AMMPDB VER.1.1

Case Study

To help the beginners (for using the database) an example using case study is incorporated under contributors page. The objective of the current study is to address the emergence of Mycobacterium tuberculosis (Mtb) resistant strains. To overcome this resistance, there is an urgent need to explore new novel targets with minimal side effects and effective Mtb inhibition. Phytochemicals-based Mtb inhibitors may reduce the disease reactivation and can be further developed as effective drugs against tuberculosis. AMMPDB (Ver 1.1.) medicinal plants and phytochemicals were used by Singh et al., 2022. In the study, PyrG (CTP synthase) is a crucial enzyme involved in the Mtb biosynthesis pathways to support its growth and is identified as a novel target for developing drugs to combat resistant Mtb strains.

Methodology 

The study screened medicinal plants listed under the AMMPDB database for novel phytochemicals as potential inhibitors of Mtb PyrG. The steps followed are:

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Step 1: Target prediction

Target prediction(gene/protein) is done through literature survey. In the present study, identified target CTP synthase PyrG is essential for the biosynthesis of a pyrimidine to support the growth of Mtb and thus possesses a therapeutic target.

Step 2: Protein and Ligand Preparation

Step 2.1 3D structure Retrieval: The three-dimensional crystallographic structure of the PyrG target protein was retrieved in the protein data bank (PDB) format from the RCSB Protein Database (https://www.rcsb. org/) using PDB ID: 4ZDK with a resolution 3.4 Å. The Dock Prep tool in UCSF Chimera (Pettersen et al 2004) was used to process and minimized the protein structure under default parameters. All the heteroatoms, native ligands, water molecules, and non-polar hydrogen atoms were removed followed by adding along with the addition of Gasteiger charges and polar hydrogen atoms from the protein structure, followed by structure minimization.

Step 2.2: 3D structure data of phytochemicals:   The 3D structure of 83 phytochemicals from the anti-tubercular medicinal plant- Withania somnifera was retrieved in SDF format from the AMMPDB database.  The ligands were prepared using the AutoDock USCF Chimera Tool by adding hydrogen and gasteiger charges under default parameters. A receptor grid was prepared to perform the virtual screening by selecting the 12 Å region around the active site residues of PyrG (Ser21, Gly23, Lys24, Gly25, Leu26, Asp78, and Ala253) using AutoGrid in USCF Chimera (Pettersen et al 2004).

Step 3: Structure-based virtual screening and Redocking

Structure-based virtual screening (SBVS) is a computational technique performed to identify the top compounds with high negative docking scores (less binding energy) possessing high predicted binding affinity. Using MtiOpenScreen (Labbé, 2015), the top compounds with notable binding energy were selected out of 83 compounds of Withania somnifera as a potent inhibitor of PyrG. Redocking of protein with the selected compounds was performed Autodock Vina and Mti-Autodock (Labbé, 2015), under default parameters. 2D and 3D interactions figures were generated using the Maestro tool of Schrödinger suite (academic version).

Step 4: Molecular Dynamic Simulation:

Molecular dynamic (MD) simulations were performed to analyse the dynamic stability and intermolecular interactions of the selected compounds within the active site residues of the Mtb PyrG protein. The MD system for top-docked complexes were prepared, neutralised minimised and stimulated using Desmond, an academic version of the Schrödinger Suite and Gromacs.

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Post-dynamics simulation analysis

Step 5: Pharmacokinetic and Toxicity:

Furthermore, the top compounds were analysed for their drug likeliness and pharmacokinetic properties such as absorption, distribution, metabolism and excretion (ADME) and Toxicity (T) were analysed using SwissADME (http://www.swissadme.ch/) (Daina, et al 2017).

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Result

Molecular docking identifies the top four phytochemicals of Withania somnifera i.e.  Quercetin 3-rutinoside-7-glucoside, Rutin, Chlorogenic acid and Isochlorogenic acid C with the substantial binding score, as potential Mtb-PyrG inhibitors. Furthermore, molecular dynamics simulation and ADME analysis support the stability of docked complexes and drug-likeness for selected compounds, respectively.

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