Molecular Modeling of HEV Core Protein and Active Compounds from Northeast Folk Medicine
Nibadita Das, Partha Pratim Kalita, Manash Pratim Sarma*, Minakshi Bhattacharjee
Abstract
The main etiological agent, which is considered to cause acute hepatitis is the Hepatitis E virus. Northeast India has a huge reservoir of medicinal plants for treating jaundice using folk medicine (ITK). The current study focuses on model 32 sequences of HEV core protein submitted in GenBank (KJ879461-KJ879492) and to evaluate the docking pattern with 10 selected active compounds (Glycyrrhizin, Lignans, Piperine, Wedelolactone, Galactomannan, Zingerone, Cajanin, Catechin, Gallic acid, Vasicinone) which are found in various medicinal plants species. Using Open Babel, the protein sequences, as well as the structures, were first converted to PDB format. The Gene Bank provided these sequences [protein sequence id: AIH14833-AIH14864]. The sequences were analyzed by PROTPARAM for chemical compositions and RaptorX for structure. Finally, PASS was applied for toxicity determination and ADME for screening the safety. The Raptor X and PROTPARAM analysis showed stable protein structures of HEV core protein. The analysis categorically showed the composition of C, H, N, O, and S in the studies sequences in a ratio of 108: 171: 35: 36: 1. However, the best results were found in Bhui-amla (Lignans) with the highest docking score of 6944 against sequence ID AIH14838. Lipinski Rule was carried out for all the active compounds and was found to be excellent. The docking score and minimum energy associated show efficient activity of the studied compounds against HEV protein and generates baseline scientific data on the use of folk medicine and the possibility of their commercial utilization.
Keywords: HEV core protein North East India, Folk medicine, RAMPAGE, PROTPARAM, RaptorX, Rasmol
Introduction
The main etiological agent, which is considered to cause acute hepatitis is the Hepatitis E virus (Albureikan, 2020; Narayana et al., 2020). Annually and worldwide, it was estimated that 20 million cases occur, causing the rates of mortality in pregnant women to reach 28%. Hepatitis E is increasing nowadays. HAV and HEV are constantly present at a high rate among the general public and affect all age groups equally. Globally, it is estimated that 2.3 billion people are infected by Hepatitis E virus (HEV), which is a significant international public health problem (Das, 2014). During the third trimester, high mortality among pregnant women is the significant difference between HEV and other causes of acute viral hepatitis (Smith et al., 2016).
HEV belongs to the family Hepeviridae and genus Orthohepevirus. Four of the species that have been defined are the ones that infect carnivores (Orthohepevirus C), soricomorphs, rodents and birds (Orthohepevirus B), and bats (Orthohepevirus D) (Knowles et al., 2011). The Orthohepevirus A comprises seven genotypes that infect human (HEV 1, 2, 3, 4 & 7), wild boar (HEV-3, 4, 5 & 6), pig (HEV- 3 & 4), deer (HEV-3), rabbit (HEV-3), mongoose (HEV-3), yak (HEV-4) and camel (HEV-7) (Sridhar et al., 2017). Annually, it was estimated that 2 million cases of hepatitis E occur in India alone in comparison with the estimated 1.4 million cases of hepatitis A. There have been consistent epidemiological characters of Hepatitis E since its first reported outbreak in New Delhi. The prevalence was found to be highest in young adults and women were reported to have high mortality rates especially in the third trimester of pregnancy (Smith et al., 2014).
Northeast India is showing a shift in hepatitis A viral seroepidemiology. Adults are affected equally by hepatitis E. Unknown herbal medications and non-ABCE AVH and ALF are very common in this region (Hughes et al., 2010). Major viral causes are constituted by HAV and HEV. Higher Mortality level can be found in females and the young ones that belong to the productive section of the society (Das, 2014; Das et al., 2016). Northeast India has a huge reservoir for treating Jaundice by folk Medicine (ITK). It is important to understand the effectiveness of the active compounds of these traditional medicines and their potential use.
