In silico investigation of Methyl parathion and Diazinon with different Metabolic protein in Drosophila melanogaster

Suman Sahoo¹, Md. Lutfur Rahman¹, Sagarika Mitra¹, Rajiniraja M.²*

¹Department of Biomedical Genetics, School of Bio Sciences and Technology,

Vellore Institute of Technology, Vellore - 632014, India.

²Department of Biotechnology, School of Bio Sciences and Technology,

Vellore Institute of Technology, Vellore - 632014, India.

*Corresponding Author E-mail: rajiniraja.m@vit.ac.in

ABSTRACT:

Chemical pollutant such as insecticide, pesticide and drugs are mainly used for agriculture, industry and economic development, which are well known for environment pollutant due to its toxicity and persistence in the nature. It can accumulate into the environment and continuously contaminate the food chain which causes threat to the health of consumer including human. Based on all these studies our investigation deals with the effects of two insecticides viz. methyl parathion and diazinon to non target organism like Drosophila melanogaster. In this study we have performed molecular modeling, docking and protein function analysis of different metabolic and physiological enzyme of Drosophila melanogaster such as acetylcholinesterase (AchE), Glutathione S-transferase D1(GST) and Protein kinase C (PKC) with these insecticides of six combinations (AchE + Diazinon, AchE + methyl parathion, GST+Diazinon, GST+Methyl parathion, PKC+Diazinon, PKC+Methyl parathion). Molecular docking results showing best binding affinity for GST+ Methyl parathion with binding energy of -4.79 kcal/mol. Overall, methyl parathion produces efficient binding toward all target protein when compare to diazinon. However, more detailed analysis need to be carried out to have an in-depth understanding of in vivo significance of these bimolecular interactions.

KEYWORDS: Insecticides, Acetylcholinesterase, Glutathione S-transferase D1, Protein kinase C, Molecular Docking, Drosophila melanogaster.

1. INTRODUCTION:

Recent studies majorly focused on environmental pollution, is a major issue in the modern society. Pesticides are used in agriculture for crop protection against pest and which is adverse effect for animal and human health. There are various pesticides classified basis of their functions like herbicide, insecticides, bactericide, antimicrobial, fungicide etc. In the several reports indicate that pesticide has a more consumption in India, have a negative effect on human, animal, birds and aquatic organism through inhalation, skin and consume food¹. When it exposed into the body different types of symptoms will be occur like skin rashes, breathing problems, heart problem, and brain disease.

Organo-phosphorus pesticides are still present in the market but most of them are banned. Some countries are still using some toxic organophosphorus insecticide such as methyl parathion (C₈H₁₀NO₅PS) and diazinon (C₁₂H₂₁N₂O₃PS) in horticultural crops, vegetables like beans, onions and many other fruits². Both of the organophosphates are toxic which inhibit many metabolic and physiological proteins like acetyl cholinesterase (AchE), glutathione S-transferases (GST), protein kinaseC (PKC), causing phenotypical abnormalities³.

AchE is one of the cholinesterase enzyme, has a major impact on proper functioning of nervous system in human, insecticide and others vertebrates. Constant firing of signals in ‘electrical switching centers’ or synapses is stimulate or inhibit neurons, which mechanism is control by the enzyme acetyl cholinesterase⁴. Some reports indicate when body expose with cholinesterase inhibiting compound cause jam in nervous system which may be lead to Alzheimer disease⁵.

The detoxification enzyme GST is one of the targeted protein which has been implicated in pesticide resistance. In insect, there are three types of GST are available: alpha GST (class I); sigma GST (class II); epsilon GST (class III) whereas in human nine GST have been identified^6-8. Biochemical mechanisms (i.e., target site resistance or metabolic target resistance) by which it involves in detoxification of organophosphate or production of less toxic derivative via conjugating the insecticide⁹. It also plays a major role in lipid peroxidation activity¹⁰. For example, sigma GST present in Drosophila model.

The third target protein, i.e. PKC is thought to be activated during exposure with insecticide. This serine/threonine kinase (i.e. family) may involve in the phosphorylation and regulate neuronal activity via signalling pathway³. Effect of the insecticide on PKC is still no clearance.

