INTRODUCTION:

More than a hundred types of cancer attack humans, and this has lead to it being placed as the second-leading cause of death worldwide. In 2015, cancer has resulted in 8.8 million mortalities and is expected to rise to 10 million in 2020¹. Some scientists believe that most cancers (90-95%) are caused by mutations that are triggered by environmental and lifestyle factors, and the rest (5-10%) are due to genetic inheritance. Globally, approximately 18% of cancer deaths are attributed to infectious agents². Inhibition of the activity of the major transcription factors appears to be a very potent approach to cancer therapy^3,4.

Human papillomavirus (HPV), a sexually transmitted virus from the papillomavirus family, is a causative agent of several types of cancer including cervical, head and neck, vaginal, penile, and anal cancer⁵. Over 200 subtypes of HPV have been identified and characterized over the years. Based on its oncogenic potential, HPV is classified into high-risk and low-risk groups^6,7. High-risk types such as HPV-18 and -16 are responsible for causing anogenital cancer which account for 15.7% and 62.6% of cervical cancers, respectively, whereas low-risk HPV-6 and -11 induce only genital warts^8,9. Thus, HPV 16 and 18 became the main targets in the design of anticancer drugs.

HPV is a double-stranded virus that invades the epithelium¹⁰. The HPV genome consists of three main regions. First, the long control region (LCR) has the function of regulating DNA replication by controlling the transcription of viral genes. Th early region (E) is responsible for encoding non-structural proteins involved in viral replication (E1,E2,E4) and oncogenesis (E5,E6,E7). Late regions are known to encode structural proteins (L1 and L2) involved in viral capsid formation, transmission, and spread of virions^11-13.

E6 is one of the oncoproteins expressed in HPV-16 and -18, which contributes to malignant transformation, immortality, and carcinogenicity². This oncoprotein targets p53, which plays an important role as a tumor suppressor and checkpoint regulator in the cell cycle¹⁴. E6 forms a heterotrimeric complex with p53 and E6-associated protein (E6AP) via the ubiquitination pathway which leads to p53 degradation¹⁵. Decreased p53 content causes it to be unable to repair DNA damage resulting in malignancy².

For more than three decades, HPV has been identified as the causative agent for cervical cancer, but finding effective therapies to fight HPV infection remains to be established¹⁶. Developing drugs for the prevention and treatment of cancer based on natural compounds of plant origin has emerged as a promising approach in recent years¹⁷.

Allicin is reported to suppress the viability of cervical cancer cells depending on the time and dose of therapy¹⁸. Apigenin arrests the cycle of human cervical cancer cells (HeLa) in the G1 phase and induces apoptosis via DNA fragmentation¹⁹. Capsaicin exhibits potent anticancer properties on various types of cancer cells, and its combination with radiotherapy or conventional chemotherapy improves sensitivity, reduces side effects, and increases patient tolerance of therapy²⁰. Cyanidin-3-O-β-glucopyranoside has an anti-proliferative effect by activating caspase-3 and inducing p21 expression on human prostate cancer cells DU145 and LnCap²¹. Fisetin which is found in many fruits and vegetables, induces apoptosis and interferes with the mitochondrial membrane potential of HeLa cell lines^22,23. Genistein triggers apoptosis in HeLa cells through increased caspase-9 and caspase-3 activity²⁴. Laricitrin can reverse lung cancer cells-induced dendritic cell paralysis, by inhibiting STAT3 (signal transducer and activator of transcription 3) phosphorylation²⁵. Naringenin, a flavanone derivative in citrus fruits, has been shown to cause cell death and inhibit the cell cycle in the G0/G1 (24 h) and S (48 h) phases in a 3D spheroid culture of cervical cancer cells, as well as to increase the therapeutic effect of cervical cancer by cisplatin^26,27. Atriplex halimus containing syringetin is also reported to have selective cytotoxicity against breast (MCF-7) and prostate (PC3) cancer cells²⁸.

Research in the field of drug design is very time consuming and expensive, and the in silico approach can be used as a preliminary study on the potency of natural compounds as drug candidates²⁹. Computational drug design simulations can predict the molecular mechanisms of candidate compounds through several analyzes based on specific research objectives, such as prediction of biological pathways, binding energies, types of interactions, and molecular dynamics³⁰. This study predicted the potential molecular mechanisms of ten different natural compounds from several plant sources as antiviral candidates for HPV-18 via an in silico approach.

