STUDY OF MOLECULAR DOCKING AND MOLECULAR DYNAMIC OF FLAVONOL COMPOUNDS AS A B-CELL LYMPHOMA-2 (BCL-2) RECEPTOR INHIBITOR IN SMALL LUNG CANCER

1. Faculty of Pharmacy, Bhakti Kencana University, Bandung, Indonesia. 2. School of Pharmacy, Bandung Institute of Technology, Bandung, Indonesia. ...................................................................................................................... Manuscript Info Abstract ......................... ........................................................................ Manuscript History Received: 05 January 2020 Final Accepted: 07 February 2020 Published: March 2020

209 drug candidates, some of which are flavonoids (Muttaqin, et al., 2020).Flavonoid compounds are often known to have antioxidant benefits, especially free radical scavengers. The antioxidant ability of flavonoids can reduce the formation of free radicals and capture free radicals (Pietta, 2000). SBDD (Structure-Based Drug design) is a drug design based on a target structure based on the receptor structure responsible for the activity and toxicity of a compound in the body. SBDD utilizes information from the structure of the target protein to find the active site of the protein that binds to the drug compound. One of the stages in the process of discovering new drug compounds is the study of interactions between drug candidate compounds and receptors. This can be done in in-silico through molecular docking and molecular dynamic simulations (Muttaqin, et al., 2017). Docking is a method for predicting the best orientation of a molecule when bound to one another to form stable complexes. In this case, molecular docking is an initial screening method used to describe the interaction between a molecule as a ligand with a receptor or protein. While molecular dynamics (MDs) simulation is a simulation of the movements of interacting molecules. MDs simulation is a simulation technique that allows us to see the movement of molecules in a material by calculating the movements of each atom one by one per unit time.
This study aims to determine the interaction between flavonol compounds and B-cell lymphoma-2 (Bcl-2) receptors as potential anti lung cancer drug candidates through molecular dynamics stability between flavonol compound and B-cell lymphoma-2 (Bcl-2) receptors from the results of molecular docking.

Macromolecule preparation:
The crystal structure of B-cell lymphoma-2 (Bcl-2) was obtained from the protein data bank https://www.rcsb.org/structure/3spfwith PDB ID: 3SPF. The small molecules (ligands) and water molecules were removed, and polar hydrogens and Kollman charges were added.

Ligand preparation:
The structures of 12 flavonol derivative compounds were built using the ChemOffice suite of programs (Table 1). Geometry optimization and density functional theory (DFT) calculations were performed in Gaussian using the Becke three-parameter Lee-Yang-Parr functional at the 6-21G basis set.

Molecular docking:
Each ligand molecule was prepared for docking using AutoDock Tools 4.2.3 (Morris, et al., 2009). Hydrogen atoms were added, and partial charges of each atom resulted from the DFT calculations were incorporated. Grid maps were created by centering the grid box at the position of the natural ligand of each macromolecule with a spacing of 0.375 Å and size covering the binding cavity of each target. Lamarckian genetic algorithm,100 Number of GA Runs and medium Number of Evals were used for each simulation (Morris, et at., 1998).

Molecular Dynamics (MDs) simulations:
Five ligands with the best docking score for each target were chosen for further MDs study. MD simulations were carried out using the AMBER18 program (Case, et al., 2018). Energy minimization was carried out on the macromolecules in vacuum using the steepest descent algorithm. Then, the macromolecules were solvated with transferable intermolecular potential with 3 points water molecules in an octahedron box. Positive and negative ions were added to the system at a concentration of 0.15 N to neutralize all charges. Energy minimization was again performed on the macromolecule/solvent/ion system to release strains resulted from the solvation procedure; the steepest descent algorithm was used again. Next, the system was carefully heated to 310 K and pressurized to 1 atm. Then, equilibration was performed to ensure that the system was in a constant state. The stability of the system was evaluated by analyzing the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) of the protein backbones. A production simulation run was carried out on each macromolecule for 50 nanoseconds (ns). Trajectory analysis of the stability of ligand-protein interactions was performed by calculating the RMSD and RMSF values of the atoms at the protein binding sites throughout the simulation (Case, et al., 2005). Table 1 The results showed that only six ligands (3, 5, 7, 8, 9, and 11) had a low binding free energy with inhibition constant lower than 1 µM (Table 2), showing a promising sign that these ligands have good affinities toward their target. Ligand eight is the best ligand which binds the active site of Bcl-2 receptor with a binding free energy of -37.739 kJ/mol and the inhibition constant of 0.246 µM. It forms four hydrogen bonds towards TYR101, GLN111, LEU112, and SER122 amino acid residues; five van der Waals interactions toward GLN125, LEU130, ASN136, GLY138, and PHE146 amino acid residues; and seven pi interactions toward PHE97, ALA104, LEU108, VAL126, ARG139, and ALA142 amino acid residues (Fig. 1).
211 Interactions between the six ligands and the active site of the macromolecular targets comprise mainly of hydrogen bonds, with the ligands mainly acting as hydrogen bond donors and the protein residues as hydrogen bond acceptors.

Interaction dynamics:
Further studies were conducted on the six ligands with the lowest binding free energy and inhibition constant. The interaction dynamics between these ligands and their target were studied using MDs simulations with explicit solvent. The purpose of such simulations was to examine the effects of ligand binding on the residues of the protein targets, especially at the binding regions. Strong binders tend to lower the movements of the atoms they bind to, and generally stabilize the binding region of the protein. This was analyzed by calculating the RMSD of the protein binding site atoms throughout the 50 ns simulation. In addition, the overall fluctuation of each atom in the binding site was also analyzed by calculating the RMSF.
The RMSD values of the Bcl-2 protein backbone were compared to those of the Bcl-2 in complex with ligand 3, 5, 7, 8, 9, and 11. In figures 2 and 3, it can be observed that overall the system had increased RMSD backbone which showed that the structure of the protein started to open at about 1 ns at a distance of about 1 Å. An increase in the value of RMSD showed that the structure of the protein started to open and the test compound began to look for the suitable bonding site to the protein. However, it can be observed that only ligand 7 and 11 which were able to stabilize the protein, marked by lower RMSD values compared to the lone protein (Fig.2). While ligand 3, 5, 8, and 9 were still not able to stabilize the protein until 50 ns MDs simulation (Fig.3), but at the end of the simulation, the fourth complex's RMSD curve tended to decrease.
Most enzyme inhibitors work by binding strongly to the active sites of the enzymes and competing with their natural substrates, and also stabilizing the enzyme structure and prevents conformation changes that are required for the enzyme to catalyze reactions. Hence, by reducing the atomic deviations of the protein target, ligands 7 and 11 have shown the potential to act as Bcl-2 inhibitors. It was also supported by examining the RMSF values of the binding site atoms of the enzyme (Fig. 4).