Table 1. List of active compounds against HEV core protein and their source
|
Sl. No. |
Scientific Name |
Common name |
Active compounds |
|
1 |
Glycyrrhiza glabra |
Liquorice |
Glycyrrhizin |
|
2 |
Phyllanthus niruri |
Bhui-amla |
Lignans |
|
3 |
Piper longum |
Long pepper |
Piperine |
|
4 |
Trigonella foenum graecum |
Fenugreek seeds |
Galactomannan |
|
5 |
Eclipta alba |
Bhringraj |
Wedelolactone |
|
6 |
Cajanus cajan |
Pigeon pea, Arhar |
Cajanin |
|
7 |
Camellia sinensis |
Green tea |
Catechin |
|
8 |
Lawsonia inermis |
Henna leaf |
Gallic acid |
|
9 |
Justicia adhatoda |
Malabur nut |
Vasicinone |
|
10 |
Zingiber officinale |
Ginger |
Zingerone |
We have selected a list of active compounds against HEV core protein and their source as shown in Table 1. Glycyrrhizin is a glycoside obtained from roots and stolon of Liquorice (Glycyrrhiza glabra). It helps the liver to detoxify drugs and is used for the treatment of liver disease. Glycyrrhizin exhibits activities like antihepatotoxic activity (Amagaya et al., 1984; Cosmetic Ingredient Review Expert Panel, 2007) while phyllanthin and hypophyllanthin belong to the lignan (Phyllantus niruri) category and have been shown to possess hepatoprotective and anti-genotoxic activities (Dahanayake et al., 2020). The major plant alkaloid Piperine, which is found in P. longum Linn (Long pepper) has bioavailability enhancing activity for some drugs nutritional and some substances and is known to exhibit a hepatoprotective activity apart from exhibiting a toxic effect against hepatocytes (Matsuda et al., 2008; Shukla et al., 2011; Panahi et al., 2015). Wedelolactone (7-methoxy-5, 11, 12-trihydroxy-coumestan) is a natural plant product, which is primarily synthesized by the members of the Asteraceae family (Kaushik-Basu et al., 2008; Ding et al., 2017). WDL is abundantly found in the plant genus Eclipta (or Bhringaraj). It is an acrid, bitter herb medicine traditionally used extensively for the prevention of liver damage due to alcohol overdose and jaundice and for hair and skin health (Singh et al., 2001; Patel et al., 2008; Roy et al., 2008). Also, in India, it was also used for the treatment of infective hepatitis (Singh et al., 2001; Patel et al., 2008; Roy et al., 2008). Cajanus cajan is a perennial member of the family Fabaceae with the presence of two globulins, cajanin, and concajanin (Zu et al., 2010). It has been used widely for many years for treating dysentery, sores, skin irritations, measles, jaundice, diabetes, hepatitis and many other illnesses; for expelling bladder stones and stabilizing menstrual period (Zu et al., 2010). Likewise, the rare risk of hepatotoxicity in a few individuals have been associated with Catechins of green tea extract (Teschke et al., 2014). On the other hand, in many regions, Lawsonia inermis (Henna) is a shrub or small tree cultivated as commercial dye crop and an ornamental (Muthumani et al., 2010) is as astringent, hypertensive, jaundice, and against a headache, sedative, and leprosy (Saadabi, 2007; Muthumani et al., 2010). Vasicinone was isolated from the leaves of Justicia adhatoda and its crude extract has been reported to have hepatoprotective activity (Sarkar et al., 2014). The major pungent compounds in Zingiber officinale (Ginger) of rhizome extract consists of potentially active gingerols, which can be converted to shogaols, zingerone, and paradol (Govindarajan & Connell, 1983; Jolad et al., 2004). Lastly, hepatoprotective activity were noticed in the Seeds of fenugreek, which were annual herbs (Kaviarasan et al., 2007).
