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Insulin formulations with diverse oligomerization states are the hallmark of interventions for the treatment of diabetes. Here using single-molecule recordings we firstly reveal that insulin oligomerization can operate via monomeric additions and secondly quantify the existence, abundance and kinetic characterization of diverse insulin assembly and disassembly pathways involving addition of monomeric, dimeric or tetrameric insulin species. We propose and experimentally validate a model where the insulin self-assembly pathway is rerouted, favoring monomeric or oligomeric assembly, by solution concentration, additives and formulations. Combining our practically complete kinetic characterization with rate simulations, we calculate the abundance of each oligomeric species from nM to mM offering mechanistic insights and the relative abundance of all oligomeric forms at concentrations relevant both for secreted and administrated insulin. These reveal a high abundance of all oligomers and a significant fraction of hexamer resulting in practically halved bioavailable monomer concentration. In addition to providing fundamental new insights, the results and toolbox presented here can be universally applied, contributing to the development of optimal insulin formulations and the deciphering of oligomerization mechanisms for additional proteins.

Macromolecules organize themselves into discrete membrane-less compartments. Mounting evidence has suggested that nucleosomes as well as DNA itself can undergo clustering or condensation to regulate genomic activity. Current in vitro condensation studies provide insight into the physical properties of condensates, such as surface tension and diffusion. However, methods that provide the resolution needed for complex kinetic studies of multicomponent condensation are desired. Here, we use a supported lipid bilayer platform in tandem with total internal reflection microscopy to observe the two-dimensional movement of DNA and nucleosomes at the single-molecule resolution. This dimensional reduction from three-dimensional studies allows us to observe the initial condensation events and dissolution of these early condensates in the presence of physiological condensing agents. Using polyamines, we observed that the initial condensation happens on a time scale of minutes while dissolution occurs within seconds upon charge inversion. Polyamine valency, DNA length, and GC content affect the threshold polyamine concentration for condensation. Protein-based nucleosome condensing agents, HP1α and Ki-67, have much lower threshold concentrations for condensation than charge-based condensing agents, with Ki-67 being the most effective, requiring as low as 100 pM for nucleosome condensation. In addition, we did not observe condensate dissolution even at the highest concentrations of HP1α and Ki-67 tested. We also introduce a two-color imaging scheme where nucleosomes of high density labeled in one color are used to demarcate condensate boundaries and identical nucleosomes of another color at low density can be tracked relative to the boundaries after Ki-67-mediated condensation. Our platform should enable the ultimate resolution of single molecules in condensation dynamics studies of chromatin components under defined physicochemical conditions.

Amyloid aggregation is associated with many diseases and may also occur in therapeutic protein formulations. Addition of co-solutes is a key strategy to modulate the stability of proteins in pharmaceutical formulations and select inhibitors for drug design in the context of diseases. However, the heterogeneous nature of this multi-component system in terms of structures and mechanisms poses a number of challenges for the analysis of the chemical reaction. Combining a spatially resolved fluorescence approach with single molecule microscopy and machine learning approaches, we disentangle the different contributions from multiple species within a single aggregation experiment. Moreover, we link the presence of interfaces to the degree of heterogeneity of the aggregation kinetics and retrieve the rate constants and underlying mechanisms for single aggregation events, providing a general tool for a comprehensive analysis of self-assembly reactions.Table of Contents

The morphology of protein assemblies impacts their behavior and contributes to beneficial and aberrant cellular responses. While single-molecule localization microscopy provides the required spatial resolution to investigate these assemblies, the lack of universal robust analytical tools to extract and quantify underlying structures limits this powerful technique. Here we present SEMORE, a semi-automatic machine learning framework for universal, system and input-dependent, analysis of super-resolution data. SEMORE implements a multi-layered density-based clustering module to dissect biological assemblies and amorphology fingerprintingmodule for quantification by multiple geometric and kinetics-based descriptors. We demonstrate SEMORE on simulations and diverse raw super-resolution data; time-resolved insulin aggregates and imaging of nuclear pore complexes. SEMORE extracts and quantifies all protein assemblies enabling classification of heterogeneous insulin aggregation pathways and NPC geometry in minutes. SEMORE is a general analysis platform for super-resolution data, and being the first time-awar e framework can also support the rise of 4D super-resolution data.

