Fruit Bromelain-Derived Peptide Potentially Restrains the Attachment of SARS-CoV-2 Variants to hACE2: A Pharmacoinformatics Approach
Abstract
:1. Introduction
2. Materials and Methods
2.1. Multiple Sequence Alignment of Wild-Type RBD and Its Variants
2.2. Three-Dimensional Structures of the Bromelain-Derived Peptide and the RBD Variants
2.3. Physicochemical Properties Analysis
2.4. Allergenicity and Toxicity Prediction
2.5. IC50 Prediction
2.6. Molecular Docking
2.7. Equilibrium Dissociation Constant Analysis
2.8. Analysis of MM-GBSA Free Energy
2.9. Analysis of the Complex Interface
2.10. Molecular Dynamics Simulations Study
2.11. Binding-Free Energies Calculation
3. Results and Discussion
3.1. The 3D and 2D Structures of Bromelain-Derived Peptide
3.2. Physicochemical Properties
3.3. Allergenicity and Toxicity Prediction
3.4. The Predicted IC50 Value of Bromelain-Derived Peptide
3.5. Analysis of the Interaction between Bromelain-Derived Peptide and RBD Variants
3.6. Prediction of the Position of Bromelain-Derived Peptide Inhibition between the RBD and hACE2
3.7. Molecular Dynamics Simulations Study
3.8. MM-PBSA Binding-Free Energy
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Sample Availability
References
- Planas, D.; Bruel, T.; Grzelak, L.; Guivel-Benhassine, F.; Staropoli, I.; Porrot, F.; Planchais, C.; Buchrieser, J.; Rajah, M.M.; Bishop, E.; et al. Sensitivity of infectious SARS-CoV-2 B.1.1.7 and B.1.351 variants to neutralizing antibodies. Nat. Med. 2021, 27, 917–924. [Google Scholar] [CrossRef] [PubMed]
- Padilla-Sanchez, V. SARS-CoV-2 Structural Analysis of Receptor Binding Domain New Variants from United Kingdom and South Africa. Res. Ideas Outcomes 2021, 7, e62936. [Google Scholar] [CrossRef]
- Albini, A.; Di Guardo, G.; Noonan, D.M.C.; Lombardo, M. The SARS-CoV-2 receptor, ACE-2, is expressed on many different cell types: Implications for ACE-inhibitor- and angiotensin II receptor blocker-based cardiovascular therapies. Intern. Emerg. Med. 2020, 15, 759–766. [Google Scholar] [CrossRef]
- Lukassen, S.; Chua, R.L.; Trefzer, T.; Kahn, N.C.; Schneider, M.A.; Muley, T.; Winter, H.; Meister, M.; Veith, C.; Boots, A.W.; et al. SARS -CoV-2 receptor ACE 2 and TMPRSS 2 are primarily expressed in bronchial transient secretory cells. EMBO J. 2020, 39, e105114. [Google Scholar] [CrossRef] [PubMed]
- Mahase, E. Covid-19: Where are we on vaccines and variants? BMJ 2021, 372, n597. [Google Scholar] [CrossRef]
- Cele, S.; Gazy, I.; Jackson, L.; Hwa, S.H.; Tegally, H.; Lustig, G.; Giandhari, J.; Pillay, S.; Wilkinson, E.; Naidoo, Y.; et al. Escape of SARS-CoV-2 501Y.V2 from neutralization by convalescent plasma. Nature 2021, 593, 142–146. [Google Scholar] [CrossRef]
- Bian, L.; Gao, F.; Zhang, J.; He, Q.; Mao, Q.; Xu, M.; Liang, Z. Effects of SARS-CoV-2 variants on vaccine efficacy and response strategies. Expert Rev. Vaccines 2021, 20, 365–373. [Google Scholar] [CrossRef] [PubMed]
- Rathod, S.B.; Prajapati, P.B.; Punjabi, L.B.; Prajapati, K.N.; Chauhan, N.; Mansuri, M.F. Peptide modelling and screening against human ACE2 and spike glycoprotein RBD of SARS-CoV-2. Silico Pharmacol. 2020, 8, 3. [Google Scholar] [CrossRef]
- Han, Y.; Král, P. Computational Design of ACE2-Based Peptide Inhibitors of SARS-CoV-2. ACS Nano 2020, 14, 5143–5147. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Odolczyk, N.; Marzec, E.; Winiewska-Szajewska, M.; Poznański, J.; Zielenkiewicz, P. Native Structure-Based Peptides as Potential Protein–Protein Interaction Inhibitors of SARS-CoV-2 Spike Protein and Human ACE2 Receptor. Molecules 2021, 26, 2157. [Google Scholar] [CrossRef]
- Hilpert, K. Peptides in COVID-19 Clinical Trials—A Snapshot. Biologics 2021, 1, 300–311. [Google Scholar] [CrossRef]
