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Vehicle-to-everything (V2X) communication is seen as one of the main enabling technologies for automated vehicles. Collective perception is especially promising, as it allows connected traffic participants to “see through the eyes of others” by sharing sensor-detected objects via V2X communication. Its benefit is typically assessed in terms of the increased object update rate, redundancy, and awareness. To determine the safety improvement thanks to collective perception, the authors introduce new metrics, which quantify the environmental risk awareness of the traffic participants. The performance of the V2X service is then analyzed with the help of the test platform TEPLITS, using real traffic traces from German highways, amounting to over 100 h of total driving time. The results in the considered scenarios clearly show that collective perception not only contributes to the accuracy and integrity of the vehicles’ environmental perception, but also that a V2X market penetration of at least 25% is necessary to increase traffic safety from a “risk of serious traffic accidents” to a “residual hypothetical risk of collisions without minor injuries” for traffic participants equipped with non-redundant 360° sensor systems. These results support the ongoing worldwide standardization efforts of the collective perception service.

Controlling protein crystallization processes is essential for improving downstream processing in biotechnology. This study investigates the combination of machine learning-based image analysis and in situ microscopy for real-time monitoring of protein crystallization kinetics. The experimental research is focused on the batch crystallization of an alcohol dehydrogenase from Lactobacillus brevis (LbADH) and two selected rational crystal contact mutants. Technical protein crystallization experiments were performed in a 1 L stirred crystallizer by adding polyethyleneglycol 550 monomethyl ether (PEG 550 MME). The estimated crystal volumes from online microscopy correlated well with the offline measured protein concentrations in solution. In addition, in situ microscopy was superior to offline data if amorphous protein precipitation occurred. Real-time image analysis provides the data basis for online estimation of important batch crystallization performance indicators like yield, crystallization kinetics, crystal size distributions, and number of protein crystals. Surprisingly, one of the LbADH mutants, which should theoretically crystallize more slowly than the wild type based on molecular dynamics (MD) simulations, showed better crystallization performance except for the yield. Thus, online monitoring of scalable protein crystallization processes with in situ microscopy and real-time image analysis improves the precision of crystallization studies for industrial settings by providing comprehensive data, reducing the limitations of traditional analytical techniques, and enabling new insights into protein crystallization process dynamics.

Protein crystallization is an alternative to well-established but cost-intensive and time-consuming chromatography in biotechnological processes, with protein crystallization defined as an essential unit operation for isolating proteins, e.g., active pharmaceutical ingredients. Crystalline therapeutic proteins attract interest in formulation and delivery processes of biopharmaceuticals due to the high purity, concentration, and stability of the crystalline state. Although improving protein crystallization is mainly achieved by high-throughput screening of crystallization conditions, recent studies have established a rational protein engineering approach to enhance crystallization for two homologous alcohol dehydrogenases from Lactobacillus brevis (LbADH) and Lactobacillus kefiri (LkADH). As generalizing crystallization processes across a wide range of target proteins remains challenging, this study takes a further step by applying the successful crystal contact engineering strategies for LbADH/LkADH to a non-homologous protein, an NADH-binding derivative of the Nostoc sp. PCC 1720 ene reductase (NspER1-L1,5). Here, the focus lies on introducing electrostatic interactions at crystal contacts, specifically between lysine and glutamic acid. Out of the nine tested NspER1-L1,5 mutants produced in E. coli, six crystallized, while four mutants revealed an increased propensity to crystallize in static µL-batch crystallization compared to the wild type: Q204K, Q350K, D352K, and T354K. The best-performing mutant Q204K was selected for upscaling, crystallizing faster than the wild type in a stirred batch crystallizer. Even when spiked with E. coli cell lysate, the mutant maintained increased crystallizability compared to the wild type. The results of this study highlight the potential of crystal contact engineering as a reliable tool for improving protein crystallization as an alternative to chromatography, paving the way for more efficient biotechnological downstream processing.

Protein crystallization is nowadays performed at the micro to macro scale in academia and industry, being particularly interesting for pharmaceutical applications. Protein crystallization offers an attractive alternative to chromatography as a downstream processing step in the biotechnology industry, but also in the food and chemical industries. Monitoring of the protein crystallization process is required to understand the crystal growth mechanism and to obtain the information necessary for efficient process control, which needs to comply with the critical quality attributes of the product. Since a wide range of monitoring techniques have already been developed and established, this review focuses on the recent advances of selected techniques in monitoring protein crystallization processes such as the focused beam reflectance method (FBRM), and machine learning-based image analysis for solid-phase monitoring, as well as the spectroscopic methods for liquid-phase monitoring, such as attenuated total reflectance Fourier transform infrared (ATR-FTIR) and UV/Vis spectroscopy.

