1
|
Kumar N, Singh A, Gulati HK, Bhagat K, Kaur K, Kaur J, Dudhal S, Duggal A, Gulati P, Singh H, Singh JV, Bedi PMS. Phytoconstituents from ten natural herbs as potent inhibitors of main protease enzyme of SARS-COV-2: In silico study. Phytomed Plus 2021; 1:100083. [PMID: 35403086 PMCID: PMC8180089 DOI: 10.1016/j.phyplu.2021.100083] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/31/2021] [Accepted: 06/01/2021] [Indexed: 04/13/2023]
Abstract
BACKGROUND Lack of treatment of novel Coronavirus disease led to the search of specific antivirals that are capable to inhibit the replication of the virus. The plant kingdom has demonstrated to be an important source of new molecules with antiviral potential. PURPOSE The present study aims to utilize various computational tools to identify the most eligible drug candidate that have capabilities to halt the replication of SARS-COV-2 virus by inhibiting Main protease (Mpro) enzyme. METHODS We have selected plants whose extracts have inhibitory potential against previously discovered coronaviruses. Their phytoconstituents were surveyed and a library of 100 molecules was prepared. Then, computational tools such as molecular docking, ADMET and molecular dynamic simulations were utilized to screen the compounds and evaluate them against Mpro enzyme. RESULTS All the phytoconstituents showed good binding affinities towards Mpro enzyme. Among them laurolitsine possesses the highest binding affinity i.e. -294.1533 kcal/mol. On ADMET analysis of best three ligands were simulated for 1.2 ns, then the stable ligand among them was further simulated for 20 ns. Results revealed that no conformational changes were observed in the laurolitsine w.r.t. protein residues and low RMSD value suggested that the Laurolitsine-protein complex was stable for 20 ns. CONCLUSION Laurolitsine, an active constituent of roots of Lindera aggregata, was found to be having good ADMET profile and have capabilities to halt the activity of the enzyme. Therefore, this makes laurolitsine a good drug candidate for the treatment of COVID-19.
Collapse
Key Words
- ACE-2, Angiotensin converting enzyme- 2
- ADMET
- ADMET, absorption, Distribution, metabolism, excretion and toxicity
- Ala, Alanine
- Approx., approximately
- Arg, arginine
- Asn, Asparagine
- Asp, Aspartic acid
- CADD, Computer Aided Drug Design
- CHARMM, Chemistry at Harvard Macromolecular Mechanics
- COV, coronavirus
- COVID, Novel corona-virus disease
- Covid-19
- Cys, cysteine
- DSBDS, Dassault's Systems Biovia's Discovery studio
- Gln, Glutamine
- Glu, glutamate
- Gly, Glycine
- His, histidine
- Ile, isoleucine
- K, Kelvin
- Kcal/mol, kilo calories per mol
- Leu, Leucine
- Leu, leucine
- Lys, Lysine
- MD, Molecular Dynamics
- Met, Methionine
- MoISA, Molecular Surface Area
- Molecular dynamic simulations
- Mpro protein
- Mpro, Main protease enzyme
- N protein, nucleocapsid protein
- NI, N-(4-methylpyridin-3-yl) acetamide inhibitor
- NPT, amount of substance (N), pressure (P) and temperature (T)
- NVT, amount of substance (N), volume (V) and temperature (T)
- Natural Antiviral herbs
- PDB, protein data bank
- PPB, plasma protein binding
- PSA, Polar Surface Area
- Phi, Phenylalanine
- Pro, Proline
- RCSB, Research Collaboratory for Structural Bioinformatics
- RMS, Root Mean Square
- RMSD, Root Mean Square Deviation
- RMSF, root mean square fluctuations
- RNA, Ribonucleic acid
- SAR-COV-2, severe acute respiratory syndrome coronavirus 2
- SDF, structure data format
- Ser, serine
- T, Temperature
- Thr, Threonine
- Trp, Tryptophan
- Tyr, Tyrosine
- Val, Valine
- kDa, kilo Dalton
- nCOV-19, Novel Coronavirus 2019
- ns/nsec, nano seconds
- ps, pentoseconds
- rGyr, Radius of gyration
- w.r.t., with respect to
- Å, angstrom
- α, alpha
- β, beta
Collapse
Affiliation(s)
- Nitish Kumar
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
- Drug and Pollution testing Lab, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Atamjit Singh
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Harmandeep Kaur Gulati
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Kavita Bhagat
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Komalpreet Kaur
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Jaspreet Kaur
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Shilpa Dudhal
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Amit Duggal
- Drugs Control Wing, Sector 16, Chandigarh, India, 160015
| | - Puja Gulati
- School of Pharmacy, Desh Bhagat University, Mandi Gobindgarh, Punjab, India, 147301
