AI‐Assisted Digital Single‐Molecule Activity Tracker for Decoupling Intrinsic Heterogeneity from Photo‐Oxidative Damage in High‐Photon‐Flux Enzymology

ABSTRACT

Single‐molecule enzymes serve as molecular motors for long‐read sequencing, where laser tolerance under high photon flux is a critical limiting factor for ultra‐long reads. However, elucidating the mechanism of laser‐induced enzyme inactivation remains a technical bottleneck due to the lack of long‐term, high‐throughput single‐molecule evaluation methods to decouple intrinsic heterogeneity from photodamage. Here, a digital Single‐Molecule Activity Tracker (dSMAT) is presented, combining deep learning with high‐throughput digital microfluidics to enable the precision tracking of thousands of compartmentalized single‐molecule reactions for 15 h. This strategy reveals a distinct photoinactivation mechanism designated as oxidative scarring through comparative tracking of individual polymerases before and after laser irradiation. This process is driven by the stochastic accumulation of photochemical lesions on redox‐sensitive residues (specifically Methionine, Tryptophan, and Cysteine) within functionally accessible pathways, creating a kinetically disordered subpopulation. A synergistic reductive‐antioxidant buffer system is engineered to mitigate this effect and rescue kinetic homogeneity. Quantitative cross‐platform validation via single‐molecule real‐time sequencing confirms that dSMAT‐derived kinetic metrics—including catalytic rate, heterogeneity, and temporal stability—deterministically govern sequencing read limits. This work establishes a mechanistically sound biophysical framework for the rational design of photostable molecular motors, offering a generalizable strategy for enhancing high‐photon‐flux enzymology across genomic and biotechnological applications.