Protein crystallization as opposed to well-established chromatography processes has the benefits to reduce production costs while reaching a comparable high purity. However, monitoring crystallization processes remains a challenge as the produced crystals may interfere with analytical measurements. Especially for capturing proteins from complex feedstock containing various impurities, establishing reliable process analytical technology (PAT) to monitor protein crystallization processes can be complicated. In heterogeneous mixtures, important product characteristics can be found by multivariate analysis and chemometrics, thus contributing to the development of a thorough process understanding. In this project, an analytical set-up is established combining offline analytics, on-line ultraviolet visible light (UV/Vis) spectroscopy, and in-line Raman spectroscopy to monitor a stirred-batch crystallization process with multiple phases and species being present. As an example process, the enzyme Lactobacillus kefir alcohol dehydrogenase (L k ADH) was crystallized from clarified Escherichia coli ( E. coli ) lysate on a 300 mL scale in five distinct experiments, with the experimental conditions changing in terms of the initial lysate solution preparation method and precipitant concentration. Since UV/Vis spectroscopy is sensitive to particles, a cross-flow filtration (cross-flow filtration)-based bypass enabled the on-line analysis of the liquid phase providing information on the lysate composition regarding the nucleic acid to protein ratio. A principal component analysis (PCA) of in situ Raman spectra supported the identification of spectra and wavenumber ranges associated with productspecific information and revealed that the experiments followed a comparable, spectral trend when crystals were present. Based on preprocessed Raman spectra, a partial least squares (PLS) regression model was optimized to monitor the target molecule concentration in real-time. The off-line sample analysis provided information on the crystal number and crystal geometry by automated image analysis as well as the concentration of Lk ADH and host cell proteins (HCPs) In spite of a complex lysate suspension containing scattering crystals and various impurities, it was possible to monitor the target molecule concentration in a heterogeneous, multi-phase process using spectroscopic methods. With the presented analytical set-up of off-line, particle-sensitive on-line, and in-line analyzers, a crystallization capture process can be characterized better in terms of the geometry, yield, and purity of the crystals.

Industrial biotechnology offers a potential ecological solution for PET recycling under relatively mild reaction conditions via enzymatic degradation, particularly using the leaf branch compost cutinase (LCC) quadruple mutant ICCG. To improve the efficient downstream processing of this biocatalyst after heterologous gene expression with a suitable production host, protein crystallization can serve as an effective purification/capture step. Enhancing protein crystallization was achieved in recent studies by introducing electrostatic (and aromatic) interactions in two homologous alcohol dehydrogenases (Lb/LkADH) and an ene reductase (NspER1-L1,5) produced with Escherichia coli. In this study, ICCG, which is difficult to crystallize, was utilized for the application of crystal contact engineering strategies, resulting in ICCG mutant L50Y (ICCGY). Previously focused on the Lys-Glu interaction for the introduction of electrostatic interactions at crystal contacts, the applicability of the engineering strategy was extended here to an Arg-Glu interaction to increase crystallizability, as shown for ICCGY T110E. Furthermore, the rationale of the engineering approach is demonstrated by introducing Lys and Glu at non-crystal contacts or sites without potential interaction partners as negative controls. These resulting mutants crystallized comparably but not superior to the wild-type protein. As demonstrated by this study, crystal contact engineering emerges as a promising approach for rationally enhancing protein crystallization. This advancement could significantly streamline biotechnological downstream processing, offering a more efficient pathway for research and industry.

Protein crystallization can serve as a purification step in biotechnological processes but is often limited by the non-crystallizability of proteins. Enabling or improving crystallization is mostly achieved by high-throughput screening of crystallization conditions and, more recently, by rational crystal contact engineering. Two selected rational crystal contact mutations, Q126K and T102E, were transferred from the alcohol dehydrogenases of Lactobacillus brevis (LbADH) to Lactobacillus kefir (LkADH). Proteins were expressed in E. coli and batch protein crystallization was performed in stirred crystallizers. Highly similar crystal packing of LkADH wild type compared to LbADH, which is necessary for the transfer of crystal contact engineering strategies, was achieved by aligning purification tag and crystallization conditions, as shown by X-ray diffraction. After comparing the crystal sizes after crystallization of LkADH mutants with the wild type, the mean protein crystal size of LkADH mutants was reduced by 40–70% in length with a concomitant increase in the total amount of crystals (higher number of nucleation events). Applying this measure to the LkADH variants studied results in an order of crystallizability T102E > Q126K > LkADH wild type, which corresponds to the results with LbADH mutants and shows, for the first time, the successful transfer of crystal contact engineering strategies.

