{"id":58555,"date":"2024-06-25T11:22:25","date_gmt":"2024-06-25T11:22:25","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=58555"},"modified":"2024-07-03T17:06:57","modified_gmt":"2024-07-03T17:06:57","slug":"computational-investigation-of-bioactive-phytoconstituents-as-sars-cov-2-main-protease-inhibitors-through-molecular-docking-and-interaction-fingerprint-studies","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol17no2\/computational-investigation-of-bioactive-phytoconstituents-as-sars-cov-2-main-protease-inhibitors-through-molecular-docking-and-interaction-fingerprint-studies\/","title":{"rendered":"Computational Investigation of Bioactive Phytoconstituents as Sars-Cov-2 Main Protease Inhibitors Through Molecular Docking and Interaction Fingerprint Studies"},"content":{"rendered":"\n

Introduction<\/strong><\/p>\n\n\n\n

The\nCOVID-19 pandemic caused by SARS-CoV-2 (severe acute respiratory\nsyndrome-coronavirus-2) is a worldwide health emergency. Since last two decades, the two main members of the\nCoronaviridae<\/em> family that periodically cause pneumonia and respiratory syndromes, SARS-CoV and MERS-CoV, have drawn attention worldwide. SARS-CoV-2 belongs to Coronavirinae<\/em>\nsubfamily of Coronaviridae<\/em> and is a very significant and dreadful virus 1-4<\/sup>. According to the World\nHealth Organization’s global situation update, as of 10 Jan 2023, there have been 660,131,952 COVID-19\nconfirmed cases, including 6,690,473 deaths, reported to WHO 5<\/sup>.\nThese findings demonstrate that the rise of this viral contagious disease,\nwhich now holds a significant position in global incidence of transmissible\ndiseases, is continuing in developing nations. Despite all the improvements in\nconventional and contemporary medicine, many attempts to control this pandemic\nhad negative health effects on people. As a result, conventional medicine has\nbecome more interested in providing healthcare services. Moreover, at least 25%\nof all modern medications are thought to be derived directly or indirectly from\ntraditional medicines, primarily through the integration of cutting-edge\ntechnologies with age-old knowledge. A wide range\nof natural extracts and phytoconstituents have been investigated for their\nability to act as drug-like molecules against the SARS-CoV-2 proteases and\nfound to possess good inhibitory activities 6-10<\/sup>. Although few drugs like remdesivir\ngained urgent approval, search for more safer & efficient treatment is\nstill required 11, 12<\/sup>. <\/p>\n\n\n\n

Mpro, papain-like\nprotease, RdRp (RNA dependent RNA polymerase) are few of the SARS-CoV-2\ndruggable targets that have been identified. The\npapain-like protease is capable of recognizing ubiquitin’s C-terminal region.\nSo, papain-like\nprotease inhibitors would also inhibit deubiquitinases\nof host cell, which would make drug-discovery against papain-like protease\ncomplicated. In contradistinction, the main protease particularly cleaves\npolypeptide sequence after glutamine. Enzymes like RdRp cannot completely operate without\nprevious proteolytic release, thus making Mpro<\/sup> as a main enzyme in\nvirus replication cycle. The SARS-CoV-2 Mpro<\/sup> is a cysteine protease,\nthat shares 96% amino acid identity with SARS-CoV Mpro 13-19<\/sup>. Mpro<\/sup>\nforms a homodimer that consists of 306 amino acid residues in each monomer.\nEach monomer consists of three domains: Domain I (8\u2013 101 residues), domain II\n(102\u2013184 residues) and Domain III (201\u2013306 residues). Domain I & II were\nprimarily made up of antiparallel \u03b2-barrel whereas domain III was made up of\n\u03b1-helices. Domains II & III are connected by loop of 15 residues (residues\n185\u2013 200). The protomers attach to one another through an N-terminal finger\n(residues 1-7) that forms a substrate-binding site in a cleft between domains I\nand II and connects domains II & III. Four pockets (S1′, S1, S2 & S3)\nmake up the substrate-binding site of Mpro<\/sup>, with the S10 pocket\nconsisting of a catalytic dyad. This catalytic dyad is composed of Cys145 &\nHis41and placed in a gap between domains I & II. Domain III promotes the\nformation of the dimer, by salt-bridge interaction of Glu290 form one monomer\nwith Arg4 of the other. Dimerization is\nessential for enzyme\u2019s catalytic activity, as the N-finger of each monomer\ninteracts with Glu166 of the other to shape the S1 pocket of substrate-binding\nsite. The N-finger\nis compressed between\ndomains II & III of the parent protomer and domain II of the other in order to reach the interaction site. Mpro\n(nsp5) auto cleaves between non-structural protein 4 (nsp 4) & nsp6, and\nthen cleaves polyproteins 1a & 1ab at 11 specific sites; with a unique\ncleavage specificity Leu-Gln\u2193 (Ser, Ala, Gly), to generate 12 mature nsp and\nprobably, functional intermediates for viral RNA replication and transcription 20-25<\/sup>.\n<\/p>\n\n\n\n