Molecular docking is a tool for molecular structure and drug design. It can be help in binding and interact between protein and ligand at their atomic level. Aim of these research to focus on interaction study on three proteins in Drosophila melanogaster which are exposed by the two different organophosphorus pesticides and compare two pesticide which one is more toxic for the Drosophila melanogasterusing bioinformatics tools and software.

2. MATERIALS AND METHODS:

2.1 Model description:

The crystal structures of three different proteins: AchE, GST, PKC were obtained from Protein Data Bank (PDB) website (http://www.rcsb.org/)³. For further analysis, we selected Drosophila AchE (PDBID: 1DX4) which has a 2.7Å. The 2nd one was the crystal structure of Drosophila GST (PDBID: 3EIN) which has 1.126Å resolution. Homology modelling has done for PKC with the template (PDB ID: 3TXO) using modeller V 9.19 software.

2.2 Model Validation:

The structure of different protein such as AchE, GST, and PKC were verified by using PROCHECK, ERRAT SCORE and VERIFY 3D programs via Saves v5.0 online software^3,11. The overall quality factor of different protein structure was determined by ERRAT SCORE where higher score indicating higher quality¹¹. The PROCHECK program was considered for checking the stereochemistry of different protein structure using Ramachandran plot. The compatibility of an atomic model (3D) was analysed by using Verify 3D server¹¹.

2.3 Active site prediction:

Active site was predicted using a software, Computed Atlas of surface topography of protein (CASTp) website http://cast.engr.uic.edu. It helps to locate, delineate and measure the surface region of 3D structure of protein.

2.4 Molecular docking:

2.4.1 Preparation of ligand:

The 3D structure of two organophosphorous pesticides (ligand) i.e. Methyl parathion (MP) and Diazinon were obtained from PubChem. The PubChem IDs of methyl parathion and diazinon are: CID4130, CID3017 respectively and opened with PyMol software.

2.4.2 Preparation of protein:

The different 3D structure of AchE and GST were obtained from pdb and modelling of PKC (as no information in pdb) using modeller. For further analysis, different structure were opened with PyMol viewer.

2.4.3 Docking study using AutoDock 4.0:

The calculation of molecular modelling and docking were studied using Autodock 4.0 following Lamarckian genetic algorithm. The clustering were generated based on the root mean square (RMS) tolerance (4Å) at each ligand conformation¹². The docking study was done between different targeted protein with two pesticides (i.e.MP and Diazinon): AchE+MP, AchE+Diazinon, GST+MP, GST+Diazinon, PKC+MP, PKC+Diazinon. Docking results were viewed using PyMol.

3. RESULTS:

3.1 Model description:

The retrieve pdb structure of different proteins: AchE, GST and modelled PKC are described as text file (PDB format), which form with different necessary information include number of atoms, name of atoms, bond distance, dihedral angles, and the number of residues³. Multiple sequence alignment (Clustal W) showed that sequences are not identical.

3.2 Model Validation:

Stereo chemical properties of AchE, GST and PKC were determined using PROCHECK calculation which showed residues in most favoured region (i.e.1DX4-85.6%, 3EIN-92.9% and PKC-94.3%), additional allowed region (i.e. 1DX4-12.7%, 3EIN-6.5% and PKC-4.4%), generously allowed regions (i.e~0.66%) and disallowed region can be ignorable(as ~0%) [Table1]. Overall quality factors were generated from ERRAT server which showed 86.014, 98.9848 and 89.7959 for 1DX4 (AchE), 3EIN(GST) and PKC respectively [Fig.1, Table1]. Verify 3D scores showed 88.08%, 89.37% (3D-1D score >=2) for 1DX4 and 3EIN respectively [Table1].

3.3 Ligand binding site analysis:

Different potential active site of AchE, GST and PKC were predicted of using CASTp server. Different proteins showed different ligand binding sites [Table 2]

3.4 Molecular docking study:

Molecular docking approaches helps to predict the interaction between ligand molecule (such as pesticides and others small molecules etc.) with the target protein¹³. We used the docking tool autodock 4.0 for interaction study. Docking score demonstrate how ligands can form strong complexes with specific target protein: low or more negative binding energy gives stronger complexes; in contrast, high binding energy gives lower affinity towards targeted protein¹⁴

Table 1: Ramachandran plot of AchE, GST, PKC and their respective PDB IDs: 1DX4, 3EIN, modelled PKC were calculated using SAVS v5.0.

