Vol.:(0123456789) 1 3 Journal of Molecular Modeling (2022) 28:212 https://doi.org/10.1007/s00894-022-05213-9 ORIGINAL PAPER Bioactive components of different nasal spray solutions may defeat SARS‑Cov2: repurposing and in silico studies Mohammad Faheem Khan 1 · Waseem Ahmad Ansari 1 · Tanveer Ahamad 1 · Mohsin Ali Khan 1 · Zaw Ali Khan 1 · Aqib Sarfraz 1 · Mohd Aamish Khan 1 Received: 8 May 2021 / Accepted: 1 July 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract The recent outbreak “Coronavirus Disease 2019 (COVID-19)” is caused by fast-spreading and highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). This virus enters into the human respiratory system by binding of the viral surface spike glycoprotein (S-protein) to an angiotensin-converting enzyme2 (ACE2) receptor that is found in the nasal passage and oral cavity of a human. Both spike protein and the ACE2 receptor have been identified as promising therapeutic targets to develop anti-SARS-CoV2 drugs. No therapeutic drugs have been developed as of today except for some vaccines. Therefore, potent therapeutic agents are urgently needed to combat the COVID-19 infections. This goal would be achieved only by applying drug repurposing and computational approaches. Thus, based on drug repurposing approach, we have investigated 16 bioactive components ( 1 – 16 ) from different nasal spray solutions to check their efficacies against human ACE2 and SARS-CoV2 spike proteins by performing molecular docking and molecular dynamic (MD) simulation studies. In this study, three bioactive components namely ciclesonide ( 8 ), levocabastine ( 13 ), and triamcinolone acetonide ( 16 ) have been found as promising inhibitory agents against SARS-CoV2 spike and human ACE2 receptor proteins with excellent binding affinities, comparing to reference drugs such as nafamostat, arbidol, losartan, and benazepril. Furthermore, MD simulations were performed (triplicate) for 100 ns to confirm the stability of 8 , 13 , and 16 with said protein targets and to compute MM-PBSA-based binding-free energy calculations. Thus, bioactive components 8 , 13 , and 16 open the door for researchers and scientist globally to investigate them against SARS-CoV2 through in vitro and in vivo analysis. Keywords Nasal spray solution · SARS-CoV2 · In silico · Ciclesonide · Levocabastine · Triamcinolone acetonide Introduction Since the first outbreak in Wuhan, China, the novel coro- navirus disease 2019 (COVID ‐ 19) is still an unprecedented respiratory health problem, as of today July 06, 2022, caus- ing the death of more than ~ 6.364 millions people globally. COVID-19 is caused by the severe acute respiratory coro- navirus 2 (SARS ‐ CoV2), a single-stranded RNA-enveloped virus that enters into the human body mainly through the nose. SARS-CoV2 also enters through the mouth but to a lesser extent [1]. The virus invades the nasal and oral pas- sage through the binding of its surface spike (S) protein with the human angiotensin–converting enzyme 2 (ACE2) recep- tors and other targets [2]. After enters into the human, the virus then synthesizes RNA via its RNA-dependent RNA polymerase, which leads to the formation of main protease and complete viral assembly. Once completion of assembly, viral particles release into the lower respiratory tract as well as in the external environment. In the respiratory system, they cause mild respiratory symptoms, but, in some cases, intense inflammatory host response may triggered, leading to a life-threatening acute respiratory syndrome [3]. Thus, SARS-CoV2 resides in the nasal and oral passages and then propagates around in the environment. Medical practition- ers, especially front-line health workers are at higher risk of getting the infection through direct contact or aerosol transmission of viral particles. From all over the world, researchers and health practitioners are working incessantly to understand and to find out the cure for this deadly respira- tory disease. By the date of writing this study, no specific * Mohammad Faheem Khan faheemkhan35@gmail.com 1 Department of Biotechnology, Era’s Lucknow Medical College, Era University, Sarfarazganj, Hardoi Road, Lucknow 226003, UP, India Journal of Molecular Modeling (2022) 28:212 1 3 212 Page 2 of 16 drug is available against COVID-19. Numerous treatments and suggestions including the use of surface disinfectants, social distancing, mask, and implementation of herbal/tra- ditional medicines have been studied and published to pre- vent SARS-CoV2 transmission [4, 5]. Therefore, effective therapeutic agents are in urgent need. New drug discovery or therapeutic treatment against any disease is a difficult task because it takes a long time up to 14–15 years, and expenses more than a billion dollars along with a low success (2.01%) rate [6]. Thus, these challenges are stand up like a hard wall for the development of a new therapeutic agent to combat the COVID-19 globally. Keeping the time and cost in view, the