Single-Molecule Study of Thermomyces lanuginosus Lipase in a Detergency Application System Reveals Diffusion Pattern Remodeling by Surfactants and Calcium

Single-Particle and Single-Molecule Characterization of Immobilized Enzymes: A Multiscale Path toward Optimizing Heterogeneous Biocatalysts

Heterogeneous biocatalysis is highly relevant in biotechnology as it offers several benefits and practical uses. To leverage the full potential of heterogeneous biocatalysts, the establishment of well-crafted protocols, and a deeper comprehension of enzyme immobilization on solid substrates are essential. These endeavors seek to optimize immobilized biocatalysts, ensuring maximal enzyme performance within confined spaces. For this aim, multidimensional characterization of heterogeneous biocatalysts is required. In this context, spectroscopic and microscopic methodologies conducted at different space and temporal scales can inform about the intraparticle enzyme kinetics, the enzyme spatial distribution, and the mass transport issues. In this Minireview, we identify enzyme immobilization, enzyme catalysis, and enzyme inactivation as the three main processes for which advanced characterization tools unveil fundamental information. Recent advances in operando characterization of immobilized enzymes at the single-particle (SP) and single-molecule (SM) levels inform about their functional properties, unlocking the full potential of heterogeneous biocatalysis toward biotechnological applications.

Deep learning assisted single particle tracking for automated correlation between diffusion and function

Sub-cellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with an unprecedented level of precision. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the sub-cellular environment, is labor-intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework to interpret the diffusional 2D or 3D temporal behavior of objects in a rapid and efficient manner, agnostically. Demonstrating its versatility, we have applied DeepSPT to automated mapping of the early events of viral infections, identifying distinct types of endosomal organelles, and clathrin-coated pits and vesicles with up to 95% accuracy and within seconds instead of weeks. The fact that DeepSPT effectively extracts biological information from diffusion alone illustrates that besides structure, motion encodes function at the molecular and subcellular level.

Deep learning assisted single particle tracking for automated correlation between diffusion and function

Sub-cellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with an unprecedented level of precision. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the sub-cellular environment, is labor-intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework to interpret the diffusional 2D or 3D temporal behavior of objects in a rapid and efficient manner, agnostically. Demonstrating its versatility, we have applied DeepSPT to automated mapping of the early events of viral infections, identifying distinct types of endosomal organelles, and clathrin-coated pits and vesicles with up to 95% accuracy and within seconds instead of weeks. The fact that DeepSPT effectively extracts biological information from diffusion alone indicates that besides structure, motion encodes function at the molecular and subcellular level.

Single-Particle Tracking of Thermomyces lanuginosus 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.

Enhanced hexamerization of insulin via assembly pathway rerouting revealed by single particle studies

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.

Structural features, temperature adaptation and industrial applications of microbial lipases from psychrophilic, mesophilic and thermophilic origins

Microbial lipases are very prominent biocatalysts because of their ability to catalyze a wide variety of reactions in aqueous and non-aqueous media. Here microbial lipases from different origins (psychrophiles, mesophiles, and thermophiles) have been reviewed. This review emphasizes an update of structural diversity in temperature adaptation and industrial applications, of psychrophilic, mesophilic, and thermophilic lipases. The microbial origins of lipases are logically dynamic, proficient, and also have an extensive range of industrial uses with the manufacturing of altered molecules. It is therefore of interest to understand the molecular mechanisms of adaptation to temperature in occurring lipases. However, lipases from extremophiles (psychrophiles, and thermophiles) are widely used to design biotransformation reactions with higher yields, fewer byproducts, or useful side products and have been predicted to catalyze those reactions also, which otherwise are not possible with the mesophilic lipases. Lipases as a multipurpose biological catalyst have given a favorable vision in meeting the needs of several industries such as biodiesel, foods, and drinks, leather, textile, detergents, pharmaceuticals, and medicals.

Physical and barrier changes in gastrointestinal mucus induced by the permeation enhancer sodium 8-[(2-hydroxybenzoyl)amino]octanoate (SNAC)

Drug delivery systems (DDS) for oral delivery of peptide drugs contain excipients that facilitate and enhance absorption. However, little knowledge exists on how DDS excipients such as permeation enhancers interact with the gastrointestinal mucus barrier. This study aimed to investigate interactions of the permeation enhancer sodium 8-[(2-hydroxybenzoyl)amino]octanoate (SNAC) with ex vivo porcine intestinal mucus (PIM), ex vivo porcine gastric mucus (PGM), as well as with in vitro biosimilar mucus (BM) by profiling their physical and barrier properties upon exposure to SNAC. Bulk mucus permeability studies using the peptides cyclosporine A and vancomycin, ovalbumin as a model protein, as well as fluorescein-isothiocyanate dextrans (FDs) of different molecular weights and different surface charges were conducted in parallel to mucus retention force studies using a texture analyzer, rheological studies, cryo-scanning electron microscopy (cryo-SEM), and single particle tracking of fluorescence-labelled nanoparticles to investigate the effects of the SNAC-mucus interaction. The exposure of SNAC to PIM increased the mucus retention force, storage modulus, viscosity, increased nanoparticle confinement within PIM as well as decreased the permeation of cyclosporine A and ovalbumin through PIM. Surprisingly, the viscosity of PGM and the permeation of cyclosporine A and ovalbumin through PGM was unaffected by the presence of SNAC, thus the effect of SNAC depended on the regional site that mucus was collected from. In the absence of SNAC, the permeation of different molecular weight and differently charged FDs through PIM was comparable to that through BM. However, while bulk permeation of neither of the FDs through PIM was affected by SNAC, the presence of SNAC decreased the permeation of FD4 and increased the permeation of FD150 kDa through BM. Additionally, and in contrast to observations in PIM, nanoparticle confinement within BM remained unaffected by the presence of SNAC. In conclusion, the present study showed that SNAC altered the physical and barrier properties of PIM, but not of PGM. The effects of SNAC in PIM were not observed in the BM in vitro model. Altogether, the study highlights the need for further understanding how permeation enhancers influence the mucus barrier and illustrates that the selected mucus model for such studies should be chosen with care.

Direct observation of heterogeneous formation of amyloid spherulites in real-time by super-resolution microscopy

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.

Real-time super-resolution microscopy analysis reveals the growth kinetics, morphology, and abundance of human insulin amyloid spherulites with different growth pathways.