In-silico docking studies with chebulagic acid and E2 of chikungunya virus using bioinformatics tools

AnushkaVerma^* , Anjana Sharma , SumanTapryal

Department of Biotechnology, School of Life Sciences, Central University of Rajasthan, Bandar Sindri 305817, Ajmer-Rajasthan, India

Corresponding Author Email: anushka.chdcity@gmail.com

DOI : http://dx.doi.org/10.5281/zenodo.8129195

Abstract

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Introduction

Chikungunya was first reported in Kenya in 1952, and it has since spread to many parts of the world. However, there has been a rapid increase in the number of cases in recent past [1]. At first, it was thought to be due to dengue as both were expressing similar symptoms however later on identified to be chikungunya[2]. The patients usually experience fever, rash, joint pain, and sometimes neurological problems,etc[3]. The joint pain can be adverse and can lead to arthritis. The annual epidemic outbreaks of CHIKV occur in tropical and subtropical regions, where two mosquitoes including Aedesaegypti and Aedesalbopictushave been playing substantial rolesas important vectors of the virus[4]. CHIKV encodes various structural (E2, C, E1, 6K, E3) and non-structural (nsP1-4) proteins which can be matured and activated via proteolytic cleavage by viral or host enzymes[5]. The interactions of viral proteins such as E1 (fusion protein), E2 or p62 (glycoprotein adhesion), and temporary E2-binding protein E3 (unknown function) with the host cell surface initiate and develop the viral pathogenesis[6]. The E2 has been recognized as the main component to inducethe immune responses. The three different lineages of the virus have nearly 92.5–98% similarity at the amino acid sequence levels. In the absence of an efficient preventive or approved therapeutic approach to control the virus; identifying an appropriate vaccine would be of utmost importance to the patients affected by the virus. The mapping of INF-γ, B cells, and T cells using rapid and cost-effective immune informatics approaches in the process of vaccine design, has been put forward[7]. A low-cost, safe, and effective vaccine would be appreciated in countries facing endemicCHIKV.Traditional methods of drug discovery could take years, whereas in silico-docking analysis enables large-scale screening fast, reliable, and cheaper than conventional drug development[8].New scientific approaches include molecular docking, ADMET studies, and molecular dynamics simulation to determine targets and lead compounds[9]. Docking is a computer-aided prediction of the size and conformation of drug and enzyme/protein seeking to find the best match between two molecules[10]. The molecular docking procedure generates multiple ligand conformations and orientations that fit against the target and selects appropriate matches[11]. The less the binding free energy of a complex, the more stable it is.

CHIKV genome structure

The genome is a positive-sense single-stranded RNA virus. The size of its genome is about 11.8 kb in length composed of single-stranded RNA with a 5′ 7-methylguanosine cap and a 3′ poly-A tail[12]. Four non-structural proteins and five structural proteins, including capsid protein and glycoproteins, are encoded by this genome (C, E1, E2, E3, and 6k)[13]. For viral replication, non-structural proteins (nsp1-4) are required. Glycoprotein E1 is involved in cell fusion, while E2 interacts with host cell receptors. The virion of CHIKV is composed of a lipid bilayer envelope connected to an icosahedral nucleocapsid shell that contains the RNA genome[14]. E1 and E2 glycoproteins form heterodimers that form trimmers that form an icosahedral8 lattice embedded in the viral envelope. Within E1/E2 heterodimer, E1 laterally contacts E2 along the central domain II and partialdomain III. The E1 hydrophobic fusion peptide is buried in a grove between domains A and B of E2, thereby preventing pre-mature activation of the membrane fusion machinery of the virus. The non-structural gene encodes for nsP1, nsP2 and helicase nsP2, and polymerase nsP4. These proteins associatedwith the viral replication complex needed for downstream viral genome replication[15].

Figure 1. Diagrammatic representation of typical structure of the chikungunya virus	Figure 2. Diagrammatic representation Pathogenesis of CHIKV11inside human body

Once inside the human body, the chikungunya virus replicates in the skin and spreads to the liver, joints, and brain. When the viral load reaches its peak, the levels of type 1 interferons, pro-inflammatory cytokines, and chemokines skyrocket. In the initial phase of infection, the virus replicates in monocyte-derived macrophages, and later on the virus spreads to different parts of the infectedbody. After the virus completes its life cycle CHIKV specific adaptive immunity is gained, however, some symptoms like joint pain can still prevail.

