Molecular docking of substituted pyrazolone analogs targeting MAO-B as therapeutic site for Parkinson: an in-silico study

doi:10.21203/rs.3.rs-1437749/v1

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Molecular docking of substituted pyrazolone analogs targeting MAO-B as therapeutic site for Parkinson: an in-silico study

https://doi.org/10.21203/rs.3.rs-1437749/v1

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Parkinson’s disease is considered as common age-related progressive neuronal disorder characterized by motor, sensory, and cognitive dysfunction. Current medication for PD offers only symptomatic relief but no cure. Although it is considered that these medicines can produce acute or chronic side effects, and becomes less effective over a time. Owing to this concerns, the discovery of novel molecule is of considerable interest. Depletion of dopamine is one of the major cause of PD which is regulated by Monoamines. In this regards monoamine oxidase (MAO-B) inhibitors are of great interest to combat PD. Among several known molecules Pyrazolone has been evident for the MAO-B inhibitory potential. Therefore, in the present study MAO-B is selected as a key target to examine the interaction of Pyrazolone derivatives with crystal structure of MAO-B. For this purpose, Molecular docking, Quick prop module, and Mechanics/generalized born surface area (MM-GBSA) parameters are analyzed, to evaluate Molecular interaction, Pharmacokinetic parameters, and Binding energy. Further results show that compounds attached with chloride and nitrogen group have better binding efficiency, therefore they can be selected as a promising drug candidate for the treatment of PD.

In-silico

Parkinson’s disease

Pyrazolone

MAO-B inhibitors

Neurodegenerative diseases are characterized by progressive degeneration of neurons in the cerebral region. Several factors can lead to this degeneration such as ageing, environmental, and genetic factors [1–3]. NDDs are classified into Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, Prion like diseases, and amyotrophic lateral sclerosis [2, 4, 5]. Among which AD and PD are familial as well sporadic type[6]. prevalence of PD is expected to crossover AD if not treated [7–9].

PD is a motor disorder and its onset leads to degeneration of dopamine neurons at the substantia nigra pars compacta, and causes deficiency of a neurotransmitter dopamine. Symptoms like tremors, bradykinesia, akinesia, and postural instability are broad-spectrum [10–11]. The pathogenesis of PD is a complex interplay of oxidative stress and cellular signaling, which results in to mitochondrial damage, neuroinflammation, protein accumulation, autophagy, and so on [12–14]. Presently more than 10 million people are suffering from PD all over the world. Its prevalence is increasing among people of 60 years and above, which is expected to be doubled by 2040[15–16].

Despite decades of research, there is no absolute medication available to treat PD [17]. Current therapies for PD provide relief from the symptoms, although the unavoidable side effects and instability are documented after long-term use [10–12]. Therefore, it is necessary to develop a novel drug for better therapeutic activity to meet the clinical demand.

New drug development cost has exceeded 1 billion US dollar since 2000 and it is rising continuously [18–19]. Hence various artificial intelligence techniques are used in drug discovery to overcome the economic burden, which include target protein structure prediction, drug protein interaction, de novo drug design, and so on [20]. In that scenario in-silico approach or structure based virtual screening has become a potential technique in drug discovery [21, 22].

Drug repositioning is an alternative approach in the enhancement of efficiency of drugs. It identifies new clues for existing drugs, along with predicted drug safety profile [23, 24]. Among several molecules used in drug repositioning Edaravone is one which can be a potential scaffold for other NDDs. It show neuroprotective effect via antioxidant activity [25]. Hence applying repositioning technique, might help in discovery of novel Pyrazolone derivatives for other NDDs.

Mary LC Hare in 1928 discovered mono amino oxidase protein, which are family members of flavin adenine dinucleotide cofactor-dependent enzymes present on the mitochondrial membrane. MAO oxidize variety of amine substrates like small mono-amino molecules, polyamines, and modified protein amino acids [26]. Therefore, MAO plays an important role in modulating monoamine neurotransmitter levels in central and peripheral nervous systems. Dopamine is a neurotransmitter that is oxidized by MAO-B at substantia nigra of basal ganglia. In PD dopamine level in the SNPC region is reduced, and the level of dopamine can be maintained by MAO-B inhibitors. Pyrazolones are also potent inhibitors of the MAO enzyme family, and it is one of the therapeutic pathways used to treat PD, therefore Pyrazolones can be a promising scaffold for the treatment of PD [27, 28].

