Insights into the Kinetic Partitioning Folding Dynamics of the Human Telomeric G-Quadruplex from Molecular Simulations and Machine Learning.

Abstract

The human telomeric DNA G-quadruplex follows a kinetic partitioning folding mechanism. The underlying folding landscape potentially has many minima separated by high free-energy barriers. However, using current theoretical models to characterize this complex folding landscape has remained a challenging problem. In this study, by developing a hybrid atomistic structure-based model that merges structural information on the hybrid-1, hybrid-2, and chair-type G-quadruplex topologies, we investigated a kinetic partitioning folding process of human telomeric DNA involving three native folds. The model was validated as it reproduced the experimental observation that the hybrid-1 conformation is the major fold and the hybrid-2 conformation is kinetically more accessible. A three-step mechanism was revealed for the formation of the hybrid-1 conformation, while a two-step mechanism was demonstrated for the formation of hybrid-2 and chair-type conformations. Likewise, a class of state in which structures adopted inappropriate combinations of syn/anti guanine nucleotides was found to greatly slow down the folding process. In addition, by employing the XGBoost machine learning algorithm, three interatom distances and six dihedral angles were identified as essential internal coordinates to represent the low-dimensional folding landscape. The strategy of coupling the multibasin model and the machine learning algorithm may be useful to investigate the conformational dynamics of other multistate biomolecules.