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Anti-Infective Agents

Author(s): Huda Mando* and Iyad Allous

DOI: 10.2174/0122113525280410240106122715

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Hierarchical Virtual Screening of SARS-CoV-2 Main Protease Potential Inhibitors: Similarity Search, Pharmacophore Modeling, and Molecular Docking Study

Article ID: e310124226577 Pages: 15

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Abstract

Background: The outbreak of COVID-19 caused by severe acute respiratory syndrome coronavirus2 (SARS-CoV-2) resulted in a widespread pandemic. Various approaches involved the repositioning of antiviral remedies and other medications. Several therapies, including oral antiviral treatments, represent some approaches to adapting to the long existence of the COVID-19 pandemic. In silico studies provide valuable insights throughout drug discovery and development in compliance with global efforts to overcome the pandemic. The main protease is an essential target in the viral cycle. Computer-aided drug design accelerates the identification of potential treatments, including oral therapy.

Aims: This work aims to identify potential SARS-CoV-2 main protease inhibitors using different aspects of in silico approaches.

Methods: In this work, we conducted a hierarchical virtual screening of SARS-CoV-2 main protease inhibitors. A similarity search was conducted to screen molecules similar to the inhibitor PF-07321332. Concurrently, structure-based pharmacophores, besides ligand-based pharmacophores, were derived. A drug-likeness filter filtered the compounds retrieved from similarity search and pharmacophore modeling before being subjected to molecular docking. The candidate molecules that showed higher affinity to the main protease than the reference inhibitor were further filtered by absorption, distribution, metabolism, and excretion (ADME) parameters.

Results: According to binding affinity and ADME analysis, four molecules (CHEMBL218022, PubChem163362029, PubChem166149100, and PubChem 162396459) were prioritized as promising hits. The compounds above were not reported before; no previous experimental studies and bioactive assays are available.

Conclusion: Our time-saving approach represents a strategy for discovering novel SARS-CoV- 2 main protease inhibitors. The ultimate hits may be nominated as leads in discovering novel SARS-CoV-2 main protease inhibitors.