- Bruno, B.J.; Miller, G.D.; Lim, C.S. Basics and recent advances in peptide and protein drug delivery. Ther. Deliv. 2013, 4, 1443–1467. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hansen, A.; Schäfer, I.; Knappe, D.; Seibel, P.; Hoffmann, R. Intracellular toxicity of proline-rich antimicrobial peptides shuttled into mammalian cells by the cell-penetrating peptide penetratin. Antimicrob. Agents Chemother. 2012, 56, 5194–5201. [Google Scholar] [CrossRef] [Green Version]
- Lei, J.; Sun, L.C.; Huang, S.; Zhu, C.; Li, P.; He, J.; Mackey, V.; Coy, D.H.; He, Q.Y. The antimicrobial peptides and their potential clinical applications. Am. J. Transl. Res. 2019, 11, 3919–3931. [Google Scholar] [PubMed]
- Gautam, A.; Chaudhary, K.; Singh, S.; Joshi, A.; Anand, P.; Tuknait, A.; Mathur, D.; Varshney, G.C.; Raghava, G.P.S. Hemolytik: A database of experimentally determined hemolytic and non-hemolytic peptides. Nucleic Acids Res. 2014, 42, D444–D449. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Schütz, D.; Ruiz-Blanco, Y.B.; Münch, J.; Kirchhoff, F.; Sanchez-Garcia, E.; Müller, J.A. Peptide and peptide-based inhibitors of SARS-CoV-2 entry. Adv. Drug Deliv. Rev. 2020, 167, 47–65. [Google Scholar] [CrossRef]
- Rakib, A.; Nain, Z.; Islam, M.A.; Sami, S.A.; Mahmud, S.; Islam, A.; Ahmed, S.; Siddiqui, A.B.F.; Babu, S.M.O.F.; Hossain, P.; et al. A molecular modelling approach for identifying antiviral selenium-containing heterocyclic compounds that inhibit the main protease of SARS-CoV-2: An in silico investigation. Brief. Bioinform. 2021, 22, 1476–1498. [Google Scholar] [CrossRef]
- Castel, G.; Chtéoui, M.; Heyd, B.; Tordo, N. Phage display of combinatorial peptide libraries: Application to antiviral research. Molecules 2011, 16, 3499–3518. [Google Scholar] [CrossRef] [Green Version]
- Murugan, N.A.; Raja, K.M.P.; Saraswathi, N.T. Peptide-Based Antiviral Drugs. Adv. Exp. Med. Biol. 2021, 1322, 261–284. [Google Scholar] [CrossRef] [PubMed]
- Ahmadi, K.; Farasat, A.; Rostamian, M.; Johari, B.; Madanchi, H. Enfuvirtide, an HIV-1 fusion inhibitor peptide, can act as a potent SARS-CoV-2 fusion inhibitor: An in silico drug repurposing study. J. Biomol. Struct. Dyn. 2021, 1–11. [Google Scholar] [CrossRef]
- Rakib, A.; Sami, S.A.; Islam, M.A.; Ahmed, S.; Faiz, F.B.; Khanam, B.H.; Marma, K.K.S.; Rahman, M.; Uddin, M.M.N.; Nainu, F.; et al. Epitope-Based Immunoinformatics Approach on Nucleocapsid Protein of Severe Acute Respiratory Syndrome-Coronavirus-2. Molecules 2020, 25, 5088. [Google Scholar] [CrossRef]
- Chakrabarti, S.; Guha, S.; Majumder, K. Food-Derived Bioactive Peptides in Human Health: Challenges and Opportunities. Nutrients 2018, 10, 1738. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gautam, S.S.; Mishra, S.K.; Dash, V.; Goyal, A.K.; Rath, G. Comparative study of extraction, purification and estimation of bromelain from stem and fruit of pineapple plant. Thai J. Pharm. Sci. 2010, 34, 67–76. [Google Scholar]
- Rathnavelu, V.; Alitheen, N.B.; Sohila, S.; Kanagesan, S.; Ramesh, R. Potential role of bromelain in clinical and therapeutic applications (Review). Biomed. Rep. 2016, 5, 283–288. [Google Scholar] [CrossRef] [Green Version]
- Chakraborty, A.J.; Mitra, S.; Tallei, T.E.; Tareq, A.M.; Nainu, F.; Cicia, D.; Dhama, K.; Emran, T.B.; Simal-Gandara, J.; Capasso, R. Bromelain a Potential Bioactive Compound: A Comprehensive Overview from a Pharmacological Perspective. Life 2021, 11, 317. [Google Scholar] [CrossRef]
- Sagar, S.; Rathinavel, A.K.; Lutz, W.E.; Struble, L.R.; Khurana, S.; Schnaubelt, A.T.; Mishra, N.K.; Guda, C.; Broadhurst, M.J.; Reid, P.M.; et al. Bromelain inhibits SARS-CoV-2 infection in VeroE6 cells. bioRxiv 2020. [Google Scholar] [CrossRef]
- Tallei, T.E.; Fatimawali; Yelnetty, A.; Idroes, R.; Kusumawaty, D.; Emran, T.B.; Yesiloglu, T.Z.; Sippl, W.; Mahmud, S.; Alqahtani, T.; et al. An Analysis Based on Molecular Docking and Molecular Dynamics Simulation Study of Bromelain as Anti-SARS-CoV-2 Variants. Front. Pharmacol. 2021, 12, 2192. [Google Scholar] [CrossRef] [PubMed]
