Biology subjects

Hatzakis, N. S.

Publications and source records attributed to Hatzakis, N. S..

31 records · Page 2Linked to original sources

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↗

bioRxiv2022

In vitro and in vivo immunogenicity assessment of protein aggregate characteristics

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 {micro}m) aggregates were found to be more immunogenic than sub-micron (0.1-1 {micro}m) 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 provides a universal workflow for reliable particle analysis in biotherapeutics.

immunology↗

bioRxiv2022

Morphological integrity of insulin amyloid-like aggregates depends on preparation methods and post-production treatments

Protein aggregates are often varying extensively in their morphological characteristics, which may lead to various biological outcomes related to e.g., immunogenicity risk. However, isolation of aggregates with a specific morphology within an ensemble is often challenging. To gain vital knowledge on the effects of aggregate characteristics, samples containing a single morphology must be produced by direct control of the aggregation process. Moreover, the formed aggregates need to be in a solvent suitable for biological assays, while keeping their morphology intact. Here we evaluated the dependence of morphology and integrity of amyloid-like fibrils and spherulites on preparation conditions and post-treatment methods. Samples containing either amyloid-like fibrils or spherulites produced from human insulin in acetic acid solutions are dependent on the presence of salt (NaCl). Moreover, mechanical shaking (600 rpm) inhibits spherulite formation, while only affecting the length of the formed fibrils compared to quiescent conditions. Besides shaking, the initial protein concentration in the formulation was found to control fibril length. Surprisingly, exchanging the solvent used for aggregate formation to a physiologically relevant buffer, had a striking effect on the morphological integrity of the fibril and spherulite samples. Especially the secondary structure of one of our spherulite samples presented dramatic changes of the aggregated {beta}-sheet content after solvent exchange, emphasizing the importance of the aggregate stability. These results and considerations have profound implications on the data interpretation and should be implemented in the workflow for both fundamental characterization of aggregates as well as assays for evaluation of their corresponding biological effects.

biophysics↗

bioRxiv2022

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

Insulin formulations are the hallmark of interventions for treatment of diabetes. Understanding the mechanism that governs insulin self assembly or disassembly --and the role of stabilizing additives--are essential for improving insulin formulations. We report here the real-time direct observation of single insulin self-assembly and disassembly events using single molecule fluorescence microscopy. Our direct observations revealed previously unaccounted monomeric additions to occur to all types of assemblies and allowed us to quantify the existence, abundance and kinetic characterization of diverse assembly pathways involving monomeric dimers or tetrameric insulin species. We proposed and experimentally validated a model where the insulin self-assembly pathway is rerouted favoring monomeric or oligomeric assembly events by solution concentration, additives and formulations. Our rate simulation predicted the abundance of each oligomeric species across a concentration range of 6 orders of magnitude. Besides providing fundamental new insights, the results and toolbox here can be universally applied contributing to the development of optimal insulin formulations and the deciphering of oligomerization mechanisms for other proteins.

biophysics↗

bioRxiv2022

A single vesicle fluorescence-bleaching assay for multi-parameter analysis of proteoliposomes by total internal reflection fluorescence microscopy

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.

biochemistry↗

bioRxiv2021

Inferring kinetic rate constants from single-molecule FRET trajectories - a blind benchmark of kinetic analysis tools

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.

biophysics↗

bioRxiv2021

Direct Observation of Anisotropy-Driven Formation of Amyloid Protein Core-Shell Structures in Real-time by Super-resolution Microscopy

The misfolding of proteins and their aggregation in the form of fibrils or amyloid-like spherulites are involved in a spectrum of pathological abnormalities. Our current understanding of protein amyloid 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 LOcalisation Microscopy (REPLOM) super-resolution method to directly observe and quantify the existence and abundance of diverse aggregate morphologies below the diffraction limit and extract their heterogeneous growth kinetics. Our results revealed that even the growth of a 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.

biophysics↗

bioRxiv2021

Single particle combinatorial multiplexed liposome fusion mediated by DNA

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.

biophysics↗

bioRxiv2020

Across kingdom biased CYP-mediated metabolism via small-molecule ligands docking on P450 oxidoreductase

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.

biophysics↗

bioRxiv2020

DeepFRET: Rapid and automated single molecule FRET data classification using deep learning

Single molecule Forster 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 (<1% as compared to human operators) on ground truth data. Its flawless and rapid operation on real data demonstrates its wide applicability. This level of classification was achieved without any preprocessing or parameter setting by human operators, demonstrating DeepFRETs capacity to objectively quantify biomolecular dynamics. The provided a standalone executable based on open source code capitalises on the widespread adaptation of machine learning and may contribute to the effort of benchmarking smFRET for structural biology insights.

biophysics↗

bioRxiv2020

Label free fluorescence quantification of hydrolytic enzyme activity on native substrates reveal how lipase function depends on membrane curvature

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.

biochemistry↗