Rational design based on multi-monomer simultaneous docking for epitope imprinting of SARS-CoV-2 spike protein.

Among biomimetic strategies shaping engineering designs, molecularly imprinted polymer (MIP) technology stands out, involving chemically synthesised receptors emulating natural antigen-antibody interactions. These versatile 'designer polymers' with remarkable stability and low cost, are pivotal for in vitro diagnostics. Amid the recent global health crisis, we probed MIPs' potential to capture SARS-CoV-2 virions. Large biotemplates complicate MIP design, influencing generated binding site specificity. To precisely structure recognition sites within polymers, we innovated an epitope imprinting method supplemented by in silico polymerization component screening. A viral surface Spike protein informed epitope selection was targeted for MIP development. A novel multi-monomer docking approach (MMSD) was employed to simulate classical receptor-ligand interactions, mimicking binding reinforcement across multiple amino acids. Around 40 monomer combinations were docked to the epitope sequence and top performers experimentally validated via rapid fluorescence binding assays. Notably, high imprinting factor polymers correlated with MMSD predictions, promising rational MIP design applicable to diverse viral pathologies.

Multidomain Protein-Urea Interactions: Differences in Binding Behavior Lead to Different Destabilization Tendencies for Monoclonal Antibodies.

We study the influence of urea on the stability of monoclonal antibodies (mAbs) using molecular dynamics (MD) simulations in combination with differential scanning fluorimetry (DSF). We show that a denaturing cosolute such as urea binds strongly to the protein, which can lead to denaturation and enhanced aggregation behavior at high temperatures. The interaction between protein and urea crucially depends on the surface properties of the individual mAb domains and therefore affects the general binding to the protein differently. The study of these mechanisms for proteins with multiple domains, such as mAbs, encounters significant limitations in experimental analysis methods due to their complexity. Using computational and experimental methods, we are able to separate the protein-urea interaction by domain and show that Lennard-Jones interactions are mainly responsible for significant binding effects. Our results emphasize the potential of MD simulations in combination with Kirkwood-Buff theory to study the interactions between proteins with multiple domains and cosolutes as formulation excipients for drug discovery and development.

Decoding the mannose receptor-mAb interaction: the importance of high-mannose N-glycans and glycan-pairing.

During the development process of therapeutic monoclonal antibodies (mAbs), it is crucial to control (critical) quality attributes such as N-glycosylation influencing pharmacokinetics (PK) and Fc effector functions. Previous reports have shown that mAbs containing high-mannose N-glycans are cleared faster from blood circulation, leading to reduced half-lives. The high-mannose N-glycan content of mAbs can be influenced during the cell culture process by factors such as cell lines, process conditions, and media. Furthermore, mAbs have either one high mannose N-glycan (asymmetrical high-mannose glyco-pair) or two high mannose N-glycans (symmetrical high-mannose glyco-pair). The hypothesis that the mannose receptor (MR, CD206) accelerates clearance by facilitating their internalization and subsequent lysosomal degradation is widespread. However, the interaction between MR and mAbs has not been explicitly demonstrated. This study aimed to investigate this interaction, providing the first systematic demonstration of MR binding to the Fc region of mAbs with high-mannose N-glycans. Two novel analytical methods, MR surface plasmon resonance and MR affinity chromatography, were developed and applied to investigate the MR-mAb interaction. The interaction is found to be dependent on high-mannose content, but is independent of the mAb format or sequence. However, different glyco-pairs exhibited varying binding affinities to the MR, with the symmetrical high-mannose glyco-pair showing the strongest binding properties. These findings strengthen the hypothesis for the MR-mediated mAb interaction and contribute to a deeper understanding of the MR-mAb interaction, which could affect the criticality of high-mannose containing mAbs development strategies of IgG-based molecules and improve their PK profiles.

Intelligent Microcontroller-Based Infrared Attenuated Total Reflection Spectroscopy for High-Throughput Screening and Discrimination of Foodborne Fungi.

