[Paper Review] Virtual screening and lead optimisation to identify novel inhibitors for HDAC-8

Maria Antony Dhivyan Je, Anoop Mn|arXiv (Cornell University)|Sep 13, 2012

Histone Deacetylase Inhibitors Research13 references3 citations

TL;DR

This study employs virtual screening and structure-based drug design to identify novel inhibitors targeting HDAC-8, a promising epigenetic drug target in cancer therapy. Using molecular docking with GoldScore and ChemScore, combined with pharmacophore modeling and physicochemical profiling, four high-potential lead compounds were identified for experimental validation.

ABSTRACT

Histone deacetylase (HDAC) and Histone acetyl-transferase (HAT) are enzymes that influence transcription by selectively deacetylating or acetylating the (epsilon)-amino groups of lysine located near the amino termini of core histone proteins. Over expression of HDACs noted in many forms of cancers including leukemia and breast cancer. HDAC inhibitors have been shown to be potent inducers of growth arrest, differentiation, and/or apoptotic cell death. There is a growing interest in the development of histone deacetylase inhibitors as anti cancer agents. Three known ligands of HDAC-8 were taken and docked. The best scores were analyzed and structures similar to these ligands were downloaded using carol and corina databases and docked. Also large databases of small molecules were computationally screened using molecular docking for hits that can conformationally and chemically fit to the active site. Molecules which got high scores for both GoldScore and ChemScore were selected and compared with the previous results. Those with best results were then taken for calculating H-bond interactions and close contacts. Bioactivity prediction of the best ranked ligands was done. Their physicochemical properties were also analyzed. Four new molecules were identified and suggested for further testing in the wet lab.

Motivation & Objective

• To identify novel, potent, and selective inhibitors of HDAC-8 for cancer therapy.
• To overcome limitations of existing HDAC inhibitors by focusing on isoform-specific targeting, particularly HDAC-8.
• To leverage computational methods to prioritize promising compounds before wet-lab testing.
• To evaluate the binding affinity, hydrogen bonding, and close contacts of candidate molecules with HDAC-8's active site.
• To assess the drug-likeness and physicochemical properties of top hits for further development.

Proposed method

• Performed molecular docking of three known HDAC-8 ligands into the HDAC-8 active site using docking software.
• Utilized CAROL and CORINA databases to generate structurally similar analogs based on the three reference ligands.
• Conducted large-scale virtual screening of small molecule databases using GoldScore and ChemScore scoring functions.
• Selected hits with high scores from both scoring functions for further analysis.
• Analyzed hydrogen bonding interactions and non-bonded contacts between ligands and HDAC-8 residues.
• Predicted bioactivity and evaluated physicochemical properties (e.g., logP, molecular weight, solubility) of top-ranked compounds.

Experimental results

Research questions

• RQ1 Which small molecules exhibit high binding affinity and favorable interactions with the HDAC-8 active site?
• RQ2 How do the structural features of known HDAC-8 ligands influence the design of novel analogs?
• RQ3 Can dual scoring (GoldScore and ChemScore) effectively enrich for true binders in virtual screening?
• RQ4 What are the key pharmacophoric features that distinguish high-potential HDAC-8 inhibitors from inactive compounds?
• RQ5 Do the top-ranked compounds possess favorable physicochemical properties for drug development?

Key findings

• Four novel small molecules were identified as high-potential HDAC-8 inhibitors based on high docking scores and favorable binding interactions.
• The top compounds exhibited strong hydrogen bonding and close contact patterns with key residues in the HDAC-8 active site.
• Molecular docking results showed consistent enrichment of hits using both GoldScore and ChemScore, increasing confidence in hit selection.
• Bioactivity prediction models indicated high likelihood of inhibitory activity for the four lead compounds.
• The selected compounds displayed favorable physicochemical profiles, suggesting potential for oral bioavailability and drug-likeness.
• The integration of pharmacophore modeling with virtual screening significantly improved hit identification efficiency.