Molecular\ndocking is important method in structural molecular biology and computer-aided\ndrug discovery that predicts the predominant interactions between a ligand and\nprotein with a known three-dimensional structure 26<\/sup>. Accurate ADME\nproperty predictions can prevent the needless testing of compounds that will\nultimately fail before costly experimental processes, thus allowing for\ninformed decisions about a molecule’s suitability. In contrast to\nfragment-based techniques, QikProp can predict attributes for both molecules\nwith new scaffolds and analogues of well-known medications with comparable\naccuracy. QikProp bases its predictions on the whole 3D molecular structure 27<\/sup>.\n<\/p>\n\n\n\n

Hence, in order to explore potent Mpro<\/sup> inhibitors, molecular docking study was performed for\nvarious phytoconstituents from different antiviral medicinal plants against\nSARS-CoV-2 Mpro<\/sup> and the phytoconstituents pharmacological\ndescriptors and ADME properties were also predicted. In addition, interaction\nfingerprint was also generated for best docked phytoconstituents and SARS-CoV-2\nMpro<\/sup> complexes and compared with that of reference crystal ligand to\nidentify any similarities in binding interactions.<\/p>\n\n\n\n

Materials and Methods<\/strong><\/p>\n\n\n\n

Protein preparation <\/strong><\/p>\n\n\n\n

A total of 602 crystal structures of SARS-CoV-2 main protease bound with various inhibitors are currently uploaded to the Protein Data Bank (PDB) database, containing 439 crystal ligands. Among the available structures of SARS-CoV-2 Mpro<\/sup>, the three-dimensional structure of SARS-CoV-2 main protease in a covalent complex with a pyridine derivative of ABT-957, compound 1 (PDB: 7AEH) with resolution 1.30 A0<\/sup>; was considered in this study to propose novel SARS-CoV-2 Mpro<\/sup> inhibitors through molecular docking studies (https:\/\/www.rcsb.org\/). <\/p>\n\n\n\n

Target protein structure\nwas imported to Maestro v11.1 (Schr\u00f6dinger LLC, 2019) (Sun Microsystems,\nSchrodinger, New York, USA) workstation running on CentOS 6. Protein\npreparation tasks were performed with the protein preparation wizard and\nprepared before docking to add hydrogen atoms,\nadjustment of protonation states for ionizable molecules, formal charge and\nbond order correction, expelling atomic clashes, modification of tautomeric\nforms and repositioning of reorientable hydrogens and other operations which\nwere not part of X-ray crystal structure refinement process. At neutral pH, the\nprotein structure minimization was done using the OPLS-2005 force field, by\nconverging the heavy atoms to RMSD of 0.3A0<\/sup>. Based on the already\nbound inhibitor in the crystal structure, the binding site on the receptor\nmolecule was identified and a grid box of 10A0<\/sup> \u00d710 A0<\/sup> \u00d7\n10 A0<\/sup> was generated around the substrate binding site residues of Mpro <\/sup>using Glide v7.1., residues\nwere cross validated using PDBsum 28<\/sup>. <\/p>\n\n\n\n

Identification of best crystal ligand through molecular docking studies<\/strong><\/p>\n\n\n\n

All the 439 crystal ligands of covid-19 Mpro<\/sup> crystal\nstructures which were available in PDB, were collected and prepared using\nligprep module of schrodinger. All the prepared and optimized ligands were\nsubjected to molecular docking studies with selected target by Glide XP docking\n28<\/sup>.<\/p>\n\n\n\n

Collection and preparation of phytoconstituents <\/strong><\/p>\n\n\n\n

A total of 274 phytoconstituents inhibitors, known for\ntheir antiviral activity were sourced from literature and were extracted from\nPubChem and DrugBank v5.0 databases 29-176<\/sup>.\nDrugBank v5.0, a specialized bioinformatics and drug cheminformatics resource, provided\ncomprehensive data encompassing chemical, pharmacological, and pharmaceutical\ndetails for the identified compounds. Subsequently, the structures of the\nselected phytoconstituents were meticulously prepared, involving the creation\nof three-dimensional geometries, assignment of proper bond orders, and the\ngeneration of accessible tautomer and ionization states. This preparatory phase\nwas executed using the LigPrep module. For molecular docking studies, the\nSchr\u00f6dinger Epik module was employed in conjunction with LigPrep to ensure a\nrobust analysis of the interactions between the phytoconstituents and their\ntarget molecules 177<\/sup>.<\/p>\n\n\n\n

Molecular\ndocking of phytoconstituents with Mpro<\/sup> of SARS-CoV-2<\/strong><\/p>\n\n\n\n

