INTRODUCTION:

Active constituents obtained from plant often serve as starting point or as a lead for the drug discovery for a particular class of disease target. Piper nigrum is a very large genus of shrubs (rarely herbs and trees), belonging to the Piperaceae family. The species of the genus Piper are among the important medicinal plants used in various systems of medicine¹. Piperine (trans-trans isomer of 1-piperoyl piperidine) an alkaloid, has received enormous attention in the last two decades as a versatile bioactive molecule. The structure consists of three important components as: methylenedioxyphenyl ring, side chain with conjugated double bond and basic piperidine moiety attached through a carbonylamide linkage to the side chain^2,3. Piperine is found to be a bioenhancer⁵ produced antidepressant effect on mice⁶ and antidepressant and cognitive enhancing effect on wistar male rats⁷.

Piperine also produces Anti-inflammatory⁸, Antioxidant⁹, Anti-platelet¹⁰, Antithyroid⁹, Antitumor¹¹, Antiasthmatic¹², Antihypertensive¹³, Hepatoprotective¹⁴, Fertility Enhancer¹⁵, Antitubercular¹⁶, Anticonvulsant¹⁷, Anxiolytic^18,Anticancer¹⁹, Antileishmanial²⁰ and Antitrypanosomal²¹.

Monoamine oxidase (MAO) plays important role in several psychiatric and neurological disorders by catalysing the oxidative deamination of monoamine neurotransmitters such as serotonin, dopamine, and norepinephrine^22-23. Based on their amino acid sequence, substrate and inhibitor selectivity, and tissue distribution MAO has been divided into two subtypes MAO-A and MAO-B²⁴. MAO-A inhibitors are useful in the treatment of mental disorders as antidepressants, whereas MAO-B inhibitors are useful in the treating Parkinson’s and Alzheimer’s diseases²⁵. Studies done on piperine and its derivative have demonstrated that they have sedative-hypnotic, muscle-relaxing actions, tranquilizing and can intensify depressive action of other depressants²⁶. Hence it might be suggested that piperine could be useful or CNS related conditions. In present work, we have done the in-silico predictions of pieprine analogues as MOA-A and MOB-B inhibitors by using best QSAR model, and designed compounds were studied using molecular docking.

EXPERIMENTAL:

Materials and Methods:

MAO-A and MAO-B analogues were selected from literature^27-28 for QSAR and docking studies. All the structures had methylenedioxyphenyl ring with a conjugated side chain as same pharmacophore with variable substitutions which contributes difference in the observed Monoamine oxidase (MAO) inhibitory activity.

QSAR studies:

Piperine derivatives possessing MAO inhibitory activity (60) were searched from the literature^27-28 and divided into training set (42 compounds) and test set (18 compounds) respectively. The structures of the compounds were drawn and optimized by ChemDraw software. Eight hundred descriptors (Physico-chemical, Alignment Independent and Atom Type descriptors) were calculated using Dragon software. Multiple linear regression analysis method was used to identify the best model. Statistical parameters as r², q², S² and F were estimated for the regression equation to determine the quality of the data fit and the predictive capability for the model. Based on the QSAR results, we have designed seventy novel derivatives of piperine and predicted their activities with the help of best QSAR model.

Docking studies:

The protein 2BXR for MAO-A inhibitory activity and 2VZ2 for MAO-B inhibitory activity were selected from the PDB. The target sequences were retrieved from NCBI which is having Uniprot Id: P21397 and P27338 for MAO-A and MAO-B respectively. The sequence similarity was checked by Psi blast and templates were obtained by Phyre2 and analysed using Ramachandran plot. The Ramachandran plots for both the proteins were shown in Plot1 and 2. The modelled proteins 2BXR and 2VZ2 were validated using procheck and analysed. Using Chemdraw ultra ligands were generated and the 3D optimizations binding interaction of the ligands where performed according to the calculation method by Autodock4.2. Binding mode of ligand was determined by evaluating number of ligand conformations and their binding energy of interactions with target molecules. Ligand candidates with the best conformational and energetic results were selected. Molecular interaction studies between protein and ligands were performed. The possible binding sites of proteins were searched using CAST_P¹⁴. All the compounds were docked into the active site of receptor proteins. After completion of docking, the docked protein (protein-ligand complex) was analysed. Further the docking poses were saved for each compound.

