Molecular Docking Studies on Various Food Grade Dyes as a Potential Inhibitor of COVID-19 ^†

Abstract

In December 2019, the Coronavirus disease-2019 (COVID-19) virus emerged in Wuhan, China. The first resolved COVID-19 crystal structure (main protease) has been developed and various repurposing activities are in process. In this study, a knowledge gap in relation to COVID-19, with the previously known fatal Coronavirus (CoV) epidemics, Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) CoVs, is covered by the investigation of sequence statistics, molecular modelling, virtual screening, docking, and sequence comparison statistics of the COVID-19 main protease. COVID-19 main protease Mpro formed a sequence similarity group with SARS CoV that was distant from MERS CoV. The identity % was 96 and 51 for COVID-19/SARS and COVID-19/MERS CoV sequence comparisons, respectively. We used molecular docking and a molecular interaction approach to identify small-molecules that bind to the isolated Viral S-protein at its host receptor region. These molecules have good solubility and pharmacodynamics properties. They also obey Lipinski’s rule, which makes them promising compounds to pursue further biochemical and cell-based assays to explore their potential for use against COVID-19. We hypothesize that the top score identified molecules that may be used to limit viral recognition of host cells and/or disrupt host-virus interactions. A ranked list of selected compounds is given that can be tested experimentally.

1. Introduction

On the penultimate day of 2019, health officials at the Wuhan Municipal Health Commission (Hubei Province, Wuhan, China) reported an occurrence of concentrated pneumonia in the city of Wuhan. Shortly after reporting the outbreak, the Chinese Centre for Disease Control (Chinese CDC) and local Chinese health workers determined that the cause of the outbreak was a novel coronavirus, i.e., nCov-2019 [1,2,3]. On 11 March 2020, WHO declared it as a pandemic. The symptoms of Coronavirus disease-2019 (COVID-19) infection are mild respiratory symptoms and a fever that occurs on an average of 5–6 days after infection (mean incubation period 5–6 days, range 1–14 days) [4,5,6]. The current treatment options are use of antivirals and antimalarials. The first available crystal structure of COVID-19 proteins was Mpro, which was published in February 2020 (Protien data bank (PDB ID) 6lu7). In this study, the first virtual screening study against the first known COVID-19 was performed. The obtained results will help in identifying some potential inhibitors to combat the recent dangerous COVID-19. We propose to use food grade dyes that could acts as a treatment option in case of COVID-19 patients. We have used computational methods, e.g., molecular docking, to evaluate the activity as well as the interactions.

2. Materials and Methods

2.1. Retrieval of Mpro Sequences

The NCBI GenBank or GISAID (Available Online: https://www.gisaid.org/ (accessed on 10 October 2020)) were used to obtain the COVID-19 sequences. SARS Coronavirus (CoV) and MERS CoV sequences were obtained from the GenBank [7,8].

2.2. Sequence Alignment and Multiple Sequence Comparisons

Pairwise and multiple sequence comparisons of Mpro were done using CLC genomics software (Qiagen Inc., USA). The sequence comparison matrix was generated, including the number of gaps, number of different residues, and identity %.

Sequences alignments of Mpro were from SARS CoV, MERS CoV, and COVID-19.

(A) Pairwise with dots for identities sequence alignments of Coronavirus disease-2019 (COVID-19) and SARS Coronaviruses (CoVs).

Identities 294/306 (96%)

SARS Mpro 2AMD     SGFRKMAFPSGKVEGCMVQVTCGTTTLNGLWLDDTVYCPRHVICTAEDMLNPNYEDLLIR 65

COVID-19 Mpro YP_009725301 ...................................................................................................V........................S.................................... 60

SARS Mpro 2AMD      KSNHSFLVQAGNVQLRVIGHSMQNCLLRLKVDTSNPKTPKYKFVRIQPGQTFSVLACYNG    125

COVID-19 Mpro YP_009725301........N............................................................................V.K.........A........................................................... 120

SARS Mpro 2AMD      SPSGVYQCAMRPNHTIKGSFLNGSCGSVGFNIDYDCVSFCYMHHMELPTGVHAGTDLEGK  185

COVID-19 Mpro YP_009725301..............................................................F.................................................................................................N 180