In this study, we hypothesized that MAO-B inhibition with Pyrazolone derivatives may result in ceasing PD symptoms and possibly it may cure PD. For that purpose, library of compounds was designed and virtually docked for binding efficiency, the ADME predictions were performed by Qikprop analysis, Molecular Mechanics /generalized born surface area analysis was carried out to identify the drug-receptor complex's, binding free energy, and five compounds were selected that fit on all parameters.

The identification of novel MAO-B inhibitors and the study of their inhibitory mechanism will be helpful for the development of possible MAO-B targeting Parkinson’s disease

Preparation of protein

The MAO-B enzyme with co-crystalline ligand, water molecule, metal ions, and cofactors (PDB ID: 2V5Z 1.60 A⁰) were yielded from the protein data bank. Protein preparation wizard of Schrodinger Suite 2019-2 used to establish protein target. Briefly H2O molecules > 5 A⁰, lacking H-bonds were removed. The Prime Module of the Schrodinger Suite 2019-2 was used to include lacking particles of amino acid sequence. All potential ionization states for the heteroatoms in the protein were generated, the state which include maximum degree of stability was picked. To control energy minimization of the polypeptides in order to rearrange the side-chain OH-groups and limit the chance of steric conflicts, Optimized Potentials for Liquid Simulations-3 (OPLS3) force field was utilized [22].

Receptor grid set to generation: -

The co-crystallized ligand was isolated from active site of designated protein. The atoms had a size of 1.0> Van der Waals radii and a restricted atomic charge of not more than 0.25 defaults. The active site is a cuboid that surrounds the workspace ligand's centroid. Following this procedure, a grid centred on the ligand was created by the default glide set up. All ligands were docked into generated grid structure [16].

Preparation of ligand

The structural arrangement of ligand was built by Chemsketch software subsequently the dataset was prepared by the LigPrep module of the Schrodinger suite 2019. To provide the best grades, the ligand that are docked must be appropriate depictions of the actual ligand structures as they would occur in a protein-ligand composite. Therefore, ligands were transformed to 3D structures through stereo concoction, ionization, and tautomerism, as well as vitality minimization and optimization of geometry, in addition they were dissolved and restructured for the absence of H-atoms as well as chirality. Ionization and tautomeric states were generated in the pH range of 6.8 and 7.2 using the Epik module. Finally, the mixes were limited using, power field OPLS-3 in the Schrodinger Impact package until a root mean square deviation of 1.8A^owas attained. A single low-energy ring confirmation was made for each ligand, and the efficient ligands were used for molecular docking studies [29].

Glide ligand docking

The ligands were located in the pocket of the MAO-B protein with the Schrodinger suite 2019-2's Glide module (PDB ID: 2V5Z). The glide score (G-score) is utilized to choose the ligands that are best enclosed. The Glide ligand docking module was used to assess active interactions between ligands and target receptor or enzyme. Docking was performed in a versatile docking mode that automatically generates conformations for each input ligand, using extra precision mode and the OPLS-3 power field. Positive relations for instance lipophilia, H-bonding, and metal-linking are rewarded, whereas steric conflicts are punished. Finally, re-scoring of a few positions was done via G-Score's ability to score. The molecular docking outcomes were analysed by means of the Glide module's XP visualizer. Electrostatic forces and hydrogen bonding to various ligands are other parameters improving binding efficacy. Few negative parameters, such as XP penalties and rotational penalties, can slow down movements.

Bind Free Energy Calculation Using MM-GBSA

The Schrodinger Suite 2019-2 Prime module, the OPLS3 power field and the dissolvable model VSGB, was used to process the drug-receptor complex's binding free energy via MM-GBSA.

ADME prediction

QikProp performed a theoretical calculation of the ADME characteristics of the data set. QikProp looked at around eleven physically significant characteristics and pharmacological characteristics of the substances. Many ADME features, including absorption, distribution, transport, and eventually bioavailability, are influenced by the solubility of substances in water. The solubility of Pyrazolone derivatives was within the range. Finally, the derivatives of Pyrazolone meet Lipinski's RO5 and the QikProp rule, indicating that they are promising medication candidates [29].

