F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.25076.1 Research Article Articles Molecular docking analysis of selected phytochemicals on two SARS-CoV-2 targets

[version 1; peer review: 1 approved, 1 approved with reservations]

 Ubani Amaka Conceptualization Methodology Software 1 Agwom Francis Conceptualization Methodology Software Writing – Review & Editing 2 Morenikeji Oluwatoyin Ruth Conceptualization Methodology Software 1 Shehu Nathan Yakubu Conceptualization Methodology Writing – Review & Editing 3 Umera Emmanuel Arinze Conceptualization Writing – Review & Editing 1 Umar Uzal Conceptualization Methodology Writing – Review & Editing 1 Omale Simeon Conceptualization Methodology Writing – Review & Editing https://orcid.org/0000-0001-7987-5325 1 4 Aguiyi John Chinyere Conceptualization Methodology Supervision Writing – Review & Editing 1 Nnadi Nnaemeka Emmanuel Conceptualization Methodology Supervision Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-8424-4859 a 5 Luka Pam Dachung Conceptualization Methodology Writing – Review & Editing 6 African Centre of Excellence in Phytomedicine Research and Development, University of Jos, Jos, Nigeria Department of Pharmaceutical & Medicinal Chemistry,Faculty of Pharmaceutical Sciences,, University of Jos, Jos, Nigeria Department of Infectious Medicine, Jos University Teaching Hospital Jos, Jos, Nigeria Department of Pharmacology and Toxicology, Faculty of Pharmaceutical Sciences, University of Jos, Jos, Nigeria Department of Microbiology, Faculty of Natural and Applied Sciences, Plateau State University, Bokkos, Nigeria Biotechnology Centre, National Veterinary Research Institute, Vom, Nigeria, Vom, Nigeria eennadi@gmail.com

No competing interests were disclosed.

 21 9 2020 2020 9 1157 7 9 2020 Copyright: © 2020 Ubani A et al. 2020 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background:The coronavirus spike (S) glycoprotein and M protease are two key targets that have been identified for vaccines and drug development against COVID-19.

Methods:Virtual screening of some compounds of plant origin that have shown antiviral activities were carried out on the two targets, the M protease (PDB ID 6LU7) and S glycoprotein (PDB ID 6VSB), by docking with PyRx software. The binding affinities were compared with other compounds and drugs already identified as potential ligands for the M protease and S glycoprotein, as well as chloroquine and hydroxychloroquine. The docked compounds with best binding affinities were also filtered for drug likeness using the SwissADME and PROTOX platforms on the basis of physicochemical properties and toxicity, respectively.

Results:The docking results revealed that scopadulcic acid and dammarenolic acid had the best binding affinity for the S glycoprotein and M pro protein targets, respectively. Silybinin, through molecular docking, also demonstrated good binding affinity for both protein targets making it a potential candidate for further evaluation as repurposed candidate for SARS-CoV-2, with likelihood of having multitarget activity as it showed activities for both targets.

Conclusions:The study proposes that scopadulcic acid and dammarenolic acid be further evaluated in vivo for drug formulation against SARS-COV-2 and possible repurposing of Silybinin for the management of COVIV-19.

 SARS-CoV-2 Molecular docking COVID-19 World Bank Group ACE-018 African Union HRST/ST/AURG-II/CALL1/2016 This research was partially funded by the African Union research grant ASF-RESIST(HRST/ST/AURG-II/CALL1/2016) to P.D.L. and by a World Bank Africa Centre of Excellence in Phytomedicine Research and Development grant (ACE-018) to J.C.A. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Introduction

Since 2003, three coronaviruses have been associated with pneumonia, the first was severe acute respiratory syndrome coronavirus (SARS-CoV-1) 1 which affected 8,098 people causing 774 deaths between 2002 and 2003 2 , the second was Middle-East respiratory syndrome coronavirus (MERS-CoV) 3 and the third is SARS-CoV-2 which, as at 8 th July, 2020, has affected 11,669,259 globally and is responsible for 539,906 deaths. SARS-CoV-2 is a human pathogen which has been declared a global pandemic by the World Health Organization 4 and is responsible for the Coronavirus disease 2019 (COVID-19) (1). . The bats have been identified as the possible reservoir and origin for both the SARS-CoV-2 and SARS-COV-1 3 . Unfortunately, there has been no known cure for COVID-19 to date. 3

The entry into the host cell by the coronaviruses is usually mediated by the spike (S) glycoprotein 3 . This glycoprotein interacts with the angiotensin-converting enzyme 2 (ACE2), enabling the virus penetration into the host. The main protease (M protease, also known as 3CL protease) has also been found to be essential for processing of translated polyproteins for the SARS_CoV-2 virus. The two targets, the S glycoprotein, Sgp and M Pro proteins have therefore been considered as important drug targets against the SARS-CoV-2 virus. 5 . For this study, these two drug targets were selected and used to virtually screen some phytochemicals for possible activity against the SARS-CoV-2 virus.