Reconstitution of membrane proteins into model membranes is an essential approach for their functional analysis under chemically defined conditions. Established model-membrane systems used in ensemble average measurements are limited by sample heterogeneity and insufficient knowledge of lipid and protein content at the single vesicle level, which limits quantitative analysis of vesicle properties and prevents their correlation with protein activity. Here, we describe a versatile total internal reflection fluorescence microscopy-based bleaching protocol that permits parallel analysis of multiple parameters (physical size, tightness, unilamellarity, membrane protein content, and orientation) of individual proteoliposomes prepared with fluorescently tagged membrane proteins and lipid markers. The approach makes use of commercially available fluorophores including the commonly used nitrobenzoxadiazole dye and may be applied to deduce functional molecular characteristics of many types of reconstituted fluorescently tagged membrane proteins.

The immunogenicity risk of therapeutic protein aggregates has been extensively investigated over the past decades. While it is established that not all aggregates are equally immunogenic, the specific aggregate characteristics, which are most likely to induce an immune response, remain ambiguous. The aim of this study was to perform comprehensive in vitro and in vivo immunogenicity assessment of human insulin aggregates varying in size, structure and chemical modifications, while keeping other morphological characteristics constant. We found that flexible aggregates with highly altered secondary structure were most immunogenic in all setups, while compact aggregates with native-like structure were found to be immunogenic primarily in vivo. Moreover, sub-visible (1–100 µm) aggregates were found to be more immunogenic than sub-micron (0.1–1 µm) aggregates, while chemical modifications (deamidation, ethylation and covalent dimers) were not found to have any measurable impact on immunogenicity. The findings highlight the importance of utilizing aggregates varying in few characteristics for assessment of immunogenicity risk of specific morphological features and may provide a workflow for reliable particle analysis in biotherapeutics.

Real-time super-resolution microscopy analysis reveals the growth kinetics, morphology, and abundance of human insulin amyloid spherulites with different growth pathways.Protein misfolding in the form of fibrils or spherulites is involved in a spectrum of pathological abnormalities. Our current understanding of protein aggregation mechanisms has primarily relied on the use of spectrometric methods to determine the average growth rates and diffraction-limited microscopes with low temporal resolution to observe the large-scale morphologies of intermediates. We developed a REal-time kinetics via binding and Photobleaching LOcalization Microscopy (REPLOM) super-resolution method to directly observe and quantify the existence and abundance of diverse aggregate morphologies of human insulin, below the diffraction limit and extract their heterogeneous growth kinetics. Our results revealed that even the growth of microscopically identical aggregates, e.g., amyloid spherulites, may follow distinct pathways. Specifically, spherulites do not exclusively grow isotropically but, surprisingly, may also grow anisotropically, following similar pathways as reported for minerals and polymers. Combining our technique with machine learning approaches, we associated growth rates to specific morphological transitions and provided energy barriers and the energy landscape at the level of single aggregate morphology. Our unifying framework for the detection and analysis of spherulite growth can be extended to other self-assembled systems characterized by a high degree of heterogeneity, disentangling the broad spectrum of diverse morphologies at the single-molecule level.