- Secor, E.R.; Szczepanek, S.M.; Singh, A.; Guernsey, L.; Natarajan, P.; Rezaul, K.; Han, D.K.; Thrall, R.S.; Silbart, L.K. LC-MS/MS identification of a bromelain peptide biomarker from ananas comosus merr. Evid.-Based Complement. Altern. Med. 2012, 2012, 548486. [Google Scholar] [CrossRef] [Green Version]
- Pettersen, E.F.; Goddard, T.D.; Huang, C.C.; Couch, G.S.; Greenblatt, D.M.; Meng, E.C.; Ferrin, T.E. UCSF Chimera—A visualization system for exploratory research and analysis. J. Comput. Chem. 2004, 25, 1605–1612. [Google Scholar] [CrossRef] [Green Version]
- Schwede, T.; Kopp, J.; Guex, N.; Peitsch, M.C. SWISS-MODEL: An automated protein homology-modeling server. Nucleic Acids Res. 2003, 31, 3381–3385. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Waterhouse, A.; Bertoni, M.; Bienert, S.; Studer, G.; Tauriello, G.; Gumienny, R.; Heer, F.T.; De Beer, T.A.P.; Rempfer, C.; Bordoli, L.; et al. SWISS-MODEL: Homology modelling of protein structures and complexes. Nucleic Acids Res. 2018, 46, W296–W303. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Laskowski, R.A.; Jabłońska, J.; Pravda, L.; Vařeková, R.S.; Thornton, J.M. PDBsum: Structural summaries of PDB entries. Protein Sci. 2018, 27, 129–134. [Google Scholar] [CrossRef]
- Wilkins, M.R.; Gasteiger, E.; Bairoch, A.; Sanchez, J.C.; Williams, K.L.; Appel, R.D.; Hochstrasser, D.F. Protein identification and analysis tools in the ExPASy server. Methods Mol. Biol. 1999, 112, 531–552. [Google Scholar] [CrossRef] [PubMed]
- Dimitrov, I.; Naneva, L.; Doytchinova, I.; Bangov, I. AllergenFP: Allergenicity prediction by descriptor fingerprints. Bioinformatics 2014, 30, 846–851. [Google Scholar] [CrossRef]
- Banerjee, P.; Eckert, A.O.; Schrey, A.K.; Preissner, R. ProTox-II: A webserver for the prediction of toxicity of chemicals. Nucleic Acids Res. 2018, 46, W257–W263. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Qureshi, A.; Tandon, H.; Kumar, M. AVP-IC50Pred: Multiple machine learning techniques-based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50). Biopolymers 2015, 104, 753–763. [Google Scholar] [CrossRef]
- Van Zundert, G.C.P.; Rodrigues, J.P.G.L.M.; Trellet, M.; Schmitz, C.; Kastritis, P.L.; Karaca, E.; Melquiond, A.S.J.; Van Dijk, M.; De Vries, S.J.; Bonvin, A.M.J.J. The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes. J. Mol. Biol. 2016, 428, 720–725. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Xue, L.C.; Rodrigues, J.P.; Kastritis, P.L.; Bonvin, A.M.; Vangone, A. PRODIGY: A web server for predicting the binding affinity of protein-protein complexes. Bioinformatics 2016, 32, 3676–3678. [Google Scholar] [CrossRef] [PubMed]
- Weng, G.; Wang, E.; Wang, Z.; Liu, H.; Zhu, F.; Li, D.; Hou, T. HawkDock: A web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA. Nucleic Acids Res. 2019, 47, W322–W330. [Google Scholar] [CrossRef] [PubMed]
- Laskowski, R.A.; Swindells, M.B. LigPlot+: Multiple ligand-protein interaction diagrams for drug discovery. J. Chem. Inf. Model. 2011, 51, 2778–2786. [Google Scholar] [CrossRef] [PubMed]
- Abraham, M.J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J.C.; Hess, B.; Lindahl, E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015, 1, 19–25. [Google Scholar] [CrossRef] [Green Version]
- Lindorff-Larsen, K.; Piana, S.; Palmo, K.; Maragakis, P.; Klepeis, J.L.; Dror, R.O.; Shaw, D.E. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins Struct. Funct. Bioinform. 2010, 78, 1950–1958. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bussi, G.; Donadio, D.; Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 2007, 126, 014101. [Google Scholar] [CrossRef] [Green Version]
- Parrinello, M.; Rahman, A. Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 1981, 52, 7182–7190. [Google Scholar] [CrossRef]
- Homeyer, N.; Gohlke, H. Free energy calculations by the Molecular Mechanics Poisson-Boltzmann Surface Area method. Mol. Inform. 2012, 31, 114–122. [Google Scholar] [CrossRef]