Possibly, potent inhibitors of these two targets will be able to interfere with the SARS-CoV-2 replication process and thus serves as potential drugs for the management of the COVID-19. Hence, this work is aimed at identifying some potential lead compounds of plant origin that can serve as candidates for testing against the SARS-CoV-2 virus.

 Methods Mining of compounds from Pubchem

Plant compounds reported in the literatures that have been demonstrated to have antiviral activities (list of all the compounds analyzed is available as Extended data 6 ) were selected, alongside also hydroxychloroquine, remdesivir and favipiravir, which have been used in the treatment and management of COVID-19 and mined from the PubChem database.

 Protein preparation

Two proteins including crystal structure of the SARS-CoV-2 main protease (PDB ID 6LU7) 7 in complex with an inhibitor N3 and the S glycoprotein in complex with N-acetyl-D-glucosamine (NAG) (PDB ID 6VSB) were downloaded from the Protein Data Bank (www.rcsb.org). The proteins were prepared by removing water molecules and co-crystalized ligands (highlighting the water molecules and co-crystalized ligands and deleting) on the proteins using Discovery Studio version 2017R2 (19). The UCSF Chimera molecular modeling package is an open access equivalent that could be sued to perform the same function 8 .

 Molecular docking and visualization

The already prepared protein and ligands were loaded on to the PyRx docking software (PyRx-Python Prescription 0.8), where molecular docking was done in the AutodockVina mode. Visualization to see how the ligands fitted and bound into the binding pockets on the protein and also the interactions between the protein and the ligands was done using Discovery Studio version 2017R2 (19) by first loading the saved PDBqt output file of the target protein from PyRx and then inserting the output of the binding modes of the different ligands and then viewed under the Receptor-Ligand interaction platform of Discovery Studio software.

Physicochemical properties and toxicity prediction of compounds

The Physicochemical properties and drugability of selected compounds were predicted using the free online versions of SwissADME 9 and Molinspiration 10 platforms and their predicted toxicity profile also compared using the PROTOX toxicity prediction platform 11 . In each case the ligands were loaded onto the platforms as SMILES structures obtained from PubChem.

 Results Ligands composition and filtering

A library of 22 compounds of plant origin known to have antiviral activity was obtained from the PubChem database (see Extended data 6 ). Though the compounds are chemically diverse, they consist of largely flavonoids and terpenes. Some compounds from the citrus family were found among the library, though they could not make it among the top six selected compounds that demonstrated good binding affinities for the two targets. Most of the compounds has showed similar binding affinities to the selected protein targets (6LU7 (M protease) and 6VSB (S glycoprotein)) compared to the training sets of known ligands to the selected targets (see Table 1).

Comparison of Binding affinities of library to some known ligands and the co-crystalized ligand.
Protein target Co-crystalized ligand Ligands used in treatment (kcal/mol) Phytochemicals (kcal/mol)
6LU7 Remdesivir -7.1 Dammarenolic acid -7.2
Favipiravir -5.3 Quercetin -7.1
Chloroquine -5.2 Solanidine -7.0
Silymarin -6.9
Silvestrol -6.7
Shikonin -6.6
6VSB Remdesivir -7.3 Scopadulcic acid -9.6
Favipiravir -5.3 Baicalin -9.4
Chloroquine -5.2 Legalon -9.2
Solanidine -9.1
Naringenin -9.0
Oleanane -9.0
Silymarin -8.6

However, the top six compounds with most favorable binding affinity were selected for each of the targets. The outcomes of the binding affinities of the selected compounds on the 6LU7 and 6VBS targets are presented in Table 2 and Table 3, respectively.