In the discovery of new mechanism-or structure-based drugs, drug design assisted by computer have allowed many success stories by new molecular modeling approaches, which are driven by these fast-developing computational platforms (De Ruyck et al., 2016). Molecular modeling tools are extensively used in drug designing. These tools consider 3D molecular structures and their relevant physicochemical properties. The research work aimed to investigate the efficacy of the prevalent medicines against the HEV virus using CADD(www.bioinfo3d.cs.tau.ac.il) (Computer-aided drug designing and to comment on the promising compound of NE folk for future use.
Materials and Methods
Retrieving the Gene Bank HEV Sequences
A total of 32 HEV sequences which were submitted by the author of this manuscript [KJ879461-92[1]] were converted into FASTA and the protein sequences were converted into PDB format using open babel.
Selection of Active Compounds
A total of 10 active compounds were selected which are present in the folk medicine used to treat jaundice in North East India and whose source parts are abundant in nature. The 2D structures of these active compounds were retrieved in SDF form using NCBI and were further converted mol2, mol, and pdb format.
Structure Prediction
RaptorX was carried out for predicting the structures of HEV protein sequences. Protparam was done for computing the physical and chemical parameters of these protein sequences.[2]
3D Structure Analysis
RASMOL was used for visualization of 3D structures of the studied protein. Using Rampage, to understand the stability of these proteins, Further Ramachandran plots were accessed.[3]
Molecular Docking
Molecular docking was carried out by the PATCHDOCK server where the PDB format of protein sequences and selected active compounds was taken.
Toxicity Test
PASS (Prediction of activity spectra for substances) was carried out for prediction of toxicity of selected active compounds while ADME for screening the safety of the selected active compounds (active compounds were in mol format).
Potentiality as Drug
Lipinski Rule of 5 was done to evaluate drug-likeness. Results were displayed using RASMOL.
Results and Discussion
Table 2. Docking Profile of All the Active Compounds (De Ruyck et al., 2016)
|
Common name |
Scientific name |
Active compounds |
Protein ID |
Best score |
|
Liquorice |
Glycyrrhiza glabra |
Glycyrrhizin |
AIH14839 |
5464 |
|
Bhui-amla |
Phyllanthus niruri |
Lignans |
AIH14838 |
6944 |
|
Long pepper |
Piper longum |
Piperine |
AIH14839 |
5606 |
|
Fenugreek seeds |
Trigonella foenum graecum |
Galactomannan |
AIH14834 |
3170 |
|
Bhringraj |
Eclipta alba |
Wedelolactone |
AIH14839 |
6728 |
|
Pigeon pea, Arhar |
Cajanus cajan |
Cajanin |
AIH14846 |
6756 |
|
Green tea |
Camellia sinensis |
Catechin |
AIH14853 |
5388 |
|
Henna leaf |
Lawsonia inermis |
Gallic acid |
AIH14842 |
2290 |
|
Malabur nut |
Justicia adhatoda |
Vasicinone |
AIH14863 |
6132 |
|
Ginger |
Zingiber officinale |
Zingerone |
AIH14839 |
3368 |
Table 2 shows the best result found from molecular docking of the active compounds against the proteins sequence which were collected from Gene Bank carried out using Patch DOCK. The best result was seen in Bhui-amla (Lignans) with the highest docking score of 6944. A table of docking profile of Bhui-amla against all the protein sequences is given in Table 3.
Table 3. Docking Profile of Lignans (Bhui-amla) against Studied Proteins
|
Sl No. |
Protein Id |
Receptor |
Score |
Area |
Ace |
Transformation |
|
1. |
AIH14833 |
302746 |
5648 |
739.80 |
-32.25 |
-2.53 -0.19 0.51 2.61 -9.79 36.36 |
|
2. |
AIH14834 |
302747 |
5530 |
703.00 |
-210.62 |
2.43 -0.00 -3.07 15.51 -5.29 -6.45 |
|
3. |
AIH14835 |
302748 |
5146 |
578.70 |
-123.17 |
1.69 -0.09 -2.36 21.35 8.25 4.24 |
|
4. |
AIH14836 |
302749 |
5562 |
709.00 |
-113.13 |
1.61 0.96 0.81 -11.85 6.03 3.89 |
|
5. |
AIH14837 |
302752 |
6108 |
825.90 |
-260.98 |
1.31 0.71 -3.01 12.88 1.16 -9.33 |
|
6. |
AIH14838 |
302753 |
6944 |
831.50 |
-114.26 |
-1.99 -0.31 -1.46 -6.10 9.99 2.38 |
|
7. |
AIH14839 |
302754 |
6340 |
884.60 |
-333.88 |
1.63 0.30 1.22 0.59 -4.97 20.89 |
|
8. |
AIH14840 |
302810 |
5186 |
744.10 |
-74.80 |
-0.36 -0.46 -1.76 -7.58 12.69 9.78 |
|
9. |
AIH14841 |
303094 |
5820 |
759.60 |
-45.67 |
-2.54 1.39 -1.38 -3.86 10.31 11.80 |
|
10. |
AIH14842 |
303095 |
6260 |
838.30 |
-251.68 |
1.67 1.29 -2.98 10.70 -19.85 6.95 |
|
11. |
AIH14843 |
303096 |
6236 |
766.60 |
-171.93 |
-1.12 -0.40 -2.98 2.93 1.19 11.87 |
|
12. |
AIH14844 |
303097 |
5360 |
695.80 |
-148.29 |
0.21 1.34 -2.27 2.43 -9.77 1.37 |
|
13. |
AIH14845 |
303098 |
5614 |
836.40 |
-240.54 |
0.72 0.41 -1.83 13.59 12.30 29.08 |
|
14. |
AIH14846 |
303100 |
6034 |
793.40 |
-152.09 |
-0.53 -0.79 -3.11 14.23 8.76 9.75 |
|
15. |
AIH14847 |
303101 |
4840 |
570.80 |
-168.84 |
0.62 0.79 0.65 12.37 7.51 0.43 |
|
16. |
AIH14848 |
303102 |
5564 |
689.20 |
-188.51 |
2.96 -0.71 -0.79 8.17 9.54 43.99 |
|
17. |
AIH14849 |
303106 |
5624 |
746.40 |
-243.42 |
1.41 0.80 -1.23 5.66 11.82 34.67 |
|
18. |
AIH14850 |
303107 |
5210 |
670.80 |
-180.86 |
0.65 0.59 -0.64 -6.50 21.27 -0.89 |
|
19. |
AIH14851 |
303108 |
4958 |
652.90 |
-51.76 |
0.35 0.62 -1.71 6.08 -15.28 -0.49 |
|
20. |
AIH14852 |
303110 |
5544 |
654.50 |
-121.53 |
2.24 1.25 -0.14 -10.61 21.53 23.16 |
|
21. |
AIH14853 |
303153 |
5598 |
639.00 |
-158.99 |
-1.88 1.27 -0.07 -6.88 -21.86 9.13 |
|
22. |
AIH14854 |
303157 |
6196 |
800.20 |
-229.59 |
-1.45 1.25 2.91 -0.43 7.45 27.75 |
|
23. |
AIH14855 |
303160 |
4726 |
546.40 |
-202.31 |
2.90 -0.40 2.00 0.73 -3.79 38.21 |
|
24. |
AIH14856 |
303163 |
5602 |
738.10 |
-229.32 |
0.21 -1.30 -0.64 -11.11 16.05 8.81 |
|
25. |
AIH14857 |
303166 |
5090 |
653.10 |
-202.47 |
-2.31 0.06 2.94 22.03 5.18 20.97 |
|
26. |
AIH14858 |
303167 |
6626 |
846.50 |
-233.11 |
-1.88 -0.85 -1.38 -5.79 14.16 20.40 |
|
27. |
AIH14859 |
303168 |
5636 |
741.80 |
-176.59 |
-0.76 -0.35 1.99 30.32 -6.17 23.72 |
|
28. |
AIH14860 |
303169 |
5314 |
701.60 |
-2.43 |
-1.09 0.23 0.84 9.69 -16.01 16.19 |
|
29. |
AIH14861 |
303170 |
5504 |
683.00 |
-186.13 |
2.75 -0.78 0.97 3.92 7.25 10.50 |
|
30. |
AIH14862 |
303172 |
4748 |
560.90 |
-44.75 |
-0.41 0.16 -1.06 -6.56 13.97 3.68 |
|
31. |
AIH14863 |
303173 |
6240 |
755.00 |
-232.47 |
0.03 0.04 -1.59 8.30 9.47 6.44 |
|
32. |
AIH14864 |
303174 |
5028 |
656.70 |
14.03 |
1.55 1.18 -0.09 0.77 12.54 10.46 |
Without close homolog’s in the Protein Data Bank (PDB), raptorx is a protein structure prediction server developed by the Xu group, which excels at predicting 3D structures for protein sequences[4]. Raptorx helps in the structural prediction of the protein sequences which is been retrieved from Gene Bank under accession number KJ879461-92. There are 32 sequences present and a single representative structure has been represented in Figure 1.