Protein crystallization can function as an effective method for protein purification or formulation. Such an application requires a comprehensive understanding of the intermolecular protein–protein interactions that drive and stabilize protein crystal formation to ensure a reproducible process. Using alcohol dehydrogenase from Lactobacillus brevis (LbADH) as a model system, we probed in our combined experimental and computational study the effect of residue substitutions at the protein crystal contacts on the crystallizability and the contact stability. Increased or decreased contact stability was calculated using molecular dynamics (MD) free energy simulations and showed excellent qualitative correlation with experimentally determined increased or decreased crystallizability. The MD simulations allowed us to trace back the changes to their physical origins at the atomic level. Engineered charge–charge interactions as well as engineered hydrophobic effects could be characterized and were found to improve crystallizability. For example, the simulations revealed a redesigning of a water mediated electrostatic interaction (“wet contact”) into a water depleted hydrophobic effect (“dry contact”) and the optimization of a weak hydrogen bonding contact towards a strong one. These findings explained the experimentally found improved crystallizability. Our study emphasizes that it is difficult to derive simple rules for engineering crystallizability but that free energy simulations could be a very useful tool for understanding the contribution of crystal contacts for stability and furthermore could help guide protein engineering strategies to enhance crystallization for technical purposes.

Since preparative chromatography is a sustainability challenge due to large amounts of consumables used in downstream processing of biomolecules, protein crystallization offers a promising alternative as a purification method. While the limited crystallizability of proteins often restricts a broad application of crystallization as a purification method, advances in molecular biology, as well as computational methods are pushing the applicability towards integration in biotechnological downstream processes. However, in industrial and academic settings, monitoring protein crystallization processes non-invasively by microscopic photography and automated image evaluation remains a challenging problem. Recently, the identification of single crystal objects using deep learning has been the subject of increased attention for various model systems. However, the advancement of crystal detection using deep learning for biotechnological applications is limited: robust models obtained through supervised machine learning tasks require large-scale and high-quality data sets usually obtained in large projects through extensive manual labeling, an approach that is highly error-prone for dense systems of transparent crystals. For the first time, recent trends involving the use of synthetic data sets for supervised learning are transferred, thus generating photorealistic images of virtual protein crystals in suspension (PCS) through the use of ray tracing algorithms, accompanied by specialized data augmentations modelling experimental noise. Further, it is demonstrated that state-of-the-art models trained with the large-scale synthetic PCS data set outperform similar fine-tuned models based on the average precision metric on a validation data set, followed by experimental validation using high-resolution photomicrographs from stirred tank protein crystallization processes.

Salmochelins represent novel carbohydrate containing catecholate siderophores, which are excreted by Salmonella enterica and uropathogenic Escherichia coli strains under low-iron stress. While previous analytical data showed salmochelins to contain 2,3-dihydroxybenzoyl-L-serine and glucose, the molecular structure remained elusive. Structure elucidation with electrospray ionization-Fourier transform ion cyclotron resonance-mass spectrometry (ESI-FTICR-MS), GC-MS and 2D-NMR now revealed that salmochelins are enterobactin-related compounds, which are beta-C-glucosylated at the 5-position of a 2,3-dihydroxybenzoyl residue. The key compound salmochelin S4 is a twofold beta-C-glucosylated enterobactin analogue. Comparison of partial structures of salmochelin with a C-glycosylated compound previously characterized by another group strongly suggest that salmochelins represent the long sought compounds termed Salmonella resistance factors (SRF) or pacifarins. Transformation of iro-genes into enterobactin-producing E. coli K12 confers the ability to produce salmochelins. A detailed analysis proved iroB to be the sole gene with glycosyltransferase activity necessary for salmochelin production. Salmochelins compared to enterobactin are the better siderophores in the presence of serum albumin. This may indicate for salmochelins a considerably more important role for pathogenic processes in certain Escherichia coli and Salmonella infections than formerly assigned to enterobactin. This conclusion is supported by the location of the iro genes on pathogenicity islands of uropathogenic E. coli strains.