Binding affinities of the compounds on the 6vsb and their Interaction with the binding site.
Serial Number Ligands Binding affinity (kcal/Mol) Hydrogen bond interaction with residues Hydrophobic bond interaction with residues
1. Scopadulcic Acid -9.6 GLN B: 913 TYR C:904, GLY B:1093, VAL B:911, ARG B: 1107, ASN B:907, THR B:912, ASN B:1119, GLN B:1113, GLY B: 910, GLN B:1106, GLU B:1092, PHE A:1121, ARG A:1091
2. Baicalin -9.4 ARG B:1039, ARG A:1039, ARG C:1039, ALA B:1020, ASN C:1023 ALA C:1026, LEU B:1024, GLN C:784, SER C:1030, ASP B:1041, LEU C:1024, THR C:1027, PHE B:1042, PHE C:1042, PHE A:1042, THR B:1027, SER B:1021, GLU C:780.
3. Sylibinin -9.2 GLU A:954, ARG B:765 GLU A:1017, ARG A:1014, ALA B:766, LYS B:776, LYS A:947, LEU A:948, PRO A:728, VAL A:951, ILE A:1018, ALA B:766, GLN A:957, GLN B:762, GLN A:1010, ILE A:1013, LEU B:1012, ARG B:1019, GLU B:773.
4. Solanidine -9.1 TYR A:369, TYR C:489, ARG C:454, PRO C:491, TYR C:421, ASN C:460, LEU C:461
5. Naringenin -9.0 GLU C:1092, ARG C:1107, ASN C:1108, GLY C:910, ILE C:909 ASN C:907, THR C:912, GLN C:1113, ARG B:1091, GLU B:1092, GLY C:1093, GLN C:1106, TYR A:904
6. Oleanane -9 LEU A:1141, ASP C:1118, LEU C:1141, PRO A:1140, ASP A:1118, THR A:1117, THR B:1116, ASP B:1118, PRO B:1140, GLU B:1144, ASP A:1139, GLU A:1144
Binding affinities of the compounds on the 6LU7 and their interaction with the binding site.
S/N Ligands Binding affinity (kcal/Mol) Hydrogen bond interaction with residues Hydrophobic bond interaction with residues
1. Dammarenolic acid -7.2 THR A: 111, GLN A: 110, GLN A: 107 VAL A:104, ARG A: 105, ILE A: 106, THR A: 292, PHE A: 112, GLN A: 127, ASP A: 295, ASN A: 152, PHE A: 294, PHE A: 294, PHE A: 8, ASP A: 153, SER A: 158, ILE A: 152
2. Quercetin -7.1 ------ -----
3. Solanidine -7.0 ------- VAL A: 104, LYS A: 102, SER A: 158, ASP A: 153, ILE A: 152, ASN A: 151, ASN A: 151, PHE A: 8, ARG A: 105, GLN A: 107, ILE A: 106
4. Silybinin -6.8 ARG A: 105, ASP A: 176 ASN A: 180, GLU A: 178, PHE A: 103, VAL A: 104, SER A: 158, ASN A: 151, THR A: 111, GLN A: 110, ILE A: 106, GLN A: 107
5. Loliolide -6.7 ------     ------
6. Shikonin -6.6 THR A: 111, SER A: 158 PHE A: 294, PHE A: 8, GLN A: 110, ILE A: 106, ASN A: 151, PHE A: 112, LYS A: 102, ASP A: 153, VAL A: 104
Molecular docking analysis

The binding affinities of the top six compounds ( Table 1) on the 6vsb target are comparable to each other, i.e. they all lie within a close range of 9 to 9.6 kcal/mol indicating that they might likely have equal or comparable potential as lead compounds for the 6vsb S glycoprotein.

One of the compounds sylibinin 12 is an FDA approved drug, which showed up as active on both M protease and S glycoprotein will make a good candidate of repurposing. Finding Quercetin as a potential inhibitor of the M protease Protein (6LU7) of SARS-CoV-2 corresponds with an earlier report 13 .

 Physicochemical screening of ligands

Looking at the octanol–water coefficient (cLogP) of the compounds, there was no correlation observed between the lipophilicity and the interaction with the receptors. However, for the compounds acting on 6LU7 (Serial numbers 3, 4, 7, 8, 9 and 10 in Table 4), interaction with the receptor is correlated with low lipophilicity, with the exception of solanidine and dammarenolic acid, which have high cLogP values, although both compounds also use their polar functional groups in interacting with the receptor. Bacailin ( Figure 1) and naringenin showed good hydrogen bond interaction with the 6VSB receptor due to their polarity.