- Baker, N.A.; Sept, D.; Joseph, S.; Holst, M.J.; McCammon, J.A. Electrostatics of nanosystems: Application to microtubules and the ribosome. Proc. Natl. Acad. Sci. USA 2001, 98, 10037–10041. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Korber, B.; Fischer, W.M.; Gnanakaran, S.; Yoon, H.; Theiler, J.; Abfalterer, W.; Hengartner, N.; Giorgi, E.E.; Bhattacharya, T.; Foley, B.; et al. Tracking Changes in SARS-CoV-2 Spike: Evidence that D614G Increases Infectivity of the COVID-19 Virus. Cell 2020, 182, 812–827. [Google Scholar] [CrossRef]
- Sharun, K.; Tiwari, R.; Dhama, K.; Emran, T.B.; Rabaan, A.A.; Al Mutair, A. Emerging SARS-CoV-2 variants: Impact on vaccine efficacy and neutralizing antibodies. Hum. Vaccin. Immunother. 2021, 17, 3491–3494. [Google Scholar] [CrossRef]
- Smaoui, M.R.; Yahyaoui, H. Unraveling the stability landscape of mutations in the SARS-CoV-2 receptor-binding domain. Sci. Rep. 2021, 11, 9166. [Google Scholar] [CrossRef] [PubMed]
- Craik, D.J.; Fairlie, D.P.; Liras, S.; Price, D. The future of peptide-based drugs. Chem. Biol. Drug Des. 2013, 81, 136–147. [Google Scholar] [CrossRef] [PubMed]
- Matsson, P.; Kihlberg, J. How Big Is Too Big for Cell Permeability? J. Med. Chem. 2017, 60, 1662–1664. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Y.; Lu, H.; Lin, Y.; Cheng, J. Water-Soluble Polypeptides with Elongated, Charged Side Chains Adopt Ultra-Stable Helical Conformations. Macromolecules 2011, 44, 6641–6644. [Google Scholar] [CrossRef] [Green Version]
- Gorham, R.D.; Forest, D.L.; Khoury, G.A.; Smadbeck, J.; Beecher, C.N.; Healy, E.D.; Tamamis, P.; Archontis, G.; Larive, C.K.; Floudas, C.A.; et al. New Compstatin Peptides Containing N-Terminal Extensions and Non-Natural Amino Acids Exhibit Potent Complement Inhibition and Improved Solubility Characteristics. J. Med. Chem. 2015, 58, 814–826. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Volpe, D.A. Application of method suitability for drug permeability classification. AAPS J. 2010, 12, 670–678. [Google Scholar] [CrossRef] [Green Version]
- Huby, R.D.; Dearman, R.J.; Kimber, I. Why are some proteins allergens? Toxicol. Sci. 2000, 55, 235–246. [Google Scholar] [CrossRef] [Green Version]
- Mahmud, S.; Biswas, S.; Kumar Paul, G.; Mita, M.A.; Afrose, S.; Robiul Hasan, M.; Sharmin Sultana Shimu, M.; Uddin, M.A.R.; Salah Uddin, M.; Zaman, S.; et al. Antiviral peptides against the main protease of SARS-CoV-2: A molecular docking and dynamics study. Arab. J. Chem. 2021, 14, 103315. [Google Scholar] [CrossRef] [PubMed]
- Nagata, M.; Nakagome, K.; Soma, T. Mechanisms of eosinophilic inflammation. Asia Pac. Allergy 2020, 10, e14. [Google Scholar] [CrossRef]
- Mousavi, S.S.; Karami, A.; Haghighi, T.M.; Tumilaar, S.G.; Fatimawali; Idroes, R.; Mahmud, S.; Celik, I.; Ağagündüz, D.; Tallei, T.E.; et al. In Silico Evaluation of Iranian Medicinal Plant Phytoconstituents as Inhibitors against Main Protease and the Receptor-Binding Domain of SARS-CoV-2. Molecules 2021, 26, 5724. [Google Scholar] [CrossRef] [PubMed]
- Zhao, H.; To, K.K.W.; Sze, K.H.; Yung, T.T.M.; Bian, M.; Lam, H.; Yeung, M.L.; Li, C.; Chu, H.; Yuen, K.Y. A broad-spectrum virus- and host-targeting peptide against respiratory viruses including influenza virus and SARS-CoV-2. Nat. Commun. 2020, 11, 5724. [Google Scholar] [CrossRef]
- Yea, C.S.; Ebrahimpour, A.; Hamid, A.A.; Bakar, J.; Muhammad, K.; Saari, N. Winged bean [Psophorcarpus tetragonolobus (L.) DC] seeds as an underutilised plant source of bifunctional proteolysate and biopeptides. Food Funct. 2014, 5, 1007–1016. [Google Scholar] [CrossRef]
- Mahmud, S.; Paul, G.K.; Afroze, M.; Islam, S.; Gupt, S.B.R.; Razu, M.H.; Biswas, S.; Zaman, S.; Uddin, M.S.; Khan, M.; et al. Efficacy of Phytochemicals Derived from Avicennia officinalis for the Management of COVID-19: A Combined In Silico and Biochemical Study. Molecules 2021, 26, 2210. [Google Scholar] [CrossRef]