Graphical Abstract

[1]
Sawicka, B.; Aslan, I.; Della Corte, V.; Periasamy, A. The coronavirus global pandemic and its impacts on society; Coronavirus Drug Discov, 2022, pp. 267-311.
[2]
Rohan, R. Understanding the dynamics of COVID-19 outbreak: Structure, diagnosis, prevention and treatment. Antiinfect. Agents, 2021, 19(4)
[3]
Yuan, Y.; Jiao, B.; Qu, L.; Yang, D.; Liu, R. The development of COVID-19 treatment. Front. Immunol., 2023, 14, 1125246.
[4]
Ng, YL.; Salim, CK. Chu, JJH Drug repurposing for COVID-19: Approaches, challenges and promising candidates. Pharmacol. Ther., 2021, 228, 107930.
[5]
Tanni, S.E.; Silvinato, A.; Floriano, I.; Pneumologia, D. Use of remdesivir in patients with COVID-19: A systematic review and meta-analysis. J. Bras. Pneumol., 2022, 48(1), e20210393.
[6]
Mehta, J.; Rolta, R.; Mehta, B.B.; Kaushik, N.; Choi, E.H. Role of dexamethasone and methylprednisolone corticosteroids in coronavirus disease 2019 hospitalized patients: A review. Front. Microbiol., 2022, 13, 1-17.
[7]
Zhan, X.; Dowell, S.; Shen, Y. Leea, DL Chloroquine to fight COVID-19: A consideration of mechanisms and adverse effects? Heliyon, 2020, 6(9), e04900.
[8]
Gasmi, A.; Peana, M.; Noor, S.; Lysiuk, R.; Menzel, A.; Benahmed, A.G. Chloroquine and hydroxychloroquine in the treatment of COVID-19: The never-ending story. Appl. Microbiol. Biotechnol., 2021, 105, 1333-1343.
[9]
Niraj, N.; Mahajan, S.S.; Prakash, A.; Sarma, P.; Medhi, B. Paxlovid: A promising drug for the challenging treatment of SARS-CoV-2 in the pandemic era. Indian J. Pharmacol., 2022, 54(6), 452-458.
[10]
Haddad, F.; Dokmak, G. A comprehensive review on the efficacy of several pharmacologic agents for the treatment of COVID-19. Life, 2022, 12, 1-36.
[11]
Rahmah, L.; Sunny, O. Oral antiviral treatments for COVID-19: Opportunities and challenges. Pharmacol. Rep., 2022, 74, 1255-1278.
[12]
Monica, G. La; Bono, A; Lauria, A; Martorana, A. Targeting SARS-CoV-2 main protease for treatment of COVID-19: Covalent inhibitors structure-activity relationship insights and evolution perspectives. J. Med. Chem., 2022, 65(19), 12500-12534.
[13]
Rungruangmaitree, R.S.P. Aunlika Chimprasit, PS Structural analysis of the coronavirus main protease for the design of pan-variant inhibitors. Sci. Rep., 2023, 13, 7055.
[14]
Stan, D.; Enciu, A.; Mateescu, A.L.; Ion, A.C.; Brezeanu, A.C.; Stan, D. Natural compounds with antimicrobial and antiviral effect and nanocarriers used for their transportation. Front. Pharmacol., 2021, 12, 1-25.
[http://dx.doi.org/10.3389/fphar.2021.723233]
[15]
Sajjad, A. In silico methods and tools for drug discovery. Comput. Biol. Med., 2021, 137.
[16]
Moradi, M.; Golmohammadi, R.; Najafi, A.; Moghaddam, M.M.; Fasihi-Ramandi, M.; Mirnejad, R. A contemporary review on the important role of in silico approaches for managing different aspects of COVID-19 crisis. Inform. Med. Unlocked, 2022, 28, 100862.
[17]
Chan, W.K.B.; Olson, K.M.; Wotring, J.W.; Sexton, J.Z.; Carlson, H.A.; Traynor, J.R. in silico analysis of SARS-CoV-2 proteins as targets for clinically available drugs. Sci. Rep., 2022, 12.
[18]
Luo, L.; Qiu, Q.; Huang, F.; Liu, K.; Lan, Y.; Li, X. Drug repurposing against coronavirus disease 2019 (COVID-19): A review. J. Pharm. Anal., 2021, 11(6), 683-690.
[19]
Mei, X.; Gu, P.; Shen, C.; Lin, X. Computer-based immunoinformatic analysis to predict candidate T-cell epitopes for SARS-CoV-2 vaccine design. Front. Immunol., 2023, 13, 847617.
[20]