Comparison of the calculated cLogP values for the selected compounds.
S/N Compound SwissADME cLog P Molinspiration cLog p Mean calculated cLogP
1. Scopodulcic acid 4.57 5.01 4.79
2. Baicalin 0.22 0.55 0.39
3. Sylibinin 1.59 1.47 1.53
4. Solanidine 5.01 5.93 5.47
5. Naringenin 1.84 2.12 1.98
6. Oleanane 8.57 8.86 8.72
7. Dammarenolic acid 6.74 8.08 7.41
8. Quercetin 1.23 1.68 1.46
9. Loliolide 1.53 1.84 1.69
10. Shikonin 2.08 2.02 2.05

cLogP: octanol-water coffecient.

Best binding pose and interaction of ( A) scopodulcic acid and ( B) baicalin on the spike glycoprotein.

 Drug likeness and predicted toxicity profiles of ligands

Filtering the compounds for drug likeness on the basis of Linpinski’s and/or Veber’s rule showed that all the compounds have drug-like properties except baicalin, which failed the two filtering scales applied ( Table 5). This implies that baicalin is not worth considering further without any structural modification. The predicted toxicity profile of the selected compounds shows ( Table 6) that all the compounds are likely to be relatively safe, which makes them good potential candidates for anti-infectives because the chances of achieving selective toxicity is high. Baicalin is thus most likely the safest.

Drug likeness.
S/N Compound Mol. Wt(g/mol) TPSA HBA HBD RB cLogP Lipinski filter Veber filter
1. Scopodulcic acid 438.56 80.67 5 1 4 4.79 + +
2. Baicalin 446.36 187.12 11 6 4 0.77 - -
3. Sylibinin 482.44 155.14 10 5 4 3.06 + -
4. Solanidine 397.64 23.47 2 1 0 5.47 + -
5. Naringenin 272.25 86.99 5 3 1 1.98 + +
6. Oleanane 412.75 0.00 0 0 0 8.72 + +
7. Dammarenolic acid 458.72 57.53 3 2 1 7.41 + +
8. Quercetin 302.24 131.36 7 5 1 1.46 + +
9. Loliolide 196.24 46.53 3 1 0 1.69 + +
10. Shikonin 288.3 94.83 5 3 3 2.05 + +

Mol. Wt.: molecular weight; TPSA: total polar surface area; HBA: number of hydrogen bond acceptors; HBD: number of hydrogen bond donors; RB: number of rotatable bonds; cLogP: mean of calculated logP values.

 Predicted toxicity profile of the compounds using PROTOX II.
S/N Compound Hepatotoxicity Immunotoxicity Carcinogenicity Mutangenicity Cytoxicity Possible toxicity targets
1. Scopodulcic acid - ++ - - - AR, AO, PGS
2. Baicalin -- -- + - -- AO, PGS
3. Sylibinin -- ++ - -- -- PGS
4. Solanidine -- ++ - -- + AR, PGS
5. Naringenin - -- - -- + AR, PGS
6. Oleanane -- -- ++ -- --
7. Dammarenolic acid - - -- -- -- AR, AO PGS
8. Quercetin - + -- - -- AO, AR, PGS
9. Loliolide - + -- - -- AO, PGS
10. Shikonin - - ++ - + PG

--: inactive; -: less inactive; +: active; ++: more active; AR: androgen receptor; AO: amine oxidase A; PGS: prostaglandin G/H synthase 1.

 Discussion

Two compounds among the top six selected for each target, solanidine and sylibinin, were observed to have good binding affinity on both the 6VSB and the 6FLU7 proteins. This makes them potential multitarget acting inhibitors on the SARS-CoV-2. Solanidine is a steroidal glycoalkaloid found in potatoes 14 . Although toxic to humans and animals, solanidine has been reported to be effective against herpes viruses (HSV), herpes genitalis and herpes zoster 15 Its activity against HSV is attributed to the presence of a sugar moiety 16 . In silico drug screening using PROTOX II showed that solanidine is very likely to be cytotoxic and immunotoxic. PROTOTOX II is a cost- and time-saving approach for testing and determining the toxicity of a compound to be considered a drug of choice 17 . PROTOTOX II predicts the toxicity outcome of a potential drug of choice, it incorporates machine-learning models which use a combination of fragment propensities, molecular similarity, pharmacophores, to predict toxicity endpoints, such as acute toxicity, cytotoxicity, , carcinogenicity , hepatotoxicity, immunotoxicity, mutagenicity and toxicity targets 17

A safe drug must not be toxic to its host target. Based on the PROTOX II evaluation of toxicity, dammarenolic acid emerges as the compound of choice with the least toxicity. Dammarenolic acid has been reported as effective antiviral agents dammarenolic acid potently inhibited the in vitro replication of other retroviruses, including simian immunodeficiency virus and murine leukemic virus in vector-based antiviral screening studies and has been proposed as a potential lead compound in the development of anti-retrovirals 18 . The compound is cytotoxic and demonstrate potential against respiratory syncytial virus 19 . We therefore propose that the evaluation of dammarenolic acid might hold the key to a safe and effective anti-SARS-CoV-2 drug considering its drugability and low toxicity.

