Computational Characterization of Flavonoid Recognition of CAG-Repeat Mismatch Motifs Using Molecular Docking, Global Reactivity Descriptors, and Pharmacokinetic Profiling

Trinucleotide repeat expansions underlie several neurodegenerative diseases, including Huntington’s disease and other polyQ-related disorders. This work investigates the therapeutic potential of dietary flavonoids, naturally occurring polyphenolic compounds, as candidate small-molecule ligands for recognizing A·A mismatch motifs within CAG trinucleotide repeats and examines how flavonoid chemical structure and electronic descriptors contribute to predicted binding. Fifteen flavonoids representing five subclasses were studied together with naphthyridine-azaquinolone (NA) as a reference ligand using density functional theory, global reactivity descriptors, hydration free-energy analysis, molecular docking, and ADME prediction. NA has been reported to specifically bind slipped-CAG DNA intermediates of expansion mutations. Quercetin and myricetin showed low energy gaps and global hardness comparable to NA, whereas catechin, EGCG, and naringenin demonstrated high global hardness. Docking revealed ligand- and receptor-dependent recognition, with EGCG exhibiting the strongest predicted binding affinity, nobiletin the weakest, and several flavonols displaying favorable interactions across different CAG-repeat structures. Binding strength correlated moderately with chemical potential (R = 0.62, p = 0.011) and strongly with hydration free energy (R = −0.82, p = 0.0001). ADME analysis showed that the predicted binding did not necessarily translate into favorable pharmacokinetic behavior; EGCG showed less favorable permeability- and metabolism-related scores, whereas 7,8-dihydroxyflavone, catechin, naringenin, and nobiletin presented balanced final consensus ADME scores. To our knowledge, this work represents one of the few integrated computational studies exploring flavonoids as small-molecule scaffolds for targeting trinucleotide CAG-repeat mismatch motifs.