MATERIALS AND METHODS:

Hardware and Softwares:

The study was conducted on Dell Latitude E7240 with Intel^® Core™ i7-4600U and 2.10 GHz processor, 16 GB RAM, and 250 GB hard disk drive. The bioinformatics softwares used were PyRx 0.8 and PyMol 2.0. Online resources such as PubChem (https://pubchem.ncbi.nlm.nih.gov/), UniProt (https://www.uniprot.org/), RCSB Protein Data Bank (PDB) (https://www.rcsb.org/), SwissADME (http://www.swissadme.ch), and Protein Plus (https://proteins.plus/) were also utilized in this study.

Compounds and Protein Preparation:

This study involved ten natural compounds namely allicin, apigenin, capsaicin, cyanidin, fisetin, genistein, laricitrin, naringenin, piperine, and syringetin. Their chemical 3D structures were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/). RCSB PDB (https://www.rcsb.org/) was used to reveal the HPV-18 E6 protein target samples.

Drug-Likeness Analysis:

Drug-likeness analysis was performed to qualitatively assess the possibility of a molecule becoming an oral drug based on its bioavailability. It was done on the SwissADME (http://www.swissadme.ch) and referred to Lipinski’s rule of five^31,32.

Molecular Docking Study:

The compounds that met Lipinski’s rule were analyzed for their binding energy with HPV-18 E6 protein by using docking analysis. A blind docking type was performed using the Vina program in PyRx 0.8³³.

Protein-Ligand Interactions:

This analysis identified the interaction position and the type of chemical bond formed when a potential compound binds to the E6 protein target. The Protein Plus web server (https://proteins.plus/) was involved in this analysis³⁴.

Molecular Visualization:

PyMol 2.0 was employed for visualization of representative color and structural selection³⁵.

RESULTS AND DISCUSSION:

The Potency of Natural Compounds as Drug Candidates:

Ten samples of chemical compounds namely allicin, apigenin, capsaicin, cyanidin, fisetin, genistein, laricitrin, naringenin, piperine, and syringetin were obtained from the PubChem database. The sample data consist of the 3-dimensional structure of the target compound in structural data format (.sdf) which was then converted into a protein data bank (.pdb) format that can be used for subsequent analysis. The selection of the target compound structure is shown in the representative form and colored based on the chemical building blocks in the PyMol 2.0 software (Figure1).

The drug-ability of these chemical compounds was then predicted on the SwissADME server (http://www.swissadme.ch) based on Lipinski’s rule of five. The rule of five (Ro5) is defined as a combination of parameters capable of identifying potential drug candidates that may cause problems with absorption and permeability³⁶. Lipinski’s rule states that oral drugs must not have more than one violation of the five criteria that consist of molecular weight ≤ 500 Dalton, hydrogen bond donors ≤ 5, hydrogen bond acceptors ≤ 10, lipophilicity ≤ 5, and molar refractivity between 40-130^37,38. The prediction results show that all the chemical compounds are drug-like (Table 1). These compounds can proceed to the next stage of analysis, i.e. the determination of the binding energy to the target protein.

Table 1. Prediction results of drug-like molecules based on Lipinski’s rule.

Compounds	Lipinski’s Rule of Five
Compounds	MW (Da)	HBD	HBA	Log P	MR
Allicin	162.27	0	1	1.18	45.88
Apigenin	270.24	3	5	0.52	73.99
Capsaicin	305.41	2	3	2.69	90.52
Cyanidin	287.24	5	6	0.32	76.17
Fisetin	286.24	4	6	-0.03	76.01
Genistein	270.24	3	5	0.52	73.99
Laricitrin	332.26	5	8	-0.83	84.53
Naringenin	272.25	3	5	0.71	71.57
Piperine	285.340	0	3	2.39	85.47
Syringetin	346.29	4	8	-0.59	89.00

Note. MW: Molecular Weight; HBD: Hydrogen Bond Donors; HBA: Hydrogen Bond Acceptors; Log P: Lipophilicity; MR: Molar Refractivity.

Interaction Study of Natural Ligands with HPV-18 E6 via Docking Analysis:

Molecular docking is a valuable computational modeling technique for estimating the interaction of ligand-receptor macromolecules and determining the binding energies generated^39,40. The protein was prepared using PyMol 2.0 software to eliminate the water molecules⁴¹. In this study, the blind docking method was employed due to the fact that the functional domain of the target receptor was unknown⁴².