Moussa, N.; Mando, H. Novel and predictive QSAR model and molecular docking: New natural sulfonamides of potential concern against SARS-Cov-2. Anti-infect. Agents, 2023, 21(5)
[21]
Elend, L.; Jacobsen, L.; Cofala, T.; Prellberg, J.; Teusch, T. Kramer, O Design of SARS-CoV-2 main protease inhibitors using artificial intelligence and molecular dynamic simulations. Molecules, 2022, 27(13), 4020.
[22]
Babar, Z.; Khan, M.; Zahra, M.; Anwar, M.; Noor, K.; Hashmi, H.F. Drug similarity and structure-based screening of medicinal compounds to target macrodomain-I from SARS-CoV-2 to rescue the host immune system: A molecular dynamics study. J. Biomol. Struct. Dyn., 2020, 1-15.
[PMID: 32897173]
[23]
Hassan, M.; Raza, H.; Athar, M. Moustafa, AA Biomedicine & Pharmacotherapy The exploration of novel Alzheimer ’ s therapeutic agents from the pool of FDA approved medicines using drug repositioning, enzyme inhibition and kinetic mechanism approaches. Biomed. Pharmacother., 2019, 109, 2513-2526.
[24]
Alhadrami, H.A.; Sayed, A.M.; Al-khatabi, H.; Alhakamy, N.A.; Rateb, M.E. Scaffold hopping of α -rubromycin enables direct access to FDA-approved cromoglicic acid as a SARS-CoV-2 M pro inhibitor. Pharmaceuticals, 2021, 14(6), 541.
[25]
Najjar, A.; Platzer, C.; Luft, A.; Aßmann, C.A.; Hany, N. Ghazawy, A Computer-aided design, synthesis and biological characterization of novel inhibitors for PKMYT1. Eur. J. Med. Chem., 2018, 1(161), 479-492.
[26]
Cereto-massagué, A.; José, M.; Valls, C.; Mulero, M.; Garcia-vallvé, S.; Pujadas, G. Molecular fingerprint similarity search in virtual screening. Methods, 2015, 71, 58-63.
[http://dx.doi.org/10.1016/j.ymeth.2014.08.005]
[27]
Negi, P.; Prakash, S.; Patil, V.M. Structure based drug design approach to identify potential SARS-CoV-2 polymerase inhibitors. Coronaviruses, 2021, 2(4), 507-515.
[28]
Saeed, M.; Saeed, A.; Alam, M.J. Receptor-based pharmacophore modeling in the search for natural products for COVID-19 mpro. Molecules, 2021, 26(6), 1549.
[29]
Wang, H.; Wen, J.; Yang, Y.; Liu, H.; Wang, S.; Ding, X. Identification of highly effective inhibitors against SARS-CoV-2 main protease: From virtual screening to in vitro study. Front. Pharmacol., 2022, 13, 1036208.
[30]
Koes, D.R. The pharmit backend: A computer system approach to enabling interactive online drug discovery. IBM J. Res. Develop., 2018, 62(6), 31-36.
[http://dx.doi.org/10.1147/JRD.2018.2883977]
[31]
El-ashrey, M.K.; Bakr, R.O.; Fayed, M.A.A.; Refaey, R.H. Pharmacophore based virtual screening for natural product database revealed possible inhibitors for SARS-CoV-2 main protease. Virology, 2022, 570, 18-28.
[32]
Torres, P.H.M.; Sodero, A.C.R.; Jofily, P.; Silva-Jr, F.P. Key topics in molecular docking for drug design. Int. J. Mol. Sci., 2019, 20(18), 4574.
[http://dx.doi.org/10.3390/ijms20184574]
[33]
Verma, D.K. Kapoor, S Potential inhibitors of SARS-CoV-2 Main Protease (Mpro) identified from the library of FDA-approved drugs using molecular docking studies. Biomedicines, 2022, 11(1), 85.
[34]
Dawood, A.A. The efficacy of Paxlovid against COVID-19 is the result of the tight molecular docking between Mpro and antiviral drugs (nirmatrelvir and ritonavir). Adv. Med. Sci., 2023, 68(1), 1-9.
[35]
Aki-Yalcin, M.T.M.; Molecular Docking, E. Molecular docking: Principles, advances, and its applications in drug discovery. Lett. Drug Des. Discov., 2022, 20.
[36]
Meng, X-Y.; Zhang, H-X.; Mezei, M. Cu, M Molecular Docking: A powerful approach for structure-based drug discovery. Curr Comput aided. Drug Des., 2011, 2011, 157340911795677602.
