Histone deacetylase inhibitors: structure-based modeling and isoform-selectivity prediction

Area of Science:

• Medicinal Chemistry
• Computational Biology
• Drug Discovery

Background:

• Histone deacetylases (HDACs) are crucial drug targets, with eleven human isoforms exhibiting distinct roles.
• Diverse structure-activity relationship (SAR) data, including crystal structures of various ligands with different enzyme isoforms, present challenges for integrative analysis.
• Existing computational tools may not effectively handle such inhomogeneous datasets for drug discovery.

Purpose of the Study:

• To develop and apply a novel computational tool, COMBINEr, for integrative data mining of diverse SAR data.
• To build robust 3D Quantitative Structure-Activity Relationship (QSAR) models for human zinc-based HDACs.
• To predict the isoform selectivity of potential HDAC inhibitors.

Main Methods:

• An enhanced version of Comparative Binding Energy (COMBINE) analysis, termed COMBINEr, was developed.
• COMBINEr integrates both ligand-based and structure-based alignments for model building.
• The approach was applied to a dataset of eleven human HDACs and their inhibitors, utilizing freely available academic software.

Main Results:

• Several 3D QSAR models were successfully built for the eleven human zinc-based HDACs.
• The COMBINEr approach demonstrated effectiveness in mining inhomogeneous structure-activity data.
• The developed models showed potential for predicting HDAC inhibitor isoform selectivity.

Conclusions:

• COMBINEr offers a novel and comprehensive solution for analyzing diverse structure-activity data in drug discovery.
• The tool facilitates the prediction of isoform selectivity for HDAC inhibitors, aiding in the development of targeted therapies.
• The use of freely available academic software makes COMBINEr an accessible resource for researchers.