Food safety became one of the most critical issues owing to the large expansion of international trading and emission of various pollutants in air, water and soil. Fungal contamination of food and feed has attracted most of the attention in the last decade because of the emerging analytical tools that facilitate the detection and discrimination of fungal species in imported foodstuff, seeds, grains, plants, meats …etc. In this work, we give an insight on the application of integrated attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy and artificial-intelligence algorithms to the determination and discrimination of fungal species/strains which potentially infect plants, seeds and grains. The proposed method is based on a microcontroller which allows the PC to analyze a large number of samples via serial connection with an UART module. Penicillium chrysogenum, Aspergillus niger, Aspergillus fumigatus, Aspergillus solani, Aspergillus flavus and two different strains of Fusarium oxysporum were used as model microorganisms. The use of artificial-intelligence algorithms herein provides the advantage of automation enabling high throughput screening of large numbers of food samples in less than 5 s. In addition, the classification accuracy is enhanced by applying these machine-learning classification techniques. Principle component analysis (PCA) was used in order to extract the spectral discriminative features from the recorded fungal FTIR spectra. Three intelligent methods of classification; namely, artificial neural network (ANN), support-vector machine (SVM) and k-nearest neighbor (KNN), were used in this study in order to prove that integration of spectroscopic measurements with varying machine-learning methods give a simple analytical tool for detection and classification of foodborne pathogens. All the utilized classifiers gave an accuracy of 100 % and were able to discriminate different species and/or strains of the investigated fungi in few milliseconds.

Development of super nanoantimicrobials combining AgCl, tetracycline and benzalkonium chloride.

In this work, we demonstrate that a simple argentometric titration is a scalable, fast, green and robust approach for producing AgCl/antibiotic hybrid antimicrobial materials. We titrated AgNO3 into tetracycline hydrochloride (TCH) aqueous solution, thus forming AgCl/TCH in a one-step procedure. Furthermore, we investigated the one-pot synthesis of triply synergistic super-nanoantimicrobials, combining an inorganic source of Ag+ ions (AgCl), a disinfecting agent (benzyl-dimethyl-hexadecyl-ammonium chloride, BAC) and a molecular antibiotic (tetracycline hydrochloride, TCH). Conventional antimicrobial tests, industrial biofilm detection protocols, and in situ IR-ATR microbial biofilm monitoring, have been adapted to understand the performance of the synthesized super-nanoantimicrobial. The resulting hybrid AgCl/BAC/TCH nanoantimicrobials are found to be synergistically active in eradicating Salmonella enterica and Lentilactobacillus parabuchneri bacteria and biofilms. This study paves the way for the development of a new class of super-efficient nanoantimicrobials that combine relatively low amounts of multiple active species into a single (nano)formulation, thus preventing the development of antimicrobial resistance towards a single active principle.

Comprehensive analysis and characterization of glycan pairing in therapeutic antibodies and Fc-containing biotherapeutics: Addressing current limitations and implications for N-glycan impact.

N-glycosylation of the Fc part is a (critical) quality attribute of therapeutic antibodies and Fc-containing biotherapeutics, that impacts their stability, immunogenicity, pharmacokinetics, and effector functions. Current glycosylation analysis methods focus on the absolute amounts of glycans, neglecting the apparent glycan distribution over the entirety of proteins. The combination of the two Fc N-glycans, herein referred to as glyco-pair, therefore remains unknown, which is a major drawback for N-glycan impact assessment. This study presents a comprehensive workflow for the analysis and characterization of Fc N-glycan pairing in biotherapeutics, addressing the limitations of current glycosylation analysis methods. The applicability of the method across various biotherapeutic proteins including antibodies, bispecific antibody formats, and a Fc-Fusion protein is demonstrated, and the impact of method conditions on glycan pairing analysis is highlighted. Moreover, the influence of the molecular format, Fc backbone, production process, and cell line on glycan pairing pattern was investigated. The results underscore the significance of comprehensive glycan pairing analysis to accurately assess the impact of N-glycans on important product quality attributes of therapeutic antibodies and Fc-containing biotherapeutics.

Ultratrace eNose Sensing of VOCs toward Breath Analysis Applications Utilizing an eNose-Based Analyzer.

This proof-of-principle study presents the ability of the recently developed iLovEnose to measure ultratrace levels of volatile organic compounds (VOCs) in simulated human breath based on the combination of multiple gas sensors. The iLovEnose was developed by our research team as a test bed for gas sensors that can be hosted in three serially connected compact low-volume and temperature-controlled compartments. Herein, the eNose system was equipped with conventional semiconducting metal oxide (MOX) gas sensors using a variety of base technologies providing 11 different sensor signals that were evaluated to determine six VOCs of interest at eight low to ultralow concentration levels (i.e., ranging from 3 to 0.075 ppm) at humid conditions (90% rh at 22 °C). The measurements were randomized and performed four times over a period of 2 weeks. Partial least-squares regression analysis was applied to estimate the concentration of these six analytes. It was shown that the iLovEnose system is able to discriminate between these VOCs and provide reliable quantitative information relevant for future applications in exhaled breath analysis as a diagnostic disease detection or monitoring device.