Chemical compounds consisting of allicin, apigenin, capsaicin, cyanidin, fisetin, genistein, laricitrin, naringenin, piperine, and syringetin were used as ligands and E6 (2I04) as the target. Docking simulations for them were carried out in PyRx 0.8 software with a grid docking center x: -6.8275; y: -10.748; z:11.2834 and dimensions (Å) x: 47.2043; y: 54.2914; z: 44.6647. Molecular visualization of docking results was conducted by structural selection and coloring using PyMol 2.0 software (Figure 2).

Figure 2. Molecular visualization of natural compound ligands that bind to the HPV-18 E6 receptor. The E6 protein is shown with a transparent surface and a violet cartoon structure

Since all observed natural compounds interact with E6 protein in different conformations and binding energies, the molecular complex with the lowest binding energy was selected for analysis. Molecular docking results showed that of all the compounds tested, fisetin had the lowest binding energy of -8.1 kcal/mol to HPV-18 E6. Nine other natural ligands i.e. apigenin, naringenin, syringetin, laricitrin, piperine, cyanidin, genistein, capsaicin, and allicin were observed to bind to the E6 receptor with a binding energy range of -7.4 to -3.4 kcal/mol (Table 2). In line with the objectives of this study, the tested compounds were predicted to inhibit E6 activity. The ligand with the lowest binding energy value affected the biological activity of a protein target. The lowest binding energy allowed the formation of molecular complexes under constant temperature and pressure⁴³. The binding energy value is also influenced by the presence of amino acid residues in the binding region of the target protein and the chemical interaction type³³ so that fisetin was further evaluated for its binding location and the type of chemical interactions formed.

Table 2. The result of molecular docking simulation

Compound	ID	Protein Target	Binding Energy (kcal/mol)
Allicin	65036	E6	-3.4
Apigenin	5280443	E6	-7.4
Capsaicin	1548943	E6	-5.9
Cyanidin	128861	E6	-6.6
Fisetin	5281614	E6	-8.1
Genistein	5280961	E6	-6.5
Laricitrin	5282154	E6	-6.8
Naringenin	932	E6	-7.3
Piperine	638024	E6	-6.8
Syringetin	5281953	E6	-6.9

Evaluation of bond type and molecular interaction position of the natural compound with the lowest binding energy was carried out on the Protein Plus webserver. Fisetin interacts with the HPV-18 E6 domain through hydrophobic bonds at Tyr398B, Phe396B, Phe396A, Asn374B, and Ser394A, as well as hydrogen bonds at the Val397B, Phe396B, Ser394A, Ser375B, Phe396A, and Tyr398B positions (Figure 3).

Figure 3. Chemical interaction between Fisetin and HPV-18 E6 binding domain

Chemical interactions that occur between a ligand and a protein domain have an important role in influencing the binding energy. The types of chemical interactions that can be formed are hydrophobic and hydrogen bonds³³. This interaction is classified as a weak bond that can encourage the performance of the biological activity of certain proteins⁴⁴. Chemical bond interactions and the position of binding amino acid residues are very important factors in determining the probability of drug molecules through computer-based studies³⁰. Thus, fisetin has a good potential as a candidate for an antiviral and anticancer drug because it can bind to the HPV-18 E6 protein with the lowest binding energy and interact with hydrogen and hydrophobic bonds.

CONCLUSION:

HPV-18 antiviral molecular activities of several natural compounds were examined regarding their inhibitory mechanisms against E6 protein, and fisetin was found to be the best candidate. We suggest that the in silico simulation results from this study be used as a reference for drug development through in vitro and in vivo studies.

CONFLICT OF INTEREST:

The authors declare no conflict of interest.

