Computational Screening and Identification of Efficient Drug Candidates for Niemann-Pick Disease

Abstract

Niemann-Pick disease (NPD) is a rare genetic ail- ment that impairs the body’s capacity to metabolise lipids, leading to the accumulation of fatty substances in a number of organs, including the liver, spleen, lungs, bone marrow, and brain.There is currently no cure for NPD, and treatment is primarily focused on managing symptoms and providing supportive care. This study aims to provide an efficient drug candidate for NPD. By identifying the binding pockets of the NPD, suitable inhibitors were screened in PDB databases followed by molecular docking, molecular dynamics was performed to find its binding affinity and stability, followed by binding free energy estimation. The drug QDG has a high binding affinity of -7.95 kcal/mol with the NPD protein target. The 5U74-QDG complex was more stable, with an RMSD of 0.37 nm and two hydrogen bonds formed between ALA885 and LEU1045 of the 5U74 protein and theQDG drug. Additionally, it has the lowest binding free energy of

-41.63 kJ/mol. The effectiveness of pharmaceuticals is shown by employing computational tools to analyse various medications. These strategies include docking, molecular simulation, and binding free energy. Based on the findings, we conclude that medicine QDG is a far more potent inhibitor of Niemann-Pick disease than competing medications.