Machine learning approaches for the identification of ligands of the autophagy marker LC3

Abstract

The LC3 proteins play a crucial role in autophagy by participating to the formation of the autophagosome. Modulation of autophagy by molecular interference with LC3 proteins could help to understand this complex fundamental biological process and how it is involved in several pathologies. Identifying new LC3 ligands is a useful contribution to this aim. In the present study, we created a PubChem library of 749 compounds having a structure based on the central scaffold of novobiocin, a reported LC3A ligand. A robust, rapid and exhaustive algorithm was used for docking each compound of this database as ligands within the dihydronovobiocin binding site, providing a docking score. Remarkable reliability and consistency between docking scores and the reported binding efficiencies of known ligands was observed, validating the machine leaning protocol used in this study. Investigation of the binding mode of the ligands having the best docking score provides additional insights in possible mode of actions of the LC3 identified ligands.

Keywords

• LC3
• Ligand
• Docking simulations
• Autophagy
• Deep learning