Biology subjects

Kaestel-Hansen, J.

Publications and source records attributed to Kaestel-Hansen, J..

6 recordsLinked to original sources

bioRxiv2024

Proteolytic Performance is Dependent on Binding Efficiency, Processivity and Turnover: Single Protease Insights

Proteases are essential enzymes for a plethora of biological processes and biotechnological applications, e.g., within the dairy, pharmaceutical, and detergent industries. Decoding the molecular level mechanisms that drive protease performance is key to designing improved biosolutions. However, direct dynamic assessment of the fundamental partial reactions of substrate binding and activity has proven a challenge with conventional ensemble approaches. We developed a single-molecule (SM) assay for the direct and parallel recording of the stochastic binding interaction of Savinase, a serine-type protease broadly employed in biotechnology, with casein synchronously with monitoring proteolytic degradation of the substrate. SM recordings enabled us to determine how the overall activity of Savinase and two mutants relies on binding efficiency, enzymatic turnover and activity per binding event. Analysis of residence times revealed three characteristic binding states. Mutations were found to dominantly alter the likelihood of sampling the long lived state, with lifetimes longer than 30 seconds, indicating this state contributes to overall activity and supporting a level of processivity for Savinase. This observation challenges conventional expectations, as the protease has no characterized substrate binding site, or binding domain, aside from the active site. These insights, inaccessible through conventional assays, offer new perspectives for engineering proteases with improved hydrolytic performance.

biophysics↗

bioRxiv2024

Defect-Engineered Metal-Organic Frameworks as Nanocarriers for Pharmacotherapy: Insights into Intracellular Dynamics at The Single Particle Level

NanoMOFs are widely implemented in a host of assays involving drug delivery, biosensing catalysis, and bioimaging. Despite their wide use, the cell entry pathways and cell fate remain poorly understood. Here we have synthesized a new fluorescent nanoMOF integrating ATTO 655 into surface defects of colloidal nano UiO-66 that allowed us to track the spatiotemporal localization of Single nanoMOF in live cells. Density Functional Theory(DFT) reveals the stronger binding of ATTO 655 to the uncoordinated saturated Zr6 cluster nodes compared with phosphate and Alendronate Sodium (AL). Parallelized tracking of the spatiotemporal localization of tens of thousands of nanoMOFs and analysis using machine learning platforms revealed whether nanoMOFs remain outside as well as their cellular internalization pathways. To quantitatively assess their colocalization with endo/lysosomal compartments, we developed a colocalization proxy approach relying on the nanoMOF detection of particles in one channel to the signal in the corresponding endo/lysosomal compartments channel, considering signal vs local background intensity ratio (S/B) and signal-to-noise ratio (SNR). This strategy effectively mitigates the potential inflation of colocalization values arising from the heightened expression of signals originating from endo/lysosomal compartments, it also overcomes limitations of low SNRs in the endo/lysosomal compartments marker channel, which incapacitates any trajectory-trajectory colocalization assessment. The results accurately measure the amount of nanoMOFs colocalization in real-time from early (EE) to late endosomes(LE) and lysosomes(LY) and emphasize the importance of understanding their intracellular dynamics based on single-particle tracking (SPT) for optimal and safe drug delivery.

biophysics↗

bioRxiv2023

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.

biophysics↗

bioRxiv2023

Assessing the performance of protein regression models

To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimization process. A natural question is: How much progress are we making with such predictions, and how important is the choice of regressor and representation? In this paper, we demonstrate that different assessment criteria for regressor performance can lead to dramatically different conclusions, depending on the choice of metric, and how one defines generalization. We highlight the fundamental issues of sample bias in typical regression scenarios and how this can lead to misleading conclusions about regressor performance. Finally, we make the case for the importance of calibrated uncertainty in this domain.

bioinformatics↗

bioRxiv2023

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

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 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 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.

biophysics↗

bioRxiv2023

Dimensional Reduction for Single Molecule Imaging of DNA and Nucleosome Condensation by Polyamines, HP1α and Ki-67

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, such studies lack the resolution needed for complex kinetic studies of multicomponent condensation. Here, we use a supported lipid bilayer platform in tandem with total internal reflection microscopy to observe the 2-dimensional movement of DNA and nucleosomes at the single-molecule resolution. This dimensional reduction from 3-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 timescale of minutes while dissolution occurs within seconds upon charge inversion. Polyamine valency, DNA length and GC content affect threshold polyamine concentration for condensation. Protein-based nucleosome condensing agents, HP1 and Ki-67, have much lower threshold concentration for condensation than charge-based condensing agents, with Ki-67 being the most effective as low as 100 pM for nucleosome condensation. In addition, we did not observe condensate dissolution even at the highest concentrations of HP1 and Ki-67 tested. We also introduce a two-color imaging scheme where nucleosomes of high density labeled in one color is 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. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/522433v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1ed0cc9org.highwire.dtl.DTLVardef@1e26b5dorg.highwire.dtl.DTLVardef@1f6e73corg.highwire.dtl.DTLVardef@c7227b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