The\nbinding affinities of selected phytoconstituents with SAS-CoV-2 Mpro<\/sup>\nwere analyzed by performing molecular docking studies for identification of the\nbest phytoconstituent with good binding affinity. The prepared\nphytoconstituents were docked into 7AEH. The binding affinity between the\ntarget and phytoconstituents was studied using the grid-based ligand docking\nwith energetic (GLIDE) XP (extra precision) docking technique. Then the\nprepared phytoconstituents were docked into the grid utilizing Monte Carlo-based\nsimulated algorithm minimization approach. Glide Scores (Gscore) and Molecular\nMechanics-Generalized Born Surface Area (MM-GBSA) were used to analyze\ncalculations for the binding free energies, affinities, orientation, and\nranking of the protein ligand complex utilizing the Prime module\nof Schrodinger suite 2021-2 that incorporates the OPLS3 force field and VSGB\ndissolvable model to look through calculations. Ten poses were created for each ligand\nduring XP docking, and the best pose was preserved after post-docking\nminimization 178<\/sup>.<\/p>\n\n\n\n

Prediction of pharmacological descriptors and ADME properties<\/strong><\/p>\n\n\n\n

Selected phytoconstituents were subjected to\nQikProp module of Schr\u00f6dinger suite to predict pharmacological, ADME\nproperties. Additionally, SASA and other related values were also predicted by\nSchr\u00f6dinger suite 179, 180<\/sup>.<\/p>\n\n\n\n

Generation of Interaction fingerprints for\nbest docked phytoconstituents and best crystal ligand<\/strong><\/p>\n\n\n\n

Docking interactions of best docked\nphytoconstituents were further analyzed using interaction fingerprint analysis\nto observe if they shared any similarities with the best co-crystal ligand’s\ninteractions. For the best docked compounds and co-crystal ligand docked\ncomplexes, an interaction fingerprint was created that translates the\nthree-dimensional structural binding information from a protein-ligand complex\ninto a one-dimensional binary string. Each fingerprint reflects \u201cstructural\ninteraction profile\u201d of complex that can be utilized to organize, analyze, and visualize\nthe extensive amount of information encoded in ligand receptor complexes. Value\n1 indicates that the specified interaction is established, while 0 indicates\nthat there is no such interaction 180<\/sup>.<\/p>\n\n\n\n

Results and Discussion<\/strong><\/p>\n\n\n\n

Protein preparation <\/strong><\/p>\n\n\n\n

The three-dimensional co-crystal structure of SARS-Cov-2 Mpro<\/sup> (PDB I\u2019D: 7AEH) was retrieved from PDB and prepared. The substrate-binding site residues were specified within the 4 \u00c5 region of co-crystal ligand using PDBsum. The substrate-binding pocket of Mpro<\/sup> complex comprises residues such as Thr 25, Thr 26, His 41, Phe 140, Leu 141, Asn 142, Gly 143, Ser 144, Cys 145 His 163, His 164, Glu 166, Gln 189 and Thr 190 within the 4 \u00c5 region around the crystal ligand 181<\/sup>.<\/p>\n\n\n\n

Molecular docking study for the identification of best co-crystal ligand of Mpro<\/sup><\/strong><\/p>\n\n\n\n

All the prepared crystal ligands were docked with SARS-Cov-2Mpro<\/sup> and results were represented in Table 1<\/strong>. Among all the crystal ligands, HF0 exhibited good docking score -7.872 Kcal\/Mol and selected as reference crystal ligand.<\/p>\n\n\n\n

Table 1: List of best docked crystal ligands of SARS-CoV-2 and docking scores with SARS-CoV-2 Mpro <\/sup>(PDB: 7AEH)<\/strong>.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n
\n

S.No.<\/strong><\/p>\n<\/td>\n

\n

Crystal ligand Code<\/strong><\/p>\n<\/td>\n

\n

Crystal ligand Name<\/strong><\/p>\n<\/td>\n

\n

PDB I\u2019D<\/strong><\/p>\n<\/td>\n

\n

Docking score<\/strong><\/p>\n<\/td>\n

\n

Interacting Amino acids<\/strong><\/p>\n

 <\/strong><\/p>\n<\/td>\n

\n

N<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

XP G Score<\/strong><\/p>\n<\/td>\n

\n

MMGBSA<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

1.<\/p>\n<\/td>\n

\n

HF0<\/strong><\/p>\n<\/td>\n

\n

7-O-methyl-dihydromyricetin<\/p>\n

<\/p>\n<\/td>\n

\n

7DPV<\/p>\n<\/td>\n

\n

-7.872<\/p>\n<\/td>\n

\n

-67.5446<\/p>\n<\/td>\n

\n

Hie 164, Gln 189<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n<\/tr>\n

\n

2.<\/p>\n<\/td>\n

\n

MYC<\/strong><\/p>\n<\/td>\n

\n

Myricetin<\/p>\n<\/td>\n

\n

7B3E<\/p>\n<\/td>\n

\n

-7.812<\/p>\n<\/td>\n

\n

-60.0807<\/p>\n<\/td>\n

\n

Hie 164, Glu 166, Gln 189<\/p>\n<\/td>\n

\n

3<\/p>\n<\/td>\n<\/tr>\n

\n

3.<\/p>\n<\/td>\n

\n

HER<\/p>\n<\/td>\n

\n

7-O-methyl-myricetin<\/p>\n<\/td>\n

\n

7DPU<\/p>\n<\/td>\n

\n

-7.576<\/p>\n<\/td>\n

\n

-67.1016<\/p>\n<\/td>\n

\n

Hie 164, Gln 189<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n<\/tr>\n

\n

4.<\/p>\n<\/td>\n

\n

RVW<\/p>\n<\/td>\n

\n

(2~{S},3~{R},4~{R},5~{S},6~{S})-2-(hydroxymethyl)-6-sulfanyl-oxane-3,4,5-triol<\/p>\n<\/td>\n