REFERENCES:

1. Chang Y. Moore PS. Weiss RA. Human oncogenic viruses: nature and discovery. Philosophical transactions of the Royal Society of London. Series B, Biological Sciences. 2017; 372. doi.org/10.1098/rstb.2016.0264

2. Nabati F. Moradi M. Mohabatkar H. In silico analyzing the molecular interactions of plant-derived inhibitors against E6AP, p53, and c-Myc binding sites of HPV type 16 E6 oncoprotein. Molecular Biology Research Communications. 2020; 9(2): 71-82. doi.org/10.22099/mbrc.2020.36522.1483

3. Johari B. Ebrahimi-Rad M. Maghsood F. Lotfinia M. Saltanatpouri Z. Teimoori-Toolabi L. Sharifzadeh Z. Karimipoor M. Kadivar M. Myc decoy oligodeoxynucleotide inhibits growth and modulates differentiation of mouse embryonic stem cells as a model of cancer stem cells. Anti-cancer Agents in Medicinal Chemistry. 2017; 17: 1786-1795. doi.org/10.2174/1871521409666170412142507

4. Johari B. Zargan J. Simultaneous targeted inhibition of Sox2‐Oct4 transcription factors using decoy oligodeoxynucleotides to repress stemness properties in mouse embryonic stem cells. Cell Biology International. 2017; 41: 1335-1344. doi.org/10.1002/cbin.10847

5. Tambunan USF. Parikesit AA. HPV Bioinformatics: In silico detection, drug design and prevention agent development. In: Topics on Cervical Cancer With an Advocacy for Prevention. InTech. 2012 doi.org/10.5772/27456

6. Kumar S. Jena L. Mohod K. Daf S. Varma AK. Virtual screening for potential inhibitors of high-risk human papillomavirus 16 E6 protein. Interdisciplinary Sciences, Computational Life Sciences. 2015; 1-7. doi:10.1007/s12539-013-0213-6

7. Park MS. Chang BS. Ultrastructural characteristics of hpv in women’s vaginal cells. Research Journal of Pharmacy and Technology. 2019; 12(9): 4305-4309. doi.org/10.5958/0974-360X.2019.00740.6

8. Kumar S. Jena L. Sahoo M. Kakde M. Daf S. Varma AK. In silico docking to explicate interface between plant-originated inhibitors and E6 oncogenic protein of highly threatening human papillomavirus 18. Genomics & Informatics. 2015; 13(2): 60-67. doi.org/10.5808/GI.2015.13.2.60

9. Merkhofer C. Maslow J. Human Papilloma virus (HPV) infection and non-cervical oncogenic disease states. Virology & Mycology. 2015; 4: 2. doi.org/10.4172/2161-0517.1000144

10. Bharatha Soruba Rani S. HPV infection and cervical cancer. International Journal of Nursing Education and Research. 2015; 3(2): 229-231. doi.org/10.5958/2231–5713

11. Fernandes JV. Fernandes TAAM. Human papillomavirus: biology and pathogenesis. In: Human papillomavirus and related diseases – from bench to bedside – a clinical perspective. InTech. 2012; 1-5. doi.org/10.5772/27154

12. Pinidis P. Tsikouras P. Iatrakis G. Zervoudis S. Koukouli Z. Bothou A. Galazios G. Vladareanu S. Human papilloma virus’ life cycle and carcinogenesis. MAEDICA – a Journal of Clinical Medicine. 2016; 11(5): 48-54. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5394500

13. de Sanjose S. Brotons M. Pavon MA. The natural history of human papillomavirus infection. Best practice & research. Clinical Obstetrics & Gynaecology. 2018; 47: 2-13. doi.org/10.1016/j.bpobgyn.2017.08.015

14. Kharisma VD. Ansori ANM. Widyananda MH. Utami SL. Nugraha AP. Molecular simulation: The potency of conserved region on E6 HPV-16 as a binding target of black tea compounds against cervical cancer. Biochemical and Cellular Archives. 2020; 20(1): 2795-2802. doi.org/10.35124/bca.2020.20.S1.2795

15. Messa L. Celegato M. Bertagnin C. Mercorelli B. Nannetti G. Palù G. Loregian A. A quantitative LumiFluo assay to test inhibitory compounds blocking p53 degradation induced by human papillomavirus oncoprotein E6 in living cells. Scientific Reports. 2018; 8: 1-11. doi.org/10.1038/s41598-018-24470-4

16. Marimuthu N. Viswanathan T. Radha M. Suganya J. Computational Screening of the phytocompounds from the plant Ballota nigra Linn against the human papillomavirus (HPV) E6. Research Journal of Pharmacy and Technology. 2017; 10(9): 3095-3097. doi.org/10.5958/0974-360X.2017.00549.2