SARS Mpro 2AMD      FYGPFVDRQTAQAAGTDTTITLNVLAWLYAAVINGDRWFLNRFTTTLNDFNLVAMKYNYE 245

COVID-19 Mpro YP_009725301...........................................................................V....................................................................................... 240

SARS Mpro 2AMD      PLTQDHVDILGPLSAQTGIAVLDMCAALKELLQNGMNGRTILGSTILEDEFTPFDVVRQC   305

COVID-19 Mpro YP_009725301..............................................................S.................................................AL............................................. 300

SARS Mpro 2AMD    SGVTFQ 311

COVID-19 Mpro YP_009725301 ...... 306

(B) Pairwise with dots for identities sequence comparison of COVID-19 and MERS CoVs.

Identities 157/310 (51%)

MERS Mpro 5C3N      SGLVKMSHPSGDVEACMVQVTCGSMTLNGLWLDNTVWCPRHVMCPADQLSDPNYDALLIS 60

COVID-19 Mpro YP_009725301.........FR........AF...........K........G...............TT..............DV.......Y............I.......TSEDMLN.........ED..........R 60

MERS Mpro 5C3N      MTNHSFSVQKHIGAPANLRVVGHAMQGTLLKLTVDVANPSTPAYTFTTVKPGAAFSVLAC  120

COVID-19 Mpro YP_009725301 KS.......N......L.......---AGNVQ........I.......S.......NCV........K.......T........K.......K......K......VRIQ.......QT.....  117

MERS Mpro 5C3N      YNGRPTGTFTVVMRPNYTIKGSFLCGSCGSVGYTKEGSVINFCYMHQMELANGTHTGSAF  180

COVID-19 Mpro YP_009725301...........S.......S........VYQCA...........F............N............FNIDYDCVS..........H........PT......V.......A.......TDL 177

MERS Mpro 5C3N      DGTMYGAFMDKQVHQVQLTDKYCSVNVVAWLYAAILNGCAWFVKPNRTSVVSFNEWALAN 240

COVID-19 Mpro YP_009725301 E.......NF........P.......V....R....TA....AAG.....TTIT......L............VI.....DR.....LNRFT....TLND.....LV....MKY  237

MERS Mpro 5C3N      QFTEFVGTQSVDM---LAVKTGVAIEQLLYAIQQLY-TGFQGKQILGSTMLEDEFTPEDV     296

COVID-19 Mpro YP_009725301 NY-.....PLTQDH......ILGP.....SAQ......I.....VLDMCASLKE.....LQN.....MN.....RT...........AL..............F.. 296

MERS Mpro 5C3N      NMQIMGVVMQ 306

COVID-19 mpro YP_009725301 VR.CS..TF.     306

2.3. Docking

The structure of COVID-19 virus Mpro in complex with N3 provides a model for identifying lead inhibitors to target COVID-19 virus Mpro through in silico screening. We used a molecular docking approach to predict the binding energy and inhibition constants of various food grade dyes under study [9,10]. We docked our ligands into the main protease of COVID-19 and screened them for their activity against COVID-19.

2.4. Predictive ADME Studies

Predictive Absorption, Distribution, Metabolism, Excretion (ADME) studies were performed by using SWISS tools*, an online tool that requires the structure or the SMILES for calculating the parameters.

The test compounds were built within the window by using the drawing tools of the online server, otherwise SMILES could be directly copied instead of drawing the structures [11]. To assure a drug-like pharmacokinetic profile in rational drug designing, predictive ADME calculations are done on the basis of Lipinski’s rule of five.

2.5. Toxicity

The toxicity of the molecules were predicted by using Toxtree [12], a free offline tool available for the prediction of toxicity. It requires the SMILES format of structures to calculate the toxicity.

The SMILES format of the compounds were pasted in the chemical identifier bar, and then their toxicity was estimated on the basis of creamer rules. The compounds were categorized into three classes, i.e., Low (Class I), Intermediate (Class II), and High (Class III).

