Virtual screening, XGBoost based QSAR modelling, Molecular Docking and Molecular Dynamics Simulation approach to discover a new inhibitor targeting ErbB1 Protein

Abstract

ErbB1 is a protein found on certain types of human cells that binds to a substance called epidermal growth factor (EGFR). The ErbB1 protein is involved in cell signalling pathways that control cell division, proliferation, and survival. Sometimes, mutations in the EGFR gene cause ErbB1 proteins to be made in higher-than-normal amounts on some types of cancer cells. The aim of this study is using the virtual screening based on ligand and structure drug design using respectively QSAR, molecular docking & molecular dynamics simulations approaches to identify novel antitumor small molecules. Therefore, the QSAR model was developed and validated using XGBOOST as a learning algorithm classifier trained on 5215 compounds. The validated model is used for screening of more than 80k natural products downloaded and prepared from the ZINC database to offer us only 36 as potent predicted inhibitors against ErbB1. The selected active compounds were docked against the target represented by the PDB ID: 3POZ. The obtained top five scoring compounds were compared to the reference ligand TAK285, to the Lapatinib and the Erlotinib drugs, after this phase their stability into the ErbB1 protein binding site has been validated using the molecular dynamics simulation.