repurposing approach of existing drugs may be a use- ful strategy to fight against SARS-CoV2, by boosting up the drug development process within a short period for the treatment of COVID-19. Nasal sprays are the liquid (solutions) medicines, which are delivered locally in the nasal cavity to help relieve acute or chronic congestion (stuffiness), rhinitis, common cold, sinusitis, hay fever, or other types of allergies [7]. Various types of nasal spray solutions are available in the market as antihistaminic formulations to reduce the inflammatory effects, or as steroid formulations to relieve the symptoms of common cold, sinusitis, hay fever, allergic rhinitis, and other allergies, or as salt-solution (also referred to as saline nasal spray) that can loosen up the mucous or as topical decon- gestant solutions to constrict the blood vessels at the time of inflammation process [8, 9]. Numerous studies showed that the bioactive components of different nasal spray solu- tions have the ability to deactivate the virus within a few seconds. During the condition of the common cold, mil- lions of viruses are present in the nasal passages that are transmitted from person to person. The use of nasal spray inhibits the transmission cycle of the virus and reduce the symptoms of common cold [10]. Hypertonic saline solution (NaCl) has an antiviral effect via reduction of viral load in the upper respiratory tract region [11]. The drug repurposing approach has been proved as an effective way by explor- ing the new therapeutic targets previously approved drugs against many diseases [12]. In a recent outbreak, everybody globally was terrified about COVID-19 infection due to the absence of effective therapeutic agents that may act directly at SARS-CoV2 encoded proteins [13]. During this time, the drug repurposing approach has gained much interest, or it can be said that it is the only option over the traditional drug discovery process because of shorter time, low cost, and easy availability to reach us timely. It may reveal novel targets for the previously approved drug to combat SARS- CoV2 at each step of transmission, infectivity, as well as raised co-morbidities. Numerous FDA-approved antiviral drugs that have pre- viously been used in the treatment of malaria, MERS, and SARS either have been tested or are being used to cure COVID-19 infection. For example, remdesivir, a potent antiviral drug of nucleotide analog has been used success- fully for the treatment of COVID-19, paving the way of repurposing approach for other approved drugs like dexa- methasone and tocilizumab. Several studies based on clinical trials have been conducted to test the efficiency of remde- sivir in COVID-19 patients [14]. However, remdesivir has been approved by FDA as a recommended drug for those COVID-19 patients who are on supplemental oxygen but not on mechanical ventilation. Favipiravir, lopinavir, and ritonavir have similar mechanisms to prevent SARS-CoV2 via inhibition of RNA-dependent RNA polymerase enzyme in the treatment of mild-to-moderate–infected patients; however, they are not as effective as remdesivir [15, 16]. Numerous protease inhibitors, originally developed as anti- HIV drugs, such as saquinavir, amprenavir, indinavir, and nelfinavir have been repurposed effectively against SARS- CoV2 proteases enzymes as validated by many in silico and in vitro studies [17, 18]. In a study, arbidol, a potent inhibi- tor of hepatitis C and influenza virus, has shown promising inhibitory effects to prevent COVID-19 infection in health care workers. Arbidol has also shown synergistic effects to enhance immune response when combined with interferons in SARS-CoV2-infected patients [19, 20]. Several studies suggested that antimalarial drugs including chloroquine and hydroxychloroquine interrupt the binding efficiency of SARS-CoV2 spike protein to the human cell membrane receptor angiotensin–converting enzyme 2 (ACE2), improv- ing the clinical outcomes of COVID-19 patients. In some clinical studies, hydroxychloroquine has been found to be effective in combination with azithromycin or more effective alone than chloroquine in mildly infected patients. However, FDA has raised safety concerns about the use of both drugs because of reporting side effects like irregular heart rhythm, blood circulation disorders, kidney, and liver problems in post-COVID-19 patients [21]. In addition, a potent antiparasitic agent named ivermectin was also studied against COVID-19, but has been stopped by FDA in March 2021 because of its interactions with other medications like blood thinners that may pose a risk of bleeding [22]. Cytokine storm, a leading cause of sever- ity in COVID-19 patients, produces a hyperinflammatory state which results in elevation of interleukin 1 (IL-1) and interleukin 6 (IL-6). Thus, inhibition of cytokine storm is a key step in the treatment of the severity of COVID-19 infection. Drugs in this category including anakinra (anti- IL-1 