Identification and Classification of Functional Split G‑Quadruplexes Using Machine Learning-Guided Activity Screening

Split G-quadruplexes are considered
excellent tools for
biosensing
and diagnostics, but splitting G-quadruplexes may often lead to a
loss of function, limiting their effectiveness. This study aims to
identify and classify functional split G-quadruplexes based on the
ability of the G-quadruplex motif to generate a fluorescence turn-on
response and undergo phase separation. A series of split G-quadruplexes
were designed, and their characterization was conducted using fluorescence
spectroscopy, fluorescence microscopy, UV–vis spectroscopy,
and circular dichroism to investigate their functional properties
(fluorogenic response, phase separation, and DNAzyme activity). Multivariate
analysis and machine learning-based pattern recognition revealed that
structural changes due to the splitting of G4-forming sequences correlate
with their ability to form phase-separated condensates, which enhance
their fluorogenic and DNAzyme activity. The machine learning-based
activity screening was used to identify split G-quadruplexes, which
may have high, moderate, or low functional activity. This integrative
approach provides a predictive framework for engineering functionally
active split G-quadruplexes and establishes a platform for their application
in molecular diagnostics.