Methods and materials

Molecular dynamic simulation was performed to confirm the docking studies and to analyze the stability of the structure. The molecular docking model has allowed the evaluation of biochemical activities between one ligand and one receptor target protein or enzyme. The success of themodel has been assessedby the scoring function that estimates the free energy change upon the binding process. Most biochemical activities performed in silico such as anticancer, 3–5 enzyme a-glucosidase inhibition,6 antibacterial activity, and anti-inflammatory have been based on the inhibition mechanism explanations of inhibition of the biosynthesis of bacterial and fungal cell walls[16]. The molecular docking model must assess the validation via the value of RMSD between one reference pose that is available in an enzyme or protein and a ranked pose or ligand.Modeling and simulation methods provide a unique means to explore biological and physicochemical processes down to the atomic level. This can guide physical experimentation, accelerating the discovery and development process. Insilico molecular docking is a useful tool to check the binding affinity, and conformation of protein-ligand interactions [9]. For the construction of E2-CH1, the truncated E2 was attached to constant a region of heavy chain antibody to create this vaccine candidate.

Figure 3(a) and 3(b): Two-dimensional structures of Chebulinic acid and Chebulagic acid with a molecular weight of 956.7g/mol and 954.7g/molrespectively.

Accession of the target protein

The Protein Data Bank (PDB)is a database for the three-dimensional structural data of large biological molecules, such as proteins and nucleic acids. The PDB iskey in areas of structural biology, such as structural genomics. Today considereda leading resource for experimental data central to scientific discovery the global level and provides access to 3D structure data for the molecules of life, found in all organisms on the planet. Knowing the 3D structure of a biological macromolecule is essential for understanding its role in human and animal health and disease, its function in plants and food and energy production, and its importance to other topics related to global prosperity and sustainability. The enormous wealth of 3D structure data stored in the PDB has underpinned significant advances in the understanding of protein architecture, culminating in recent breakthroughs in protein structure prediction accelerated by artificial intelligence approaches and deep or machine learning methods.

Ligand selection

A ligand is considered that it is interacting well with the enzyme when the three parts of the ligand, including the capping group, linker, and functional group bond well to the active center of the enzyme. Molecular Docking generates a more accurate biophysical simulation of theprotein–ligand complex binding to explain the model complex’s stability. PubChemis an open chemistry database at the National Institutes of Health. PubChem mostly contains small molecules, but also larger molecules such as nucleotides, carbohydrates, lipids, peptides, and chemically-modified macromolecules. Each month this site with programmatic services provides data to several million users worldwide. The MOL SDF format of the ligand was converted to PDBQT file using PyRx tool to generate atomic coordinates. PyRx is Virtual Screening software for Computational Drug Discovery that can be used to screen libraries of compounds against potential drug targets. PyRx enables Medicinal Chemists to run Virtual Screening from any platform and helps users in every step of this process – from data preparation to job submission and analysis of the results. ThePyRx includeda docking wizard with an easy-to-use user interface which makes it a valuable tool for Computer-Aided Drug Design.

Target and ligand optimization

After downloading the structures, furtherpreparation for docking was carried out with the What-if software. WHAT IF provides a flexible environment to display, manipulate, and analyze small molecules, proteins, nucleic acids, and their interactions. One notable use was detecting many millions of errors (often small, but sometimes catastrophic) in Protein Data Bank (PDB) files. WHAT IF also provides an environment forhomology modeling of protein tertiary structures and quaternary structures; validating protein structures, notably those deposited in the PDB; correcting protein structures; visualizing macromolecules and their interaction partners, and manipulating macromolecules interactively. For docking analysis, PDB coordinates of the target protein and ligand molecule were optimized by Drug Discovery Studio version 3.0 software.

Results and discussion

Molecular docking analysis

PyMOL is an open-source but proprietary molecular visualization system created by Warren Lyford DeLano. The PyMOL Molecular Graphics System, Version 1.2r3pre, Schrödinger, LLC has been utilized as 3D visualization tool used to analyze protein molecules.  Discovery Studio (BIOVIA, DassaultSystèmes, BIOVIA Workbook, Release 2020; BIOVIA Pipeline Pilot, Release 2020, San Diego: DassaultSystèmes, [2021]) used to analyze protein molecules in 3D visualization. Today’s biopharmaceutical growing market demandsimproved specificity and safety, novel treatment classes, and more intricate mechanisms of disease. Keeping up with this complexity requires a deeper understanding of therapeutic behavior. BIOVIA Discovery Studio provides world-class in silico techniques such as molecular mechanics, free energy calculations, biotherapeutics development ability, and more in a common environment [10]. It provides researchers with a complete toolset to explore the nuances of protein chemistry and catalyze the discovery of small and large molecule therapeutics from Target ID to Lead Optimization.