Docking studies were performed to know the interactions between ligands and proteins also to investigate their binding mode. Gasteiger charges within active sites of amino acids were computed and the charges are spread across total residue of polar and non-polar hydrogen bonds. By selecting a flexible protein and rigid molecule to create grid map of size 40x40x40 from x, y and z-axis and the resultant compounds were used to compute molecular stimulation parameters by Lamarckian genetic algorithm. Each simulation was carried about 10 times which ultimately yielded 10 docked conformations. From this, the least energy conformation was regarded as the best binding conformation. The results were analysed based on clusters of RMSD and hydrogen bonding interactions. The conformational protein structure is modelled and visualized using Swiss PDB Viewer.

RESULTS AND DISCUSSION:

The 60 structures of MAO inhibitors were collected from literature and the data was divided into training set (48 compounds) and test set (12 compounds). Their descriptors were calculated and MLRA was used to generate QSAR models, which showed significantly good statistical results. The best equations for their activity and r², F, S² and q² values are given in Table 1. The best model for MAO-A inhibitors best model showed r² 0.7970 and q² 0.6657 and MAO-B inhibitors showed r² 0.8427 and q² 0.7852. The models generated were validated using test set (12 compounds). The correlation plot between calculated and experimental activities of test sets of MAO-A inhibitors & MAO-B inhibitors are given in Table 3&4. It is evident from the figures and tables that the QSAR models are quite robust and possess good predictive ability.

The results of QSAR model were used to designed 70 new piperine analogues as MAO inhibitors. The in silico activity was predicted by molecular docking studies using proteins 2BXR and 2VZ2 from PDB for MAO-A and MAO-B respectively. As depicted in Ramchandran plot (fig 2 & 34) 2BXR and 2VZ2 modelled structures were found to 98.4% residues in favoured region, 1.6%residues in allowed region and 0.0% residues in outlier region. These results are clear indicators of the very good quality of docking poses. The active sites of 2BXR comprises of amino acid residues GLY66, GLY110, ALA111, ARG129, ILE131, ALA174, PHE177, ASN181, VAL182, THR211, TYR264, CYS266, ILE273, ARG284, LEU287, ASP328, GLU329, ASP330, ALA331, ARG356, TYR407, ARG421, ILE423, TYR444, VAL473 and active ligand binding sites of 2VZ2 comprises of amino acid residues as ASN3, LYS4, GLY57, PRO102, ASN116, PHE118, ARG120, MET122, ASP132, LEU167, VAL173, THR202, ALA263, ILE264, LEU278, GLU303, ASP318, GLY319, GLU321, ALA322, TYR398, GLY411, ARG412, ARG415, GLN416, TYR435. The docking results provided information about the binding affinity, ligand-protein interaction and binding energy to inhibit selected proteins. Our hit molecules P11 and P12 (fig. 4 and 5 respectively) for MAO-A inhibitory activity showed binding energy of -5.89 kcal/mol. and -6.43 kcal/mol with the formation of five and four hydrogen bonds within the active site of protein respectively. Other compounds from the series P28, P32, P545, P605, P1088, P1117, P1118 formed three hydrogen bonds with varying binding energies. Compounds P1078 and P1090 (Fig. 6 and 7 respectively) showed binding energy of -5.86 kcal/mol. and -8.56 kcal/mol with five and four hydrogen bonds respectively for MAO-B inhibitory activity. Compounds P16, P1117, P1118, P1119 formed three hydrogen bonds within active site of amino acids with varying binding energies (table 5).

The results of docking studies suggest that the compounds P11 and P12 interact with the protein 2BXR (MAO- A inhibitor) through oxygen of 1,3-dioxol ring; two nitrogen presents in thiadiazole ring and mercapto group present in the system. The presence of an electron withdrawing group linked with mercapto is also helping in enhancement of binding affinity of the ligands with protein. Whereas in case of 2VZ2 (MAO-B Inhibitor) the best binding affinity of compound P1078 is because of presence of 1,3,4-oxadiazole which showing binding interaction through both nitrogen present in the ring system. The oxygen of 1,3-dioxol ring is also contributing in binding to amino acids presence in the binding cavity of protein. Compound P1090 showing good binding interaction by forming bonds from the carbonyl oxygen as well as from two oxygen of nitro group.

