Using graph-based structural signatures and machine learning algorithms for molecular docking assessment of histone deacetylases and small ligands

• Alessandra G. Cioletti UFMG
• Rafael P. Lemos UFMG
• Lucas M. dos Santos UFMG
• Diego Mariano UFMG
• Raquel C. de Melo-Minardi UFMG

DOI: https://doi.org/10.5753/bsb.2024.245612

Resumo

Histone deacetylases (HDACs) are enzymes that play an essential role in regulating gene expression, with recent studies linking their inhibition to autism spectrum disorders (ASD). As a result, there is growing interest in understanding the effects of HDAC inhibition. In this paper, we used molecular docking to investigate the binding between HDACs and small ligands, focusing on two enzymes involved in embryonic development: Histone deacetylase 1 (H1) and Histone deacetylase 2 (H2). Using a graph-based structural signature algorithm, we extracted features from the resulting complexes and employed machine learning algorithms to distinguish natural ligands from decoys, achieving 72% of accuracy in the classification test.

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