INSILCO DESIGN OF NOVEL ISOFORM SELECTIVE HISTONE DEACETYLASE INHIBITOR AS A THERAPEUTIC APPROACH FOR ALZHEIMER'S DISEASE USING MULTIPLE SEQUENCE ALIGNMENT, MACHINE LEARNING, MOLECULAR DOCKING, ADME, AND MUTATION ANALYSIS

Abstract

Histone deacetylases (HDAC) are Zn2+ dependent cofactors or class of proteins that play a positive part in cellular transcription and functioning. Overexpression of these proteins are common in the progression of various discrepancies in brain tissues brings about the deregulation of different target proteins engaged with cell development and growth connected with Alzheimer's ailment that causes the shortage in memory and learning capacity. Although various approaches have been applied to control the higher expression of HDACs by repressing them with different compound inhibitors yet constrained effectiveness has been accomplished. In this study, we used ligand-based quantitative structure-activity relationship approach followed by machine learning model generation, molecular docking, and mutation studies in order to predict novel HDAC isoform-selective inhibitor by taking into consideration the previous studied binding calculations and morphological and chemical molecular descriptors. Total 11 novel compounds were selected having quite high binding affinities with different types of HDACs. Out of these compounds, one compound named as ChEMBL1834473 were able to interact at the central Zn2+ with both class I and class II HDAC members. Overall chemical bioactivity and binding efficiency of anticipated compound recommended that the proposed compound tend to be a compelling inhibitor for Alzheimer’s disease.