Peptide Hydrolases
Subject
2019 novel coronavirus
Predicate
LOCATION_OF
Object
Peptide Hydrolases
2. Peptide Hydrolases PART_OF 2019 novel coronavirus
Subject
Peptide Hydrolases
Predicate
PART_OF
Object
2019 novel coronavirus
3. Peptide Hydrolases DISRUPTS Virus Replication
Subject
Peptide Hydrolases
Predicate
DISRUPTS
Object
Virus Replication
4. 2019 novel coronavirus LOCATION_OF Peptide Hydrolases
Subject
2019 novel coronavirus
Predicate
LOCATION_OF
Object
Peptide Hydrolases
5. Peptide Hydrolases PART_OF 2019 novel coronavirus
Subject
Peptide Hydrolases
Predicate
PART_OF
Object
2019 novel coronavirus
6. Peptide Hydrolases DISRUPTS Virus Replication
Subject
Peptide Hydrolases
Predicate
DISRUPTS
Object
Virus Replication
ABSTRACT
COVID-19 pandemic has created a healthcare crisis across the world and has put human life under life-threatening circumstances. The recent discovery of the crystallized structure of the main protease (Mpro) from SARS-CoV-2 has provided an opportunity for utilizing computational tools as an effective method for drug discovery. Targeting viral replication has remained an effective strategy for drug development. Mpro of SARS-COV-2 is the key protein in viral replication as it is involved in the processing of polyproteins to various structural and nonstructural proteins. Thus, Mpro represents a key target for the inhibition of viral replication specifically for SARS-CoV-2. We have used a virtual screening strategy by targeting Mpro against a library of commercially available compounds to identify potential inhibitors. After initial identification of hits by molecular docking-based virtual screening further MM/GBSA, predictive ADME analysis, and molecular dynamics simulation were performed. The virtual screening resulted in the identification of twenty-five top scoring structurally diverse hits that have free energy of binding (ΔG) values in the range of -26-06 (for compound AO-854/10413043) to -59.81 Kcal/mol (for compound 329/06315047). Moreover, the top-scoring hits have favorable AMDE properties as calculated using in silico algorithms. Additionally, the molecular dynamics simulation revealed the stable nature of protein-ligand interaction and provided information about the amino acid residues involved in binding. Overall, this study led to the identification of potential SARS-CoV-2 Mpro hit compounds with favorable pharmacokinetic properties. We believe that the outcome of this study can help to develop novel Mpro inhibitors to tackle this pandemic.Communicated by Ramaswamy H. Sarma.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Molecular Dynamics Simulation / COVID-19 Type of study: Diagnostic study / Prognostic study / Screening study Limits: Humans Language: English Journal: J Biomol Struct Dyn Year: 2022 Document Type: Article Affiliation country: 07391102.2020.1848636

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Molecular Dynamics Simulation / COVID-19 Type of study: Diagnostic study / Prognostic study / Screening study Limits: Humans Language: English Journal: J Biomol Struct Dyn Year: 2022 Document Type: Article Affiliation country: 07391102.2020.1848636