Automated kinetic-scheme-free sorting of single-molecule fluorescence events using a deep learning based hidden-state method

Single-molecule fluorescence techniques have revolutionized our ability to probe biomolecular dynamics by resolving molecular heterogeneity and transient states inaccessible to ensemble measurements. However, conventional analysis pipelines remain constrained by several factors that compromise both reproducibility and the detection of rare but biologically significant events. To overcome these challenges, we developed DASH, an automated framework that integrates bidirectional long short-term memory networks, conditional random fields, and hidden Markov models for unbiased analysis of single-molecule fluorescence trajectories. This unified platform performs three critical functions: (1) automated trajectory classification, (2) conformational state assignment, and (3) kinetic-scheme-free event sorting, all without requiring user-defined kinetic models. We demonstrate DASH’s broad applicability across diverse protein and nucleic acid systems. By eliminating manual intervention and any predefined kinetic schemes in the event-sorting stage, DASH provides a standardized, generalizable platform for extracting comprehensive mechanistic insights from single-molecule kinetics, particularly for systems exhibiting complex dynamic heterogeneity. Single-molecule fluorescence reveals hidden biomolecular dynamics, but analysis relies on manual curation and user-defined kinetic models. Here, authors present DASH, an automated framework that sorts events without predefined kinetic schemes, enabling unbiased discovery of molecular complexity.