[http://dx.doi.org/10.2174/157340911795677602]
[37]
Madan, R.; Pandit, K.; Kumar, H.; Kumari, N.; Singh, S. Principles and aspects of molecular docking: A bird ’ s eye view. Hans Shodh Sudha., 2020, 1(1), 110-121.
[38]
Eberhardt, Jerome AutoDock Vina 1.2.0: New docking methods, expanded force field, and Python bindings. J. Chem. Inf. Model., 2021, 61(8), 3891-3898.
[39]
Adamu, U Molecular docking studies, drug-likeness and in-silico ADMET prediction of some novel β-Amino alcohol grafted 1,4,5- trisubstituted 1,2,3-triazoles derivatives as elevators of p53 protein levels. Sci. African, 2020, 10.
[40]
Oliveira, T.A. de; Silva, MP; da; Maia, EHB; Silva, AM; Taranto, A Virtual screening algorithms in drug discovery: A review focused on machine and deep learning methods. Drugs Drug Candi., 2023, 2(2), 311-334.
[41]
Suay-García, B.; Bueso-Bordils, J.; Falcó, A.; Antón-Fos, G.M.; Alemán-López, P.A. Virtual combinatorial chemistry and pharmacological screening: A short guide to drug design. Mol. Sci., 2022, 23(3), 1620.
[http://dx.doi.org/10.3390/ijms23031620]
[42]
Zoete, V.; Daina, A.; Bovigny, C.; Michielin, O. SwissSimilarity: A web tool for low to ultra high throughput ligand-based virtual screening. J. Chem. Inf. Model., 2016, 56, 1399-1404.
[43]
Bragina, M.E.; Daina, A.; Perez, M.A.S.; Michielin, O.; Zoete, V. The swisssimilarity 2021 web tool: Novel chemical libraries and additional methods for an enhanced ligand-based virtual screening experience. Int. J. Mol. Sci., 2022, 23, 811.
[44]
Dotolo, A.F. Pharmacophore modeling, virtual computational screening and biological evaluation studies. PeerJ Prepr., 2017, 1-5.
[45]
Sunseri, J.; Koes, D.R. Pharmit: Interactive exploration of chemical space. Nucleic Acids Res., 2016, 44, 442-448.
[http://dx.doi.org/10.1093/nar/gkw287]
[46]
Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feneey, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev., 1997, 23(1-3), 3-25.
[http://dx.doi.org/10.1016/S0169-409X(96)00423-1]
[47]
Bickerton, G.R.; Paolini, G.V.; Besnard, J.; Muresan, S.; Andrew, L. Quantifying the chemical beauty of drugs. Nat. Chem., 2012, 4(2), 90-98.
[http://dx.doi.org/10.1038/nchem.1243]
[48]
Halimi, M.; Bararpour, P. Natural inhibitors of SARS CoV 2 main protease: Structure based pharmacophore modeling, molecular docking and molecular dynamic simulation studies. J. Mol. Model., 2022, 28, 279.
[49]
Zadorozhnii, P.V.; Kiselev, V.V.; Kharchenko, A.V. in silico drug-likeness assessment and ADME screening were performed using the freely accessible web tool SwissADME. Fut. Pharmacol., 2022, 2(2), 160-197.
[50]
Benet, L.Z.; Hosey, C.M.; Ursu, O.; Oprea, T.I. BDDCS, the rule of 5 and drugability. Adv. Drug Deliv. Rev., 2016, 101, 89-98.
[http://dx.doi.org/10.1016/j.addr.2016.05.007]
[51]
Arup, K. A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. J. Comb. Chem., 1999, 1(1), 55-68.
[52]
Daniel, F. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem., 2002, 45(12), 2615.
[53]
Kralj, S. Molecular filters in medicinal chemistry. Encyclopedia, 2023, 3, 501-511.
[54]
Muegge, I. Simple selection criteria for drug-like chemical matter. J. Med. Chem., 2001, 44(12), 1841-1846.
[55]
Vieira, T.F. Comparing autodock and vina in ligand/decoy discrimination for virtual screening. Appl. Sci., 2019, 9(21), 4535.
[56]
Olson, O.T. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading. J. Comput. Chem., 2011, 31(2), 455-461.
[57]
