Rtt109 is a fungal-specific histone acetyltransferase that acetylates histone H3 residues and is a promising antifungal drug target. To identify novel Rtt109 inhibitors as potential drug scaffolds, we employed in vitro high throughput screening (HTS) and various computer-assisted strategies, including docking, 3D-QSAR, pharmacophore modeling, shape-based searching, and Support Vector Machine (SVM) mining. Experimental HTS on our in-house collection yielded 213 hits with low-micromolar activity against Rtt109. Validated pharmacophore hypotheses developed from these hits pinpoint key Rtt109 ligand-receptor interactions. 3D-QSAR CoMFA and CoMSIA models were also derived from the hit series, and display high self-consistency (r2 = 0.985 [CoMFA] and r2 = 0.976 [CoMSIA]) and robust internal predictivity (rcv2 = 0.754 [CoMFA] and rcv2 = 0.654 [CoMSIA]). Notably, the pharmacophore and 3D-QSAR models agree well with each other and with experimentally defined structural features in the Rtt109 lysine binding tunnel, suggesting that our screening hits may target Rtt109 at that location.

Identification of novel fungal histone acetyltransferase Rtt109 inhibitors using in vitro HTS, pharmacophore mapping, 3D-QSAR, shape-based searching, and SVM

244th National Meeting (2012)

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