Conformational Plasticity of Parallel G-Quadruplex─Implications on Duplex-Quadruplex Motifs.

DNA G-quadruplexes are essential motifs in molecular biology performing a wide range of functions enabled by their unique and diverse structures. In this study, we focus on the conformational plasticity of the most abundant and biologically relevant parallel G-quadruplex topology. A multipronged approach of structure survey, solution-state NMR spectroscopy, and molecular dynamics simulations unravels subtle yet essential features of the parallel G-quadruplex topology. Stark differences in flexibility are observed for the nucleotides depending upon their positioning in the tetrad planes that are intricately correlated with the conformational sampling of the propeller loop. Importantly, the terminal nucleotides in the 5'-end versus the 3'-end of the parallel quadruplex display differential dynamics that manifests their ability to accommodate a duplex on either end of the G-quadruplex. The conformational plasticity characterized in this study provides essential cues toward biomolecular processes such as small molecular binding, intermolecular quadruplex stacking, and implications on how a duplex influences the structure of a neighboring quadruplex.

Characterization of DNA G-quadruplex Topologies with NMR Chemical Shifts.

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.