Forli, S.; Huey, R.; Pique, M.E.; Sanner, M.F.; Goodsell, D.S. Olson, AJ Computational protein - ligand docking and virtual drug screening with the AutoDock suite. Nat. Protoc., 2016, 11(5), 905-919.
[58]
Melissa, FPLIP 2021: Expanding the scope of the protein-ligand interaction profiler to DNA and RNA. Nucleic Acids Res., 2021, 49(1), 530-534.
[59]
Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug- likeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 2017, 7, 1-13.
[60]
Riyadi, P.H. SwissADME predictions of pharmacokinetics and drug- likeness properties of small molecules present in Spirulina platensis. IOP Conf. Ser. Earth Environ. Sci., 2021.
[61]
Sahin, S. A single-molecule with multiple investigations: Synthesis, characterization, computational methods, inhibitory activity against Alzheimer’s disease, toxicity, and ADME studies. Comput. Biol. Med., 2022, 146, 105514.
[62]
Akshay, B. P-glycoprotein substrate assessment in drug discovery: Application of modeling to bridge differential protein expression across in vitro tools. J. Pharm. Sci., 2021, 110(1)
[63]
Nishino, K.; Yamasaki, S.; Nakashima, R.; Zwama, M.; Hayashi-Nishino, M. Function and inhibitory mechanisms of multidrug efflux pumps. Front. Microbiol., 2021, 12, 737288.
[http://dx.doi.org/10.3389/fmicb.2021.737288]
[64]
Guéniche, N.; Huguet, A.; Bruyere, A. Habauzit, Denis Comparative in silico prediction of P-glycoprotein-mediated transport for 2010-2020 US FDA-approved drugs using six Web-tools. Biopharm. Drug Dispos., 2021, 42(8), 393-398.
[65]
Refaey, R.H.; El-ashrey, M.K.; Nissan, Y.M. Repurposing of renin inhibitors as SARS-CoV-2 main protease inhibitors: A computational study. Virology, 2021, 554, 48-54.
[66]
Mochizuki, Masahiro QEX: Target-specific druglikeness filter enhances ligand-based virtual screening. Mol. Divers., 2019, 23, 11-18.
[67]
Chatterjee, A.; Walters, R.; Shafi, Z.; Ahmed, O.S.; Sebek, M.; Gysi, D. Improving the generalizability of protein-ligand binding predictions with AI-Bind. Nat. Commun., 2023, 14, 1989.
[68]
Yu, R.; Chen, L.; Lan, R. Shen, R Computational screening of antagonists against the SARS-CoV-2 (COVID-19) coronavirus by molecular docking. Int. J. Antimicrob. Agents, 2020, 56(2), 106012.
[69]
Ferreira, J.C.; Fadl, S.; Villanueva, A.J. Rabeh, WM Catalytic dyad residues His41 and Cys145 impact the catalytic activity and overall conformational fold of the main SARS-CoV-2 protease 3-chymotrypsin-like protease. Front Chem., 2021, 9, 692168.
[70]
Al-Bustany, H.A.; Ercan, S.; Ince, E.; Pirinccioglu, N. Investigation of angucycline compounds as potential drug candidates against SARS Cov-2 main protease using docking and molecular dynamic approaches. Mol. Divers., 2021, 26, 293-308.
[71]
Antonopoulou, I.; Sapountzaki, E.; Rova, U.; Christakopoulos, P. Inhibition of the main protease of SARS-CoV-2 (M pro) by repurposing/designing drug-like substances and utilizing nature’s toolbox of bioactive compounds. Comput. Struct. Biotechnol. J., 2022, 20, 1306-1344.
[72]
Hai, Ping Shao; Wang, TH; Zhai, HL; Bi, KX; Zhao, BQ Discovery of inhibitors against SARS-CoV-2 main protease using fragment-based drug design. Chem. Biol. Interact., 2023, 371.
[73]
Akbulut, E. Mutations in main protease of SARS CoV-2 decreased boceprevir affinity. Biol. Appl. Sci., 2021, 64, e21200803.
[http://dx.doi.org/10.1590/1678-4324-2021200803]
[74]
Ryunosuke, Yoshino NY Identification of key interactions between SARS-CoV-2 main protease and inhibitor drug candidates. Sci. Rep., 2020, 10.