Machine Learning Aids Classification and Discrimination of Noncanonical DNA Folding Motifs by an Arrayed Host:Guest Sensing System

An arrayed host:guest fluorescence sensor system can discriminate among and classify multiple different noncanonical DNA structures by exploiting selective molecular recognition. The sensor is highly selective and can discriminate between folds as similar as native G-quadruplexes and those with bulges or vacancies. The host and guest can form heteroternary complexes with DNA strands, with the host acting as mediator between the DNA and dye, modulating the emission. By applying machine learning algorithms to the sensing data, prediction of the folding state of unknown DNA strands is possible with high fidelity.

Automated summary

An arrayed host:guest fluorescence sensor system can accurately discriminate and classify multiple different noncanonical DNA structures through selective molecular recognition. The sensor is highly selective and can distinguish between folds with subtle differences, such as native G-quadruplexes and those with bulges or vacancies. By forming heteroternary complexes with DNA strands, the host mediates the interaction between DNA and dye to modulate emission, and machine learning algorithms enable high-fidelity prediction of the folding state of unknown DNA strands.