Prediction of Deoxynivalenol Contamination in Wheat via Infrared Attenuated Total Reflection Spectroscopy and Multivariate Data Analysis.

The climate crisis further exacerbates the challenges for food production. For instance, the increasingly unpredictable growth of fungal species in the field can lead to an unprecedented high prevalence of several mycotoxins, including the most important toxic secondary metabolite produced by Fusarium spp., i.e., deoxynivalenol (DON). The presence of DON in crops may cause health problems in the population and livestock. Hence, there is a demand for advanced strategies facilitating the detection of DON contamination in cereal-based products. To address this need, we introduce infrared attenuated total reflection (IR-ATR) spectroscopy combined with advanced data modeling routines and optimized sample preparation protocols. In this study, we address the limited exploration of wheat commodities to date via IR-ATR spectroscopy. The focus of this study was optimizing the extraction protocol for wheat by testing various solvents aligned with a greener and more sustainable analytical approach. The employed chemometric method, i.e., sparse partial least-squares discriminant analysis, not only facilitated establishing robust classification models capable of discriminating between high vs low DON-contaminated samples adhering to the EU regulatory limit of 1250 µg/kg but also provided valuable insights into the relevant parameters shaping these models.

Rational design of MIPs for the detection of Myxovirus resistance protein A (MxA), a biomarker for viral infection.

Accurate diagnosis is crucial for effective patient care and the containment of antimicrobial resistance outbreaks. The intricate challenge of distinguishing bacterial from viral infections, coupled with limited diagnostic tools and overlapping symptoms has driven the utilization of molecular imprinting techniques. This study focuses on developing cost-effective, chemically stable antibody analogs for the interferon-induced protein myxovirus resistance protein A (MxA). MxA is an intracellular, cytoplasmic GTPase having activity against a wide range of viruses and serves as a distinctive biomarker for viral infections. We utilized computational design to guide the polymer assembly, centering on epitope imprinting to target MxA-specific regions crucial for interaction. Molecular docking calculations, alongside a pioneering multi-monomer simultaneous docking (MMSD) protocol, efficiently elucidate cooperativity during pre-polymerization. Monomer binding affinity scores, such as for APTMS, exhibited notable increase, ranging from -3.11 to -13.03 kcal/mol across various MMSD combinations compared to a maximum of -2.78 kcal/mol in single monomer docking, highlighting the capacity of MMSD in elucidating crucial monomer-monomer interactions. This computational approach provides a theoretical alternative to labor-intensive experimental optimization, streamlining the development process for synthetic receptors. Simulations reveal unique interactions enhancing MIP-peptide complementarity, yielding optimized receptors selectively binding to MxA epitopes. The obtained MIPs demonstrated a maximum adsorption capacity of approximately 12 mg/g and captured 1.6 times more epitope and 2.6 times more epitope containing MxA protein than corresponding NIPs. A proof-of-concept study demonstrates MxA protein binding to synthetic receptors, highlighting the potential of MIPs, analogous to antibodies, in overcoming current diagnostic challenges for precise detection of viral infection.

An Algebraic Blueprint for Predicting Turnover Numbers and Endpoints in Photocatalysis.

The front cover artwork is provided by the Institute of Analytical and Bioanalytical Chemistry and the Institute of Inorganic Chemistry I at Ulm University within the Collaborative Research Center TRR 234 CataLight. The image shows an algebraic approach to generically calculate and predict the turnover number (TON) and the endpoint of photocatalytic hydrogen gas evolution experiments. Read the full text of the Research Article at 10.1002/cphc.202300767.

A scalable route to quaternary ammonium-functionalized AgCl colloidal antimicrobials inhibiting food pathogenic bacteria and biofilms.

This study explores how a simple argentometric titration-like approach could be evolved into a versatile, scalable, fast, and robust strategy for the production of AgCl/quaternary ammonium compounds (QACs) colloidal nanoantimicrobials (NAMs). These systems, which are green, stable, cost-effective, and reproducible are found to be effective against a wide range of food pathogenic bacteria and biofilms. The option of a large-scale production for such colloidal suspensions was explored via the use of a peristaltic pump. The utilization of various types of biosafe QACs and a wide range of solvents including aqueous and organic ones renders this system green and versatile. Nanocolloids (NCs) were characterized using UV-Vis, X-ray photoelectron and Fourier transform infrared (FTIR) spectroscopies. Their morphology and crystalline nature were investigated by transmission electron microscopy (TEM) and selected area diffraction pattern (SAED). Nanoparticle (NP) size distribution and hydrodynamic radius were measured by dynamic light scattering (DLS), while the ζ-potential was found to be highly positive, thus indicating significant colloidal stability and antimicrobial activity. In fact, the higher the NP surface charge, the stronger was their bioactivity. Furthermore, the antibacterial and antibiofilm effects of the as-prepared NCs were tested against Gram-positive bacteria, such as Staphylococcus aureus (ATCC 29213) and Listeria monocytogenes 46, and Gram-negative bacteria, such as Escherichia coli (ATCC 25922) and Pseudomonas aeruginosa (ATCC 27853). The results clearly indicate that AgCl/QACs provide pronounced antibiofilm activity with long-term bacteriostatic effects against foodborne pathogenic bacteria rendering them an ideal choice for active food packaging systems.