receptor blocker) and tocilizumab (humanized anti-IL-6 monoclonal antibody) have been administered successfully in COVID-19 patients without adverse effects [23]. Besides the above medications, corticosteroids have also been stud- ied. Several approved corticosteroids dexamethasone, meth- ylprednisolone, and prednisolone were found to increase survival rate and reduce morbidity in moderate or severe Journal of Molecular Modeling (2022) 28:212 1 3 Page 3 of 16 212 COVID-19 patients [24]. Some studies conducted on hos- pitalized individuals showed that dexamethasone effectively reduce the deaths by 1/3 in patients who were on respiratory support. However, these drugs were less effective on patients without oxygen support. Interestingly, corticosteroids have been found to reduce the fluid accumulation in the lungs, as well as alveolar damage, and thus decreased respiratory failure by improving hypoxia conditions [25]. Although no study is reported on the repurposing of corticosteroids that act directly on SARS-CoV2 encoded proteins to kill them, some in vitro and in vivo studies showed the effective role of ciclesonide, fluticasone propionate, etc. via suppressing ACE2 expression, and thus preventing the SARS-CoV2 cell entry in the nasal mucosa [26, 27]. In a previous study, oxymetazoline and xylometazoline displayed the reduction of rhinovirus infections as shown in a study [28]. Moreover, povidone iodine–containing nasal spray to reduce the naso- pharyngeal viral load in patients with COVID-19 is under clinical trials [29, 30]. In this context, because of tedious and traditional methods of drug development, the drug repur- posing approach can help to develop the drugs against this disease, which can be quickly clinically applied within a short period of time. In order to explore the drug repurposing approach to prevent COVID-19 transmission, the compounds (Fig. 1) namely fluticasone ( 1 ), fluticasone furoate ( 2 ), fluticasone propionate ( 3 ), mometasone ( 4 ), mometasone furoate ( 5 ), beclomethasone dipropionate ( 6 ), budesonide ( 7 ), cicleson- ide ( 8 ), flunisolide ( 9 ), oxymetazoline ( 10 ), xylometazoline ( 11 ), azelastine ( 12) , levocabastine ( 13 ), olopatadine ( 14 ), phenylephrine ( 15 ), and triamcinolone acetonide ( 16 ) were selected as a major bioactive components that are presents in FDA-approved nasal spray solutions. In the present study, all these ligands ( 1 – 16 ) were subjected to an in silico analy- sis using molecular docking and MD simulation methods against the active sites of spike protein of SARS-CoV2 as well as human ACE2 target receptor. As per our prediction, this study will provide useful data that can be utilized for preclinical and clinical studies to get mechanistic insights into mode of action of nasal spray solutions with the help of computational tools. Material and methods Preparation of ligands Structure-based repurposing of clinically approved bioactive components of different nasal spray solutions were selected for screening to find out the potent anti-SARS-CoV2 agents. The chemical structures, as shown in Fig. 1, of bioactive components as ligands were retrieved from the PubChem site including fluticasone ( 1 ) (PubChem ID: 5311101), fluticasone furoate ( 2 ) (PubChem ID: 9854489), fluticasone propionate ( 3 ) (PubChem ID: 444036), mometasone ( 4 ) (PubChem ID: 441336), mometasone furoate ( 5 ) (PubChem ID: 441,336), beclomethasone dipropionate ( 6 ) (PubChem ID: 21700), budesonide ( 7 ) (PubChem ID: 5281004), ciclesonide ( 8 ) (PubChem ID: 6918155), flunisolide ( 9 ) (PubChem ID: 82153), oxymetazoline ( 10 ) (PubChem ID: 4636), xylometazoline ( 11 ) (PubChem ID: 5709), azelastine ( 12 ) (PubChem ID: 2267), levocabastine ( 13 ) (PubChem ID: 54385), olopatadine ( 14 ) (PubChem ID: 5281071), phe- nylephrine ( 15 ) (PubChem ID: 6041), and triamcinolone acetonide ( 16 ) (PubChem ID: 6436). The geometries of all ligands were optimized with the help of Discovery Studio 3.0 by using a clean geometry option and the CHARMm (MMFF94) force field was applied on the ligand molecules and then saved as “sdf” or “pdb” format. The ligand prepara- tion was done by detecting root, set number of torsions, and aromaticity criteria using AutoDock Tool. Preparation of receptors To get mode of interactions of ligands within the binding pockets of human angiotensin–converting enzyme (ACE2) and spike protein of SARS-CoV-2, we retrieved the 3D struc- tures of both the proteins with PDB-IDs 1R4L (resolution of crystal structure 3.00 Å) and 6LZG (resolution of crystal structure 2.50 Å) respectively from the protein data bank (PDB) website (https://www.rcsb.org/). The 1R4L is a crys- tal structure of human ACE2 receptor in a complex with an inhibitor XX5 [31]. The covalent bonds between XX5 ligand and protein-binding sites (S1 subset unit) were cleaved. The S1 subset is made up of interacting His374, His378, Glu375, Thr371, Asp368, His345, Glu402, Arg514, Tyr515, Tyr510, His505, Arg278, Phe274 