Cell-penetrating peptides (CPPs) are known to interact with cell membranes and by doing so enhance cellular interaction and subsequent cellular internalization of nanoparticles. Yet, the early events of membrane interactions are still not elucidated, which is the aim of the present work. Surface conjugation of polymeric nanoparticles with cationic CPPs of different architecture (short, long linear, and branched) influences the surface properties, especially the charge of the nanoparticles, and therefore provides the possibility of increased electrostatic interactions between nanoparticles with the cell membrane. In this study, the physicochemical properties of CPP-tagged poly(lactic

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 a long running time and excessive materials cost. We here present a single-particle combinatorial multiplexed liposome fusion mediated by DNA for parallelized multistep and non-deterministic fusion of individual subattolitre 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 single-stranded DNA and fluorescently barcoded by a distinct ratio of chromophores. The stochastic fusion resulted in a distinct permutation of fusion sequences for each autonomous nanocontainer. Real-time total internal reflection imaging allowed the direct observation of >16,000 fusions and 566 distinct fusion sequences accurately classified using machine learning. The high-density arrays of surface-tethered target nanocontainers (~42,000 containers per mm2) offers entire combinatorial multiplex screens using only picograms of material. [Figure not available: see fulltext.]

Reconstitution of membrane proteins into model membranes is an essential approach for their functional analysis under chemically defined conditions. Established model-membrane systems used in ensemble average measurements are limited by sample heterogeneity and insufficient knowledge of lipid and protein content at the single vesicle level, which limits quantitative analysis of vesicle properties and prevents their correlation with protein activity. Here, we describe a versatile total internal reflection fluorescence microscopy-based bleaching protocol that permits parallel analyses of multiple parameters (physical size, tightness, unilamellarity, membrane protein content and orientation) of individual proteoliposomes prepared with fluorescently tagged membrane proteins and lipid markers. The approach makes use of commercially available fluorophores including the commonly used nitrobenzoxadiazole (NBD) dye and may be applied to deduce functional molecular characteristics of many types of reconstituted fluorescently tagged membrane proteins.

Biological membranes have distinct geometries that confer specific functions. However, the molecular mechanisms underlying the phenomenological geometry/function correlations remain elusive. We studied the effect of membrane geometry on the localization of membrane-bound proteins. Quantitative comparative experiments between the two most abundant cellular membrane geometries, spherical and cylindrical, revealed that geometry regulates the spatial segregation of proteins. The measured geometry-driven segregation reached 50-fold for membranes of the same mean curvature, demonstrating a crucial and hitherto unaccounted contribution by Gaussian curvature. Molecular-field theory calculations elucidated the underlying physical and molecular mechanisms. Our results reveal that distinct membrane geometries have specific physicochemical properties and thus establish a ubiquitous mechanistic foundation for unravelling the conserved correlations between biological function and membrane polymorphism., Cellular organelles display highly conserved morphologies, e.g., cylindrical (tubes) or spherical (vesicles), and here we show that their Gaussian curvature differences can regulate protein recruitment.

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.

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.

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.

The dopamine transporter facilitates dopamine reuptake from the extracellular space to terminate neurotransmission. The transporter belongs to the neurotransmitter:sodium symporter family, which includes transporters for serotonin, norepinephrine, and GABA that utilize the Na+ gradient to drive the uptake of substrate. Decades ago, it was shown that the serotonin transporter also antiports K+, but investigations of K+-coupled transport in other neurotransmitter:sodium symporters have been inconclusive. Here, we show that ligand binding to the Drosophila- and human dopamine transporters are inhibited by K+, and the conformational dynamics of the Drosophila dopamine transporter in K+ are divergent from the apo- and Na+-states. Furthermore, we find that K+ increases dopamine uptake by the Drosophila dopamine transporter in liposomes, and visualize Na+ and K+ fluxes in single proteoliposomes using fluorescent ion indicators. Our results expand on the fundamentals of dopamine transport and prompt a reevaluation of the impact of K+ on other transporters in this pharmacologically important family.