| | - Harbinder Singh
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Jatinder Vir Singh
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | | |
Collapse
|
2
|
Kumar N, Singh A, Gulati HK, Bhagat K, Kaur K, Kaur J, Dudhal S, Duggal A, Gulati P, Singh H, Singh JV, Bedi PMS. Phytoconstituents from ten natural herbs as potent inhibitors of main protease enzyme of SARS-COV-2: In silico study. Phytomed Plus 2021. [PMID: 35403086 DOI: 10.1016/j.phyplu.2021.100139] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
BACKGROUND Lack of treatment of novel Coronavirus disease led to the search of specific antivirals that are capable to inhibit the replication of the virus. The plant kingdom has demonstrated to be an important source of new molecules with antiviral potential. PURPOSE The present study aims to utilize various computational tools to identify the most eligible drug candidate that have capabilities to halt the replication of SARS-COV-2 virus by inhibiting Main protease (Mpro) enzyme. METHODS We have selected plants whose extracts have inhibitory potential against previously discovered coronaviruses. Their phytoconstituents were surveyed and a library of 100 molecules was prepared. Then, computational tools such as molecular docking, ADMET and molecular dynamic simulations were utilized to screen the compounds and evaluate them against Mpro enzyme. RESULTS All the phytoconstituents showed good binding affinities towards Mpro enzyme. Among them laurolitsine possesses the highest binding affinity i.e. -294.1533 kcal/mol. On ADMET analysis of best three ligands were simulated for 1.2 ns, then the stable ligand among them was further simulated for 20 ns. Results revealed that no conformational changes were observed in the laurolitsine w.r.t. protein residues and low RMSD value suggested that the Laurolitsine-protein complex was stable for 20 ns. CONCLUSION Laurolitsine, an active constituent of roots of Lindera aggregata, was found to be having good ADMET profile and have capabilities to halt the activity of the enzyme. Therefore, this makes laurolitsine a good drug candidate for the treatment of COVID-19.
Collapse
Key Words
- ACE-2, Angiotensin converting enzyme- 2
- ADMET
- ADMET, absorption, Distribution, metabolism, excretion and toxicity
- Ala, Alanine
- Approx., approximately
- Arg, arginine
- Asn, Asparagine
- Asp, Aspartic acid
- CADD, Computer Aided Drug Design
- CHARMM, Chemistry at Harvard Macromolecular Mechanics
- COV, coronavirus
- COVID, Novel corona-virus disease
- Covid-19
- Cys, cysteine
- DSBDS, Dassault's Systems Biovia's Discovery studio
- Gln, Glutamine
- Glu, glutamate
- Gly, Glycine
- His, histidine
- Ile, isoleucine
- K, Kelvin
- Kcal/mol, kilo calories per mol
- Leu, Leucine
- Leu, leucine
- Lys, Lysine
- MD, Molecular Dynamics
- Met, Methionine
- MoISA, Molecular Surface Area
- Molecular dynamic simulations
- Mpro protein
- Mpro, Main protease enzyme
- N protein, nucleocapsid protein
- NI, N-(4-methylpyridin-3-yl) acetamide inhibitor
- NPT, amount of substance (N), pressure (P) and temperature (T)
- NVT, amount of substance (N), volume (V) and temperature (T)
- Natural Antiviral herbs
- PDB, protein data bank
- PPB, plasma protein binding
- PSA, Polar Surface Area
- Phi, Phenylalanine
- Pro, Proline
- RCSB, Research Collaboratory for Structural Bioinformatics
- RMS, Root Mean Square
- RMSD, Root Mean Square Deviation
- RMSF, root mean square fluctuations
- RNA, Ribonucleic acid
- SAR-COV-2, severe acute respiratory syndrome coronavirus 2
- SDF, structure data format
- Ser, serine
- T, Temperature
- Thr, Threonine
- Trp, Tryptophan
- Tyr, Tyrosine
- Val, Valine
- kDa, kilo Dalton
- nCOV-19, Novel Coronavirus 2019
- ns/nsec, nano seconds
- ps, pentoseconds
- rGyr, Radius of gyration
- w.r.t., with respect to
- Å, angstrom
- α, alpha
- β, beta
Collapse
Affiliation(s)
- Nitish Kumar
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
- Drug and Pollution testing Lab, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Atamjit Singh
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Harmandeep Kaur Gulati
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Kavita Bhagat
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Komalpreet Kaur
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Jaspreet Kaur
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Shilpa Dudhal
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Amit Duggal
- Drugs Control Wing, Sector 16, Chandigarh, India, 160015
| | - Puja Gulati