\n

7ARF<\/p>\n<\/td>\n

\n

-6.512<\/p>\n<\/td>\n

\n

-45.2359<\/p>\n<\/td>\n

\n

Glu 166, Thr 190<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n<\/tr>\n

\n

5.<\/p>\n<\/td>\n

\n

93J<\/p>\n<\/td>\n

\n

Pelitinib<\/p>\n<\/td>\n

\n

7AXM<\/p>\n<\/td>\n

\n

-6.423<\/p>\n<\/td>\n

\n

-81.9294<\/p>\n<\/td>\n

\n

Glu 166<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

*N: No. of interactions<\/span><\/p>\n\n\n

Ligand preparation<\/strong><\/p>\n\n\n\n

The structures of selected\nphytoconstituents were preparedprior to molecular docking using Ligprep module of the Schrodinger. The preparation was conducted at a pH of 7.0 \u00b1 2, employing the OPLS_3\nforcefield, and involved the enhancement of protonation states and\nconsideration of ligand stereochemical nature. Energy minimization was\nperformed as part of the ligand preparation process.  <\/p>\n\n\n\n

Molecular Docking<\/strong><\/p>\n\n\n\n

Molecular docking study was performed for selected phytoconstituents against SARS-CoV-2 Mpro<\/sup> to identify potent inhibitors. All the selected phytoconstituents and crystal ligands were docked into the substrate-binding site of Mpro<\/sup> and binding energies were represented in Table 2. Furthermore, protein-ligand binding energies revealed that seven phytoconstituents strongly bind to substrate binding site of main protease with more binding affinity than best co-crystal ligand HF0. Among all the selected phytoconstituents, 3,4-dicaffeoylquinic acid showed better XP G score -9.721 Kcal\/Mol than best co-crystal ligand HF0 (-7.872kcal\/Mol). It was clearly observed thatall the best docked phytoconstituents and best crystal ligand HF0 showed at least two H-bond interactions with substrate binding site. <\/p>\n\n\n\n

Table 2:<\/strong> Docking scores of best docked phytoconstituents and best crystal-ligand HF0 against SARS-CoV-2 Mpro<\/sup><\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

S. No.<\/strong><\/p>\n<\/td>\n

\n

Phytoconstituents<\/strong><\/p>\n<\/td>\n

\n

Docking scores with Mpro <\/sup>(Kcal\/Mol)<\/strong><\/p>\n<\/td>\n

\n

Interacting Amino Acids <\/strong><\/p>\n<\/td>\n

\n

N<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

XP G Score <\/strong><\/p>\n<\/td>\n

\n

MMGBSA<\/strong> (<\/strong>\u2206G)<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