The dopamine transporter (DAT) facilitates dopamine reuptake from the extracellular space, and thereby terminates neurotransmission and refills cellular stores of dopamine. DAT belongs to the neurotransmitter:sodium symporter (NSS) family, which includes similar transporters for serotonin, norepinephrine, and GABA. A hallmark of NSS proteins is their ability to utilize the energy stored in the inward-directed Na+ gradient to drive the uphill transport of substrate. Decades ago, it was shown that the serotonin transporter also counter-transports K+, but investigations of K+-coupled transport in other NSSs have been inconclusive. Here, we show that the Drosophila dopamine transporter (dDAT) counter-transports K+. We found that ligand binding to both dDAT and human DAT is inhibited by K+ and that the conformational dynamics of dDAT in K+ is highly divergent from both the apo- and Na+-bound conformations. Furthermore, we found that K+ increased dopamine uptake by purified dDAT reconstituted in liposomes, and we visualized, in real-time, Na+ and K+ fluxes in single proteoliposomes using fluorescent ion indicators. Our results expand on the fundamentals of dopamine transport and prompt a reevaluation of the impact of K+ on other NSSs, including whether K+ counter-transport is a common mechanism for this pharmacologically important protein family.

Peptide YY3-36 (PYY3-36) is an endogenous ligand of the neuropeptide Y2 receptor (Y2R), on which it acts to reduce food intake. Chemically modified PYY3-36 analogues with extended half-lives are potential therapeutics for the treatment of obesity. Here we show that the common half-life extending strategies PEGylation and lipidation not only control PYY3-36's pharmacokinetics but also affect central aspects of its pharmacodynamics. PEGylation of PYY3-36 inhibited endocytosis by increasing receptor dissociation rates (koff), which reduced arrestin-3 (Arr3) activity. This is the first link between Arr3 recruitment and Y2R residence time. C16-lipidation of PYY3-36 had a negligible impact on Y2R signaling, binding, and endocytosis. In contrast, C18acid-lipidation minimized endocytosis, which indicated a decreased internalization through non-arrestin-related mechanisms. We propose a temporal model that connects the properties and position of the half-life extender with receptor Gi versus Arr3 signaling bias. We believe that this will be important for future design of peptide therapeutics.

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., P450 oxidoreductase (POR) selectively activates numerous cytochromes P450 (CYP), crucial for metabolism of drugs, steroids and xenobiotics and natural product biosynthesis. Here, the authors identify ligands that bind POR and bias its specificity towards CYP redox partners, activating distinct metabolic cascades in cells.

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 non-deterministic 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 mm2 offers entire combinatorial multiplex screens using only picograms of material.

Significance Single-particle tracking (SPT) analysis of individual biomolecules is an indispensable tool for extracting quantitative information from dynamic biological processes, but often requires some a priori knowledge of the system. Here we present “single-particle diffusional fingerprinting,” a more general approach for extraction of diffusional patterns in SPT independently of the biological system. This method extracts a set of descriptive features for each SPT trajectory, which are ranked upon classification to yield mechanistic insights for the species under comparison. We demonstrate its capacity to yield a dictionary of diffusional traits across multiple systems (e.g., lipases hydrolyzing fat, transcription factors diffusing in cells, and nanoparticles in mucus), supporting its use on multiple biological phenomena (e.g., drug delivery, receptor dynamics, and virology)., Single-particle tracking (SPT) is a key tool for quantitative analysis of dynamic biological processes and has provided unprecedented insights into a wide range of systems such as receptor localization, enzyme propulsion, bacteria motility, and drug nanocarrier delivery. The inherently complex diffusion in such biological systems can vary drastically both in time and across systems, consequently imposing considerable analytical challenges, and currently requires an a priori knowledge of the system. Here we introduce a method for SPT data analysis, processing, and classification, which we term “diffusional fingerprinting.” This method allows for dissecting the features that underlie diffusional behavior and establishing molecular identity, regardless of the underlying diffusion type. The method operates by isolating 17 descriptive features for each observed motion trajectory and generating a diffusional map of all features for each type of particle. Precise classification of the diffusing particle identity is then obtained by training a simple logistic regression model. A linear discriminant analysis generates a feature ranking that outputs the main differences among diffusional features, providing key mechanistic insights. Fingerprinting operates by both training on and predicting experimental data, without the need for pretraining on simulated data. We found this approach to work across a wide range of simulated and experimentally diverse systems, such as tracked lipases on fat substrates, transcription factors diffusing in cells, and nanoparticles diffusing in mucus. This flexibility ultimately supports diffusional fingerprinting’s utility as a universal paradigm for SPT diffusional analysis and prediction.

