Pretrained Deep Neural Network Kin-SiM for Single-Molecule FRET Trace Idealization.

ABSTRACT: 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 system

SUBMITTER: Zhang L 

PROVIDER: S-EPMC12922942 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature