Globally, snakes are the most dreaded venomous animals since they induce morbidity and mortality greatly. Snakebite envenomation induces local tissue damage including myonecrosis and inflammation. Pathogenesis induced by snakebite is multifactorial and complex. Many studies reported the association of clinical symptoms with the biochemical variation of venom composition (20). Snake venom has evolved into a wide range of proteins and peptides that induce neurotoxic, hemotoxic, cytotoxic, inflammatory effects, etc. If not treated envenomation results in severe morbidity and even death. The main protein classes in snake venom causing a severe pathological effect in victims are three-finger toxins (3FTXs), phospholipase A2 (PLA A2), snake venom serine protease (SVSP), and snake venom metalloproteinase (SVMP) (21). Previously we reported the interaction of A.paniculata phytochemicals with PLA A2 of Russell’s viper. Phytochemicals had a great affinity towards the residues responsible for the myotoxic and enzymatic activity of PLA A2 (22).
Next to PLA A2, the predominant SVMP becomes the potent target for small pharmaceutical biomolecules. Apart from the hemorrhagic activity of snake venom metalloproteinase other activities namely fibro(ogen)olytic, prothrombin activation, activation of blood coagulation factor X, apoptosis, inhibition of platelet aggregation, pro-inflammatory and blood serine proteinase inhibitor inactivation are also attributed to SVMPs (23). All these factors have envisioned SVMP as a potential target.
SVMP (PDB ID: 2E3X) selected for the study represents class P-IIId, a heterotrimeric class of SVMP, which has MDC (M - metalloproteinase, D - Disintegrin-like, C - Cysteine-rich) domain along with snaclec domain. It is made up of a heavy chain and two light chains namely LA and LB. Cys133, present in the C-terminal of the light chain, bonds (disulfide bond) with Cys389 present in the heavy chain’s hyper variable region (HVR) (24). Zinc and calcium are involved in the catalytic activity and structural stabilization of SVMP respectively (25). Russell's viper metalloproteinase is a strong activator of the blood coagulating factor X (FX) (26). During the physiological coagulation, FX is activated upon R194-I195 bond cleavage by the factors IXa or VIIa resulting in the removal of the 52 residues at the N-terminal of the FX heavy chain which is heavily glycosylated. This results in the formation of a catalytic triad (the active site in this case) of the serine proteinase domain. Thus, the activated FX (FXa), in turn, facilitates the conversion of prothrombin to thrombin which eventually forms a hemostatic plug (27-29). Hence, SVMP is seen as one of the crucial proteins mediating the venomous activity of snake venom.
In cellular processes, the interaction of the protein with other molecules is needed for performing their biological function. Hence knowledge of interacting/functional sites would help us to develop inhibitors for receptor proteins. Therefore, the primary prerequisite step for protein-ligand docking studies is the identification of ligand binding sites Here the binding sites of SVMP are predicted using the metal pocket tool.
MetaPocket analyzes and combines the results obtained from eight predictor tools namely POCASA, LIGSITECS (LCS), Fpocket (FPK), GHECOM (GHE), Q-SiteFinder, ConCavity (CON), SURFNET, and PASS (PAS) to improve the prediction success rate. Metapocket combines' z-score of all 8 predicted autonomous tools which run simultaneously to produce a total z-score. Based on spatial similarity the pocket sites are determined and finally, all the clusters are arranged based on the total z-score as predicted by metapocket. The results are presented in the form of a table highlighting the functional residues which are found in the vicinity of the pocket sites (13). The results are summarized in Table 1, which shows the binding site at the third pocket (C3), with x, y, and z grid points 45.169, 39.898, and -4.220 of the pocket and its total z-score 1.72. Pockets that are ranked first were used for further molecular docking study.
Autodock 4.2 was used to study the binding affinity of various ligands towards SVMP at the molecular level. A two-dimensional structure representing the interaction of A.paniculata phytochemicals and batimastat with SVMP is presented in Fig3. Binding energy, inhibition constant, interaction, and interacting amino acids are tabulated in Table 2.
Binding energy is better defined as the aggregate of the torsional free energy and the intermolecular energy of the compounds. In simple terms, the energy released during bond formation or the protein-ligand interaction can be defined as binding energy. However, for any favorable reaction to occur the free energy should be negative. The lower the binding energy, the greater is the protein-ligand binding. The binding energy of 14-acetylandrographolide with SVMP is higher than -9.32kCal/mol whereas the least binding phytochemical is bisandrographolide with -4.09kCal/mol.
Inhibition constant is the required concentration of inhibitor to produce half-maximum inhibition. Autodock calculates Ki as follows:
Where ΔG is the docking energy,
Rcal is 1.987
TK is 298.15
Docking energy is the summation of the ligand’s internal energy and the intermolecular energy (30).