Target identification

Targets for PD are enzymatic and genetical such as MAO inhibitors, catechol-o-methyl transferase inhibitors, α synuclein accumulation, dopamine receptors, PARKIN, PINK-1, LRRK-2, DJ-1, and SNCA. Among which MAO-B is one of the major enzyme which is responsible to produce Dopamine metabolites at SNPC. Elevated MAO-B occurred due to blockade of voltage-dependent Na+ and Ca2+ channels and inhibition of glutamate release that govern reduction in the Dopamine level which drive PD condition. Hence target structure of MAO-B;2V5Z (Fig: 1) is retrieved from the PDB.

Molecular Docking study of Pyrazolone derivatives with MAO-B

After preparation of ligand and protein, the ligand was docked with crystal structure of protein MAO-B. The most stable conformation was selected by lowest energy complex with largest amino acids chain. RMSD value was calculated with reference to MAO-B crystal structure. The molecular docking results produce by compounds (PPC1-PPC14) with co-crystalized structure of MAO-B is detailed in terms of G-score (Table-2). The ligands G-score ranges for the compound PPC1-14 is from -10.965 to -7.4 kcal/mol. Thr 462, Gly 40, Arg 42, Glu 34, Ala 35, Ala 263, Arg 36, Val 235, Leu 268, Tyr 393 are the energetic amino acids in co crystalized MAO-B. Compound PPC-4 has the maximum G-score of -10.965 kcal/mol, followed by compound PPC-13, PPC-6, PPC-9, and PPC-12 with -10.582, 10.563, -10.319, and -10.06 kcal/mol respectively. Table-1 represents the structures, code, and IUPAC name of the designed compounds and Fig- 6 illustrate the 2-D docking interaction of top 5 compounds with enzyme MAO-B. For PPC-4 forms H-bond between CYS 172 and oxygen molecule, pi-pi stacking between TYR 398 and TRP 119 with F and Cl containing rings respectively, PPC-13 and PPC12 forms pi-cation between TYR 435 and 398 with nitro group, PPC-9 and PPC-6 forms H-bond between CYS 172 with oxygen. Some of the other designed ligands PPC5, PPC7, PPC11, and PPC14 along with above mentioned top compounds exhibited remarkable G-scores when assessed with the standard drugs Selegiline, Entacapone and L-Dopa.

Analysis of ADMET properties

The ADMET analysis is important factor for designing and proposing a novel drug molecule which is anticipated by Schrödinger suite 2019-2 Qikprop module (Table-3). The molecular weight (MW) of the molecules were <500, the dipole moments are in between 8.7-1.4 which implies compounds followed Lipinski’s RO5. The amount of acceptors and donors of H-bond, and Log P estimates were within the adequate limits. Qikprop also benefits to distinguish #metab which implies the number of possible metabolic responses. It helps for the prediction of how the ligand may possibly approach the target. The molecules were within the suggested array of 1-8. Owing to the fact that the % oral absorption of the compounds found >80%, except PPC12 (52.18%) there would be no effect on the bioavailability of the compounds, in case of deviation from the Lipinski’s RO5. Among the designed compounds PPC4-PPC11 displayed 100% human oral absorption.

MM-GBSA Assay

The results of MM-GBSA assay shows that energies of ligand binding in the binding pocket of co-crystalized structure of MAO-B enzyme are non-polar solvation (ΔGLipo) and Van der Waals energy (ΔGvdW) due to the high -ve values shown by all the compounds. The other energies, namely covalent energy (ΔGCov) and electrostatic solvation (ΔGSolv) energy do not strongly favor receptor binding. Further, the greater values of ΔGVdW and ΔGLipo in the -ve range show notable hydrophobic interaction with enzyme MAO-B and ligands PPC1-14. Highly preferred ligand binding was observed in compounds PPC4, PPC6, PPC9, PPC12, and PPC13. This result can be correlated to the G score as well since compound PPC12 displayed highest docking score implying that columb energy (ΔGCoul- 34.09 to -3.97 kcal mol-1). plays a vital role in the drug-receptor interaction. It can be observed through the MM-GBSA assay, that strong binding affinities of the compounds PPC5, PPC7, PPC11, PPC12, and PPC14 the receptor is visible.

