Structure-based virtual screening for novel potential selective inhibitors of class IIa histone deacetylases for cancer treatment. (June 2021)

• Record Type: Journal Article
• Authors: Elmezayen, Ammar D.; Kemal, Yelekçi
• Is Part Of: Computational biology and chemistry. Volume 92(2021)
• Publication Date: 2021-06
• Subjects: Structure-based virtual screening -- Selective HDAC inhibitors -- ADMET -- MD simulation -- MM-PBSA; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2021.107491
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: HDACs are vital enzymes that control several cellular processes, where they regulate cell differentiation and organize the gene expression. Class IIa HDACs have significant impacts on several disorders' development such as cancer, diabetes, muscle degenerative disorders, neurological and immunological disorders. A total of ∼10 million drug-like compounds were screened in the present study against class II HDAC members. Thorough and systematic virtual screening, physiochemical properties analysis, MD simulation, and MM-PBSA calculations were carried out for designing potential selective inhibitors against class II HDACs. Seven potential inhibitors showed isoform selectivity and interacted with their corresponding targets with high affinity and good binding mode. Abstract: The fundamental cause of human cancer is strongly influenced by down- or up-regulations of epigenetic factors. Upregulated histone deacetylases (HDAC) have been shown to be effectively neutralized by the action of HDACs inhibitors (HDACi). However, cytotoxicity has been reported in normal cells because of non-specificity of several available HDACis that are in clinical use or at different phases of clinical trials. Because of the high amino acid sequence and structural similarity among HDAC enzymes, it is believed to be a challenging task to obtain isoform-selectivity. The essential aim of the present research work was to identify isoform-selective inhibitors against class IIa HDACs via structure-based drug design. Based on the highest binding affinity and isoform-selectivity, the top-ranked inhibitors were in silico tested for their absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties, which were classified as drug-like compounds. Later, molecular dynamics simulation (MD) was carried out for all compound-protein complexes to evaluate the structural stability and the biding mode of the inhibitors, which showed high stability throughout the 100 ns simulation. Free binding energy predictions by MM-PBSA method showed the high binding affinity of the identified compounds toward their respective targets. Hence, these inhibitors could be used as drug candidates or as lead compounds for more in silico or in vitro optimization to design safe isoform-selective HDACs inhibitors.