|
|
|
Figure 1. A representative protein structure found by RaptorX (AIH14833) |
The Ramachandran plotswere constructed for each protein sequence. For a clear view, one of the Ramachandran plots has been displayed in Figure 2.
|
|
|
Figure 2. Ramachandran Plot for a given protein sequence(AIH14838) |
The figure shows that the red regions correspond with conformations. These are the allowed regions, where there are no steric clashes like the alpha-helical and beta-sheet conformations. The yellow areas show the allowed regions if slightly shorter Van der Waals radii are used in the calculation, i.e. the atoms are allowed to come a little closer together. This brings out an additional region that corresponds to the left-handed alpha-helix[5].
RasMol is a computer program written for molecular graphics visualization. It is mainly used to explore and depict biological macromolecule structures, such as those found in the Protein Data Bank[6]. RASMOL was used to visualize all the results as well as all the structure of all the protein sequences.
Table 4. Result of Lipinski Rule for All the Active Compounds
|
Compound |
Mass |
HBD |
HBA |
cLOGP |
Molar Refractivity |
|
Glycyrrhizin |
912.000000 |
0 |
0 |
0.000000 |
0.000000 |
|
Lignans |
912.000000 |
0 |
0 |
0.000000 |
0.000000 |
|
Piperine |
342.000000 |
0 |
3 |
9.319986 |
139.000488 |
|
Galactomannan |
576.000000 |
0 |
0 |
0.000000 |
0.000000 |
|
Wedelolactone |
364.000000 |
10 |
7 |
6.725702 |
116.919746 |
|
Cajanin |
364.000000 |
7 |
6 |
8.785706 |
135.272186 |
|
Catechin |
342.000000 |
5 |
6 |
6.780993 |
123.389862 |
|
Gallic acid |
202.000000 |
4 |
5 |
3.909029 |
68.664085 |
|
Vasicinone |
242.000000 |
9 |
2 |
3.086290 |
82.836876 |
|
Zingerone |
230.000000 |
4 |
3 |
5.821792 |
93.607079 |
Drug-like and non-drug like molecules can be differentiated by Lipinski Rule of 5. It predicts a high probability of success or failure due to drug-likeness for molecules[7]. Lipinski Rule was carried out for all the active compounds. The results are given in Table 4.
Prot Param is a tool that computes the various chemical and physical parameters for a given protein sequence which is given by the user. The parameters which are computed include the extinction coefficient, atomic composition, theoretical PI, molecular weight, amino acid composition, have estimated half-life, instability index, aliphatic index, and grand average of hydropathicity (Table 5)[8].
