RESUMO
G-quadruplexes are nucleic acid motifs formed by stacking of guanosine-tetrad pseudoplanes. They perform varied biological roles, and their distinctive structural features enable diverse applications. High-resolution structural characterization of G-quadruplexes is often time-consuming and expensive, calling for effective methods. Herein, we develop NMR chemical shifts and machine learning-based methodology that allows direct, rapid, and reliable analysis of canonical three-plane DNA G-quadruplexes sans isotopic enrichment. We show, for the first time, that each unique topology enforces a specific distribution of glycosidic torsion angles. Newly acquired carbon chemical shifts are exquisite probes for the dihedral angle distribution and provide immediate and unambiguous backbone topology assignment. The support vector machine learning methodology aids resonance assignment by providing plane indices for tetrad-forming guanosines. We further demonstrate the robustness by successful application of the methodology to a sequence that folds in two dissimilar topologies under different ionic conditions, providing its first atomic-level characterization.