- Rendon-Marin, S.; Martinez-Gutierrez, M.; Whittaker, G.R.; Jaimes, J.A.; Ruiz-Saenz, J. SARS CoV-2 Spike Protein in silico Interaction with ACE2 Receptors from Wild and Domestic Species. Front. Genet. 2021, 12, 571707. [Google Scholar] [CrossRef]
- Celik, I.; Yadav, R.; Duzgun, Z.; Albogami, S.; El-Shehawi, A.M.; Idroes, R.; Tallei, T.E.; Emran, T.B. Interactions of the receptor binding domain of SARS-CoV-2 variants with hACE2: Insights from molecular docking analysis and molecular dynamic simulation. Biology 2021, 10, 880. [Google Scholar] [CrossRef]
- Abelian, A.; Dybek, M.; Wallach, J.; Gaye, B.; Adejare, A. Chapter 6—Pharmaceutical chemistry. In Remington: The Science and Practice of Pharmacy, 23rd ed.; Adejare, A., Ed.; Academic Press: London, UK, 2021; pp. 105–128. [Google Scholar] [CrossRef]
- Wu, M.Y.; Dai, D.Q.; Yan, H. PRL-dock: Protein-ligand docking based on hydrogen bond matching and probabilistic relaxation labeling. Proteins Struct. Funct. Bioinform. 2012, 80, 2137–2153. [Google Scholar] [CrossRef] [PubMed]
- Lan, J.; Ge, J.; Yu, J.; Shan, S.; Zhou, H.; Fan, S.; Zhang, Q.; Shi, X.; Wang, Q.; Zhang, L.; et al. Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor. Nature 2020, 581, 215–220. [Google Scholar] [CrossRef] [Green Version]
- Yan, R.; Zhang, Y.; Li, Y.; Xia, L.; Guo, Y.; Zhou, Q. Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2. Science 2020, 367, 1444–1448. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Koley, T.; Madaan, S.; Chowdhury, S.R.; Kumar, M.; Kaur, P.; Singh, T.P.; Ethayathulla, A.S. Structural analysis of COVID-19 spike protein in recognizing the ACE2 receptor of different mammalian species and its susceptibility to viral infection. 3 Biotech 2021, 11, 109. [Google Scholar] [CrossRef]
- Khan, A.; Zia, T.; Suleman, M.; Khan, T.; Ali, S.S.; Abbasi, A.A.; Mohammad, A.; Wei, D.Q. Higher infectivity of the SARS-CoV-2 new variants is associated with K417N/T, E484K, and N501Y mutants: An insight from structural data. J. Cell. Physiol. 2021, 236, 7045–7057. [Google Scholar] [CrossRef] [PubMed]
- Shang, J.; Wan, Y.; Luo, C.; Ye, G.; Geng, Q.; Auerbach, A.; Li, F. Cell entry mechanisms of SARS-CoV-2. Proc. Natl. Acad. Sci. USA 2020, 117, 11727–11734. [Google Scholar] [CrossRef]
- Mahmud, S.; Uddin, M.A.R.; Paul, G.K.; Shimu, M.S.S.; Islam, S.; Rahman, E.; Islam, A.; Islam, M.S.; Promi, M.M.; Emran, T.B.; et al. Virtual screening and molecular dynamics simulation study of plant derived compounds to identify potential inhibitor of main protease from SARS-CoV-2. Brief. Bioinform. 2021, 22, 1402–1414. [Google Scholar] [CrossRef]
- Setny, P.; Baron, R.; McCammon, J.A. How can hydrophobic association be enthalpy driven? J. Chem. Theory Comput. 2010, 6, 2866–2871. [Google Scholar] [CrossRef] [PubMed]
- Ferreira De Freitas, R.; Schapira, M. A systematic analysis of atomic protein-ligand interactions in the PDB. Med. Chem. Comm. 2017, 8, 1970–1981. [Google Scholar] [CrossRef] [Green Version]
- Obaidullah, A.J.; Alanazi, M.M.; Alsaif, N.A.; Albassam, H.; Almehizia, A.A.; Alqahtani, A.M.; Mahmud, S.; Sami, S.A.; Emran, T.B. Immunoinformatics-guided design of a multi-epitope vaccine based on the structural proteins of severe acute respiratory syndrome coronavirus 2. RSC Adv. 2021, 11, 18103–18121. [Google Scholar] [CrossRef]
- Wong, F.-C.; Ong, J.-H.; Chai, T.-T. Identification of Putative Cell-entry-inhibitory Peptides against SARS-CoV-2 from Edible Insects: An in silico Study. eFood 2020, 1, 357–368. [Google Scholar] [CrossRef]
- Wong, F.-C.; Ong, J.-H.; Kumar, D.T.; Chai, T.-T. In Silico Identification of Multi-target Anti-SARS-CoV-2 Peptides from Quinoa Seed Proteins. Int. J. Pept. Res. Ther. 2021, 27, 1837–1847. [Google Scholar] [CrossRef] [PubMed]
- Verma, S.; Pandey, A.K. Factual insights of the allosteric inhibition mechanism of SARS-CoV-2 main protease by quercetin: An in silico analysis. 3 Biotech 2021, 11, 67. [Google Scholar] [CrossRef]
