Inferring kinetic rate constants from single-molecule FRET trajectories – a blind benchmark of kinetic analysis tools

Abstract

Single-molecule FRET (smFRET) is a versatile technique to study the dynamics and function of biomolecules since it makes nanoscale movements detectable as fluorescence signals. The powerful ability to infer quantitative kinetic information from smFRET data is, however, complicated by experimental limitations. Diverse analysis tools have been developed to overcome these hurdles but a systematic comparison is lacking. Here, we report the results of a blind benchmark study assessing eleven analysis tools used to infer kinetic rate constants from smFRET trajectories. We tested them against simulated and experimental data containing the most prominent difficulties encountered in analyzing smFRET experiments: different noise levels, varied model complexity, non-equilibrium dynamics, and kinetic heterogeneity. Our results highlight the current strengths and limitations in inferring kinetic information from smFRET trajectories. In addition, we formulate concrete recommendations and identify key targets for future developments, aimed to advance our understanding of biomolecular dynamics through quantitative experiment-derived models.

Heterogeneous migration routes of DNA triplet repeat slip-outs

“…Although, hidden smFRET states have been documented previously, our system (with six degenerate FRET states) is more complex than most systems that have been reported to date. ( 44 , 45 , 46 , 47 ) As a result of the heterogeneity in FRET states, we are unable to unambiguously assign the individual FRET states to particular 3WJ structures. However, it is clear that almost all transitions are possible.…”

“…( 30 ) Dedicated HMM analysis software for smFRET data has been freely available for over 15 years,( 28 ) and this continues to be an active area of research. ( 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ) Previously, Hu et al. ( 19 ) employed the software HaMMy developed by McKinney et al.…”

Can DyeCycling break the photobleaching limit in single-molecule FRET?

“…The (experiment-derived) simulation resulted in 80% FRET regime during which the biomolecular dynamics are resolved. The remaining gaps can be dealt with during analysis, e.g., using Hidden Markov models (implemented in SMACKS [44] or other software packages [45]) to describe the biomolecular states, and additionally three states for the ‘dark’, ‘donor-only’, and ‘acceptor only’ regimes. In this way, the quantization of the observed biomolecular kinetics is not affected by the intermittent pauses.…”