amino acids. Among them, His345, Thr371, His505 amino acids were involved in H-bond with the ligand XX5. Similarly, the 6LZG is a crystal structure of spike protein of SARS-CoV2 with native ligand NAG [32]. The S1 subdomain of 6LZG is consisted of five stranded antiparallel β sheets ( β 1, β 2, β 3, β 4, and β 7) associates with α -helices and loops regions. Furthermore, these binding sites were retrieved to ensure structural correctness by removing Hetatm and adding a hydrogen atom to the target protein receptors with the help of Discovery Studio 3.0 Visualizer (Biovia, DS 2010). Moreover, polar hydrogen atoms and Kollman charges were also added for protein optimization via AutoDock Tool version 1.5.6 [33]. In 6LZG protein, the grid box is generated over active sites of interacting Gly446, Tyr449, Gly496, Glu498, Thr500, and Gly502 amino acids. Molecular docking Molecular docking studies of selected ligands within the target receptors (1R4L and 6LZG) were performed using Journal of Molecular Modeling (2022) 28:212 1 3 212 Page 4 of 16 AutoDock Vina [34]. To initiate the molecular docking, receptor grid box was generated by putting the binding site at coordinates ( x = 34.933, y = 8.863, z = 23.11) with dimensions of 50 Å × 50 Å × 50 Å with a suitable spac- ing around target residues for 1R4L protein. Similarly, a grid box for 6LZG was also prepared having a set of dimensions of 50 Å × 50 Å × 50 Å with the coordinates ( x = − 32.468, y = 24.877, z = − 15.026) that was centered on target residues. The empirical scoring function by using a flexible method were used for docking. Finally, the dock- ing results were analyzed by using the PyMol (Molecular Graphic System Version 2.4.0) visualizer and LigPlot [35] softwares. Fig. 1 Chemical structures of active components ( 1 – 16 ) of various nasal spray solutions, which are available in market Journal of Molecular Modeling (2022) 28:212 1 3 Page 5 of 16 212 Molecular dynamic simulation Molecular dynamics simulation were carried out 100 ns by using Desmond MD simulation package (Maestro version 12.6.144 Schrodinger 2020–4 LCC, New York USA) was used to explore the stability of protein–ligand complexes 8-1R4L, 16-1R4L, 8-6LZG, and 13-6LZG. Each selected complex was individually solvated by placing explicit water box size 10 Å with single-point charges (SPC) and selected the TIP3P water model with periodic boundary condition (PBC). The force field optimized potentials for liquid simulation extended (OPLSe) was applied and added ions (Na + , Cl − ) to make neu- tralized the complex systems at 300 K temperature and 1 bar pressure with NPT ensemble. The particle mesh Ewald method was applied to determine the electrostatic interaction between atoms, with Columbic cut-off radius of 9.0 Å. Furthermore, Martyna–Tuckerman–Klein and Nose–Hoover chain schemes were used for the generation of barostat at 2.0 ps and thermo- stat 1.0 ps. Non-bonded forces were measured with r-RESPA integrators where each step was updated to the short-range forces and each three steps was updated. The trajectories were stored for analysis at 4.8-ps intervals. The activity and inter- action of ligands with the proteins have been studied using the Desmond MD package method simulation interaction dia- gram. MD simulations have been tracked in time by analyzing the RMSD positions of the atomic ligand and protein [36]. MM‑GBSA calculation The free binding energy of three ligand–protein complexes were performed using Prime MM-GBSA (molecular mechan- ics generalized born surface area) module of Schrödinger 2020–4 LCC, New York, USA. Furthermore, the free binding energy of docked poses of complexes were minimized and calculated using OPLSe (optimized potentials for liquid simu- lation extended) force field with embedded solvation. From 100 ns MD simulation, 40 snapshots of 25 frame at 2.5 ns of whole trajectories were obtained, and thereafter binding energy was calculated by using command thermal_mmgbsa.py for each of complexes. This command extracts the Gibbs-free energy of binding (∆ G binding) using MM-GBSA method as estimated by following equations: (1) Δ G binding = Δ E MM + Δ G solvent + Δ G SA (2) Δ E MM = E complex − ( E ligand + E receptor ) (3) Δ G solvent = G ( Solv ) complex − G ( solv ) ligand + G ( solv ) receptor (4) Δ G SA = G ( SA ) complex − G ( SA ) ligand + G ( SA ) receptor where Δ E MM is the difference between energy of complex and sum of energy ligand and receptor, Δ G solvent is the dif- ference in solvation energy of the complex and sum of ligand with receptor as well as Δ G SA is difference in surface area energy of complex and the sum of ligand and receptor [37]. Results and discussion To find the potential drug candidate for the treatment of COVID-19 disease, molecular docking and molecular dynamic (MD) simulation of 16 bioactive components ( 1 – 16 ) of various nasal spray solutions were performed against the spike protein (6LZG) of SARS-CoV2 and human receptor (1R4L) ACE2 protein. The molecular docking study, predicted as docking score in Table 1, has been car- ried out by using AutoDock Vina, PyMol visualizer, and LigPlot software, respectively. The results of docking scores (negative binding energies) were calculated with the help of AutoDock Vina to investigate the way of molecular interac- tions within the binding pockets of protein receptors. In con- text to find the best ligands against said protein receptors, we have selected a scale of best docking score with a value > 9.0 for SARS-CoV2 spike (6LZG) protein and > 10.0 for human ACE2 (1R4L) protein, respectively. Analysis of molecular docking study In order to validate the generated grid for molecular dock- ing, the co-crystallized ligands such as XX5 of 1R4L and NAG of 6LZG were cleaved out and reconstructed. After this, they were re-docked into the binding pockets of said proteins via grid generation process. In our study, docking scores of ligands ( 1 – 16 ) show the negative binding energy because of obtained molecular interactions to the active sites of said proteins. The two-dimensional (2D) views of docked poses are displayed in Figs. 2, and 3 with the distinction of both hydrophilic (e.g., hydrogen bonds) and hydrophobic forces (e.g., van der Waals and pi-pi interactions) within the binding pockets of target proteins. Additionally, Table 1 shows the docking scores along with molecular interactions. Ligand 1 (fluticasone) is used to treat allergic rhinitis [38]. In our study, it showed the − 9.1 docking score via the formation of two hydrogen bonds with Arg273, Thr371, and ten hydrophobic bonds with Tyr127, Asn149, Trp271, Phe274, His345, Pro346, Leu370, His374, Glu406, Arg518 amino acids within binding pockets of 1R4L protein. In the case of docking with 6LZG protein, this ligand showed the − 7.6 docking score via formation of one hydrogen bond with Tyr196 and 13 hydrophobic bonds with Leu95, Gln98, Gln102, Gly205, Asp206, Glu208, Val209, Val212, Ala396, Lys562, Glu564, Pro565, Trp566 amino acids. Furthermore, ligands 2 (fluticasone furoate) and 3 (fluticasone propionate), Journal of Molecular Modeling (2022) 28:212 1 3 212 Page 6 of 16 Table 1 Docking score and various molecular interactions of active components ( 1 – 16 ) of different nasal spray solutions against 1R4L (human ACE2) and 6LZG (SARS-CoV2 spike protein) Ligand No Receptors Docking score Interacting amino acids Hydrogen bonds Hydrophobic bonds 1 1R4L − 9.1 Arg273, Thr371 Tyr127, Asn149, Trp271, Phe274, His345, Pro346, Leu370, His374, Glu406, Arg518 6LZG − 7.6 Tyr196 Leu95, Gln98, Gln102, Gly205, Asp206, Glu208, Val209, Val212, Ala396, Lys562, Glu564, Pro565, Trp566 2 1R4L − 9.8 Arg273, His345, Lys363, Thr371 Tyr127, Asn149, Trp271, Phe274, Pro346, Asp367, Asp368, Leu370, His374, Glu406 6LZG − 8.3 Lys187, Tyr196, Trp203, Asp206 Gln102, Tyr199, Tyr202, Gly205, Asp509, Tyr510, Ser511, Arg514 3 1R4L − 8.9 Arg273, His345, Lys363, Thr371 Tyr127, Asn149, Trp271, Phe274, Pro346, Asp367, Asp368, Leu370, His374, Glu406 6LZG − 7.3 Arg403 Asp30, Asn33, His34, Glu37, Pro389, Glu406, Lys417, Tyr505 4 1R4L − 9.2 Asp367 Asp269, Phe274, Thr276, Leu370, Thr371, Glu406, Ser409, Thr445 6LZG − 7.6 Arg273 Tyr127, Leu144, Glu145, Asn149, Asp269, Trp271, Phe274, Phe504 5 1R4L − 9.9 Lys363 Asn149, Ala153, Asp269, Phe274, Thr276, Asp367, Asp368, Leu370, Thr371, Glu406, Ser409, Thr445 6LZG − 7.9 Asp350 Phe40, Ala348, His378, Phe390, Asn394, Glu398, His401 6 1R4L − 9.0 Gln98, Asn210 Leu95, Gln102, Tyr196, Tyr202,Gly205, Glu208, Val209, Gly211, Val212, Ala396, Pro565, Trp566 6LZG − 7.7 Ser43 Phe40, Ser44, Ser47, Ala348, Trp349, Asp350, Glu375, His378, Asp382, Arg393, Asn394, His401 7 1R4L − 9.5 His345 Trp271, Arg273, Phe274, pro346, Asp367, Asp368, leu370, Thr371, His374, Glu406, Arg518 6LZG − 8.4 Ser511, Arg514 Gln102, Lys187, Tyr196, Tyr199, Tyr202, Trp203, Asp206, Asp509, Tyr510 8 1R4L − 10.3 Arg273, His345, His505, Tyr515 Asn149, Ala153, Asp269, Phe274, Pro346, Asp367, Asp368, Thr371, His374, Glu375, His378, Glu402, Glu406, Arg518 6LZG − 9.3 Ser43 Phe40, Ser44, Trp69, Ala348, Asp350, His378, Asp382, Phe390, Arg393, Asn394, His401 9 1R4L − 9.5 Gln98, Asn210, Asn394 Leu95, Ala99, Glu208, Val209, Lys562, Pro565 6LZG − 8.5 Ser511, Arg514 Gln102, Tyr199, Tyr196, Tyr202, Trp203, Asp206, 10 1R4L − 7.3 Glu406, Arg518 Phe274, Pro346, Asp367, Leu370, Thr371, Thr445 6LZG − 7.0 Asp206 Leu95, Gln98, Ala99, Gln102, Tyr196, Gly205, Tyr207, Glu208, Val209, Ala396, Asn397, Glu398, Lys562, Glu564, Pro565, Trp566 11 1R4L − 7.4 Glu406 Phe274, Thr276, Asp367, Leu370, Thr371, Ser409 6LZG − 6.8 – Leu95, Gln102, Tyr196, Gly205, Asp206, Val209, Ala396, Lys562, Glu564, Trp566 12 1R4L − 9.7 – Tyr127, Asn149, Ala153, Asp269, Trp271, Phe274, Cys344, His345, Lys363, Asp367, Thr371, 6LZG − 7.8 Tyr196, Trp566 Leu95, Gln98, Gln102, Tyr202, Gly205, Asp206, Glu208, Asn210, Ala396, Lys562 13 1R4L − 9.7 His345, Asn394, Tyr385 Phe40, Ala348, Asp350, His378, Glu402, Phe504, Tyr510, Arg514, Tyr515 6LZG − 9.0 – Phe40, Leu73, Ala99, Leu100,Gln102, Asp350, Phe390, Leu391, Arg393, Asn394 14 1R4L − 9.2 Pro346, Thr371, Tyr515 Arg273, Phe274, Thr276, His345, Asp367, Asp368, His374, Thr445, His505, Arg518 6LZG − 7.2 Tyr196, Gly205, Lys562 Leu95, Gln102, Asp206, Val209, Asn394, Gly395, Ala396, Asn397, Glu564, Pro565, Trp566 15 1R4L − 6.0 Ala348, Glu402, Arg514 Thr347, His378, Phe504, Tyr510, Tyr515 6LZG − 6.1 Glu208, Asn210 Leu95, Val209, Ala396, Lys562, Glu564, Trp566 Journal of Molecular Modeling (2022) 28:212 1 3 Page 7 of 16 212 the ester analogs of fluticasone, are most efficient for the prevention of allergic rhinitis than the previous one [39, 40]. As shown in Table 1, they displayed promising − 9.8 and − 8.3 docking scores through the formation of molecular interactions with Arg273, His345, Lys363, Thr371 (hydro- gen bonds), Tyr127, Asn149, Trp271, Phe274, Pro346, Asp367, Asp368, Leu370, His374, Glu406 (hydropho- bic bonds) and Lys187, Tyr196, Trp203, Asp206 (hydro- gen bonds), Gln102, Tyr199, Tyr202, Gly205, Asp509, Tyr510, Ser511, Arg514 (hydrophobic bonds) amino acids, respectively, within the pockets of 1R4L protein. Both the ligands 2 and 3 (Table 1) also showed the − 8.3 and − 7.3 docking scores through the formation of molecular inter- actions with Lys187, Tyr196, Trp203, Asp206 (hydrogen bonds), Gln102, Tyr199, Tyr202, Gly205, Asp509, Tyr510, Ser511, Arg514 (hydrophobic bonds) and Arg403 (hydro- gen bond), Asp30, Asn33, His34, Glu37, Pro389, Glu406, Lys417, Tyr505 (hydrophobic bonds) amino acids, respec- tively, within the pockets of 6LZG protein. The ligand 4 (mometasone) and its ester analog 5 (mometasone furo- ate, Fig. 1) are well known to treat inflammatory skin dis- orders, asthma, nasal congestion, discharge, pruritus, etc. [41, 42]. Our docking study (Table 1) claims that 4 showed significant docking score − 9.2 and − 7.6 against 1R4L and * Numbering of ligands as per chemical structures displaying in Fig. 1 Table 1 (continued) Ligand No Receptors Docking score Interacting amino acids Hydrogen bonds Hydrophobic bonds 16 1R4L − 10.0 Tyr127, Arg273, His345, Thr371 Glu145, Trp271, Phe274, Lys363, Leu370, His374 6LZG − 8.2 Gln98, Gln102, Tyr202, Arg514 Tyr196, Trp203, Gly205, Asp206, Glu208, Glu398, Lys562 Benazepril 1R4L − 7.4 Tyr127, Asn149, His345 Leu144, Asp269, Trp271, Arg273, Phe274, Thr276, Pro346, Lys363, Asp367, Thr371 Losartan − 9.1 Thr371, Asp368 Asn149, Ap269, Trp271, Phe274, Lys363, Asp367, Thr445 Nafamostat 6LZG − 8.8 Gln102, Asp350, Arg393 Phe40, Leu73, Ser77, Ala99, Leu100, Phe390, Leu391 Arbidol − 7.0 Gln102 Leu95, Gln98, Ala99,Tyr196, Tyr202, Gly205, Asp206, Glu208, Lys562 Fig. 2 Two-dimensional (2D) structures of ligand 8 ( A ) and 16 ( B ) within the binding pockets of 1R4L protein. The figure depicted the green dotted lines (for hydrogen bonds), red half circle (for hydropho- bic bonds), blue circle (for nitrogen atom), white circle (for carbon atom), and red circle (for oxygen atom) Journal of Molecular Modeling (2022) 28:212 1 3 212 Page 8 of 16 6LZG protein receptors, which were attributed to several molecular interactions with Asp367 (hydrogen bond) and Asp269, Phe274, Thr276, Leu370, thr371, Glu406, Ser409, Thr445 (hydrophobic bonds) amino acids of 1R4L protein as well as Arg273 (hydrogen bond), Tyr127, Leu144, Glu145, Asn149, Asp269, Trp271, Phe274, Phe504 (hydrophobic bonds) amino acids of 6LZG protein, whereas 5 displayed the important molecular interactions (docking score − 9.9) with Lys363 (hydrogen bond) and Asn149, Ala153, Asp269, Phe274, Thr276, Asp367, Asp368, Leu370, Thr371, Glu406, Ser409, Thr445 (hydrophobic bonds) amino acids within the pockets of 1R4L protein as well as similar molecular interactions (docking score − 7.9) with Asp350 (hydrogen bond), Phe40, Ala348, His378, Phe390, Asn394, Glu398, His401 (hydrophobic bonds) amino acids within the pockets of 6LZG protein. Ligand 6 (beclomethasone dipropionate, Fig. 1) is a diester derivative of beclomethasone steroid having pro- pionyl groups at the 17- and 21-positions [43]. It has anti- inflammatory and anti-asthmatic effects for the treatment of allergic rhinitis and nasal polyps [44]. As per our study (Table 1), the docking scores of this ligand were found as − 9.0 and − 7.7 that exhibited several molecular interac- tions by the presence of two hydrogen bonds with Gln98, Asn210, and 12 hydrophobic bonds with Leu95, Gln102, Tyr196, Tyr202, Gly205, Glu208, Val209, Gly211, Val212, Ala396, Pro565, Trp566 amino acids of 1R4L as well as three hydrogen bonds with Ser43, Phe40, Ser44, and ten hydrophobic bonds with Ser47, Ala348, Trp349, Asp350, Glu375, His378, Asp382, Arg393, Asn394, His401 amino acids of 6LZG proteins, respectively. As per Fig. 1, the ligands 7 (budesonide), 8 (ciclesonide), 9 (flunisolide), and 16 (triamcinolone acetonide) are the highly oxygen- ated glucocorticoid steroids having dioxapentacyclic ring in their basic skeleton. They are generally used as anti- inflammatory and bronchodilator drugs for the treatment of asthma, allergic rhinitis as well as nasal polyposis [45–48]. In the present study, 7 showed the − 9.5 docking score via the formation of two hydrogen bonds with His345 and Trp271 and ten hydrophobic bonds with Arg273, Phe274, Pro346, Asp367, Asp368, Leu370, Thr371, His374, Glu406, and Arg518 amino acids during molecular interac- tions with 1R4L protein. It also displayed the − 8.4 dock- ing score (Table 1) through formation of two hydrogen bonds with Ser511, Arg514, and nine hydrophobic bonds with Gln102, Lys187, Tyr196, Tyr199, Tyr202, Trp203, Asp206, Asp509, Tyr510 amino acids of 6LZG protein. In continuation, 8 shows the docking scores − 10.3 and − 9.3 attributable to the presence of numerous hydrogen bonds and hydrophobic bond interactions with Arg273, His345, His505, Tyr515, Asn149, Ala153, Asp269, Phe274, Pro346, Asp367, Asp368, Thr371, His374, Glu375, His378, Glu402, Glu406, Arg518 and Ser43, Phe40, Ser44, Trp69, Ala348, Asp350, His378, Asp382, Phe390, Arg393, Asn394, His401 amino acids within the binding pockets of 1R4L and 6LZG protein receptors, respectively. Contrary, ligand 9 also dis- played the good docking scores − 9.5 and − 8.5 which may be due to the presence of many hydrogen bonds and hydro- phobic bonds with Gln98, Asn210, Asn394, Ala99, Glu208, Val209, Lys562, Pro565 amino acids of 1R4L protein as well as with Ser511, Arg514, Gln102, Tyr199, Tyr196, Tyr202, Trp203, Asp206 amino acids of 6LZG protein. Moreover, ligand 16 displayed significant docking scores with the value of − 10.0 as − 8.2 against 1R4L and 6LZG proteins respec- tively as compared to other ligands. It has four hydrogen bond interactions with Tyr127, Arg273, His345, Thr371, and six hydrophobic bond interactions with Glu145, Trp271, Phe274, Lys363, Leu370, His374 amino acids within the pockets of 1R4L protein as well as four hydrogen bonds with Gln98, Gln102, Tyr202, Arg514, and seven hydropho- bic bonds with Tyr196, Trp203, Gly205, Asp206, Glu208, Glu398, Lys562 amino acids of 6LZG protein receptor. Fig. 3 Two-dimensional (2D) structures of ligand 8 ( A ) and 13 ( B ) within the binding pockets of 6LZG protein. The figure depicted the green dotted lines (for hydrogen bonds), red half circle and lines (for hydro- phobic bonds), blue circle (for nitrogen atom), white circle (for carbon atom), green circle (for fluorine atom), and red circle (for oxygen atom) Journal of Molecular Modeling (2022) 28:212 1 3 Page 9 of 16 212 Furthermore, Fig. 1 contains the imidazole analogs such as the ligands 10 (oxymetazoline) and 11 (xylometazoline) which are being used as vasoconstriction agents for the treat- ment of cold, hay fever, or other respiratory allergies [49, 50]. They displayed low docking scores as − 7.3 and − 7.4 against 1R4L as well as − 7.0 and − 6.8 against 6LZG pro- teins, respectively. Ligand 10 forms two hydrogen bonds with Glu406, Arg518, and six hydrophobic bonds with Phe274, Pro346, Asp367, Leu370, Thr371, Thr445 amino acids, whereas 11 forms only one hydrogen bond with Glu406 and six hydrophobic bonds with Phe274, Thr276, Asp367, Leu370, Thr371, Ser409 amino acids within the binding pockets of 1R4L protein, respectively. On the other hand, ligands 10 contain one hydrogen bond and 16 hydrophobic bond interactions with Asp206, Leu95, Gln98, Ala99, Gln102, Tyr196, Gly205, Tyr207, Glu208, Val209, Ala396, Asn397, Glu398, Lys562, Glu564, Pro565, Trp566 amino acids, whereas only ten hydrophobic bond interac- tions of ligand 11 with Leu95, Gln102, Tyr196, Gly205, Asp206, Val209, Ala396, Lys562, Glu564, Trp566 amino acids within of 6LZG protein were present. The ligand 12 (azelastine), a second-generation antihistamine drug, is used as a nasal spray solution to treat allergic rhinitis and other seasonal allergies [51]. Our docking study shows that 12 performs docking scores − 9.7 and − 7.8 against 1R4L and 6LZG proteins, respectively attributable due to the presence of eleven hydrophobic bonds with Tyr127, Asn149, Ala153, Asp269, Trp271, Phe274, Cys344, His345, Lys363, Asp367, Thr371 amino acids of 1R4L protein as well as two hydrogen bonds with Tyr196, Trp566, and ten hydrophobic bonds with Leu95, Gln98, Gln102, Tyr202, Gly205, Asp206, Glu208, Asn210, Ala396, Lys562 amino acids of 6LZG protein. The ligand 13 (levocabastine), a synthetic piperidine derivative, act as a potent H1-receptor antagonist and is extensively used in the treatment of histamine-mediated seasonal aller- gic rhinitis. In our study, the binding modes of 13 within the pockets of 1R4L protein exhibit a significant docking score (− 9.7) due to the presence of numerous molecular interac- tions via hydrogen bonds with His345, Asn394, Tyr385, and hydrophobic bonds with Phe40, Ala348, Asp350, His378, Glu402, Phe504, Tyr510, Arg514, Tyr515 amino acids of 1R4L protein. It also performs a − 9.0 docking score through hydrophobic interactions with Phe40, leu73, Ala99, Leu100, Gln102, Asp350, Phe390, Leu391, Arg393, Asn394 amino acids of 6LZG protein. Furthermore, as shown in Fig. 1, ligands 14 (olopatadine) and 15 (phenylephrine), important drugs to get relief from nasal congestion, hay fever, and other allergic reactions [52, 53], demonstrate the docking scores − 9.2 and − 6.0 against 1R4L as well as − 7.2 and − 6.1 against 6ZLG proteins respectively. As of analysis, the docking score of 14 was attributable to the interactions of three hydrogen bonds with Pro346, Thr371, Tyr515, and ten hydrophobic bonds with Arg273, Phe274, Thr276, His345, Asp367, Asp368, His374, Thr445, His505, Arg518 amino acids of 1R4L protein, whereas three hydrogen bonds with Tyr196, Gly205, Lys562 and 11 hydrophobic bonds with Leu95, Gln102, Asp206, Val209, Asn394, Gly395, Ala396, Asn397, Glu564, Pro565, Trp566 amino acids of 6LZG protein respectively. On the other hand, ligand 15 showed molecular interactions of three hydrogen bonds with Ala348, Glu402, Arg514 and five hydrophobic bonds with Thr347, His378, Phe504, Tyr510, Tyr515 amino acids of 1R4L protein while two hydrogen bond interactions with Glu208, Asn210 amino acids and six hydrophobic bond interactions with Leu95, Val209, Ala396, Lys562, Glu564, Trp566 amino acids were present within the binding pockets of 6LZG proteins, respectively. After analysis of docking scores of all the ligands with the said proteins, we had gotten three ligands 8 , 13 , and 16 with excellent modes of molecular interactions (Figs. 2 and 3) within the binding pockets of 1R4L and 6LZG proteins. To this, ligands 8 and 13 showed strong binding affinities against 6LZG, a SARS-CoV2 spike protein with the value of docking score − 9.3 and − 9.0, respectively, compared to the value of docking scores − 8.8 and − 7.0 of reference drugs such as nafamostat and arbidol, respectively. In addition, ligands 8 and 16 have shown significant binding affinities against 1R4L, a human ACE2 protein with the value of docking score − 10.3 and − 10.0, respectively, compared to the value of docking score − 7.4 and − 9.1 of reference drugs such as benazepril and losartan respectively. Additionally, the protein–protein interactions at the interface of spike protein-ACE2 (S-ACE2) complex are the key players for the entry of SARS-CoV2 into the host body. Inhibition of these interactions may be crucial to develop the therapeutic agents against COVID-19 disease. Lan et al. identified 13 polar amino acid residues such as Gln24, Asp30, Glu35, Glu37, Asp38, Tyr41, Gln42, Tyr83, Gln325, Glu329, Asn330, Lys353, Arg393 in human ACE2 protein. They form favorable molecular association at the interface of S1 subdomain of spike glycoprotein, enhanc- ing the affinity of both the receptors [54]. A study showed the numerous protein–protein interactions at the interface of S-ACE2 complex via existing of Tyr505, Ser494, Phe497, Gly496, Tyr495, Tyr453, Lys403, Arg393, Phe390, Pro389, Gln388, Ala387, Lys353, Asp38, Glu37, His34, and Asn33 amino acid residues [55]. Moreover, several experimental and in silico studies have also been reported that revealed the presence of important interacting residues while forms the S-ACE2 complex. [56, 57]. Recent study, based on amide hydrogen/deuterium exchange mass spectrometry (HDXMS) method, has indicated the main interaction sites on the pep- tide chains including 340–359, 400–420, 432–452, and 491–510 residues at the interface of said complex [58]. Furthermore, a crystal structure of such complex has iden- tified 24 key ACE2 residues, containing the peptides 16–45, Journal of Molecular Modeling (2022) 28:212 1 3 212 Page 10 of 16 79–83, 325–330, and 350–357 that are unique to SARS- CoV2 in comparison to SARS-CoV1 [59]. In our study with comparing to above studies, it has been seen (Figs. 2 and 3) that the amino acid residues (Arg393 and Phe390, Tyr505) of 1R4L and 6LZG receptors form strong molecular associa- tion with the functionalities of ligands 8 , 13 , and 16 respec- tively. Similarly, when we compare our docking scores, it can also be seen that 8 has the most negative value (− 10.3 and − 9.3) of docking scores against 1R4L and 6LZG pro- teins, respectively, revealing the effective binding of 8 with the key amino acid residues. It shows interacting amino acids (His345, Asp269, Phe274, Asp367, and Asn149) as simi- lar to reference drugs such as benazepril and losartan that were found to interact with the His345, Asp269, Phe274, Asp367 and Asn149, Asp269, Phe274, Asp367 amino acids of 1R4L protein receptor, respectively. Moreover, 8 also con- tains Phe40 and Phe390 interacting amino acids similar to reference drugs such as nafamostat and arbidol that inter- acted with the Phe40 and Phe390 amino acids of the 6LZG protein receptor, respectively. However, 13 shows docking score − 9.0 with interacting amino acids Phe40, Leu73, Ala99, Leu100, Phe390, and Leu391, similar to reference drugs nafamostat and arbidol against 6LZG protein. Dock- ing score − 10.0 is exhibited by 16 against 1R4L pr