1.<\/p>\n<\/td>\n

\n

3,4-dicaffeoylquinic acid<\/p>\n<\/td>\n

\n

-9.721<\/p>\n<\/td>\n

\n

-77.4562<\/p>\n<\/td>\n

\n

Glu 166, Gln 189, Thr 190, Ala 191<\/p>\n<\/td>\n

\n

5<\/p>\n<\/td>\n<\/tr>\n

\n

2.<\/p>\n<\/td>\n

\n

Epigallocatechingallate<\/p>\n<\/td>\n

\n

-8.925<\/p>\n<\/td>\n

\n

-73.8888<\/p>\n<\/td>\n

\n

Asn 142, Hie 164, Glu 166, Gln 189, Thr 190<\/p>\n<\/td>\n

\n

6<\/p>\n<\/td>\n<\/tr>\n

\n

3.<\/p>\n<\/td>\n

\n

Isomangiferin<\/p>\n<\/td>\n

\n

-8.747<\/p>\n<\/td>\n

\n

-55.4927<\/p>\n<\/td>\n

\n

Ser 46, Asn 142, Hie 164, Glu 166, Gln 189<\/p>\n<\/td>\n

\n

6<\/p>\n<\/td>\n<\/tr>\n

\n

4.<\/p>\n<\/td>\n

\n

Rosemarinic acid<\/p>\n<\/td>\n

\n

-8.477<\/p>\n<\/td>\n

\n

-60.0924<\/p>\n<\/td>\n

\n

Glu 166, Gln 189<\/p>\n<\/td>\n

\n

3<\/p>\n<\/td>\n<\/tr>\n

\n

5.<\/p>\n<\/td>\n

\n

Rutin<\/p>\n<\/td>\n

\n

-8.405<\/p>\n<\/td>\n

\n

-52.1528<\/p>\n<\/td>\n

\n

Hie 164, Glu 166, Pro 168<\/p>\n<\/td>\n

\n

4<\/p>\n<\/td>\n<\/tr>\n

\n

6.<\/p>\n<\/td>\n

\n

Gnetupendin<\/p>\n<\/td>\n

\n

-7.99<\/p>\n<\/td>\n

\n

-61.1349<\/p>\n<\/td>\n

\n

Glu 166, Thr 190<\/p>\n<\/td>\n

\n

3<\/p>\n<\/td>\n<\/tr>\n

\n

7.<\/p>\n<\/td>\n

\n

Amarogentin<\/p>\n<\/td>\n

\n

-7.989<\/p>\n<\/td>\n

\n

-73.8429<\/p>\n<\/td>\n

\n

Asn 142, Glu 166, Gln 189, Thr 190<\/p>\n<\/td>\n

\n

7<\/p>\n<\/td>\n<\/tr>\n

\n

8.<\/p>\n<\/td>\n

\n

HF0 (Best crystal ligand)<\/p>\n<\/td>\n

\n

-7.872<\/p>\n<\/td>\n

\n

-67.5446<\/p>\n<\/td>\n

\n

Hie 164, Gln 189<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

*N: No. of interactions<\/span><\/p>\n\n\n

Prediction of Pharmacological descriptors and ADME properties<\/strong><\/p>\n\n\n\n

Absorption, distribution, metabolism, excretion, and toxicity (ADMET) characteristics were estimated using qikprop module to measure the phytoconstituents’ potential as therapeutics. Various properties like mol. weight; volume; globularity descriptor; hydrophobic component of SASA; total solvent accessible surface area; weakly polar component of SASA; \u03c0 (carbon and attached hydrogen) component of SASA; ionization potential; H-Bond acceptors & donors; Log Po\/w; brain\/blood partition coefficient; human serum albumin binding; No. of violations of Lipinski\u2019s rule of five; No. of violations of Jorgensen\u2019s rule of three; Predicted skin permeability etc. These results indicated that pharmacological descriptors of the most of phytoconstituents were found to be with in the acceptable range for 95% of known drugs (Table 3 & 4).<\/p>\n\n\n\n

Table 3: Pharmacological descriptors of best docked phytoconstituents and best crystal ligand HF0<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Entry Name<\/strong><\/p>\n<\/td>\n

