biostudies-literatureZhang LNational Institutes of HealthNIGMS NIH HHS1167-1175https://www.ebi.ac.uk/biostudies/studies/S-EPMC12922942biostudies-literatureUnknown129(4)Single-molecule fluorescence resonance energy transfer (smFRET) has emerged as a pivotal technique for probing biomolecular dynamics over time at nanometer scales. Quantitative analyses of smFRET time traces remain challenging due to confounding factors such as low signal-to-noise ratios, photophysical effects such as bleaching and blinking, and the complexity of modeling the underlying biomolecular states and kinetics. The dynamic distance information shaping the smFRET trace powerfully uncovers even transient conformational changes in single biomolecules both at or far from equilibrium, relying on trace idealization to identify specific interconverting states. Conventional trace idealization methods based on hidden Markov models (HMMs) require substantial a priori knowledge of the systemThe journal of physical chemistry. BPretrained Deep Neural Network Kin-SiM for Single-Molecule FRET Trace Idealization.PMC12922942R35 GM131922GM131922Walter NGLi JZhang LfalsePretrained Deep Neural Network Kin-SiM for Single-Molecule FRET Trace Idealization.Single-molecule fluorescence resonance energy transfer (smFRET) has emerged as a pivotal technique for probing biomolecular dynamics over time at nanometer scales. Quantitative analyses of smFRET time traces remain challenging due to confounding factors such as low signal-to-noise ratios, photophysical effects such as bleaching and blinking, and the complexity of modeling the underlying biomolecular states and kinetics. The dynamic distance information shaping the smFRET trace powerfully uncovers even transient conformational changes in single biomolecules both at or far from equilibrium, relying on trace idealization to identify specific interconverting states. Conventional trace idealization methods based on hidden Markov models (HMMs) require substantial a priori knowledge of the system2025-01-01T00:00:00Z2025 Jan2026-07-16T12:28:44.795Z2026-07-09T10:54:44.543ZS-EPMC129229423980957310.1021/acs.jpcb.4c05276