Single-molecule Förster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated, open-source standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histograms of biomolecule behavior, is a user-adjustable quality threshold. Integrating standard features of smFRET analysis, DeepFRET consequently outputs the common kinetic information metrics. Its classification accuracy on ground truth data reached >95% outperforming human operators and commonly used threshold, only requiring ~1% of the time. Its precise and rapid operation on real data demonstrates DeepFRET’s capacity to objectively quantify biomolecular dynamics and the potential to contribute to benchmarking smFRET for dynamic structural biology.

Combinatorial high throughput methodologies are central for both screening and discovery in synthetic biochemistry and biomedical sciences. They are, however, often reliant on large scale analyses and thus limited by long running time and excessive materials cost. We herein present Single PARticle Combinatorial multiplexed Liposome fusion mediated by DNA (SPARCLD), for the parallelized, multi-step and nondeterministic fusion of individual zeptoliter nanocontainers. We observed directly the efficient (>93%), and leakage free stochastic fusion sequences for arrays of surface tethered target liposomes with six freely diffusing populations of cargo liposomes, each functionalized with individual lipidated ssDNA (LiNA) and fluorescent barcoded by distinct ratio of chromophores. The stochastic fusion results in distinct permutation of fusion sequences for each autonomous nanocontainer. Real-time TIRF imaging allowed the direct observation of >16000 fusions and 566 distinct fusion sequences accurately classified using machine learning.The high-density arrays of surface tethered target nanocontainers ~42,000 containers per mm 2 offers entire combinatorial multiplex screens using only picograms of material.

Metabolic control is mediated by the dynamic assemblies and function of multiple redox enzymes. A key element in these assemblies, the P450 oxidoreductase (POR), donates electrons and selectively activates numerous (>50 in humans and >300 in plants) cytochromes P450 (CYPs) controlling metabolism of drugs, steroids and xenobiotics in humans and natural product biosynthesis in plants. The mechanisms underlying POR-mediated CYP metabolism remain poorly understood and to date no ligand binding has been described to regulate the specificity of POR. Here, using a combination of computational modeling and functional assays, we identify ligands that dock on POR and bias its specificity towards CYP redox partners, across mammal and plant kingdom. Single molecule FRET studies reveal ligand binding to alter POR conformational sampling, which results in biased activation of metabolic cascades in whole cell assays. We propose the model of biased metabolism, a mechanism akin to biased signaling of GPCRs, where ligand binding on POR stabilizes different conformational states that are linked to distinct metabolic outcomes. Biased metabolism may allow designing pathway-specific therapeutics or personalized food suppressing undesired, disease-related, metabolic pathways.

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.