- Chowdhury, K.H.; Chowdhury, M.; Mahmud, S.; Tareq, A.M.; Hanif, N.B.; Banu, N.; Reza, A.S.M.; Emran, T.B.; Simal-Gandara, J. Drug repurposing approach against novel coronavirus disease (COVID-19) through virtual screening targeting SARS-CoV-2 main protease. Biology 2021, 10, 2. [Google Scholar] [CrossRef]
- Rabaan, A.A.; Al-Ahmed, S.H.; Garout, M.A.; Al-Qaaneh, A.M.; Sule, A.A.; Tirupathi, R.; Mutair, A.A.; Alhumaid, S.; Al-Omari, A.; Hasan, A.; et al. Diverse Immunological Factors Influencing Pathogenesis in Patients with COVID-19: A Review on Viral Dissemination, Immunotherapeutic Options to Counter Cytokine Storm and Inflammatory Responses. Pathogens 2021, 10, 565. [Google Scholar] [CrossRef] [PubMed]
- Bunker, A.; Róg, T. Mechanistic Understanding from Molecular Dynamics Simulation in Pharmaceutical Research 1: Drug Delivery. Front. Mol. Biosci. 2020, 7, 604770. [Google Scholar] [CrossRef]
- Arantes, P.R.; Saha, A.; Palermo, G. Fighting COVID-19 using molecular dynamics simulations. ACS Cent. Sci. 2020, 6, 1654–1656. [Google Scholar] [CrossRef]
- Ferreira, L.G.; Dos Santos, R.N.; Oliva, G.; Andricopulo, A.D. Molecular docking and structure-based drug design strategies. Molecules 2015, 20, 13384–13421. [Google Scholar] [CrossRef] [PubMed]
- Islam, F.; Bibi, S.; Meem, A.F.K.; Islam, M.; Rahaman, M.; Bepary, S.; Rahman, M.; Elzaki, A.; Kajoak, S.; Osman, H.; et al. Natural Bioactive Molecules: An Alternative Approach to the Treatment and Control of COVID-19. Int. J. Mol. Sci. 2021, 22, 12638. [Google Scholar] [CrossRef] [PubMed]
- Mutiawati, E.; Fahriani, M.; Mamada, S.S.; Fajar, J.K.; Frediansyah, A.; Maliga, H.A.; Ilmawan, M.; Emran, T.B.; Ophinni, Y.; Ichsan, I.; et al. Anosmia and dysgeusia in SARS-CoV-2 infection: Incidence and effects on COVID-19 severity and mortality, and the possible pathobiology mechanisms-a systematic review and meta-analysis. F1000Res. 2021, 10, 40. [Google Scholar] [CrossRef] [PubMed]
Variants | PANGO Lineage | Greek Alphabet | Mutation Sites | ||||||
---|---|---|---|---|---|---|---|---|---|
WT | Wild Type | ||||||||
SA | South Africa | B.1.351 | Beta | K417N | E484K | N501Y | |||
BR | Brazil | P.1 | Gamma | K417T | E484K | N501Y | |||
UK | United Kingdom | B.1.1.7 | Alpha | N501Y | |||||
CA | California | B.1.429 | Epsilon | L452R | |||||
SG | New York | B.1.526 | Iota | S477G | |||||
SN | New York | B.1.526 | Iota | S477N | |||||
SC | Indian | B.1.617.2 | Delta | L452R | E484Q | ||||
NG | Nigeria | B.1.525 | Eta | E484K |
Parameters | WT | SA | BR | UK | CA | SG | SN | SC | IN | NG |
---|---|---|---|---|---|---|---|---|---|---|
HADDOCK Score (a.u.) | −69.3 ± 3.2 | −78.6 ± 0.7 | −72.7 ± 3.3 | −70.7 ± 5.3 | −71.0 ± 2.2 | −70.3 ± 1.5 | −70.5 ± 2.3 | −75.6 ± 0.4 | −68.6 ± 2.3 | −82.1 ± 6.0 |
MM/GBSA (kcal/mol) | −42.69 | −42.74 | −38.91 | −19.03 | −37.99 | −26.66 | 26.84 | −29.27 | −29.54 | −46.87 |
Cluster Size | 9 | 16 | 15 | 15 | 13 | 60 | 66 | 66 | 7 | 8 |
RMSD (Å) | 2.2 ± 0.1 | 0.3 ± 0.0 | 0.4 ± 0.3 | 3.0 ± 0.0 | 1.9 ± 0.1 | 2.2 ± 0.1 | 0.9 ± 0.5 | 0.8 ± 0.5 | 4.1 ± 0.0 | 0.2 ± 0.1 |
Intermolecular Van der Waals Energy (kcal/mol) | −36.7 ± 2.6 | −32.2 ± 2.2 | −33.0 ± 0.3 | −37.3 ± 3.1 | −45.0 ± 6.5 | −40.3 ± 2.4 | −36.2 ± 2.3 | −39.6 ± 1.8 | −36.7 ± 2.0 | −37.4 ± 1.3 |
Intermolecular electrostatic Energy (kcal/mol) | −156.9 ± 5.0 | −205.6 ± 12.8 | −179.2 ± 38.8 | −117.2 ± 22.2 | −122.9 ± 40.7 | −86.8 ± 8.0 | −134.7 ± 35.2 | −132.8 ± 22.1 | −128.8 ± 22.9 | −220.1 ± 10.1 |
Desolvation Energy (kcal/mol) | −4.2 ± 1.3 | −9.5 ± 2.0 | −10.0 ± 5.1 | −15.3 ± 4.5 | −5.6 ± 2.8 | −15.8 ± 1.8 | −10.8 ± 4.2 | −11.9 ± 2.5 | −11.5 ± 2.9 | −3.0 ± 2.0 |
Restraint Violation Energy (kcal/mol) | 29.4 ± 15.1 | 42.3 ± 17.2 | 60.8 ± 12.3 | 53.4 ± 31.6 | 41.9 ± 2.1 | 32.8 ± 32.2 | 34.1 ± 19.5 | 24.8 ± 18.9 | 53.7 ± 19.3 | 22.5 ± 11.3 |
Buried Surface Area (Å) | 1079.4 ± 65.4 | 1122.3 ± 27.5 | 1083.8 ± 28.1 | 1090.8 ± 51.4 | 1107.7 ± 35.2 | 1029.4 ± 22.3 | 1022.8 ± 58.6 | 1102.2 ± 52.0 | 1098.0 ± 47.5 | 1094.2 ± 30.7 |