Liposomes are versatile three-dimensional, biomaterial-based frameworks that can spatially enclose a variety of organic and inorganic biomaterials for advanced targeted-delivery applications. Implementation of external-stimuli-controlled release of their cargo will significantly augment their wide application for liposomal drug delivery. This paper presents the synthesis of a carbohydrate-derived lipid, capable of changing its conformation depending on the presence of Zn2+ : an active state in the presence of Zn2+ ions and back to an inactive state in the absence of Zn2+ or when exposed to Na2 EDTA, a metal chelator with high affinity for Zn2+ ions. This is the first report of a lipid triggered by the presence of a metal chelator. Total internal reflection fluorescence microscopy and a single-liposome study showed that it indeed was possible for the lipid to be incorporated into the bilayer of stable liposomes that remained leakage-free for the fluorescent cargo of the liposomes. On addition of EDTA to the liposomes, their fluorescent cargo could be released as a result of the membrane-incorporated lipids undergoing a conformational change.

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., eLife digest Proteins are folded into particular shapes in order to carry out their roles in the cell. However, their structures are not rigid: proteins bend and rotate in response to their environment. Identifying these movements is an important part of understanding how proteins work and interact with each other. Unfortunately, when researchers study the structures of proteins, they often look at the ‘average’ shape a protein takes, missing out on other conformations the protein might only be in temporarily. An important technique for studying protein flexibility is known as single molecule Förster resonance energy transfer (FRET). In this technique, two light-sensitive tags are attached to the same protein molecule and give off a signal when they come into close contact. This nano-scale sensor allows structural biologists to get information from individual protein movements that can be lost when looking at the average conformations of proteins. Advances in the instruments used to perform FRET have made observing the motion of individual proteins more widely accessible to non-specialists, but the analysis of the data that these instruments produce still requires a high level of expertise. To lower the barrier for non-specialists to use the technology, and to ensure that experiments can be reproduced on different instruments and by different researchers, Thomsen et al. have developed a new way to automate the data analysis. They used machine learning technology to recognize, filter and characterize data so as to produce reliable results, with the user only needing to perform a couple of steps. This new analysis approach could help expand the use of single-molecule FRET to different fields , allowing researchers to investigate the importance of protein flexibility for certain diseases, or to better understand the roles that proteins have in a cell.

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 identified ligands that dock on POR and bias its specificity towards CYP redox partners. Single molecule FRET studies revealed ligand docking to alter POR conformational sampling, which resulted 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 docking 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.

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 identified ligands that dock on POR and bias its specificity towards CYP redox partners. Single molecule FRET studies revealed ligand docking to alter POR conformational sampling, which resulted 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 docking 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.

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 &minus, 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 molecule Förster Resonance energy transfer (smFRET) is a mature and adaptable method for studying the structure of biomolecules and integrating their dynamics into structural biology. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and fully automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histogram of biomolecule behavior, is a user-adjustable quality threshold. Integrating all standard features of smFRET analysis, DeepFRET will consequently output common kinetic information metrics for biomolecules. We validated the utility of DeepFRET by performing quantitative analysis on simulated, ground truth, data and real smFRET data. The accuracy of classification by DeepFRET outperformed human operators and current commonly used hard threshold and reached >95% precision accuracy only requiring a fraction of the time (