Surprisingly, seven phytochemicals of A.paniculata showed higher binding energy than the known SVMP inhibitor batimastat. 14-acetylandrographolide, 14-deoxy-11,12didehydroandrographolide, andrograpanin, Isoandrographolide 14-deoxyandrographolide, Andropanolide, and Andrographolide are the seven phytochemicals of A.paniculata. In the present study, all the A. paniculata phytochemicals were found to interact with the M domain of the SVMP heavy chain. Phytochemicals form a non-covalent bond with SVMP residues namely GLU14, ILE57, ARG85, MET90, LYS93, SER94, HIS95, ASP96, MET119, CYS120, GLN121, ALA122, LYS197, PRO198, LYS199, CYS200, PHE202, ASN203, PRO204, PRO205, LEU206, ASP209, ARG275, ASP276, ASP279, ARG293, ASP294, GLN295, LEU296, TYR311, ASN312, GLY313, ASP314, ASP398, and PRO399. Non-covalent interactions have a pivotal role in assessing the function, structure, and dynamics of the biomolecules. They are reversible and have favorable energy at room temperature. Non-covalent interactions are strong, sufficient to bind molecules together as well as weak enough to assemble and disassemble without the usage of much energy (31). Batimastat a known inhibitor of SVMP (32) formed a conventional hydrogen bond with only two amino acid residues PHE202 and ASN203. Whereas the phytochemicals of A. paniculata found to form conventional hydrogen bonds besides PHE202 and ASN203, are ARG293 and ASP294 of calcium-binding region owing to higher binding efficiency. Catalytic calcium-binding site residues interacting with the ligands are presented in Fig4. Eighteen out of twenty-two phytochemicals formed a conventional hydrogen bond with PHE202, ASN203, ARG293, and ASP294 amino acids except for Deoxyandrographolide, andrographidine C, andrographidine E, and Bisandrographolide. Hydrogen bonding between a druggable ligand and a protein receptor is strong and has high specificity (33). Three different effects rise from hydrogen bonds in ligand binding namely: (i) positioning of ligand by a binding partner, occasionally related to molecule conformational distortion, (ii) substrates recognition, and (iii) ligand affinity. However, they affect the molecule's physiochemical properties (34).
Protein structural flexibility plays a key role in biological function. Metalloproteinase and its complex flexibility with phytochemicals were simulated using Cab Flex 2.0, an online server. Structural simulation results show that the RMSF of amino acids was comparatively low for metalloproteinase-ligand complex than the metalloproteinase unbound state. 14-deoxy-11,12didehydroandrographolide, Andrograpanin, and 14-deoxy-11-oxoandrographolide had high fluctuation when compared with 14-acetylandrographolide, Isoandrographolide, Andropanolide, andrographolide and batimastat. Molecular dynamic simulations of the metalloproteinase-ligand complex are presented in Fig5. The change in the amino acids fluctuation at the active site metalloproteinase indicates binding of phytochemicals, which ultimately enhance the rigidity of amino acids at the protein active site.
SwissADME a web tool predicts ADME parameters and calculates physiochemical descriptors along with pharmacokinetics properties, drug-likeness, and medicinal chemistry of small molecules. In our current study, ligands were evaluated for drug-likeness properties. Results were presented as BOILED-Egg model, a graphical representation. The graphical interface of BOILED-Egg can predict HIA (passive human gastrointestinal absorption), BBB (Blood-brain barrier) permeation by calculating lipophilicity (WLOGP) versus polarity (TPSA) (17). In our current study, fourteen phytochemicals showed HIA property and seven phytochemicals showed BBB permeation property. The BIOLED-Egg model for A.paniculata phytochemicals was shown in Fig6.
Structurally unrelated drugs are actively transported (efflux) out of the cell against the concentration gradient by P-glycoprotein (P-gp), membrane-bound transporter) resulting in intracellular concentration reduction, in-turn it affects the drug's oral bioavailability (35). BOILED-Egg model additionally indicated that among twenty-two phytochemicals, fifteen would function as P-gp + (P-gp substrate). The Drug-likeness property of A.paniculata phytochemicals is shown in Table 3. 14-acetylandrographolide and Andrograpanin shows P-gp+ and HIA property whereas 14-deoxy-11,12didehydroandrographolide show BBB permeation along with P-gp+ and HIA properties. The results shows the potential use of A.paniculata phytochemicals as drug, and the results also suggest ADME parameters guided structural alterations can be made to make versatile drug.
Ligand-based virtual screening (LBVS) done using SwissSimilarity to find similar analogs from FDA-approved drugs for phytochemicals LBVS works based on the hypothesis “Similar molecules are prone to exhibit similar biological activity”. It relies on molecular similarity quantification (36). Phytochemicals were analogous to drugs having antibiotic, anti-inflammatory, antihypertensive, antihypotensive, anti-arrhythmic, anticancer, antidiabetic, antilipidemic, anti-viral, anti-retroviral, anti-malarial, anti-fungal activity with similarity score ranging from 0.002 to 0.887. Ouabain, an anti-arrhythmic drug showed a high similarity score with Andropanoside. Eplerenone, an anti-hypertensive drug analogous to Paniculide A with a high similarity score of 0.513. A histogram presenting the phytochemical similarity score against the FDA approved drug is represented as Fig7. However, screening for 14-deoxy-11,12 didehydroandrographolide did not show any similar analogs. SwissSimilarity results with analogs score tabulated as Table 4.
Putative targets of A.paniculata phytochemicals were identified through SwissTargetPrediction, a web tool used to find the possible target for small molecules (37). In our current study, we found each phytochemicals would interact and target multiple proteins. This might be the polypharmacology effect of phytochemicals. The phytochemicals which targeted different classes of metalloproteinase were selected and a phytochemical-target illustration network was created using Cytoscape, a software used for visualizing network and integration biomolecular interaction (38). The phytochemical-target network results are presented in Fig8.