17. Proboningrat A. Ansori ANM. Fadholly A. Putri N. Kusala MKJ. Achmad AB. First report on the cytotoxicity of Pinus merkusii bark extract in WiDr, a human colon carcinoma cell line. Research Journal of Pharmacy and Technology. 2021; 14(3): 1685-1688. doi.org/10.5958/0974-360X.2021.00299.7

18. Zhang Q. Yang D. Allicin suppresses the migration and invasion in cervical cancer cells mainly by inhibiting NRF2. Experimental and Therapeutic Medicine. 2019; 17: 1523-1528. doi.org/10.3892/etm.2018.7104

19. Imran M. Gondal TA. Atif M. Shahbaz M. Qaisarani TB. Mughal MH. Salehi B. Martorell M. Rad JS. Apigenin as an anticancer agent. Phytotherapy Research. 2020; 1-17. doi.org/10.1002/ptr.6647

20. Zhang S. Wang D. Huang J. Hu Y. Xu Y. Application of capsaicin as a potential new therapeutic drug in human cancers Journal of Clinical Pharmacy and Therapeutics. 2019; 45: 16-28. doi.org/10.1111/jcpt.13039

21. Sorrenti V. Vanella L. Acquaviva R. Cardile V. Giofre S. Giacomo CD. Cyanidin induces apoptosis and differentiation in prostate cancer cells. International Journal of Oncology. 2015; 47: 1303-1310. doi.org/10.3892/ijo.2015.3130

22. Kumar R. Kumar R. Khursheed R. Kapoor B. Sharma N. Khurana S. Khurana N. Singh SK. Vyas M. Development and validation of uv spectroscopic method for estimation of fisetin in self nanoemulsifying drug delivery system. Research Journal of Pharmacy and Technology. 2020; 13(3): 1179-1182. doi.org/10.5958/0974-360X.2020.00217.6

23. Imran M. Saeed F. Gilani SA. Shariati MA. Imran A. Afzaal M. Atif M. Tufail T. Anjum FM. Fisetin: An anticancer perspective. Food Science & Nutrition. 2020; 9(1): 3-16. doi.org/10.1002/fsn3.1872

24. Tuli HS. Tuorkey MJ. Thakral F. Sak K. Kumar M. Sharma AK. Sharma U. Jain A. Aggarwal V. Bishayee A. Molecular mechanisms of action of genistein in cancer: recent advances. Frontiers in Pharmacology. 2019; 10: 1-16. doi.org/10.3389/fphar.2019.01336

25. Chang WA. Hung JY. Jian SF. Lin YS. Wu CY. Hsu YL. Kuo PL. Laricitrin ameliorates lung cancer-mediated dendritic cell suppression by inhibiting signal transducer and activator of transcription 3. Oncotarget. 2016; 7(51): 85220-85234. doi.org/10.18632/oncotarget.13240

26. Fadholly A. Ansori ANM. Sucipto TH. An Overview of Naringin: Potential anticancer compound of citrus fruits. Research Journal of Pharmacy and Technology. 2020; 13(11): 5613-5619. doi.org/10.5958/0974-360X.2020.00979.8

27. Rodriguez OPM. Torres AG. Salas LMA. Sanchez HH. Perez BEG. Bonilla MRT. Flores MEJ. Effect of naringenin and its combination with cisplatin in cell death, proliferation and invasion of cervical cancer spheroids. RSC Advances. 2020; 11: 129-141. doi.org/10.1039/D0RA07309A

28. El-Aasr M. Kabbash A. El-Seoud KAA. Al-Madboly LA. Ikeda T. Antimicrobial and immunomodulatory activities of flavonol glycosides isolated from Atriplex halimus L. herb. Journal of Pharmaceutical Sciences and Research. 2016; 8(10): 1159-1168. Retrieved from https://www.jpsr.pharmainfo.in/issue.php?page=86

29. Sindhu TJ. Arathi KN. Akhilesh KJ. Anju J. Binsiya KP. Thomas B. Wilson E. Antiviral screening of clerodol derivatives as COV 2 main protease inhibitor in novel corona virus disease: In silico approaches. Asian Journal of Pharmacy and Technology. 2020; 10(2): 60-64. doi.org/10.5958/2231-5713.2020.00012.4