- School of Pharmacy, Desh Bhagat University, Mandi Gobindgarh, Punjab, India, 147301
| | - Harbinder Singh
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | - Jatinder Vir Singh
- Department of Pharmaceutical Sciences, Guru Nanak Dev University, Amritsar, Punjab, India, 143005
| | | |
Collapse
|
3
|
Ambure P, Bhat J, Puzyn T, Roy K. Identifying natural compounds as multi-target-directed ligands against Alzheimer's disease: an in silico approach. J Biomol Struct Dyn 2018; 37:1282-1306. [PMID: 29578387 DOI: 10.1080/07391102.2018.1456975] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Alzheimer's disease (AD) is a multi-factorial disease, which can be simply outlined as an irreversible and progressive neurodegenerative disorder with an unclear root cause. It is a major cause of dementia in old aged people. In the present study, utilizing the structural and biological activity information of ligands for five important and mostly studied vital targets (i.e. cyclin-dependant kinase 5, β-secretase, monoamine oxidase B, glycogen synthase kinase 3β, acetylcholinesterase) that are believed to be effective against AD, we have developed five classification models using linear discriminant analysis (LDA) technique. Considering the importance of data curation, we have given more attention towards the chemical and biological data curation, which is a difficult task especially in case of big data-sets. Thus, to ease the curation process we have designed Konstanz Information Miner (KNIME) workflows, which are made available at http://teqip.jdvu.ac.in/QSAR_Tools/ . The developed models were appropriately validated based on the predictions for experiment derived data from test sets, as well as true external set compounds including known multi-target compounds. The domain of applicability for each classification model was checked based on a confidence estimation approach. Further, these validated models were employed for screening of natural compounds collected from the InterBioScreen natural database ( https://www.ibscreen.com/natural-compounds ). Further, the natural compounds that were categorized as 'actives' in at least two classification models out of five developed models were considered as multi-target leads, and these compounds were further screened using the drug-like filter, molecular docking technique and then thoroughly analyzed using molecular dynamics studies. Finally, the most potential multi-target natural compounds against AD are suggested.
Collapse
Key Words
- 3D, three-dimensional
- ACh, acetylcholine
- AChE, acetylcholinesterase
- AD, Alzheimer’s disease
- ADME, absorption, distribution, metabolism, and elimination
- APP, amyloid precursor protein
- AUROC, area under the ROC curve
- Alzheimer’s disease
- Aβ, amyloid beta
- BACE1, beta-APP-cleaving enzyme 1
- CDK5, cyclin-dependant kinase 5
- FDA, food and drug administration
- FN, false negative
- FP, false positive
- GSK-3β, glycogen synthase kinase 3β
- HTVS, high-throughput virtual screening
- InChI, International Chemical Identifier
- KNIME, Konstanz Information Miner
- LBDD, ligand-based drug design
- LDA, linear discriminant analysis
- MAO-B, monoamine oxidase B
- MMGBSA, molecular mechanics/generalized born surface area
- MMPBSA, molecular mechanics/Poisson–Boltzmann surface area
- MMPs, matched molecular pairs
- MSA, molecular spectrum analysis
- MTDLs, multi-target-directed ligands
- NMDA, N-methyl-D-aspartate
- PDB, protein data bank
- PP, posterior probability
- QSAR, quantitative structure–activity relationship
- RMSD, root-mean-square deviation
- ROC, receiver operating curve
- ROS, reactive oxygen species
- SBDD, structure-based drug design
- SDF, structure data format
- SMILES, simplified molecular-input line-entry system
- TN, true negative
- TP, true positive
- big data
- data curation
- linear discriminant analysis
- molecular docking
- molecular dynamics
- multi-target drug design
- natural compounds
Collapse
Affiliation(s)
- Pravin Ambure
- a Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology , Jadavpur University , Kolkata 700 032 , India
| | - Jyotsna Bhat
- b Laboratory of Environmental Chemometrics, Faculty of Chemistry , University of Gdańsk , ul. Wita Stwosza 63, Gdańsk 80-308 , Poland
| | - Tomasz Puzyn
- b Laboratory of Environmental Chemometrics, Faculty of Chemistry , University of Gdańsk , ul. Wita Stwosza 63, Gdańsk 80-308 , Poland
| | - Kunal Roy
- a Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology , Jadavpur University , Kolkata 700 032 , India
| |
Collapse
|