Molecular docking studies have shown that our designed compounds interacted with proteins with more binding affinity, strong interactions and lower binding energies. Molecular dynamics simulations could provide the stability of the bound complex at physiological pH, in docking studies to further strengthen our results. The encouraging results of our in silico activities can be used to design and develop these analogues further to get potent MAO inhibitors.

CONCLUSION:

Based on the results of our study we can say, that best model for MAO-A and MAO-B inhibitory activity were robust as the test set compounds showed their calculated activity very close to the experimental activity. These models were used for designing of our novel series of piperine analogues. Docking results have shown that, the presence of 1,3-dioxol nucleus in the structure along with a five-membered ring structure containing electron withdrawing groups attached to the nucleus leads to better binding of the molecules in the active site of 2BXR and 2VZ2.

Table 1: Best QSAR models of MAO-A and B inhibitors

Activity	Equation	R2	F	S2	Q2
MAO-A	A= 1.2765e3-1.1828e3ATS3e-2.1008e2MATS8v+1.3163e2 P2v -8.2458e1IVDE +1.8397e2E2e	0.7970	18.85	546.0668	0.6657
MAO-B	A= -1.3259e2+8.7067e1GATS8v+3.0122e2Mor30p -1.2142e1PHI -3.0391e1Mor15e +1.0334e2Mor23p	0.8427	25.71	467.6370	0.7852

Table 2: Calculated and experimental activities of test set MAO-A Inhibitors obtained from QSAR model (r²0.7975)

S. No.	Name	Structures	Calculated (MAO-AI)	Expt. Value (MAO-AI)	Difference
1	clorgiline		-18.7904	0.0004	-18.7908
2	selegiline		55.9299	58.9800	-3.0501
3	piperine		92.3608	100.0000	-7.6392
4	sb1		103.5741	100.0000	3.5741
5	sb2		93.9650	100.0000	-6.0350
6	sb3		101.2873	100.0000	1.2873
7	sb5		108.5015	100.0000	8.5015
8	sb6		102.3294	100.0000	2.3294
9	sb9		105.0685	100.0000	5.0685
10	sb10		24.7552	18.3400	6.4152
11	sb19		0.0684	7.5500	-7.4816
12	sb20		107.8701	100.0000	7.8701

Table 4: Calculated and experimental activities of test set MAO-B Inhibitors obtained from QSAR Model (r² 0.8427)

S. No.	Name	Structures	Calcd (MAO-BI)	Expt. value (MAO-BI)	Difference
1	clorgiline		5.2685	0.2600	5.0085
2	selegiline		7.2431	0.0139	7.2291
3	piperine		19.4780	0.4830	18.9950
4	sb5		101.7837	100.0000	1.7837
5	sb7		97.0459	100.0000	-2.9541
6	sb10		4.4541	13.5900	-9.1359
7	sb11		3.5453	0.0450	3.5003
8	sb12		-8.7889	3.3000	-5.4889
9	sb13		4.4257	0.0780	4.3477
10	sb15		-2.0909	1.5700	-3.6609
11	sb16		5.2555	0.7500	4.5055
12	sb19		-3.4266	0.3300	-3.7566

Fig1: Plot 1: Ramachandran plot of 2BXR

Fig 2: Plot 2: Ramachandran plot of 2VZ2

Fig. 3 Docked pose of Compound P11 with protein 2BXR amino acids

Fig. 4 Docked pose of Compound P12 with 2BXR amino acids

Fig. 5 Docked pose of Compound P1078 with 2VZ2 amino acids

Fig. 6: Docked pose of compound P1090 with 2VZ2 amino acids

ACKNOWLEDGEMENTS:

We are thankful to Dr CN Prashantha of Scientific Biominds, for helping us with docking studies.

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Received on 29.07.2021 Modified on 10.11.2021

Asian J. Res. Pharm. Sci. 2022; 12(2):115-122.

DOI: 10.52711/2231-5659.2022.00019

S. No.	Code	Structure	Binding Energy (kcal/mol)	H bonds	Interacting Amino acid
1.	P11		-6.43	4H	MET300, GLY404, LYS280
2.	P12		-5.89	5H	LYS280, MET300, TYR410, GLY404
3.	P1078		-5.86	5H	LYS190, SER131, ASP123, THR196
4.	P1090		-8.56	4H	TYR60, CYS172, SER59, TYR188