Z-Score | −1.6 | −2.2 | −1.8 | −1.4 | −1.6 | −1.4 | −1.7 | −2.0 | −1.2 | −2.4 |
Prodigy ΔG (kcal/mol) | −9.2 | −8.8 | −8.6 | −9.4 | −9.6 | −9.2 | −9.0 | −9.3 | −8.9 | −9.9 |
KD (M) at 37.0 °C | 3.3 × 10−7 | 6.6 × 10−7 | 9.0 × 10−7 | 2.4 × 10−7 | 1.8 × 10−7 | 3.5 × 10−7 | 4.7 × 10−7 | 2.7 × 10−7 | 5.1 × 10−7 | 9 × 10−8 |
Variants | H-Bonds | Non-Bonded Contacts | ||
---|---|---|---|---|
RBD | Bromelain-Derived Peptide | RBD | Bromelain-Derived Peptide | |
WT | Arg452 (2) | Glu7, Val5 | Arg403 | Tyr2 |
Tyr453 (2) | Tyr2 (2) | Tyr449 (6) | Gly3 (2); Ala4 (2) Val5 (2); | |
Glu484, Gln493 | Asn6 (2) | Arg452 (9) | Val5 (4); Glu7 (5) | |
Ser494 (2) | Val5 (2) | Tyr453 (4) | Tyr2 (4) | |
Gln498 | Tyr2 | Glu484 (5) | Asn6 (5) | |
Thr500 | Asp1 | Phe490 | Asn6 | |
Gln493 (4) | Ala4 (2); Asn6 (2) | |||
Ser494 (8) | Gly3; Ala4 (2); Val5 (5) | |||
Gly496 (5) | Tyr2; Gly3 (4) | |||
Gln498 (9) | Asp1 (4); Tyr2 (5); | |||
Thr500 (7) | Asp1 (7) | |||
Asn501 (10) | Asp1 (10) | |||
Gly502 | Asp1 | |||
Tyr505 (4) | Asp1 (3); Tyr2 | |||
BR | Gly482 | Lys2 | Tyr449 (3) | Asn6 (3) |
Ser494 | Asn6 | Leu452 (4) | Val8 (4) | |
Thr500 | Tyr2 | Ile472 (2); Gly482 | Lys9 (3) | |
Tyr505 | Asp1 | Lys484 (5) | Glu7 (5) | |
Phe490 (4) | Val8 (3); Lys9 | |||
Leu492 (3) | Glu7 (2); Val8 | |||
Gln493 (7) | Val5; Asn6 (5); Glu7 | |||
Ser494 (5) | Ala4; Asn6 (4) | |||
Gly496 (2) | Ala4 (2) | |||
Thr500 (2) | Tyr2 (2) | |||
Tyr501 (12) | Tyr2 (11); Ala4 | |||
Gly502 | Tyr2 (2) | |||
Tyr505 (8) | Asp1 (5); Tyr2 (3) | |||
SA | Gly482 | Lys9 | Tyr449 (4) | Asn6 (4) |
Lys484 | Glu7 | Leu452 | Val8 | |
Ser494 | Asn6 | Tyr453 | Val5 | |
Gly502 | Asp1 | Thr470; Ile472 (2); Gly482 | Lys9 (4) | |
Lys484 (4) | Glu7 (4) | |||
Phe490 (6) | Val8 (3); Lys9 (3) | |||
Leu492 | Glu7 | |||
Gln493 (9) | Val5 (2); Asn6 (4); Glu7 (3) | |||
Ser494 (7) | Val5 (2); Asn6 (5) | |||
Gly496 (2) | Ala4 (2) | |||
Thr500 (4) | Tyr2 (4) | |||
Tyr501 (20) | Asp1 (2); Tyr2 (9); Gly3 (6); Ala4 (3) | |||
Gly502 (6) | Asp1 (6) | |||
Tyr505 (8) | Asp1 (6); Tyr2 (2) | |||
UK | Asn487; Tyr489 | Glu7 (2) | Arg403 (3); Glu406 (4); Lys417 (5); Ile418 (2); Tyr453 (9) | Tyr2 (23) |
Gly496; Tyr501 | Asp1 (2) | Tyr453 | Gly3 | |
Leu455 (3) | Gly3; Ala4 (2) | |||
Phe456 (6) | Val5 (3); Asn6 (3) | |||
Ala475 (2) | Glu7; Val8 | |||
Gly485 | Glu7 | |||
Phe486 (6) | Glu7 (3); Lys9 (3) | |||
Asn487 (14) | Glu7 (10); Val8 (4) | |||
Tyr489 (8) | Glu7 (8) | |||
Gln493 | Gly3 | |||
Tyr495 (3) | Asp1; Tyr2 (2) | |||
Gly496 (4); Tyr501 (5); Tyr505 (4) | Asp1 (13) | |||
CA (USA) | Arg452; Gln493 | Asn6 (2) | Tyr449 (8) | Gly3 (2); Ala4 (2); Val5 (4) |
Ser494 | Val5 | Arg452 (2) | Asn6 (2) | |
Thr500 | Asp1 | Tyr453 (3) | Tyr2 (2) | |
Ile472; Glu484 (7) | Val8 (8) | |||
Phe490 (9) | Asn6 (6); Val8 (3) | |||
Leu492 (3) | Asn6 (3) | |||
Gln493 (8) | Ala4; Val5 (3); Asn6 (4) | |||
Ser494 | Ala4; Val5 (3) | |||
Tyr495 (2) | Tyr2 (2) | |||
Gly496 (9) | Tyr2 (4); Gly3 (5) | |||
Gln498 (4) | Asp1 (2); Gly3 (2) | |||
Thr500 (8) | Asp1 (8) | |||
Asn501 (13) | Asp1 (12); Tyr2 | |||
Tyr505 (4) | Asp1 (3); Tyr2 | |||
SG (NY1) | Arg403 | Asp1 | Arg403 (4) | Asp1 (4) |
Tyr453 | Tyr2 | Tyr449 (8) | Tyr2 (8) | |
Glu484 | Asn6 | Tyr453 | Tyr2 | |
Glu484 (6) | Asn6 (5); Val8 | |||
Gly485 (7) | Asn6 (2); Glu7 (3); Val8 (2) | |||
Phe486 (13) | Glu7 (3); Val8 (7); Lys9 (3) | |||
Asn487; Cys488 | Asn6 (2) | |||
Tyr489 (7) | Val5 (4); Asn6 (3) | |||
Gln493 (12) | Tyr2 (3); Gly3 (5); Ala4 (4) | |||
Ser494 (4) | Tyr2 (4) | |||
Gly496 (4) | Asp1 (2); Tyr2 (2) | |||
Asn501; Tyr505 (4) | Asp1 (5) | |||
SN(NY2) | Lys417 (3) | Asp1 (2); Tyr2 | Arg403 (10) | Asp1 (8); Tyr2 (2) |
Gln493 | Ala4 | Lys417 (7) | Asp1 (6); Tyr2 | |
Gly496 | Tyr2 | Tyr453 (8) | Tyr2 (6); Gly3 (2) | |
Tyr505 | Asp1 | Leu455 (4) | Tyr2; Gly3; Ala4 (2) | |
Phe456 | Val5 | |||
Glu484 (8) | Asn6 (2); Glu7 (2); Val8 (4) | |||
Gly485 (12) | Glu7 (10); Val8 (2) | |||
Phe486 (2) | Glu7 (2) | |||
