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

Modulation of insulin aggregation by betaine and proline directly observed via real-time super-resolution microscopy

Protein aggregation is associated with a spectrum of neurodegenerative diseases. Although many small ligands have been found to modulate or inhibit protein aggregation, their molecular mechanisms remain unclear. One reason for this is the inherent heterogeneity of protein aggregation pathways with different kinetics that result in the coexistence of multiple structures, for example, protein spherulites and fibrils, challenging the analysis of protein-ligand interactions. To address this issue, we evaluated the roles of betaine and proline in insulin aggregation. We employed our recently developed super-resolution microscopy real-time kinetics via binding and photobleaching localization microscopy (REPLOM) to directly observe the formation and morphological evolution of individual insulin aggregates in real time, with or without betaine/proline. Utilizing our machine learning approach, we monitor the effect of betaine and proline on the aggregation pathways and extract the growth kinetics of each individual aggregate type. Our results show that a high concentration of betaine or proline modulates the heterogeneity of the final aggregates, leading to the formation of smaller aggregates in a mixture with spherulites. The fraction of small aggregates increases with betaine/proline concentration, highlighting the heterogeneity of protein aggregation, and our toolbox can unravel the effects of small molecule ligands on individual protein aggregation pathways and the resulting aggregate types and abundances.

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

Subcellular 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 unprecedented precision. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the subcellular environment is labor intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework integrated in an analysis software, to interpret the diffusional two- or three-dimensional 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 endosomal organelles, clathrin-coated pits and vesicles among others with F1 scores of 81%, 82% and 95%, respectively, 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.

Investigating the interactions between an industrial lipase and anionic (bio)surfactants

In laundry formulations, synergies between amphiphiles and other additives such as enzymes increase sustainability through a large decrease in energy consumption. However, traditional surfactants are derived from petroleum, requiring chemical modifications (sulfonation, ethoxylation, or esterification) and generating environmental pollution through toxicity and low degradability. Use of biosurfactants removes these issues. To provide a firmer basis for the use of biosurfactants, we report on the interactions between the industrial lipase LIPEX® and three common biosurfactants, rhamnolipids, sophorolipids, and surfactin. The model surfactant sodium dodecyl sulfate (SDS) is included in the study for comparison. A thorough characterization by Small-angle X-ray scattering (SAXS) provides valuable information on the enzyme's oligomerization and the surfactant micelles' ellipsoidal morphology. Additionally, the enzymatic activity and complex formation in different surfactant mixtures are studied using isothermal titration calorimetry, activity assays, and SAXS. SDS activates the enzyme while promoting a controlled association of monomers while the biosurfactants inhibit the enzyme, independent of their effects on its quaternary structure. Rhamnolipids and surfactin promote lipase dimerization while sophorolipids have no significant effect on lipase quaternary structure. Based on these data, we propose a partial replacement that allows the enzyme to retain enzymatic activity while improving the environmental footprint of the formulation.

Chemical Modification of Insulin Using Flow Chemistry

Chemical modification of proteins is of growing importance to generate new molecular probes for chemical biology and for the development of new biopharmaceuticals. For example, two approved, long-acting insulin variants are lipidated at the LysB29 side-chain. Acylations of proteins have so far been performed in batch-mode. Here we describe the use of flow chemistry for site-selective acylation of a small protein, insulin. To the best of our knowledge this is the first report on flow chemistry for chemical modification of insulin. The first step was to develop reaction conditions for acylation of Lys B29 that gave a soluble mixture and thus was compatible with flow chemistry in a microreactor; this included selection of a soluble base. Secondly, the conditions, such as reagent ratios and flow rate were optimized. Third, the use of these conditions for the acylation with a wide range of acids was demonstrated. Finally, Boc-protected insulins were synthesized. Insulin remained stable towards these flow chemistry conditions. This use of flow chemistry for the chemical modification of insulin opens the prospect of producing chemically modified biopharmaceuticals by flow chemistry with fewer byproducts.

Deciphering Pathways and Thermodynamics of Protein Assembly Using Native Mass Spectrometry

Protein oligomerization regulates many critical physiological processes, and its dysregulation can contribute to dysfunction and diseases. Elucidating the assembly pathways and quantifying their underlying thermodynamic and kinetic parameters are crucial for a comprehensive understanding of biological processes and for advancing therapeutics targeting abnormal protein oligomerization. Established binding assays, with limited mass precision, often rely on simplified models for data interpretation. In contrast, high-resolution native mass spectrometry (nMS) can directly determine the stoichiometry of biomolecular complexes in vitro. However, quantification is hindered by the fact that the relative abundances of gas-phase ions generally do not reflect solution concentrations due to nonuniform response factors. Recently, slow mixing mode (SLOMO)-nMS, which can quantify the relative response factors of interacting species, has been demonstrated to reliably measure the affinity (Kd) of binary biomolecular complexes. Here, we introduce an extended form of SLOMO-nMS that enables simultaneous quantification of the thermodynamics in multistep association reactions. Application of this method to homo-oligomerization of concanavalin A and insulin confirmed the reliability of the assay and uncovered details about the assembly processes that had previously resisted elucidation. Results acquired using SLOMO-nMS implemented with charge detection shed new light on the binding of recombinant human angiotensin-converting enzyme 2 and the SARS-CoV-2 spike protein. Importantly, new assembly pathways were uncovered, and the affinities of these interactions, which regulate host cell infection, were quantified. Together, these findings highlight the tremendous potential of SLOMO-nMS to accelerate the characterization of protein assembly pathways and thermodynamics and, in so doing, enhance fundamental biological understanding and facilitate therapeutic development. https://orcid.org/0000-0002-3389-7112.

Improving the stability of insulin through effective chemical modifications: A Comprehensive review

Insulin, an essential peptide hormone, conjointly regulates blood glucose levels by its receptor and it is used as vital drug to treat diabetes. This therapeutic hormone may undergo different chemical modifications during industrial processes, pharmaceutical formulation, and through its endogenous storage in the pancreatic β-cells. Insulin is highly sensitive to environmental stresses and readily undergoes structural changes, being also able to unfold and aggregate in physiological conditions. Even; small changes altering the structural integrity of insulin may have significant impacts on its biological efficacy to its physiological and pharmacological activities. Insulin analogs have been engineered to achieve modified properties, such as improved stability, solubility, and pharmacokinetics, while preserving the molecular pharmacology of insulin. The casually or purposively strategies of chemical modifications of insulin occurred to improve its therapeutic and pharmaceutical properties. Knowing the effects of chemical modification, formation of aggregates, and nanoparticles on protein can be a new look at the production of protein analogues drugs and its application in living system. The project focused on effects of chemical modifications and nanoparticles on the structure, stability, aggregation and their results in effective drug delivery system, biological activity, and pharmacological properties of insulin. The future challenge in biotechnology and pharmacokinetic arises from the complexity of biopharmaceuticals, which are often molecular structures that require formulation and delivery strategies to ensure their efficacy and safety.

SEMORE: SEgmentation and MORphological fingErprinting by machine learning automates super-resolution data analysis

The morphology of protein assemblies impacts their behaviour 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 a morphology fingerprinting module 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 published data of dSTORM imaging of nuclear pore complexes, fibroblast growth receptor 1, sptPALM of Syntaxin 1a and dynamic live-cell PALM of ryanodine receptors. SEMORE extracts and quantifies all protein assemblies, their temporal morphology evolution and provides quantitative insights, e.g. classification of heterogeneous insulin aggregation pathways and NPC geometry in minutes. SEMORE is a general analysis platform for super-resolution data, and being a time-aware framework can also support the rise of 4D super-resolution data.

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.