\n

MW<\/strong><\/p>\n<\/td>\n

\n

volume<\/strong><\/p>\n<\/td>\n

\n

glob<\/strong><\/p>\n<\/td>\n

\n

SASA<\/strong><\/p>\n<\/td>\n

\n

WPSA<\/strong><\/p>\n<\/td>\n

\n

PISA<\/strong><\/p>\n<\/td>\n

\n

FOSA<\/strong><\/p>\n<\/td>\n

\n

IP(eV)<\/strong><\/p>\n<\/td>\n

\n

HBD<\/strong><\/p>\n<\/td>\n

\n

HBA<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

3,4-dicaffeoylquinic acid<\/strong><\/p>\n<\/td>\n

\n

516.4<\/p>\n<\/td>\n

\n

1516.8<\/p>\n<\/td>\n

\n

0.74617<\/p>\n<\/td>\n

\n

855.6<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

292.77<\/p>\n<\/td>\n

\n

133.5<\/p>\n<\/td>\n

\n

9.08<\/p>\n<\/td>\n

\n

7<\/p>\n<\/td>\n

\n

11.45<\/p>\n<\/td>\n<\/tr>\n

\n

Epigallocatechin gallate<\/strong><\/p>\n<\/td>\n

\n

458.3<\/p>\n<\/td>\n

\n

1255.9<\/p>\n<\/td>\n

\n

0.80723<\/p>\n<\/td>\n

\n

697.3<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

223.98<\/p>\n<\/td>\n

\n

50.6<\/p>\n<\/td>\n

\n

9.02<\/p>\n<\/td>\n

\n

8<\/p>\n<\/td>\n

\n

8.75<\/p>\n<\/td>\n<\/tr>\n

\n

Isomangiferin<\/strong><\/p>\n<\/td>\n

\n

422.3<\/p>\n<\/td>\n

\n

1134.0<\/p>\n<\/td>\n

\n

0.83403<\/p>\n<\/td>\n

\n

630.5<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

147.25<\/p>\n<\/td>\n

\n

97.6<\/p>\n<\/td>\n

\n

8.74<\/p>\n<\/td>\n

\n

7<\/p>\n<\/td>\n

\n

13<\/p>\n<\/td>\n<\/tr>\n

\n

Rosemarinic acid<\/strong><\/p>\n<\/td>\n

\n

360.3<\/p>\n<\/td>\n

\n

1106.8<\/p>\n<\/td>\n

\n

0.80032<\/p>\n<\/td>\n

\n

646.5<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

251.26<\/p>\n<\/td>\n

\n

61.8<\/p>\n<\/td>\n

\n

8.82<\/p>\n<\/td>\n

\n

5<\/p>\n<\/td>\n

\n

7<\/p>\n<\/td>\n<\/tr>\n

\n

Rutin<\/strong><\/p>\n<\/td>\n

\n

610.5<\/p>\n<\/td>\n

\n

1583.3<\/p>\n<\/td>\n

\n

0.79977<\/p>\n<\/td>\n

\n

821.4<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

196.71<\/p>\n<\/td>\n

\n

219.2<\/p>\n<\/td>\n

\n

9.19<\/p>\n<\/td>\n

\n

9<\/p>\n<\/td>\n

\n

20.5<\/p>\n<\/td>\n<\/tr>\n

\n

Gnetupendin B<\/strong><\/p>\n<\/td>\n

\n

380.3<\/p>\n<\/td>\n

\n

1169.5<\/p>\n<\/td>\n

\n

0.81961<\/p>\n<\/td>\n

\n

655.0<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

267.79<\/p>\n<\/td>\n

\n

138.4<\/p>\n<\/td>\n

\n

8.57<\/p>\n<\/td>\n

\n

5<\/p>\n<\/td>\n

\n

4.5<\/p>\n<\/td>\n<\/tr>\n

\n

Amarogentin<\/strong><\/p>\n<\/td>\n

\n

586.5<\/p>\n<\/td>\n

\n

1558.2<\/p>\n<\/td>\n

\n

0.83405<\/p>\n<\/td>\n

\n

779.3<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

259.95<\/p>\n<\/td>\n

\n

218.0<\/p>\n<\/td>\n

\n

9.18<\/p>\n<\/td>\n

\n

5<\/p>\n<\/td>\n

\n

16.4<\/p>\n<\/td>\n<\/tr>\n

\n

HF0<\/strong><\/p>\n<\/td>\n

\n

334.2<\/p>\n<\/td>\n

\n

953.3<\/p>\n<\/td>\n

\n

0.84958<\/p>\n<\/td>\n

\n

551.3<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

161.38<\/p>\n<\/td>\n

\n

118.6<\/p>\n<\/td>\n

\n

9.05<\/p>\n<\/td>\n

\n

4<\/p>\n<\/td>\n

\n

7.2<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

*<\/strong>MW: Mol. Weight (130-725); Volume (500-2000); glob: Globularity (0.75-0.95); SASA: Total solvent accessible surface area (300-1000); WPSA: Weakly polar component of SASA (0-175); PISA: \u03c0 (carbon and attached hydrogen) component of SASA (0-450); FOSA:Hydrophobic component of SASA (0-750) ; IP: Ionization potential; HBD: H-Bond donors (0-6); HBA: H-bond acceptors (2-20) 186<\/sup><\/span><\/p>\n\n\n

Table 4: ADME properties of best docked phytoconstituents and best crystal ligand HF0<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Entry Name<\/strong><\/p>\n<\/td>\n

