A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories

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 test 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

Reply to: On the statistical foundation of a recent single molecule FRET benchmark

In their ‘Matters Arising’ manuscript, Saurabh et al. discuss two issues related to single-molecule Förster resonance energy transfer (smFRET) experiments: the use of the Gaussian noise approximation and spectral crosstalk. Their arguments are based on simulations obtained with parameters that differ significantly from the typical conditions measured experimentally, and, thus, from the regime included in the original study (Götz et al.1). In addition, they make claims about our multi-lab blind study that we would like to rectify. In Table 1, we provide a list of specific statements made by Saurabh et al. with our respective explanations.In our reply, we will discuss three points, summarized here and detailed below. 1.smFRET trajectories from typical surface-tethered experiments are well described by Gaussian noise models (mean photon counts are >50 per data point). Non-Gaussian Poisson noise only becomes relevant for smFRET data with extremely low photon counts, which is generally avoided by increasing the laser power and/or integration time of the experiment. 2. Spectral crosstalk correction is relevant for determining correct FRET efficiencies and FRET-derived distances, but it does not impact the kinetic rate derivation, which is the focus of Götz et al. 3. The study of Götz et al. compares the strengths and weaknesses of currently available kinetic tools to draw lessons for further development. It does not “favor” any approaches or “lead to bias” etc. as incorrectly stated by Saurabh et al. Saurabh et al. are welcome to conduct dedicated studies on the specific features they propose to extend the work of Götz et al

Direct Observation of Sophorolipid Micelle Docking in Model Membranes and Cells by Single Particle Studies Reveals Optimal Fusion Conditions

Sophorolipids (SLs) are naturally produced glycolipids that acts as drug delivery for a spectrum of biomedical applications, including as an antibacterial antifungal and anticancer agent, where they induce apoptosis selectively in cancerous cells. Despite their utility, the mechanisms underlying their membrane interactions, and consequently cell entry, remains unknown. Here, we combined a single liposome assay to observe directly and quantify the kinetics of interaction of SL micelles with model membrane systems, and single particle studies on live cells to record their interaction with cell membranes and their cytotoxicity. Our single particle readouts revealed several repetitive docking events on individual liposomes and quantified how pH and membrane charges, which are known to vary in cancer cells, affect the docking of SL micelles on model membranes. Docking of sophorolipids micelles was found to be optimal at pH 6.5 and for membranes with −5% negatively charge lipids. Single particle studies on mammalian cells reveled a two-fold increased interaction on Hela cells as compared to HEK-293 cells. This is in line with our cell viability readouts recording an approximate two-fold increased cytotoxicity by SLs interactions for Hela cells as compared to HEK-293 cells. The combined in vitro and cell assays thus support the increased cytotoxicity of SLs on cancer cells to originate from optimal charge and pH interactions between membranes and SL assemblies. We anticipate studies combining quantitative single particle studies on model membranes and live cell may reveal hitherto unknown molecular insights on the interactions of sophorolipid and additional nanocarriers mechanism

Single-Particle Tracking of <i>Thermomyces lanuginosus</i> Lipase Reveals How Mutations in the Lid Region Remodel Its Diffusion

The function of most lipases is controlled by the lid, which undergoes conformational changes at a water–lipid interface to expose the active site, thus activating catalysis. Understanding how lid mutations affect lipases’ function is important for designing improved variants. Lipases’ function has been found to correlate with their diffusion on the substrate surface. Here, we used single-particle tracking (SPT), a powerful tool for deciphering enzymes’ diffusional behavior, to study Thermomyces lanuginosus lipase (TLL) variants with different lid structures in a laundry-like application condition. Thousands of parallelized recorded trajectories and hidden Markov modeling (HMM) analysis allowed us to extract three interconverting diffusional states and quantify their abundance, microscopic transition rates, and the energy barriers for sampling them. Combining those findings with ensemble measurements, we determined that the overall activity variation in the application condition is dependent on surface binding and lipase mobility when bound. Specifically, the L4 variant with a TLL-like lid and wild-type (WT) TLL displayed similar ensemble activity, but WT bound stronger to the surface than L4, while L4 had a higher diffusion coefficient and thus activity when bound to the surface. These mechanistic elements can only be de-convoluted by our combined assays. Our findings offer fresh perspectives on the development of the next iteration of enzyme-based detergent