3. Results and Discussions

3.1. Docking

The PDB ID of protein used was 6LU7, which was retrieved from the protein data bank. The validation of the model that was performed redocked the internal ligand/inhibitor into the active site of the macromolecule. The individual ligands were then prepared in Auto Dock 4.2.6 software, as per standard protocols, and docking was carried out. The results are listed below

Table 1. List of ligands with binding energy and inhibition constants.

S. No.	Ligands	1st Run		2nd Run		3rd Run
		Binding Energy	Inhibition Constant	Binding Energy	Inhibition Constant	Binding Energy	Inhibition Constant
1	DG01	−10.35	26.12 nM	−9.99	47.43 nM	−9.91	54.73 nM
2	DG02	−9.52	104.45 nM	−9.07	225.6 nM	−8.99	259.33 nM
3	DG03	−9.43	121.71 nM	−9.29	154.77 nM	−9.28	158.05 nM
4	DG04	−9.1	214.18 nM	−8.98	261.41 nM	−8.66	447.14 nM
5	DG05	−9.00	251.81 nM	−8.89	305.47	−8.87	314.38 nM
6	DG06	−8.86	322.93 nM	−8.63	472.32 nM	−8.63	475.09 nM
7	DG07	−8.53	555.76 nM	−8.53	561.87 nM	−8.52	571.48 nM
8	DG08	−7.97	1.44 μM	−7.6	2.67 uM	−7.11	6.1 uM
9	DG09	−7.86	1.73 μM	−7.72	2.2 uM	−7.63	2.54 uM
10	DG10	−7.81	1.87 μM	−7.81	1.87 uM	−7.80	1.92 uM
11	DG11	−7.42	3.63 μM	−7.33	4.24 uM	−7.28	4.6 uM
12	DG12	−7.35	4.12 μM	−6.33	22.87 uM	−6.30	24.27 uM
13	DG13	−7.34	4.14 μM	−7.28	4.62 uM	−7.32	4.32 uM
14	DG14	−6.14	31.82 μM	−6.13	31.97 uM	−6.12	32.46 uM
15	DG15	−6.24	26.75 μM	−4.79	307.68 uM	−5.78	58.44 uM

and Figure 1.

3.2. Predictive ADME Studies

Analysis of all the compounds was done for the physicochemically and pharmacokinetically important descriptors using SWISS tools. In order to predict the drug-alike properties of molecules, these major descriptors were required:

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Molecular weight (mol MW) (150–650)

⮚

Octanol/water partition coefficient (Log Po/w) (−2–6.5)

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Hydrogen bond donor (≤5)

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Hydrogen bond acceptor (≤10)

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Human oral absorption percentage (≥80% is high, ≤25% is poor)

The entire set of compounds showed appreciable values for the properties analyzed, as well as exhibited drug-like aspects based on Lipinski’s rule of five. The results are summarized in

Table 2. SWISS ADME for compounds DG01-15.

Compounds	DG01	DG02	DG03	DG04	DG05	DG06	DG07	DG08	DG09	DG10	DG11	DG12	DG13	DG14	DG15
Properties
MW	546.53	538.53	835.89	537.43	496.38	458.46	273.29	561.69	539.4	314.25	468.42	408.41	422.39	495.39	538.41
HBA	11	11	5	12	12	9	3	7	14	7	12	9	8	12	13
HBD	3	4	2	8	8	3	0	3	9	4	3	3	4	8	7
MR	138.93	123.99	139.61	131.61	120.15	113.81	79.7	149.36	128.28	77.74	109.69	96.31	101.04	121.7	129.89
TPSA	208.86	229.71	75.99	238.99	230.12	170.45	47.03	−1.14	273.21	132.13	220.19	170.45	183.7	235.91	236.19
LOG Po/w	1.54	1.37	5.23	−1.25	−1.25	2.8	2.05	2.94	−4	0	0.32	2.02	−0.18	−0.71	−0.31
Solubility (mg/mL)	1.13 × 10−2	6.97 × 10−2	4.44 × 10−7	6.15 × 10−3	7.05 × 10−3	4.58 × 10−3	6.63 × 10−3	4.22 × 10−6	3.51 × 10−1	4.22 × 10−2	5.74 × 10−1	5.59 × 10−2	1.63 × 10−1	5.74 × 10−4	2.90 × 10−3
G.I absorption	Low	Low	High	Low	Low	Low	High	Low	Low	High	Low	Low	Low	Low	Low
BBB Permeant	No	No	No	No	No	No	Yes	No	No	No	No	No	No	No	No
CYP1A2	No	No	Yes	No	Yes	No	Yes	No	No	No	No	No	No	Yes	Yes
CYP2D6	No	No	No	No	No	No	No	No	No	No	No	No	No	No	No
Veber	No	Yes	Yes	No	No	No	Yes	No	No	Yes	No	No	No	No	No
Lipinski	No	No	No	No	No	Yes	Yes	No	No	Yes	No	Yes	Yes	No	No
Bioavailability Score	0.11	0.11	0.17	0.11	0.11	0.11	0.55	0.11	0.11	0.56	0.11	0.11	0.11	0.11	0.11