\n

QP logPo\/w<\/strong><\/p>\n<\/td>\n

\n

QPlog BB<\/strong><\/p>\n<\/td>\n

\n

QPlog Khsa<\/strong><\/p>\n<\/td>\n

\n

Rule Of Five<\/strong><\/p>\n<\/td>\n

\n

Rule Of Three<\/strong><\/p>\n<\/td>\n

\n

QPlog Kp<\/strong><\/p>\n<\/td>\n

\n

QPlogHERG<\/strong><\/p>\n<\/td>\n

\n

QPPCaco<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

3,4-dicaffeoylquinic acid<\/strong><\/p>\n<\/td>\n

\n

0.973<\/p>\n<\/td>\n

\n

-5.481<\/p>\n<\/td>\n

\n

-0.598<\/p>\n<\/td>\n

\n

3<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

-6.96<\/p>\n<\/td>\n

\n

-5.228<\/p>\n<\/td>\n

\n

0.213<\/p>\n<\/td>\n<\/tr>\n

\n

Epigallocatechingallate<\/strong><\/p>\n<\/td>\n

\n

-0.261<\/p>\n<\/td>\n

\n

-4.364<\/p>\n<\/td>\n

\n

-0.441<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n

\n

-7.561<\/p>\n<\/td>\n

\n

-5.719<\/p>\n<\/td>\n

\n

0.971<\/p>\n<\/td>\n<\/tr>\n

\n

Isomangiferin<\/strong><\/p>\n<\/td>\n

\n

-1.814<\/p>\n<\/td>\n

\n

-3.676<\/p>\n<\/td>\n

\n

-0.877<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n

\n

-7.245<\/p>\n<\/td>\n

\n

-4.92<\/p>\n<\/td>\n

\n

2.179<\/p>\n<\/td>\n<\/tr>\n

\n

Rosemarinic acid<\/strong><\/p>\n<\/td>\n

\n

1.183<\/p>\n<\/td>\n

\n

-3.571<\/p>\n<\/td>\n

\n

-0.544<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

-5.724<\/p>\n<\/td>\n

\n

-4.154<\/p>\n<\/td>\n

\n

1.726<\/p>\n<\/td>\n<\/tr>\n

\n

Rutin<\/strong><\/p>\n<\/td>\n

\n

-2.329<\/p>\n<\/td>\n

\n

-4.567<\/p>\n<\/td>\n

\n

-1.327<\/p>\n<\/td>\n

\n

3<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n

\n

-6.86<\/p>\n<\/td>\n

\n

-5.71<\/p>\n<\/td>\n

\n

1.414<\/p>\n<\/td>\n<\/tr>\n

\n

Gnetupendin B<\/strong><\/p>\n<\/td>\n

\n

2.508<\/p>\n<\/td>\n

\n

-2.568<\/p>\n<\/td>\n

\n

0.085<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

-4.105<\/p>\n<\/td>\n

\n

-5.728<\/p>\n<\/td>\n

\n

43.341<\/p>\n<\/td>\n<\/tr>\n

\n

Amarogentin<\/strong><\/p>\n<\/td>\n

\n

0.263<\/p>\n<\/td>\n

\n

-3.108<\/p>\n<\/td>\n

\n

-0.687<\/p>\n<\/td>\n

\n

3<\/p>\n<\/td>\n

\n

2<\/p>\n<\/td>\n

\n

-4.909<\/p>\n<\/td>\n

\n

-5.457<\/p>\n<\/td>\n

\n

13.753<\/p>\n<\/td>\n<\/tr>\n

\n

HF0<\/strong><\/p>\n<\/td>\n

\n

0.278<\/p>\n<\/td>\n

\n

-2.304<\/p>\n<\/td>\n

\n

-0.402<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

1<\/p>\n<\/td>\n

\n

-5.376<\/p>\n<\/td>\n

\n

-4.735<\/p>\n<\/td>\n

\n

26.471<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

* <\/sup>QP logPo\/w: Octanol\/water partition coefficient (-2.0 to 6.5); QPlog BB: Brain\/blood partition coefficient (-3.0 to 1.2); QPlog Khsa: Binding to human serum albumin Prediction (-1.5 to 1.5); Rule Of Five: No. of violations of lipinski\u2019s rule (Max 4); Rule of three: No. of violations of Jorgensen\u2019s rule (Max 3); QPlog Kp:Predicted skin permeability (-8.0- -1) ; QPlog HERG: Predicted value of IC50 for blockage of HERG K+ channels (concern below -5); QPPCaco: Predicted apparent Caco-2 cell permeability (<25 poor, >500 great) mm\/sec  186<\/sup><\/p>\n\n\n

Generation of Interaction fingerprints for best docked phytoconstituents and best crystal ligand<\/strong><\/p>\n\n\n\n

To describe the 3D protein-ligand interactions of the best docked phytoconstituents with SARS-CoV-2 Mpro, a 9-bit interaction fingerprint was generated. Pharmacophore feature is represented by each bit of the fingerprint, which is denoted by the numbers 0 (absence of specified interaction) or 1 (presence of specified interaction) (Table 5). <\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Table 5: Interaction fingerprint for docked complexes of <\/strong>the best docked phytoconstituents and best co-crystal ligand HF0 <\/strong>with Mpro<\/sup> of SARS-CoV-2<\/strong><\/p>\n

<\/p>\n

Click here to view Table<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Interactions of best docked phytoconstituents were compared with that of best crystal ligand HF0. It was observed that all the best docked phytoconstituents binds to Mpro<\/sup> through at least one hydrogen (H)-bond with Glu 166 residue with better binding affinity than HF0 (Fig 1-7). In addition, 3,4-dicaffeoylquinic acid, epigallocatechingallate, isomangiferin, rosemarinic acid and gnetupendin B showed H-bond interactions with Gln 189 similar to that of best co-crystal ligand HF0. Docking results indicated that these H-bond interactions<\/em> with residues Glu166 and Gln189<\/em> are very important as any interaction with Glu166 can lead to inactive monomer formation that interferes with Mpro<\/sup> catalytic activity; and also, Glu166 anchor holds the ligand firmly to the central region of binding site, that facilitates the multiple interactions with remaining residues 182-184<\/sup>. Further, the H-bond interaction with Gln189 helps in inhibitor recognition through increasing S2 subsite plasticity 185<\/sup>. In addition to interaction with Glu 166 and Gln 189, 3,4-dicaffeoylquinic acid exhibited H-bond interactions with backbone residues of Thr 190 and Ala 191 (Fig 1). Epigallocatechingallate displayed H-bond interactions with backbone residues of Hie 164, Thr 190 and with side chain residues of Asn 142 (Fig 2). Isomangiferin showed H-bond interactions with the backbone residues of Hie 164; with side chain residues of Ser 46, Asn 142 (Fig 3). Rutin displayed H-bond interactions with backbone residues of Hie 164, Pro 168 (Fig 5). Gnetupendin B exhibited H-bond interaction with backbone residue of Thr 190 (Fig 6). Amarogentin showed H-bond interactions with side chain residues of Asn 142 and with backbone residue of Thr 190 (Fig 7). Best crystal ligand HF0 exhibited H-bond interactions with backbone residue of Hie 164; side chain residue of Gln 189 of Mpro<\/sup> (Figure 8). The results of the present study indicated that these best docked phytoconstituents can efficiently bind with key amino acid residues such as Glu 166, Gln 189 in the substrate binding site of SARS-CoV-2 Mpro<\/sup> with more binding affinity than the reference HF0, which can result in the formation of inactive monomer thus inhibiting the catalytic activity of main protease in virus replication. The best docked phytoconstituents also exhibited interactions with more than two amino acids in SARS-CoV-2 Mpro<\/sup> substrate binding site. However, in addition to aforementioned hits, experimental validation of computational studies by in vitro<\/em> and in vivo <\/em>methods is required to discover the therapeutic efficacy of 3,4-dicaffeoylquinic acid as novel SARS-CoV-2 Mpro<\/sup> inhibitor.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 1: Docking Interactions of 3,4-dicaffeoylquinic acid with <\/strong>SARS-CoV-2 Mpro<\/sup><\/strong><\/p>\n