Tyr489 (7) | Val5 (3); Asn6 (4) | |||
Gln493 (4) | Ala4 (4) | |||
Ser494 | Tyr2 | |||
Tyr495 (5) | Tyr2 (5) | |||
Gly496 (3) | Tyr2 (3) | |||
Tyr505 (2) | Asp1 (2) | |||
SC | Arg403 | Asp1 | Arg403 (5) | Asp1 (5) |
Tyr449 | Tyr2 | Tyr449 (9), Tyr453 | Tyr2 (10) | |
Glu484 | Asn6 | Glu484 (5) | Asn6 (5) | |
Asn487 | Glu7 | Gly485 (8) | Asn6; Glu7 (4); Val8 (3) | |
Gln493 | Ala4 | Phe486 (12) | Glu7 (4); Val8 (5); Lys9 (3) | |
Asn487 (2) | Glu7 (2) | |||
Cys488 | Asn6 | |||
Tyr489 (5) | Val5 (3); Asn6 | |||
Asn493 (14) | Tyr2 (6); Gly3 (5); Ala4 (3) | |||
Ser494 | Tyr2 | |||
Gly496 (9) | Asp1 (5); Tyr2 (4) | |||
Asn501; Tyr505 (3) | Asp1 (4) | |||
IN | Gln409; Lys417 | Asp1 (2) | Arg403 (10) | Asp1 (2); Tyr2 (4) |
Asn487 | Glu7 | Glu406 (4) | Asp1 (2); Tyr2 (2) | |
Gly496 | Tyr2 | Gln409 | Asp1 (2) | |
Gly416 (2) | Asp1 (2) | |||
Lys417 (10) | Asp1 (8); Tyr2 (2) | |||
Tyr453 (15) | Tyr2 (13); Gly3 (2) | |||
Leu455 (4) | Tyr2; Gly3; Ala4 (2) | |||
Gly485 (6) | Glu7 (2); Val8 (4) | |||
Phe486 (6) | Glu7 (3); Val8 (3) | |||
Asn487 (3) | Glu7 (3) | |||
Tyr489 (7) | Asn6 (3); Glu7 (4) | |||
Gly496 (2) | Tyr2 (2) | |||
NG | Gly482 | Lys9 | Tyr449 (4) | Ala4 (2); Asn6 (2) |
Lys484; Gln493 | Glu7 (2) | Leu452 (3) | Val8 (3) | |
Ser494 (2) | Asn6 (2) | Gly482 (3) | Lys9 (3) | |
Gly496 | Ala4 | Lys484 (5) | Glu7 (5) | |
Gly502 | Asp1 | Phe490 (11) | Glu7; Val8 (3); Lys9 (7) | |
Leu492 (2) | Glu7 (2) | |||
Gln493 (8) | Val5; Asn6 (3); Glu7 (3) | |||
Ser494 (7) | Ala4; Val5; Asn6 (5) | |||
Gly496 (7) | Gly3; Ala4 (6) | |||
Gln498 (8) | Tyr2 (5); Gly3 (3) | |||
Asn501 (13) | Asp1; Tyr2 (9); Gly3 (3) | |||
Gly502 (2) | Asp1 | |||
Tyr505 (14) | Asp1 (12); Tyr2 (2) |
Parameters (kJ/mol) | WT | BR | UK | CA | NG |
---|---|---|---|---|---|
Van der Waals Energy | −229.646 ± 21.620 | −237.086 ± 22.932 | −117.540 ± 4.519 | −130.427 ± 22.184 | −220.283 ± 21.339 |
Electrostatic Energy | −436.047 ± 66.496 | −257.948 ± 44.899 | −222.193 ± 82.117 | −365.998 ± 162.924 | −450.044 ± 49.243 |
Polar Solvation Energy | 521.232 ± 69.214 | 230.554 ± 59.360 | 266.989 ± 108.001 | 363.554 ± 183.985 | 438.652 ± 57.986 |
SASA Energy | −28.782 ± 1.707 | −22.875 ± 1.862 | −16.385 ± 2.108 | −17.589 ± 3.215 | −24.126 ± 2.072 |
Binding Energy | −173.243 ± 33.428 | −287.356 ± 32.004 | −89.129 ± 48.966 | −150.460 ± 38.762 | −255.801 ± 29.792 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Tallei, T.E.; Fatimawali; Adam, A.A.; Elseehy, M.M.; El-Shehawi, A.M.; Mahmoud, E.A.; Tania, A.D.; Niode, N.J.; Kusumawaty, D.; Rahimah, S.; et al. Fruit Bromelain-Derived Peptide Potentially Restrains the Attachment of SARS-CoV-2 Variants to hACE2: A Pharmacoinformatics Approach. Molecules 2022, 27, 260. https://doi.org/10.3390/molecules27010260
Tallei TE, Fatimawali, Adam AA, Elseehy MM, El-Shehawi AM, Mahmoud EA, Tania AD, Niode NJ, Kusumawaty D, Rahimah S, et al. Fruit Bromelain-Derived Peptide Potentially Restrains the Attachment of SARS-CoV-2 Variants to hACE2: A Pharmacoinformatics Approach. Molecules. 2022; 27(1):260. https://doi.org/10.3390/molecules27010260
Chicago/Turabian StyleTallei, Trina Ekawati, Fatimawali, Ahmad Akroman Adam, Mona M. Elseehy, Ahmed M. El-Shehawi, Eman A. Mahmoud, Adinda Dwi Tania, Nurdjannah Jane Niode, Diah Kusumawaty, Souvia Rahimah, and et al. 2022. "Fruit Bromelain-Derived Peptide Potentially Restrains the Attachment of SARS-CoV-2 Variants to hACE2: A Pharmacoinformatics Approach" Molecules 27, no. 1: 260. https://doi.org/10.3390/molecules27010260
APA StyleTallei, T. E., Fatimawali, Adam, A. A., Elseehy, M. M., El-Shehawi, A. M., Mahmoud, E. A., Tania, A. D., Niode, N. J., Kusumawaty, D., Rahimah, S., Effendi, Y., Idroes, R., Celik, I., Hossain, M. J., & Emran, T. B. (2022). Fruit Bromelain-Derived Peptide Potentially Restrains the Attachment of SARS-CoV-2 Variants to hACE2: A Pharmacoinformatics Approach. Molecules, 27(1), 260. https://doi.org/10.3390/molecules27010260