Single-Molecule FRET at 10 MHz Count Rates

Grabenhorst, Lennart; Sturzenegger, Flurin; Hasler, Moa; Schuler, Benjamin; Tinnefeld, Philip

doi:10.1021/jacs.3c13757

Cited by 3 publications

(1 citation statement)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, it is common to perform global fits to many measurements with different dye positions and solvent conditions 54 – 56 , fit hidden Markov models to photon traces 21 , 46 , 51 , or dissect the correlation between FRET efficiency and other fluorescence observables that report on shorter timescales 44 , 45 , 55 . New experimental approaches are also being developed to shorten the timescale over which FRET efficiencies are measured 57 – 61 . Nonetheless, accounting for time-averaging could dramatically improve agreement between experiments and simulations enabling these two approaches to be used even more effectively to advance our understanding of the ensemble-function relationship.…”

Section: Introductionmentioning

confidence: 99%

Accounting for fast vs slow exchange in single molecule FRET experiments reveals hidden conformational states

Miller,

Mallimadugula,

Zimmerman

et al. 2024

Preprint

View full text Add to dashboard Cite

Proteins are dynamic systems whose structural preferences determine their function. Unfortunately, building atomically detailed models of protein structural ensembles remains challenging, limiting our understanding of the relationships between sequence, structure, and function. Combining single molecule Förster resonance energy transfer (smFRET) experiments with molecular dynamics simulations could provide experimentally grounded, all-atom models of a protein’s structural ensemble. However, agreement between the two techniques is often insufficient to achieve this goal. Here, we explore whether accounting for important experimental details like averaging across structures sampled during a given smFRET measurement is responsible for this apparent discrepancy. We present an approach to account for this time-averaging by leveraging the kinetic information available from Markov state models of a protein’s dynamics. This allows us to accurately assess which timescales are averaged during an experiment. We find this approach significantly improves agreement between simulations and experiments in proteins with varying degrees of dynamics, including the well-ordered protein T4 lysozyme, the partially disordered protein apolipoprotein E (ApoE), and a disordered amyloid protein (Aβ40). We find evidence for hidden states that are not apparent in smFRET experiments because of time averaging with other structures, akin to states in fast exchange in NMR, and evaluate different force fields. Finally, we show how remaining discrepancies between computations and experiments can be used to guide additional simulations and build structural models for states that were previously unaccounted for. We expect our approach will enable combining simulations and experiments to understand the link between sequence, structure, and function in many settings.

show abstract

Section: Introductionmentioning

confidence: 99%

Accounting for fast vs slow exchange in single molecule FRET experiments reveals hidden conformational states

Miller,

Mallimadugula,

Zimmerman

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Information-theoretical limit on the estimates of dissipation by molecular machines using single-molecule fluorescence resonance energy transfer experiments

Song,

Makarov,

Vouga

2024

The Journal of Chemical Physics

View full text Add to dashboard Cite

Single-molecule fluorescence resonance energy transfer (FRET) experiments are commonly used to study the dynamics of molecular machines. While in vivo molecular processes often break time-reversal symmetry, the temporal directionality of cyclically operating molecular machines is often not evident from single-molecule FRET trajectories, especially in the most common two-color FRET studies. Solving a more quantitative problem of estimating the energy dissipation/entropy production by a molecular machine from single-molecule data is even more challenging. Here, we present a critical assessment of several practical methods of doing so, including Markov-model-based methods and a model-free approach based on an information-theoretical measure of entropy production that quantifies how (statistically) dissimilar observed photon sequences are from their time reverses. The Markov model approach is computationally feasible and may outperform model free approaches, but its performance strongly depends on how well the assumed model approximates the true microscopic dynamics. Markov models are also not guaranteed to give a lower bound on dissipation. Meanwhile, model-free, information-theoretical methods systematically underestimate entropy production at low photoemission rates, and long memory effects in the photon sequences make these methods demanding computationally. There is no clear winner among the approaches studied here, and all methods deserve to belong to a comprehensive data analysis toolkit.

show abstract