Keywords

1 Background

From very ancient days, different medicinal plants dealing with health care are part and parcel of major populations of India, and other Asian countries.

Parkinson’s disorder (PD) is a movement disorder having well-expressed symptoms like slowing of movement, tremor, rigidity or stiffness, and balancing problems. As per Dr. Dickson, the typical patient with PD has Lewy bodies (aggregates of protein, alpha synuclein or SNCA) in the brain neurons. Research suggests that the above central nervous system (CNS) disorder, PD, is a combination of environmental factors and multi-gene mutation [1,2,3,4,5,6].

In the present study for the preliminary screening, the following medicinal plants have been selected to study the scope and activity of different compounds on CNS using the above-mentioned bioinformatic parameter. These are Hydrocotyle asiatica/Centella asiatica, Bacopa monnieri, Convolvulus pluricaulis, Mucuna pruriens, Ocimum sanctum, Tinospora cordifolia, Curcuma longa, Nardostachys jatamansi, Gloriosa superba, Colchicum autumnale, etc., especially considered in this work.

Mutations in the SNCA (alpha synuclein) receptor and is noted as causal factors for many CNS disorders is used in this work [7,8,9].

SNCA

Alpha synuclein is expressed in the brain and integrates presynaptic signals and membrane traffics, mutations and defects of are implicated in the pathogenesis of PD [10, 11]. Unconventionally spliced transcripts encoding different isoforms have been identified for this gene [12]. Researchers are still much unclear about the function of SNCA, though it is seen it generally maintains a supply of synaptic vesicles in presynaptic terminals by clustering synaptic vesicles and controls the release of dopamine which is an important neurotransmitter which critically controls the voluntary and involuntary movements [13].

In this work, the 3D structure of the SNCA receptor is modeled by homology modeling, using high-throughput screening (HTS), and its ligand is selected from Indian Ayurvedic Herbs.

2 Methodology

The 3D structure of the SNCA receptors was modeled using modeler software [14]. The SNCA receptor’s amino acid sequence is downloaded from NCBI; its homologous templates were selected by BLAST (Table 1). The receptor and their corresponding templates were submitted to modeler software to model their 3D structure. Using Rampage Ramachandran plot server [15], the generated five models generated by modeler were evaluated and the selection of the best model was done (Table 2). The 3D structures of the unknown ligands (phytocompounds as in Table 3) were downloaded from various other online databases, and a phase database is generated [16]. Structure-based pharmacophore model is a unique procedure for generating energy-optimized e-pharmacophores. This was done by selecting the regular features of the 3D structure of SNCA receptor interacting with the known ligands; thus, phores were generated in the 3D structure of the SNCA receptor at the interaction sites with the above known ligands. Hence, these e-pharmacophores were used as queries for virtual screening [17, 18].

Table 1. PD receptors with their GenBank accession number and homologous templates
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Table 2. Ramachandran plot analysis of SNCA receptor’s modeler generated models
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Table 3. Ayurvedic herbs (the source of phytocompounds) used in this work.
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Docking was performed by PATCHDOCK server by selecting the best model (model 4) with the ligand selected by pharmacophore modeling, colchicine, to get the docked structure [19, 20].

3 Results and Discussions

3.1 Homology Modeling

The amino acid sequences of SNCA receptor were downloaded from NCBI (Table 1). Its homologous templates were selected by BLAST (Table 1).

The amino acid sequences of the receptors along with their homologous templates were submitted to modeler software for the generation of the 3D structures of the receptor using the principle of homology modeling [14]. Modeler generated five models for each receptor. The 3D models generated by modeler of SNCA (Table 3) are submitted to Rampage Ramachandran Plot server for model verification [15], and the best (Figs. 1 and 2) model was selected.

Fig. 1.
figure 1

Ramachandran plot analysis of SNCA receptor model 4

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Fig. 2.
figure 2

3D structure of SNCA receptor model 4

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3.2 Structure-Based Pharmacophore

Pharmacophore sites were created in the SNCA receptor (model 4) using the known ligands, viz. phenothiazine, N-acylaminophenothiazine, N-alkylphenothiazine, stimovul, etorphine, propoxyphene, and pentazdine. The above ligands are established ligands for SNCA receptor [21, 22].

Based on preliminary screening, special emphasis is given on the following herbs having known phytocompounds as given in Table 3 were screened.

As per structure-based pharmacophore results, phytocompound having the fitness score 1.089598 was selected as the best fitted ligand (Fig. 3, Table 4) and the further docking studies were done using this phytocompound.

Fig. 3.
figure 3

Pharmacophore features of phenothiazine

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Table 4. Results of pharmacophore modeling
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3.3 Molecular Docking

SNCA receptor (model 4) was docked with the phytocompound using software PATCHDOCK [19, 20]. It was seen that SNCA receptor docks with the phytocompound, colchicine, with a docking score of −5566 kcal/mol (Fig. 4, Table 5).

Fig. 4.
figure 4

Docked structure of SNCA receptor with phytocompound colchicine

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Table 5. Docking results of SNCA receptor with phytocompound colchicine, jatamanin11, and bacopaside I
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From the above result is compared with the docking interactions of SNCA receptor with known phytocompounds Jatamanin11 from Valeriana jatamansi (Fig. 5) [23, 24] and Bacopaside I from Bacopa monnieri (Fig. 6) [25] which were established as ligands for PD [23, 25] in our previous works.

Fig. 5.
figure 5

Docked structure of SNCA receptor with phytocompound jatamanin11 [23, 24]

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Fig. 6.
figure 6

Docked structure of SNCA receptor with phytocompound bacopaside I [25]

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3.4 ADME Screening

ADME is an abbreviation for absorption, distribution, metabolism, and excretion. It is used to calculate the drug-like properties of the molecules [16].

QikProp generated the following output (Tables 6 and 7) [18, 23] for the phytocompound colchicine.

Table 6. QikProp prediction [18, 23]
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Table 7. QikProp prediction [18, 23]
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4 Conclusion

As per Rampage Ramachandran Plot analysis, model 4 of SNCA receptor was selected as the best model. Further, pharmacophore and molecular docking studies prove that phytocompound colchicine can be used as ligand for SNCA receptor. Further, in vitro receptor–ligand binding studies can be performed on SNCA receptor with the above ligand to justify the selection of colchicine as the ligand for SNCA receptors and as a remedy for PD. Also, as per ADME studies, colchicine satisfies all drug-like properties which correlate with the results of Bagchi et al. [26].