Figure 4.Structural depiction of receptor binding sites and Chebulinic binding sites of E2.  (a) Side view of E2 protein (surface representation) with sites for glycosaminoglycans and prohibitin-1 binding sites being depicted in space filling representation, encircled in black and orange circles, respectively. (b) Top view of (a). (c) structural representation of E2-chebulagic acid docked conformations.

Table 1:  Binding affinities of protein-ligand docked models

Model	Binding Affinity	Interacting residues
1	-9.6	Hydrogen bond- Arg p103, Asp p143 Other bonds- Val p132, His p276, Pro p131, His p139, Pro p277, Lys p106, Asp p130, Lys p137, Met p294, His p129
2	-9.3	Hydrogen bond- Pro p131, Asp p180, Lys p137, Arg p103, Phe p138, Ile p133 Other bonds- Pro p105, Lys p106
3	-9.2	Hydrogen bond- Arg p103 Other bonds- Val p132, Pro p131, Cys p 104, Thr p91, His p139
4	-8.7	Hydrogen bond- Ala p158, His p61 Other bonds- Asp p4, Phe p6, Trp p63
5	-8.6	Hydrogen bond- Pro p131, Lys p137, His p139 Other bonds- Val p132
6	-8.6	Hydrogen bond- Arg p141, Arg p253, Glu p147, Arg p103, Asp p43 Other bonds- Thr p45, Ile p133, Cys p252, Thr p251, Gln p143, Gln p155
7	-8.5	Hydrogen bond- Asp p4, Asn p7, His p61, Thr p157
8	-8.5	Hydrogen bond-Arg p141, Arg p253, Glu p147, Gln p 155, Arg p103, Arp p43 Other bonds-Gln p143, Thr p251, Cys p252, Ile p133, Thr p45
9	-8	Hydrogen bond- Asp p116, Arg p118, His p122, Ser p27 Other bonds- Pro p31, Asp p70, Cys p22, Cys p28, His p26, Gly p23

Docking confirmation of chebulagic acid on E2 protein as observed from structural representation by docking model -1 in figure 6. While the interacting residues of E2 involved in binding chebulagic acidfound in Figure 7 had been obtained from Discovery Studio software as H bonds were represented by green bonds and light green represented van der Waal interaction, orange clour represents Pi cation interaction; pink represents Pi-alky interactions and red represents unfavorable interactions. The structural representation of docked conformation of chebulagic acid on E2 protein by using the PyMOL software based on docking model-2 had represented in figure 8 as the structure of E2 was depicted in surface form, and the ligand was represented in stick form. The two-dimensional representations of interacting residues of E2 involved in binding chebulagic acid based on docked model-2 were figured in Figure 9 with Green bonds representing H-bonds; light green represents van der Waal interaction; pink represents Pi-alky interactions and red represents unfavorable interactions. The structural and two-dimensional representations had reflected in Figures 10 and 11 respectively.  Docking model -4 was also tried for structural and two dimensional representations of interacting residuals with binding chebulagic acid and expressed in Figures 12 and 13 respectively. The details of binding affinities of protein-ligand docked models had mentioned in Table 1 for ready reference.

Figure 6. Structural representation of docked conformation of chebulagic acid on E2 protein (docking model-1).	Figure 7. Two dimensional representation of interacting residues of E2 involved in binding chebulagic acid (docked model-1).
Figure 8. The structural representation of docked conformation of chebulagic acid on E2 protein (docking model-2).	Figure 9. The 2D representation of interacting residues of E2 involved in binding chebulagic acid (docked model-2).
Figure 10. The structural representation of docked conformation of chebulagic acid on E2 protein (docking model-3).	Figure 11. The 2D representation of interacting residues of E2 involved in binding chebulagic acid (docked model-3).
Figure 12. The structural representation of docked conformation of chebulagic acid on E2 protein (docking model-4).	Figure 13. The 2D representation of interacting residues of E2 involved in binding chebulagic acid (docked model-4).

Conclusions

The need to develop a vaccine against chikungunya is now more than ever. E2-CH1 forms protein in the form of inclusion bodies and a high yield of protein was obtained using 1mM IPTG as an inducer at 37°C for 6 hours. It can be purified with SP sepharose chromatography and gives a high yield (95%) in vitro refolding using Urea. This purified and refolded vaccine candidate can be further analysed in future studies. The in silico docking of chikungunya virus with an inhibitory molecule was evaluated and the reasonable binding affinity varied from negative 9 tonegative 8 had been observed in a study which would be a favourable and spontaneous reaction.

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