Microbial-dependent trimethylamine (TMA) generation from dietary precursors such as choline was recently linked to cardiovascular diseases (CVDs) as well as chronic kidney disease (CKD). Inhibition of TMA-generating enzymes in gut bacteria would be an innovative approach to treat these diseases. The potential to accurately quantify secreted TMA levels highlights the capacity of mass spectrometry (MS) for tracking microbial TMA-lyase activity. However, high-throughput screening (HTS) by conventional MS instrumentation is hampered by limited sample throughput. Recent advancement in liquid handling and instrumentation of matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS provides an HTS-compatible MS technology. The deciphering of enzymatic reactions using this label-free readout has been successfully applied but has thus far been limited to peptide/protein-centric activity assays. Here, we demonstrate the versatile applicability of MALDI-TOF by tracking a small molecule within a highly complex sample background. The key to success for this concept was chemical derivatization of the target molecule enabling quantitative assessment of microbial TMA formation. Further, its potential was demonstrated in a side-by-side comparison to RapidFire-MS in a primary screen and subsequent dose–response experiments. Overall, the established assay enables the screening for microbial TMA-lyase inhibitors and serves as a proof of concept for the applicability of MALDI-TOF for demanding assay concepts per se.

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.

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 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.

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.