Lipases are important hydrolytic enzymes used in a spectrum of technological applications, such as the pharmaceutical and detergent industries. Because of their versatile nature and ability to accept a broad range of substrates, they have been extensively used for biotechnological and industrial applications. Current assays to measure lipase activity primarily rely on low-sensitivity measurements of pH variations or visible changes of material properties, like hydration, and often require high amounts of proteins. Fluorescent readouts, on the other hand, offer high contrast and even single-molecule sensitivity, albeit they are reliant on fluorogenic substrates that structurally resemble the native ones. Here we present a method that combines the highly sensitive readout of fluorescent techniques while reporting enzymatic lipase function on native substrates. The method relies on embedding the environmentally sensitive fluorescent dye pHrodo and native substrates into the bilayer of liposomes. The charged products of the enzymatic hydrolysis alter the local membrane environment and thus the fluorescence intensity of pHrodo. The fluorescence can be accurately quantified and directly assigned to product formation and thus enzymatic activity. We illustrated the capacity of the assay to report the function of diverse lipases and phospholipases both in a microplate setup and at the single-particle level on individual nanoscale liposomes using total internal reflection fluorescence (TIRF). The parallelized sensitive readout of microscopy combined with the inherent polydispersity in sizes of liposomes allowed us to screen the effect of membrane curvature on lipase function and identify how mutations in the lid region control the membrane curvature-dependent activity. We anticipate this methodology to be applicable for sensitive activity readouts for a spectrum of enzymes where the product of the enzymatic reaction is charged.

Lipases are important hydrolytic enzymes used in a spectrum of technological applications, such as the pharmaceutical and detergent industry. Due to their versatile nature and ability to accept a broad range of substrates they have been extensively used for biotechnological and industrial applications. Current assays to measure lipase activity primarily rely on low sensitivity measurement of pH variations or visible changes on material properties, like hydration, and often require high amount of proteins. Fluorescent readouts on the other hand offer high contrast and even single molecule sensitivity, albeit they are reliant on fluorogenic substrates that structurally resemble the native ones. Here we present a method that combines the highly sensitive readout of fluorescent techniques while reporting enzymatic lipase function on native substrates. The method relies on embedding the environmentally sensitive fluorescent dye pHrodo and native substrates into the bilayer of liposomes. The charged products of the enzymatic hydrolysis alter the local membrane environment and thus the fluorescence intensity of pHrodo. The fluorescence can be accurately quantified and directly assigned to product formation and thus enzymatic activity. We illustrated the capacity of the assay to report function of diverse lipases and phospholipases both in a microplate setup and at the single particle level on individual nanoscale liposomes using Total Internal Reflection Fluorescence (TIRF). The parallelized sensitive readout of microscopy combined with the inherent polydispersity in sizes of liposomes allowed us to screen the effect of membrane curvature on lipase function and identify how mutations in the lid region control the membrane curvature dependent activity. We anticipate this methodology to be applicable for sensitive activity readouts for a spectrum of enzymes where the product of enzymatic reaction is charged.

Inhaled antibiotic treatment of cystic fibrosis-related bacterial biofilm infections is challenging due to the pathological environment of the lungs. Here, we present an "environment adaptive" nanoparticle composed of a solid poly lactic-co-glycolic acid (PLGA) core and a mucus-inert, enzymatically cleavable shell of D-α-tocopheryl polyethylene glycol 1000 succinate (TPGS) for site-specific delivery of antibiotics to bacterial biofilms via aerosol administration. The hybrid nanoparticles with ultrasmall size were self-assembled via a nanoprecipitation process by using a facile microfluidic method. The interactions of the nanoparticles with the biological barriers were comprehensively investigated by using cutting-edge techniques (e.g. quartz crystal microbalance with dissipation monitoring, total internal reflection fluorescence microscopy-based particle tracking, in vitro biofilm model cultured in a flow-chamber system, and quantitative imaging analysis). Our results suggest that the mucus-inert, enzymatically cleavable TPGS shell enables the nanoparticles to penetrate through the mucus, accumulate in the deeper layer of the biofilms, and serve as sustained release depot, thereby, improving the killing efficacy of azithromycin (a macrolide antibiotic) against biofilm-forming Pseudomonas aeruginosa. In conclusion, the ultra-small TPGS-PLGA hybrid nanoparticles represent an efficient delivery system to overcome the multiple barriers and release antibiotics with a sustained manner in the vicinity of the biofilm-forming bacteria.

