Identification of novel potential scaffold for class I HDACs inhibition: An in-silico protocol based on virtual screening, molecular dynamics, mathematical analysis and machine learning

Abstract

Histone deacetylases (HDACs) family has been widely reported as an important class of enzyme targets for cancer therapy. Much effort has been made in discovery of novel scaffolds for HDACs inhibition besides existing hydroxamic acids, cyclic peptides, benzamides, and short-chain fatty acids. Herein we set up an in-silico protocol which not only could detect potential Zn2+ chelation bonds but also still adopted non-bonded model to be effective in discovery of Class I HDACs inhibitors, with little human's subjective visual judgment involved. We applied the protocol to screening of Chembridge database and selected out 7 scaffolds, 3 with probability of more than 99%. Biological assay results demonstrated that two of them exhibited HDAC-inhibitory activity and are thus considerable for structure modification to further improve their bio-activity.