Impact of Urea on Monoclonal Antibodies: Multiple Destabilization and Aggregation Effects for Therapeutic Immunoglobulin G Proteins.

We performed nano differential scanning fluorimetry (nanoDSF) measurements of immunoglobulin G (IgG) in urea gradient solutions under thermal unfolding. Our results show that the denaturing effect of urea on individual IgG domains can be monitored via a linear mapping of thermal shift curves to the corresponding urea concentrations. Assignment of IgG domains to each thermal shift curve allows for a reliable differentiation of the underlying mechanisms. Further results show a decisive influence of salt-induced electrostatic screening effects. We are able to explain all findings by preferential binding mechanisms in combination with electrostatic effects. The results of our study shed more light on the complex interaction mechanisms between buffer solutions and complex proteins, which are important for improving the shelf life of protein therapeutic formulation.

Fabrication of Low-Cost Miniaturized Gas Cells via SLA 3D-Printing for UV-Based Gas Sensors.

The use of 3D-printing technology for producing optical devices (i.e., mirrors and waveguides) remains challenging, especially in the UV spectral regime. Gas sensors based on absorbance measurements in the UV region are suitable for determining numerous volatile species in a variety of samples and analytical scenarios. The performance of absorbance-based gas sensors is dependent on the ability of the gas cell to propagate radiation across the absorption path length and facilitate interaction between photons and analytes. In this technical note, we present a 3D-printed substrate-integrated hollow waveguide (iHWG) to be used as a miniaturized and ultralightweight gas cell used in UV gas-sensing schemes. The substrates were fabricated via UV stereolithography and polished, and the light-guiding channel was coated with aluminum for UV reflectivity. This procedure resulted in a surface roughness of 11.2 nm for the reflective coating, yielding a radiation attenuation of 2.25 W/cm2. The 3D-printed iHWG was coupled to a UV light source and a portable USB-connected spectrometer. The sensing device was applied for the quantification of isoprene and acetone, serving as a proof-of-concept study. Detection limits of 0.22 and 0.03% in air were obtained for acetone and isoprene, respectively, with a nearly instantaneous sensor response. The development of portable, low-cost, and ultralightweight UV optical sensors enables their use in a wide range of scenarios ranging from environmental monitoring to clinical/medical applications.

An Algebraic Blueprint for Predicting Turnover Numbers and Endpoints in Photocatalysis.

Photocatalysis is a contemporary research field given that the world's fossil energy resources including coal, mineral oil and natural gas are finite. The vast variety of photocatalytic systems demands for standardized protocols facilitating an objective comparison. While there are commonly accepted performance indicators such as the turnover number (TON) that are usually reported, to date there is no unified concept for the determination of TONs and the endpoint of the reaction during continuous measurements. Herein, we propose an algebraic approach using defined parameters and boundary conditions based on partial-least squares regression for generically calculating and predicting the turnover number and the endpoint of a photocatalytic experiment. Furthermore, the impact of the analysis period was evaluated with respect to the fidelity of the obtained TON, and the influence of the data point density along critical segments of the obtained fitting function is demonstrated.

Sensing Liquid- and Gas-Phase Hydrocarbons via Mid-Infrared Broadband Femtosecond Laser Source Spectroscopy.

In this study, we demonstrate the combination of a tunable broadband mid-infrared (MIR) femtosecond laser source separately coupled to a ZnSe crystal horizontal attenuated total reflection (ATR) sensor cell for liquid phase samples and to a substrate-integrated hollow waveguide (iHWG) for gas phase samples. Utilizing this emerging light source technology as an alternative MIR radiation source for Fourier transform infrared (FTIR) spectroscopy opens interesting opportunities for analytical applications. In a first approach, we demonstrate the quantitative analysis of three individual samples, ethanol (liquid), methane (gas), and 2-methyl-1-propene (gas), with limits of detection of 0.3% (ethanol) and 22 ppmv and 74 ppmv (methane and isobutylene), respectively, determined at selected emission wavelengths of the MIR laser source (i.e., 890 cm-1, 1046 and 1305 cm-1). Hence, the applicability of a broadband MIR femtosecond laser source as a bright alternative light source for quantitative analysis via FTIR spectroscopy in various sensing configurations has been demonstrated.

The Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis.

Colorectal cancer (CRC) is the third most common malignancy and the second most common cause of cancer-related deaths worldwide. While CRC screening is already part of organized programs in many countries, there remains a need for improved screening tools. In recent years, a potential approach for cancer diagnosis has emerged via the analysis of volatile organic compounds (VOCs) using sensor technologies. The main goal of this study was to demonstrate and evaluate the diagnostic potential of a table-top breath analyzer for detecting CRC. Breath sampling was conducted and CRC vs. non-cancer groups (105 patients with CRC, 186 non-cancer subjects) were included in analysis. The obtained data were analyzed using supervised machine learning methods (i.e., Random Forest, C4.5, Artificial Neural Network, and Naïve Bayes). Superior accuracy was achieved using Random Forest and Evolutionary Search for Features (79.3%, sensitivity 53.3%, specificity 93.0%, AUC ROC 0.734), and Artificial Neural Networks and Greedy Search for Features (78.2%, sensitivity 43.3%, specificity 96.5%, AUC ROC 0.735). Our results confirm the potential of the developed breath analyzer as a promising tool for identifying and categorizing CRC within a point-of-care clinical context. The combination of MOX sensors provided promising results in distinguishing healthy vs. diseased breath samples. Its capacity for rapid, non-invasive, and targeted CRC detection suggests encouraging prospects for future clinical screening applications.

Thin-Film Waveguide Laser Spectroscopy: A Novel Platform for Bacterial Analysis.

Bacterial sensing based on quantum cascade laser spectroscopy coupled with diamond or gallium arsenide thin-film waveguides is a novel analytical tool for gaining high-resolution infrared spectroscopic information of planktonic and sessile bacteria, as shown in the present study for Escherichia coli. During observation periods of up to 24 h, diamond and gallium arsenide thin-film waveguide laser spectroscopy was compared to information obtained via conventional Fourier transform infrared spectroscopy. The proliferation behavior of E. coli at those surfaces was complementarily investigated using atomic force microscopy and scanning electron microscopy.

Graphene-enhanced quantum cascade laser infrared spectroscopy using diamond thin-film waveguides.

Diamond thin-film waveguides were combined with quantum cascade lasers augmented by drop-casted graphene enabling surface-enhanced infrared absorption spectroscopy. Enhancing the signal provides access to an even more pronounced vibrational signature suitable for analytical scenarios where only a small sample volume and/or low analyte concentration levels are prevalent. To demonstrate the utility of this concept, taurine was investigated as a model analyte.

In vivo application of magnetic molecularly imprinted polymer in rheumatoid arthritis rat model.

A magnetic molecularly imprinted polymer (MMIP) was synthesised and tested for an in vivo rheumatoid arthritis (RA) rat model. Magnetite coated with mesoporous silica (Fe2O3@mSi) was used as core for surface imprinting, dopamine was used as monomer and methotrexate (MTX) was loaded directly during polymerisation. The amount of MTX loaded on MMIPs reached 201.165 ± 0.315 µmol/g. Characterisation of the polymers was done via SEM, TEM, and FTIR. The pharmacological effect of the selected MMIP was evaluated in a Complete Freund's Adjuvant (CFA) induced arthritis rat model where a 3D magnet bearing construct was designed for targeted delivery of MMIPs. The parameters evaluated were the change in paw edoema, paw diameter, gait score, and animal's weight. Results revealed a tendency of MMIP to significantly improve the measured parameters which was confirmed with histopathological findings. In conclusion, the improvement in the arthritic signs associated with MMIP treatment compared to free MTX, indicated successful targeting of MMIPs to the site of inflammation.

A Portable Infrared Attenuated Total Reflection Spectrometer for Food Analysis.

The analytical performance of a compact infrared attenuated total reflection spectrometer using a pyroelectric detector array has been evaluated and compared to a conventional laboratory Fourier transform infrared system for applications in food analysis. Analytical characteristics including sensitivity, repeatability, linearity of the calibration functions, signal-to-noise ratio, and spectral resolution have been derived for both approaches. Representative analytes of relevance in food industries (i.e., organic solvents, fatty acids, and mycotoxins) have been used for the assessment of the performance of the device and to discuss the potential of this technology in food and feed analysis.