The molecular mechanism of proton pumping by heme-copper oxidases (HCO) has intrigued the scientific community since it was first proposed. We have recently reported a novel technology that enables the continuous characterisation of proton transport activity of a HCO and ubiquinol oxidase from Escherichia coli, cytochrome bo3, for hundreds of seconds on the single enzyme level (Li et al. J Am Chem Soc 137 (2015) 16055-16063). Here, we have extended these studies by additional experiments and analyses of the proton transfer rate as a function of proteoliposome size and pH at the N- and P-side of single HCOs. Proton transport activity of cytochrome bo3 was found to decrease with increased curvature of the membrane. Furthermore, proton uptake at the N-side (proton entrance) was insensitive to pH between pH6.4-8.4, while proton release at the P-side had an optimum pH of ~7.4, suggesting that the pH optimum is related to proton release from the proton exit site. Our previous single-enzyme experiments identified rare, long-lived conformation states of cytochrome bo3 where protons leak back under turn-over conditions. Here, we analyzed and found that ~23% of cytochrome bo3 proteoliposomes show ΔpH half-lives below 50s after stopping turnover, while only ~5% of the proteoliposomes containing a non-pumping mutant, E286C cytochrome bo3 exhibit such fast decays. These single-enzyme results confirm our model in which HCO exhibit heterogeneous pumping rates and can adopt rare leak states in which protons are able to rapidly flow back.

Proteins anchored to membranes through covalently linked fatty acids and/or isoprenoid groups play crucial roles in all forms of life. Sorting and trafficking of lipidated proteins has traditionally been discussed in the context of partitioning to membrane domains of different lipid composition. We recently showed that membrane shape/curvature can in itself mediate the recruitment of lipidated proteins. However, exactly how membrane curvature and composition synergize remains largely unexplored. Here we investigated how three critical structural parameters of lipids, namely acyl chain saturation, headgroup size, and acyl chain length, modulate the capacity of membrane curvature to recruit lipidated proteins. As a model system we used the lipidated minimal membrane anchor of the GTPase, N-Ras (tN-Ras). Our data revealed complex synergistic effects, whereby tN-Ras binding was higher on planar DOPC than POPC membranes, but inversely higher on curved POPC than DOPC membranes. This variation in the binding to both planar and curved membranes leads to a net increase in the recruitment by membrane curvature of tN-Ras when reducing the acyl chain saturation state. Additionally, we found increased recruitment by membrane curvature of tN-Ras when substituting PC for PE, and when decreasing acyl chain length from 14 to 12 carbons (DMPC versus DLPC). However, these variations in recruitment ability had different origins, with the headgroup size primarily influencing tN-Ras binding to planar membranes whereas the change in acyl chain length primarily affected binding to curved membranes. Molecular field theory calculations recapitulated these findings and revealed lateral pressure as an underlying biophysical mechanism dictating how curvature and composition synergize to modulate recruitment of lipidated proteins. Our findings suggest that the different compositions of cellular compartments could modulate the potency of membrane curvature to recruit lipidated proteins and thereby synergistically regulate the trafficking and sorting of lipidated proteins.