This study proposes a potential re-purposing of silybinin for the management of COVID19 diseases. Silybinin (silymarin) possesses antiviral ability against hepatitis C virus (HCV) 20, 21 It has been reported to have activities against a wide range of viral groups including flaviviruses (HCV and dengue virus), togaviruses (Chikungunya virus and Mayaro virus), influenza virus, human immunodeficiency virus, and hepatitis B virus 20 . In an in vivo and in vitro study, Silymarin has been proposed to inhibit HCV entry, RNA synthesis, viral protein expression and prevent infectious virus production; it can also block cell-to-cell spread of the virus 22 . In silico analysis of silybinin in this present study has shown that it can likely inhibit SARS-CoV-2 S glycoprotein and M pro targets, making it a drug to be considered with a possible multi-target activity against the SARS-CoV-2 virus.

 Conclusions

From the 22 phytocompounds that were virtually screened, scopodulcic acid and dammarenolic acid showed the best binding energies with the S glycoprotein and M pro, respectively. This makes them potential lead compounds for development into candidates against the SARS-CoV-2. Furthermore, the FDA-approved drug silybinin (Legalon) had good binding affinity for the two targets, so could be evaluated further for possible repurposing against the SARS-CoV-2 virus.

 Data availability Underlying data

All data underlying the results are available as part of the article and no additional source data are required.

 Extended data

Harvard Dataverse: Replication Data for:Molecular docking analysis of some phytochemicals on two SARS-CoV-2 targets. https://doi.org/10.7910/DVN/BB0QQK 6 .

The file within this project contains the compounds obtained from the PubChem database that were analyzed in this study.

Extended data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

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Competing interests: No competing interests were disclosed.

 6 10 2021 Copyright: © 2021 Girgis AS 2021 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

The current study titled “Molecular docking analysis of selected phytochemicals on two SARS-CoV-2 targets”, deals with an important subject. Investigation of promising anti-SARS-CoV-2 agents is a recent universal need. Computational technique can accelerate the drug discovery. Major revisions are needed for this study.  

Update what mention in the first paragraph of introduction section “the third is SARS-CoV-2 which, as at 8th July, 2020, has affected 11,669,259 globally and is responsible for 539,906 deaths”.

Figures exhibiting the best fit docking of all the tested compounds should be inserted in the supplementary file with short comment(s) explaining the interactions (hydrogen bonding, π-interactions, etc.)

Experimental data for the most successful agents discovered are the perfect support for the hypothesized/computational studies. This point is considered the major revision requested.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Organic, medicinal chemistry with special interest in computational chemistry

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

 10.5256/f1000research.27667.r84269 Reviewer response for version 1 Nayak Yogendra 1 Referee https://orcid.org/0000-0002-0508-1394 Department of Pharmacology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India

Competing interests: No competing interests were disclosed.

 4 6 2021 Copyright: © 2021 Nayak Y 2021 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

The work by Ubani et al., on molecular docking analysis of phytochemicals on SARS-CoV-2 is well presented. I recommend and endorse the study. However, few points can be improved, they are as follows:

In the abstract the abbreviation for M protease can be mentioned, it is mentioned but not corresponding to M protease. 

Generally, the molecular dynamic simulation is said to be the best indicator/predictor of biological activity. Why this was not performed? or is it not the limitation of the study?

Why only 22 compounds from plant origin are selected? How it is shortlisted? What was the rationale? Because there are more than 22 natural plant product having antiviral properties. Why only silybin, why not curcumin, which is also FDA approved. (Mohan et al. (2020 1 ))

The ligand protein interaction can be further discussed. In the discussion, at least top five compounds can be discussed.

Why not just try the silybin in clinical trial? 

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Pharmacy and Pharmacology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

References : Bioactive Natural Antivirals: An Updated Review of the Available Plants and Isolated Molecules. Molecules .2020;25(21) : 10.3390/molecules25214878 33105694 10.3390/molecules25214878