Mitochondrial dysfunction mediated by oxidative stress induced damage to proteins and lipids caused due to elevation of MAO-B plays a key role in the mechanism of PD. Currently, search is ongoing for neuroprotective potential drugs. In this context, Pyrazolone derivatives are considered as potent antioxidants and MAO-B inhibitory agent, whose neuroprotective activity has been linked with its ability to overcome PD [30]. Hence we selected the Pyrazolone derivatives for its MAO-B inhibitory activity with in-silico molecular docking studies.

Molecular docking techniques in drug discovery process is potent tool to screen the newly designed lead molecules to understand drug receptor interaction. With this in background to understand the possible mechanism of action, and the binding site of the molecules virtual screening is carried out on molecular docking plat form [31]. Binding efficiency of compounds (PPC1-PPC14) and standard Selegiline with MAO-B has given better understanding about the interactions of compounds and produced docking scores (Table-2) which implies that few designed molecules have better binding efficiency compared to standard drug. Owing to the lipophilic factors for binding affinity, indicates less than or equal to 5 N containing compound possess lipophilic nature [32]. According to the Lipinski’s RO5, the MW of the compounds, Partition coefficient, number of H-bond donors and H-bond acceptors should be ≤ 500, ≤5, ≤ 5 and ≤ 10 respectively. Designed molecules have better G-score compared to standard drug. However, its ADMET parameters follows Lipinski RO5 which implies data is in accepted range. This finding agrees with the report of chimenti and Bolasco 2004, that Pyrazolone possess MAO-B inhibitory effect [28].

Prime MM-GSBA analysis (Table-4) represents the relative energies of binding of individual compound with that of the MAO-B. It is a conclusion of various drug-receptor interactions involving polar, hydrophobic, covalent bond interactions etc. The proposed ligands, showed good free binding energy, which will adequately place into the MAO-B (Table-4). The binding free energies of the Compound PPC12 has the highest binding energy (-68.86 kcal/mol) when compared to standard drug.

The involvement of MAO-B in the maintenance of Dopamine levels through methylation is of greatest importance as it can help in the design of novel drugs for PD. Hence MAO-B has been used as potential therapeutic target for treating PD. We have designed potential inhibitors for MAO-B by using a series of in-silico approach. Molecular interaction and pharmacokinetic studies of substituted Pyrazolone derivatives with MAO-B, studied by molecular docking and ADME profile, to improve the screening accuracy and to expose the interaction between the designed compounds. In-silico Docking results predicted significant affinities of Pyrazolone derivatives with MAO-B inhibitors. In view of present study, substituted novel Pyrazolone derivatives can serve as a promising lead moiety for the development of MAO-B inhibitors as an Anti-Parkinson’s agent.

NDDs- Neurodegenerative diseases, AD-Alzheimer’s disease, PD-Parkinson’s disease, SNPC-substantia nigra pars compacta, MAO-mono amino oxidase, MM-GBSA-Molecular Mechanics /generalized born surface area, PDB-protein data bank, RMSD-root mean square deviation, RO5- Rule of five.

Acknowledgements

The authors extend their appreciation to Nitte Gulabi Shetty Memorial Institute of pharmaceutical Sciences at Nitte (Deemed to be University) for providing CADD lab facility with Schrodinger software and doctoral scholarship.

Data availability

The data for binding efficiency of designed compounds is generated from Schrodinger software and are included in this article.

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Tables 1 is available in the Supplementary Files section.

Table 2. Docking studies for compounds(PPC1-14) with MAO-B (2V5Z)

Compounds	G-score	G-EvdW	XP H Bond	G- emodel	G-Rotatable bonds	G-ecoul	XP penalties-
PPC1	-7.4	-47.024	-0.045	-43.414	5	-1.239	0
PPC2	-9.765	-40.66	0	-34.962	5	-3.714	0
PPC3	-9.083	-44.643	-0.089	0.975	5	1.583	0
PPC4	-10.965	-50.542	-0.148	-58.589	4	1.427	0
PPC5	-8.551	-42.443	0	-46.78	4	-2.198	0
PPC6	-10.319	-40.699	0	-41.378	4	-2.801	0
PPC7	-8.914	-39.182	0	-33.478	4	-2.281	0
PPC8	-9.306	-47.062	0	-53.855	4	-0.795	0
PPC9	-10.563	-42.765	0	-59.259	4	-3.902	0
PPC10	-8.774	-52.639	0	-56.292	5	-1.783	0
PPC11	-8.872	-35.793	0	-39.678	4	-2.843	0
PPC12	-10.06	-49.393	-0.071	-18.086	6	-1.006	0
PPC13	-10.582	-45.652	0	0.01	5	-4.371	0
PPC14	-9.382	-53.053	-0.046	-30.453	5	-0.38	0
SELEGILLINE	-7.28	-26.419	0	-32.983	5	-1.129	0

Table 3. The ADMET profile for proposed compounds (PPC1-14)

Compounds	MW	Dipole	Donor HB	Accept HB	QP log O/W	#metab	Rule of five	% Human oral absorption
PPC1	369.311	8.033	0	6	2.793	4	0	86.12
PPC2	369.311	2.891	0	6	2.655	3	0	80.491
PPC3	383.338	3.436	0	6	3.003	4	0	84.476
PPC4	358.759	7.199	0	5	3.889	3	0	100
PPC5	358.759	4.766	0	5	3.917	2	0	100
PPC6	393.204	3.33	0	5	4.358	2	0	100
PPC7	372.786	5.193	0	5	4.204	4	0	100
PPC8	342.304	4.641	0	5	3.638	2	0	100
PPC9	376.749	3.054	0	5	4.12	2	0	100
PPC10	354.34	5.628	0	5.75	3.469	4	0	100
PPC11	403.21	5.08	0	5	3.999	2	0	100
PPC12	414.309	2.964	0	7	2.037	4	1	52.183
PPC13	403.756	1.652	0	6	3.122	3	0	84.528
PPC14	383.338	8.7	0	6	3.119	4	0	88.012
SELEGILINE	187.284	1.074	0.5	2	3.08	3	0	100
RECOMMENDED VALUES	130-725	1–12.5	0 – 6	2 – 20	-2 - 6.5	1 – 8	Max 4	>80% is high <20% is poor

mw- molecular weight, dipole – computed dipole moment; donor hb - estimated number of hydrogen bonds that would be donated by the solute to water molecules in an aqueous solution; accept hb- estimated number of hydrogen bonds that would be accepted by the solute from water molecules in an aqueous solution; qp logpo/w - predicted octanol/water partition coefficient; # metab- number of likely metabolic reactions; rule of five- number of violations of lipinski’s ro5; %human- oral absorption- predicted human oral absorption on 0 to 100% scale.

Table 4. Binding free energy calculation using Prime/MM-GBSA approach

Compounds	Gbind (Kcal/mol)	Gbind Coulomb	Gbind covalent	Gbind Vander	Gbind H bond	Gbind Lipophilic
PPC1	-40.31	11.92	12.23	-45.56	-0.13	-29.58
PPC2	-24.23	8.83	18.76	-33.77	-0.29	-31.75
PPC3	-48.42	20.54	5.88	-46.94	-0.27	-36.66
PPC4	-33.25	3.59	10.04	-46.43	0	-38.44
PPC5	-50.04	-4.69	9.86	-42.62	-0.17	-40.85
PPC6	-38.42	-13.66	14.99	-32.39	0	-44.46
PPC7	-54.04	-4.45	9.15	-39.6	-0.16	-45.08
PPC8	-38.38	-3.57	11.23	-44.48	-0.18	-30.1
PPC9	-37.12	-14.61	14.34	-34.91	0	-37.56
PPC10	-46.03	-5.83	12.74	-48.73	-0.17	-35.91
PPC11	-55.44	-5.22	10.65	-44.6	-0.13	-39.88
PPC12	-68.86	34.09	10.21	-55.01	-0.46	-32.07
PPC13	-37.37	8.27	18.68	-37.15	-0.31	-38.6
PPC14	-52.08	11.78	15.51	-49.62	-0.34	-37.72
SELEGILINE	-23.94	-0.77	0.06	-8.42	0	-16.89

No competing interests reported.

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Molecular docking of substituted pyrazolone analogs targeting MAO-B as therapeutic site for Parkinson: an in-silico study

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