Molecular Modeling Study of the Flavanones

 Molecular Modeling Study of the Flavanones

Introduction:

In recent times, the progress of computational technologies has revolutionized

the field of drug discovery. Researchers now have access to a plethora of

online server-based databases such as Drugbank, ZINC, UniProt, ChEMBL,

RCSB-PDB, and others. These databases store vast amounts of quantitative

and qualitative data, providing invaluable resources for identifying and

predicting new drug-target correlations.

With the aid of these databases, researchers can undertake various types of

analyses for drug design, including structure-based, ligand-based, and

fragment-based approaches23. Computational chemistry, in particular, focuses

on small molecule-based drug design and can employ another important

strategy known as similarity-based drug design for drug target identification

and hit-to-lead optimization.

Similarity-based drug design relies on the concept of chemical similarity24. It

involves identifying compounds that exhibit similar bioactivities based on

their structural resemblance to known active ligands. By conducting chemical

screening of these databases, researchers can identify lead molecules that

show promise in targeting specific proteins or biological pathways.

Once a lead molecule is discovered with potential targets through this

screening process, researchers can then design a series of structural

derivatives25. These derivatives are modified versions of the lead compound,

carefully tailored to enhance its activity and improve its pharmaceutical

properties. The goal is to optimize the lead molecule to make it more effective

and safer for potential use as a drug.

Overall, the integration of computational technologies and the vast resources

provided by online databases have opened up exciting opportunities for drug

discovery. By combining different approaches, scientists can efficiently

identify potential drug candidates, predict their interactions with targets, and

optimize them to develop safer and more effective treatments for various

diseases. This approach represents a significant advancement in the field of

pharmaceutical research and has the potential to accelerate the development

of new medicines for the benefit of human health

Similarly, we have chosen three protein targets against diabetes i.e. AMPactivated

Protein Kinase, Human Salivary ɑ -amylase and Pancreatic ɑ -

amylase and docked with the flavanones that can be produced from the

different aldehydes present in our lab. Finally exploring their target specificity

via molecular modeling studies.

Experimental:

1. Protein Preparation: Crystal structures of protein were retrieved from

the protein data bank (PDB). The PDB code 6B2E, 3BAJ, 1SMD were

used in the studies, and three-dimensional configurations of those were

processed using the Maestro Version 13.0.013 platform of Schrodinger

software (Fig. 1). Protein preparation was done using the „protein

preparation wizard panel, hydrogen atoms were added, sample water

orientations were achieved using PROPKA at pH 7, and waters with

less than three hydrogen bonds to non-waters were removed from the

protein. The restrained minimization of the main protein-ligand

complex was achieved using the OPLS3e force field. Finally, the protein

was saved in PDB format for docking.

2. Ligands Preparation: All 51 ligands (Fig. 2) were drawn using

ChemDraw software and saved individually as a .cdx file. Later the ligands were imported to the project table of Schrodinger and by using

the Ligprep, all the ligands were prepared.

LigPrep is a tool employed in drug discovery and computational

chemistry to initiate and execute ligand preparation calculations. Its

main purpose is to generate stable, low-energy three-dimensional (3D)

structures of ligands, which are small molecules that can potentially

interact with a target protein or receptor. The LigPrep panel takes an

input ligand structure and performs various transformations on it to

produce multiple output structures. These transformations include

generating different protonation states (adding or removing protons to

the molecule), exploring different stereochemistry (arrangement of

atoms in 3D space), considering various tautomers (isomers with

different hydrogen arrangements), and exploring different ring

conformations (different spatial arrangements of cyclic structures). By

generating multiple output structures for each input ligand, LigPrep

helps researchers explore different potential configurations of the

molecule. This is important because the behavior and interactions of

ligands in biological systems can be influenced by their specific 3D

structures and chemical properties. By considering various protonation

states, stereochemistry, tautomers, and ring conformations, researchers

can gain insights into how the ligands might interact with their target

proteins under different physiological conditions.

3. Receptor Grid generation: After preparing the minimized protein

structure, the next step in the drug discovery process involves

generating a grid at the active site of the protein. The purpose of this

grid is to define the spatial region within which a ligand (typically a

standard ligand or known inhibitor) is already docked with the protein.

This process is facilitated by using the "receptor grid generation" panel available in the Maestro Version 13.0.013 platform of the Schrodinger

software suite.

The receptor grid generation panel allows researchers to select a specific

molecule from the prepared protein structure that represents the active

site. The active site is a region on the protein's surface or cleft where

interactions with ligands take place. By selecting this molecule, the

software can define the three-dimensional coordinates of the active site

and create a grid around it. Once the grid is generated, it serves as a

spatial framework for performing molecular docking simulations.

During docking, the ligand is virtually positioned within the grid to

explore its potential binding modes and interactions with the active site

residues of the protein. This process helps researchers predict how the

ligand will interact with the protein and estimate its binding affinity.

4. Ligand Docking: The next step is Ligand docking where the previously

generated grid file and all the prepared ligands from the Project table

were selected and performed the action using the „Ligand Docking‟

panel from the maestro. There are three types of precision i.e. Standard

Precision (SP), Extra Precision (XP), and HTVS. Generally, XP was set

for this measurement. The output of the docking process will be an XP

output file. This file contains the results of the docking simulations,

providing information on how each ligand interacts with the active site

of the protein. The docking output may include details such as the

predicted binding pose, binding affinity, and potential interactions

between the ligand and the protein. After that, the 2D images of how

the ligands interacted with the amino acids present in the active site of

that particular protein were taken using the „Ligand Interaction‟ panel.

Some of them are shown here in Fig. 3.