Table 5. Shows composition of different elements in the studied sequence (physical and chemical parameters for a given protein sequences)
|
Results of ADMET OF LIGNANS (Phyllanthus niruri) |
|
|
ID |
Value |
|
BBB |
0.0100522 |
|
Buffer_solubility_mg_L |
18.8721 |
|
Caco2 |
26.8396 |
|
CYP_2C19_inhibition |
Inhibitor |
|
CYP_2C9_inhibition |
Inhibitor |
|
CYP_2D6_inhibition |
Non |
|
CYP_2D6_substrate |
Non |
|
CYP_3A4_inhibition |
Inhibitor |
|
CYP_3A4_substrate |
Substrate |
|
HIA |
97.099394 |
|
MDCK |
14.3364 |
|
Pgp_inhibition |
Inhibitor |
|
Plasma_Protein_Binding |
76.666659 |
|
Pure_water_solubility_mg_L |
9.94881 |
|
Skin_Permeability |
-4.24331 |
|
SKlogD_value |
1.740200 |
|
SKlogP_value |
1.740200 |
|
SKlogS_buffer |
-4.341610 |
|
SKlogS_pure |
-4.619660 |
ADME was carried out for each active compound to check their skin permeability, buffer solubility, plasma protein binding, and pure water solubility, etc [9].
Rasmol is a computer program for molecular graphics visualization. It is mainly used to explore and depict biological macromolecule structures, such as those found in the Protein Data Bank[10]. RASMOL was used to visualize all the results as well as all the structures of all the active compounds and protein sequences.
This method which has been adopted in the current study has been tried and tested for identifying active compounds for the treatment of jaundice. A study has got similarity with a study carried out by Xia et al., 2011 where that target protein was HEV ORF2 protein. The procedure in their study mainly focuses on homology modeling and molecular docking. Also, the calculation of the binding domain and details of energy involved along with the configuration of hydrogen bonds are similar to our study (Xing et al., 2011).
In a study, carried out by You et al., 2014 that they predicted the epitope of 8H3 on E2S by epitope prediction software based on the combined approaches of ZDOCK. The study was to check a specific epitope of HEV E2S (You et al., 2014).
In a study carried out by Xing et al., 2011, where molecular docking of the HEV VLP crystal structure have shown that fab 224 covered three surface loops of the recombinant second open reading frame protein. Also determined the structure of a chimeric HEV VLP (Xing et al., 2011).
In a study, carried out by Quintero-Gil C et al., 2017 that they derived six antiviral peptides from the sequences of porcine Beta-Defensin-2 and bacteriocins Nisin and also generated Subtilosin by using in silico tools to propose new antiviral agents. And also, interactions between the HEV capsid protein and the six new antiviral peptide candidates were evaluated by molecular docking. (Quintero-Gil et al., 2017)
Conclusion
The selected active compounds have hepatoprotective activities of traditional medicinal plants of North-East India for treating jaundice. The present study shows that the docking score and energy associated shows the efficient activity of the studied compounds against HEV protein. Selected proteins were having 108: 171: 35: 36: 1 as the ratio for C, H, N, O, and S. All the selected proteins were rich in Leu (L), Arg (R), Ser(S), Pro (P), Glu (E). Ramachandran Plot refers to the β-sheet of the selected HEV protein. ADME results showed favorable results for the selected active compounds. Toxicity prediction of the selected active compounds was showing antitoxic and hepatoprotective properties. Therefore, in this study, we mainly tried to focus on the traditional herbs present in NE, India. The purpose of the study is to figure out the medicinal properties of the natural products present in the environment. The in silico analysis of these traditional herbs can be also useful. However, the current study finding needs to be validated by AUTODOCK results for carrying out animal model studies and human clinical trials.
Acknowledgments: The authors acknowledge the infrastructure support received from Assam down town University.
Conflict of interest: None
Financial support: None
Ethics statement: None[1] http://www.scfbio-iitd.res.in/software/drugdesign/lipinski.jsp.
[4] www.bioinfo3d.cs.tau.ac.il
[6] www.mordred.bioc.cam.ac.uk
[7] https://sourceforge.net/projects/openrasmol/
[8] http://www.scfbio-iitd.res.in/software/drugdesign/lipinski.jsp
[9] www.web.expasy.org
[10] www.mordred.bioc.cam.ac.uk
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