30. Kharisma VD. Syafrudin S. Septiadi L. Prediction of novel bioactive compound from Z. officinale as non-nucleoside reverse transcriptase inhibitors (NNRTIs) of HIV-1 through computational study. Bioinformatics and Biomedical Research Journal. 2018; 1(2): 49-55. doi.org/10.11594/bbrj.01.02.05

31. Ray NM. Singh R. Singh J. Bhati S. Kaushik V. Computational screening of thiohydantoin derivatives for antitumor activity. Research Journal of Pharmacy and Technology. 2020; 13(2): 795-800. doi.org/10.5958/0974-360X.2020.00150.X

32. Daina A. Michielin O. Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports. 2017; 7: 42717. doi.org/10.1038/srep42717

33. Kharisma VD. Ansori ANM. Nugraha AP. Computational study of ginger (Zingiber officinale) as E6 inhibitor in human papillomavirus type 16 (Hpv-16) infection. Biochemical and Cellular Archives. 2020; 20(1): 3155-3159. doi.org/10.35124/bca.2020.20.S1.3155

34. Purnama ER. Kharisma VD. Epitope mapping of capsid protein L1 from human papillomavirus to development cervical cancer vaccine through computational study. Journal of Physics: Conference Series. 2018; 1108: 1-6. doi.org/10.1088/1742-6596/1108/1/012096

35. Adianingsih OR. Kharisma VD. Study of B cell epitope conserved region of the Zika virus envelope glycoprotein to develop multi-strain vaccine. Journal of Applied Pharmaceutical Science. 2019: 9(1): 98-103. doi.org/10.7324/JAPS.2019.90114

36. Fernandes TB. Segretti MCF. Polli MC. Filho RP. Analysis of the applicability and use of Lipinski`s rule for central nervous system drugs. Letters in Drug Design & Discovery. 2016; 13: 1-8. doi.org/10.2174/1570180813666160622092839

37. Buvana C. Sumathy A. Sukumar M. In silico Identification of potential xanthine oxidase inhibitors for the treatment of gout and cardiovascular disease. Asian Journal of Pharmacy and Technology. 2013; 6(11): 1049-1053. doi.org/10.5958/0974-4150

38. Lipinski CA. Lombardo F. Dominy BW. Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews. 2001; 46(1-3): 3-26. doi.org/10.1016/s0169-409x(00)00129-0

39. Kharisma VD. Kharisma SD. Ansori ANM. Kurniawan HP. Witaningrum AM. Fadholly A. Tacharina MR. Antiretroviral effect simulation from black tea (Camellia sinensis) via dual inhibitors mechanism in HIV-1 and its social perspective in Indonesia. Research Journal of Pharmacy and Technology. 2021; 14(1): 455-460. doi.org/10.5958/0974-360X.2021.00083.4

40. Sukmanadi M. Sudjarwo SA. Effendi MH. Molecular mechanism of capsaicin from (Capsicum annuum L.) on expression of MAPK1 and AKT1 protein as candidate of anticancer drugs: In silico study. Pharmacognosy Journal. 2020; 12(4): 916-919. doi.org/10.5530/pj.2020.12.130

41. Widyananda MH. Pratama SK. Samoedra RS. Sari FN. Kharisma VD. Ansori ANM. Antonius Y. Molecular docking study of sea urchin (Arbacia lixula) peptides as multi-target inhibitor for non-small cell lung cancer (NSCLC) associated proteins. J Pharm Pharmacogn Res. 2021; 9(4): 484-496. Retrieved from https://jppres.com/jppres/sea-urchin-peptides-as-multi-target-inhibitor-of-nsclc

42. Hassan NM. Alhossary AA. Mu Y. Kwoh CK. Protein ligand blind docking using QuickVina-W with inter-process spatio-temporal integration. Scientific Reports. 2017; 7(1): 15451. doi.org/10.1038/s41598-017-15571-7

43. Ramírez D. Caballero J. Is it reliable to use common molecular docking methods for comparing the binding affinities of enantiomer pairs for their protein target? International Journal of Molecular Sciences. 2016; 17(4): 525. doi.org/10.3390/ijms17040525

44. Durell SR. Ben-Naim A. Hydrophobic-hydrophilic forces in protein folding. Biopolymers. 2017: 107: 8. doi.org/10.1002/bip.23020

Received on 10.05.2021 Modified on 24.06.2021

Research J. Pharm.and Tech 2022; 15(3):1251-1256.

DOI: 10.52711/0974-360X.2022.00209