\u00a0<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0<\/p>\n\n\n\n
\"\"<\/td>\n\n

Figure 2: Docking Interactions of Epigallocatechingallate with SARS-CoV-2 Mpro<\/sup><\/strong><\/p>\n

\u00a0<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0<\/p>\n\n\n\n
\"\"<\/td>\n\n

Figure 3: Docking Interactions of Isomangiferin with SARS-CoV-2 Mpro<\/sup><\/strong><\/p>\n

\u00a0<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0<\/p>\n\n\n\n
\"\"<\/td>\n\n

Figure 4: Docking Interactions of Rosemarinic acid with SARS-CoV-2 Mpro<\/sup><\/strong><\/p>\n

\u00a0<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0<\/p>\n\n\n\n
\"\"<\/td>\n\n

Figure 5: Docking Interactions of Rutin with SARS-CoV-2 Mpro<\/sup><\/strong><\/p>\n

\u00a0<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0<\/p>\n\n\n\n
\"\"<\/td>\n\n

Figure 6: Docking Interactions of Gnetupendin B with SARS-CoV-2 Mpro<\/sup><\/strong><\/p>\n

\u00a0<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0<\/p>\n\n\n\n
\"\"<\/td>\n\n

Figure 7: Docking Interactions of Amarogentin with SARS-CoV-2 Mpro<\/sup><\/strong><\/p>\n

\u00a0<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u00a0<\/p>\n\n\n\n
\"\"<\/td>\n\n

Figure 8: Docking interactions of best crystal ligand HF0 with SARS-CoV-2 Mpro<\/sup><\/strong><\/p>\n

\u00a0<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Conclusion<\/strong><\/p>\n\n\n\n

The SARS-CoV-2 Mpro<\/sup>\nis considered to be a potential drug target, because it differs from human\nproteases and plays important role in viral replication. Hence, the present\nstudy explored the inhibitory potentials of 274 antiviral phytoconstituents\nfrom medicinal plants against SARS-CoV-2 Mpro<\/sup>. Then, the best docked\ncrystal ligand HF0 (-7.872kcal\/Mol) was selected as reference among 439 crystal\nligands of Mpro<\/sup>. Among the phytoconstituents, 3,4-dicaffeoylquinic\nacid was found to show good binding affinity with XPG score -9.721 kcal\/Mol\nthan standard HF0. From ADMET properties prediction, it was found that most of\nthe phytoconstituents showed acceptable pharmacological properties. Interaction\nfingerprint analysis revealed that Glu 166 was present in seven best docked\nphytoconstituents, either as close contact or as participant in H-bond which\ndiminishes the catalytic activity of SARS-CoV-2 Mpro<\/sup> resulting in\ninhibition of viral replication. Thus the present study provided an insight\nabout possible mechanism of 3,4-dicaffeoylquinic acid in inhibition of\ncatalytic function of Mpro<\/sup> which would help in the further\ndevelopment of SARS-CoV-2 Mpro<\/sup> inhibitors. From above results, it\nhas been observed that the 3,4-dicaffeoylquinic acid has potential to act as\nnovel SARS-CoV-2 Mpro<\/sup> inhibitors. Further in vitro<\/em> studies will be\nrequired to understand the efficacy of 3,4-dicaffeoylquinic acid in inhibition\nof COVID-19 main protease.<\/p>\n\n\n\n

Acknowledgements\n<\/strong><\/p>\n\n\n\n

Authors\nare thankful to Department of\nBioinformatics, Sri Venkateswara Institute of Medical Sciences, Tirupati\nfor their support to perform the molecular docking studies.<\/p>\n\n\n\n

Conflict of Interest<\/strong> <\/p>\n\n\n\n

Authors declare no competing interests to\ndisclose.<\/p>\n\n\n\n

Funding Sources<\/strong><\/p>\n\n\n\n

There is no funding of Sources<\/p>\n\n\n\n

Ethical approval <\/strong><\/p>\n\n\n\n

Not applicable<\/p>\n\n\n\n

References<\/strong><\/p>\n\n\n\n

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