Utilizing the Pyrx Program to Conduct in Silico Screening of Polyphenol Compounds as Potential Inhibitors of the Epidermal Growth Factor Receptor (EGFR) in Mango Plants(Mangifera Indica)

Mango (Mangifera indica) is a tropical plant from Asia with the potential to cure diseases including colorectal cancer. The research aimed to determine the amount of free energy (ΔG) from the polyphenol compounds of mango plants on Epidermal Growth Factor Receptor (EGFR) in silico using PyRx program with molecular docking method. The receptor used was downloaded from the Protein Data Bank (PDB) database with 1YY9 codes and the ligands used were downloaded from the KNApSAcK database. The validation of the docking method showed the RMSD value of 1.645 Å in the Alpha-Mannose; 1.381 Å in Beta-D-Mannose; 1.605 Å at 2-(Aetylamino)-2-Deoxy-A-D-Glucopyronose; and 1.334 Å in N-Acetyl-D-Glucosamine. As the standard, Cetuximab was used as a therapy for colorectal cancer. The polyphenol compounds in mango plants showed bond-free energy (ΔG) with a range of values between -8.5 kcal/molto -6.0 kcal/mol, and the best value was Aurasperone D (-8.5 kcal/mol). Compared to Cetuximab (-6.4 kcal/mol), the value of bond-free energy (ΔG) of Aurasperoune compounds was smaller with quite a farther range of values -2.5 kcal/mol. Polyphenolic compounds discovered in Mango plants displayed potential as inhibitors for the Epidermal Growth Factor Receptor (EGFR).

Keywords:In Silico, Mangifera indica, Epidermal Growth Factor Receptor, Root Mean Square Deviation

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Langkah Docking Dengan Autodock Vina

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HitPick

HitPick is a web server that facilitates the analysis of chemical screenings by identifying hits and predicting their molecular targets. The target prediction functionality can also be used in a stand-alone fashion.

For hit identification, the widely used B-score method (1) is applied.

For target prediction, HitPick applies a new approach that combines two 2D molecular similarity based methods, namely, simple 1-Nearest-Neighbour (1NN) similarity searching (2) and a machine learning method based on Laplacian-modified naive Bayesian models (3).

Virtual Ligand Screening – part one

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