Molecular weight (MW), hydrogen bond acceptor (HBA), hydrogen bond donor (HBD), total polar surface area (TPSA), octanol/water partition coefficient (Log Po/w), aqueous solubility (Log S), molar refractivity (MR), Cytochrome P450 1A2 (CYP1A2), Cytochrome P450 2D6 (CYP2D6).

.

3.3. Toxicity

Toxicity prediction of the compounds is necessary before further development. The toxicity is predicted by using Craemer rules. It categorizes the compounds into the classes, i.e., Low (Class I), Intermediate (Class II), and High (Class III), depending on its toxicity index. The categories are based on different thresholds of toxicological concern, as follows:

⮚

Class I—1800 (30 µg/kg bw/d)

⮚

Class II—540 (9 µg/kg bw/d)

⮚

Class III—90 (1.5 µg/kg bw/d)

The results are summarized in

Table 3. Toxicity of the compounds DG01-15.

Compounds	Toxicity Class
DG01	High Class III
DG02	Low Class I
DG03	High Class III
DG04	High Class III
DG05	High Class III
DG06	Low Class I
DG07	High Class III
DG08	Low Class I
DG09	High Class III
DG10	High Class III
DG11	Low Class I
DG12	Low Class I
DG13	Low Class I
DG14	High Class III
DG15	High Class III

.

From the ADME studies, it was found that only a few compounds followed all the parameters for being a suitable drug candidate, but all the other compounds violated the parameters by a few factors, which, on further modifications, can be modified to promising drug candidates. The toxicity studies suggest that the therapeutic range of some compounds is very narrow, whereas some have wide therapeutic ranges, and these can be modified as per the purpose. The modifications required can be taken as a future perspective to develop these compounds as promising drug candidates.

4. Conclusions

Researchers are now focusing mainly on synthetic protease inhibitors, but natural compounds have always been found to be better than their synthetic counterparts. As natural chemists, we tried to focus on untouched natural drugs that could provide better drug therapies in the future. As per our study, the sequence identity % was 96 and 51 for COVID-19/SARS and COVID-19/MERS CoV, respectively. Docking studies revealed that Orange B (−10.35 kcal/mol) and Cochineal Red A (−9.52 kcal/mol) had the best binding affinity with the receptor. They had low GI absorption but showed no BLOOD BRAIN BARRIER (BBB) permeation activity. They obeyed the Lipinski rule and bioavailability score was 0.11 and showed drug-like aspects. Cochineal Red A was classified under Low Class I toxicity. Erythrosine, Laccaic Acid A, Laccaic Acid B, Azorubine, and Quinoline yellow also had a comparable binding affinity. These two molecules/compounds proved to be a good inhibitor against the COVID-19 main protease. Further MD simulation studies can be performed to mimic the interaction of the molecules with the receptor. These molecules can further be studied for their in vitro and in vivo activity. This work may be able to pave a new path for the development of potential drugs using food grade dyes and for the selection of compounds, as well as designing new scaffolds or novel combinatorial libraries of analogs/derivatives; however, before coming to any outcome of an in-silico study, proper in-vitro and in-vivo research works should be performed.