In silico evaluation of anti-colorectal cancer inhibitors by Resveratrol derivatives targeting Armadillo repeats domain of APC: molecular docking and molecular dynamics simulation

Abstract

Colorectal cancer is the second leading cause of cancer-related deaths. In 2018, there were an estimated 1.8 million cases, and this number is expected to increase to 2.2 million by 2030. Despite its prevalence, the current therapeutic option has a lot of side effects and limitations. Therefore, this study was designed to employ a computational approach for the identification of anti-cancer inhibitors against colorectal cancer using Resveratrol derivatives. Initially, the pass prediction spectrum of 50 derivatives was conducted and selected top seven compounds based on the maximum pass prediction score. After that, a comprehensive analysis, including Lipinski Rule, pharmacokinetics, ADMET profile study, molecular orbitals analysis, molecular docking, molecular dynamic simulations, and MM-PBSA binding free energy calculations. The reported binding affinity ranges of Resveratrol derivatives from molecular docking were -6.1 kcal/mol to -7.9 kcal/mol against the targeted receptor of human armadillo repeats domain of adenomatous polyposis coli (APC) (PDB ID: 3NMW). Specifically, our findings reported that two compounds [(03) Resveratrol 3-beta-mono-D-glucoside, and (29) Resveratrol 3-Glucoside] displayed the highest level of effectiveness compared to all other derivatives (-7.7 kcal/mol and -7.9 kcal/mol), and favorable drug-likeness, and exceptional safety profiles. Importantly, almost all the molecules were reported as free from toxic effects. Subsequently, molecular dynamic simulations conducted over 100ns confirmed the stability of the top two ligand-protein complexes. These findings suggest that Resveratrol derivatives may be effective drug candidate to manage the colorectal cancer. However, further experimental research, such as in vitro/in vivo studies, is essential to validate these computational findings and confirm their practical value.

Keywords: colorectal cancer; drug design; molecular docking; molecular dynamics simulation; resveratrol derivatives.

Figure 1

Molecular structure of selected ligands. Although, they have similar physicochemical properties. However, each of the ligands contains different structural shape, and functional unit.

Figure 2

Displayed three-dimensional structure of selected target proteins.

Figure 3

View of the molecular electrostatic potential surface of the ground state of the optimized structure of compounds, obtained by using the DFT/B3LYP/6-31G method.

Figure 4

Molecular docking pocket, and active site analysis.

Figure 5

(A) RMSD for complex systems of 03, 29, and standard protein. (B) RMSD for backbone atoms of 03, 29, and standard protein.

Figure 6

RMSF for backbone atoms of protein.

Figure 7

Represents the ROG values of the protein–ligand complexes to the protein backbone for 100 ns. RoG of 03, 29, and standard are shown in black, red, and blue respectively.

Figure 8

Represents the number of hydrogen bonds responsible for the stability of the complexes (03, 29, and standard) throughout the 100 ns.

Figure 9

Principal Component Analysis Backbone of complex 03, 29, and Standard. Every data point represents the protein’s conformation about the X and Y axes. The simulation used a chromatic arrangement of blue and red dots to depict the extent of conformational alterations. The color gradient, ranging from blue to white to red, functioned as a visual depiction of the simulation’s duration. The color blue represents the start time step, the color white represents the intermediate time step, and the color red represents the final time step.

Figure 10

Dynamic cross-correlation matrix (DCCM) plots for (03, 29, and Standard).

Figure 11

Binding free energy plots for ligand 03, ligand 29 and Standard.