Model description	1DX4	3EIN	PKC
Residues in most favoured region [A, B, L]	391/85.6%	171/92.9%	216/94.3%
Residues in additional allowed regions [a, b, l, p]	58/12.7%	12/6.5%	10/4.4%
Residues in generously allowed regions [~a, ~b, ~1, ~p]	5/1.1%	1/0.5%	1/0.4%
Residues in disallowed regions	3/0.7%	0/0.0%	2/0.9%
ERRAT overall quality factor	86.014	98.9848	89.7959
VERIFY 3D (3D-1D score>=2)	88.08%	89.37%	73.26%

Fig. 1: A, B and C represents the overall quality scores of AChE, GST and PKC using ERRAT server. Black bars indicate misfolded region, grey bars include the error value between 95% and 99%, white bars demonstrate lower error rate.

Table 2: The predicted binding site residues of AchE, GST and PKC using CASTp server

Protein	Binding site residues
AchE	VAL364, ARG365, ASP366, GLU367, GLY368, THR369, LEU372, PHE414, GLN415, TYR416, THR417, SER418, TRP419, GLU420, GLN426, GLN428, GLN429, GLN430, GLY432, ARG433, GLY436, ASP437, THR441, TYR462, THR464, TRP475, GLY477, ILE541, SER543, ASP545, LYS547, ILE548, GLY554, PRO555, LEU556, ALA558, ARG559, PHE562.
GST	LEU7, PRO8, GLY9, SER10, SER11, PRO12, ARG14, SER15, ASN33, LEU34, GLN35, GLN50, THR52, ILE53, PRO54, TRP64, GLE65, SER66, ARG67, ASP101, MET102, TYR106, GLN107, ALA110, TYR114, PHE118, ALA159, ALA162, THR163, THR166, GLU203, PHE204, LYS206, TYO207.
PKC	SER11, ILE122, ARG124, ASP125, LEU126, LYS127, MET146, LYS148, GLU174, GLY178, VAL181, TRP184, ALA185, VAL188, GLU192, PRO198, PRO199, PHE200, GLU205, LEU208, PHE209, ILE212.

Table 3: Docking result shows the details of cluster size within 4Å, binding energy, number of hydrogen bond and respective values obtained using Autodock 4.0. The amino acids involving interaction with ligands also shown.

Interacting molecule	No. of confirmation in cluster, RMSD (4Å)	Binding energy (kcal/mol)	No. of H bond	Interactive amino acids
Ache + Diazinon	12/100	-3.58	2	ARG559: HE, ARG559:HH22
Ache + MeP	10/100	-4.22	3	TRP419:HN, ARG433: HE, ARG433:HH22
GST + Diazinon	25/100	-4.37	1	ARG67:HH12
GST + MeP	67/100	-4.79	2	SER11:HN, ILE53:HN
PKC + Diazinon	14/100	-4.63	-	-
PKC + MeP	33/100	-4.04	1	VAL82:H

4. DISCUSSION:

During the study on different fish experiments many scientists have identified that AchE activity was reduced mainly in brain when exposed with different organopesticides such as methyl parathion and diazinon^15,16. Chaudhry M et al proposed AchE is the suitable target for drug therapy when people exposed with organophosphate¹⁷. Despite many reports for the inhibition of GST and PKC with other genotoxic insecticide, although there are some experimental reports indicate that GST activity increase when exposed with selected toxic insecticides^18,19,20,21. Methyl parathion showed more binding affinity towards three important metabolizing enzymes than diazinon in our study. Reports indicate these enzymes may be the solution of drug discovery for organopesticides causing complex diseases.

5. CONFLICT OF INTEREST:

The authors declare that there is no conflict of interest.

6. ACKNOWLEDGEMENT:

Authors thank the VIT for the infrastructure and instrumentation facilities.

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Received on 09.06.2020 Modified on 06.08.2020

Research J. Pharm. and Tech. 2021; 14(7):3794-3798.

DOI: 10.52711/0974-360X.2021.00657