The targeted spatial organization (sorting) of Gprotein-coupled receptors (GPCRs) is essential for their biological function and often takes place in highly curved membrane compartments such as filopodia, endocytic pits, trafficking vesicles or endosome tubules. However, the influence of geometrical membrane curvature on GPCR sorting remains unknown. Here we used fluorescence imaging to establish a quantitative correlation between membrane curvature and sorting of three prototypic class A GPCRs (the neuropeptide Y receptor Y2, the β1 adrenergic receptor and the β2 adrenergic receptor) in living cells. Fitting of a thermodynamic model to the data enabled us to quantify how sorting is mediated by an energetic drive to match receptor shape and membrane curvature. Curvature-dependent sorting was regulated by ligands in a specific manner. We anticipate that this curvature-dependent biomechanical coupling mechanism contributes to the sorting, trafficking and function of transmembrane proteins in general.

Lipases are interfacially activated enzymes that catalyze the hydrolysis of ester bonds and constitute prime candidates for industrial and biotechnological applications ranging from detergent industry, to chiral organic synthesis. As a result, there is an incentive to understand the mechanisms underlying lipase activity at the molecular level, so as to be able to design new lipase variants with tailor-made functionalities. Our understanding of lipase function primarily relies on bulk assay averaging the behavior of a high number of enzymes masking structural dynamics and functional heterogeneities. Recent advances in single molecule techniques based on fluorogenic substrate analogues revealed the existence of lipase functional states, and furthermore so how they are remodeled by regulatory cues. Single particle studies of lipases on the other hand directly observed diffusional heterogeneities and suggested lipases to operate in two different modes. Here to decipher how mutations in the lid region controls Thermomyces lanuginosus lipase (TLL) diffusion and function we employed a Single Particle Tracking (SPT) assay to directly observe the spatiotemporal localization of TLL and rationally designed mutants on native substrate surfaces. Parallel imaging of thousands of individual TLL enzymes and HMM analysis allowed us to observe and quantify the diffusion, abundance and microscopic transition rates between three linearly interconverting diffusional states for each lipase. We proposed a model that correlate diffusion with function that allowed us to predict that lipase regulation, via mutations in lid region or product inhibition, primarily operates via biasing transitions to the active states.

Transmembrane nanostructures like ion channels and transporters perform key biological functions by controlling flow of molecules across lipid bilayers. Much work has gone into engineering artificial nanopores and applications in selective gating of molecules, label-free detection/sensing of biomolecules and DNA sequencing have shown promise. Here, we use DNA origami to create a synthetic 9 nm wide DNA nanopore, controlled by programmable, lipidated flaps and equipped with a size-selective gating system for the translocation of macromolecules. Successful assembly and insertion of the nanopore into lipid bilayers are validated by transmission electron microscopy (TEM), while selective translocation of cargo and the pore mechanosensitivity are studied using optical methods, including single-molecule, total internal reflection fluorescence (TIRF) microscopy. Size-specific cargo translocation and oligonucleotide-triggered opening of the pore are demonstrated showing that the DNA nanopore can function as a real-time detection system for external signals, offering potential for a variety of highly parallelized sensing applications., Artificial nanopores can perform selective gating of molecules analogous to transmembrane proteins. Here, the authors design a DNA origami pore with a controllable lid for size-selective gating and translocation of macromolecules and evaluate its biosensing properties by single particle assay.

Proton-pumping enzymes of electron transfer chains couple redox reactions to proton translocation across the membrane, creating a proton-motive force used for ATP production. The amphiphilic nature of membrane proteins requires particular attention to their handling, and reconstitution into the natural lipid environment is indispensable when studying membrane transport processes like proton translocation. Here, we detail a method that has been used for the investigation of the proton-pumping mechanism of membrane redox enzymes, taking cytochrome bo3 from Escherichia coli as an example. A combination of electrochemistry and fluorescence microscopy is used to control the redox state of the quinone pool and monitor pH changes in the lumen. Due to the spatial resolution of fluorescent microscopy, hundreds of liposomes can be measured simultaneously while the enzyme content can be scaled down to a single enzyme or transporter per liposome. The respective single enzyme analysis can reveal patterns in the enzyme functional dynamics that might be otherwise hidden by the behavior of the whole population. We include a description of a script for automated image analysis.