Molecularly imprinted polymers (MIPs) show significant promise as effective alternatives to antibodies in disease diagnosis and therapy. However, the challenging process of screening extensive libraries of monomer combinations and synthesis conditions to identify formulations with enhanced selectivity and affinity presents a notable time constraint. The need for expedient methods becomes clear in accelerating the strategic development of MIPs tailored for precise molecular recognition purposes. In this study, an innovative high-throughput screening methodology designed to identify the optimal MIP formulation for targeting tumors is presented. Employing a microtiter plate format, over 100 polymer syntheses are conducted, incorporating diverse combinations of functional monomers. Evaluation of binding performance utilizes fluorescence-based assays, focusing on an epitope of the epidermal growth factor receptor (EGFR). Through this meticulously structured screening process, synthesis conditions that produced MIP nanoparticles exhibiting substantial specificity for EGFR targeting (KD = 10-12 m) are identified. These \"bionic antibodies\" demonstrate selective recognition of cancer cells in whole blood samples, even at concentrations as low as 5 cells mL-1. Further validation through fluorescent imaging confirms the tumor-specific localization of the MIPs in vivo. This highly efficient screening approach facilitates the strategic synthesis of imprinted polymers functioning as precision bioprobes.

Addressing the critical need for swift and precise nutritional profiling in healthcare and in food industry, this study pioneers the integration of vision-language models (VLMs) with chemical analysis techniques. A cutting-edge VLM is unveiled, utilizing the expansive UMDFood-90k database, to significantly improve the speed and accuracy of nutrient estimation processes. Demonstrating a macro-AUCROC of 0.921 for lipid quantification, the model exhibits less than 10% variance compared to traditional chemical analyses for over 82% of the analyzed food items. This innovative approach not only accelerates nutritional screening by 36.9% when tested amongst students but also sets a new benchmark in the precision of nutritional data compilation. This research marks a substantial leap forward in food science, employing a blend of advanced computational models and chemical validation to offer a rapid, high-throughput solution for nutritional analysis.

Biosensors are valuable tools in accelerating the test phase of the design-build-test-learn cycle of cell factory development, as well as in bioprocess monitoring and control. G protein-coupled receptor (GPCR)-based biosensors enable cells to sense a wide array of molecules and environmental conditions in a specific manner. Due to the extracellular nature of their sensing, GPCR-based biosensors require compartmentalization of distinct genotypes when screening production levels of a strain library to ensure that detected levels originate exclusively from the strain under assessment. Here, we explore the integration of production and sensing modalities into a single Saccharomyces cerevisiae strain and compartmentalization using three different methods: (1) cultivation in microtiter plates, (2) spatial separation on agar plates, and (3) encapsulation in water-in-oil-in-water double emulsion droplets, combined with analysis and sorting via a fluorescence-activated cell sorting machine. Employing tryptamine and serotonin as proof-of-concept target molecules, we optimize biosensing conditions and demonstrate the ability of the autocrine screening method to enrich for high producers, showing the enrichment of a serotonin-producing strain over a nonproducing strain. These findings illustrate a workflow that can be adapted to screening for a wide range of complex chemistry at high throughput using commercially available microfluidic systems.

The rapid rise of antibiotic resistance and slow discovery of new antibiotics have threatened global health. While novel phage lysins have emerged as potential antibacterial agents, experimental screening methods for novel lysins pose significant challenges due to the enormous workload. Here, the first unified software package, namely DeepLysin, is developed to employ artificial intelligence for mining the vast genome reservoirs (\"dark matter\") for novel antibacterial phage lysins. Putative lysins are computationally screened from uncharacterized Staphylococcus aureus phages and 17 novel lysins are randomly selected for experimental validation. Seven candidates exhibit excellent in vitro antibacterial activity, with LLysSA9 exceeding that of the best-in-class alternative. The efficacy of LLysSA9 is further demonstrated in mouse bloodstream and wound infection models. Therefore, this study demonstrates the potential of integrating computational and experimental approaches to expedite the discovery of new antibacterial proteins for combating increasing antimicrobial resistance.

D-amino acid oxidase (DAAO)-catalyzed selective oxidative deamination is a very promising process for synthesizing l-amino acids including l-phosphinothricin (l-PPT, a high-efficiency and broad-spectrum herbicide). However, the wild-type DAAO\'s low activity toward unnatural substrates like d-phosphinothricin (d-PPT) hampers its application. Herein, a DAAO from Caenorhabditis elegans (CeDAAO) was screened and engineered to improve the catalytic potential on d-PPT. First, we designed a novel growth selection system, taking into account the intricate relationship between the growth of Escherichia coli (E. coli) and the catalytic mechanism of DAAO. The developed system was used for high-throughput screening of gene libraries, resulting in the discovery of a variant (M6) with significantly increased catalytic activity against d-PPT. The variant displays different catalytic properties on substrates with varying hydrophobicity and hydrophilicity. Analysis using Alphafold2 modeling and molecular dynamic simulations showed that the reason for the enhanced activity was the substrate-binding pocket with enlarged size and suitable charge distribution. Further QM/MM calculations revealed that the crucial factor for enhancing activity lies in reducing the initial energy barrier of the reductive half reaction. Finally, a comprehensive binding-model index to predict the enhanced activity of DAAO toward d-PPT, and an enzymatic deracemization approach was developed, enabling the efficient synthesis of l-PPT with remarkable efficiency.

Peroxisomal compartmentalization has emerged as a highly promising strategy for reconstituting intricate metabolic pathways. In recent years, significant progress has been made in the peroxisomes through harnessing precursor pools, circumventing metabolic crosstalk, and minimizing the cytotoxicity of exogenous pathways. However, it is important to note that in methylotrophic yeasts (e.g. Pichia pastoris), the abundance and protein composition of peroxisomes are highly variable, particularly when peroxisome proliferation is induced by specific carbon sources. The intricate subcellular localization of native proteins, the variability of peroxisomal metabolic pathways, and the lack of systematic characterization of peroxisome targeting signals have limited the applications of peroxisomal compartmentalization in P. pastoris. Accordingly, this study established a high-throughput screening method based on β-carotene biosynthetic pathway to evaluate the targeting efficiency of PTS1s (Peroxisome Targeting Signal Type 1) in P. pastoris. First, 25 putative endogenous PTS1s were characterized and 3 PTS1s with high targeting efficiency were identified. Then, directed evolution of PTS1s was performed by constructing two PTS1 mutant libraries, and a total of 51 PTS1s (29 classical and 22 noncanonical PTS1s) with presumably higher peroxisomal targeting efficiency were identified, part of which were further characterized via confocal microscope. Finally, the newly identified PTS1s were employed for peroxisomal compartmentalization of the geraniol biosynthetic pathway, resulting in more than 30% increase in the titer of monoterpene compared with when the pathway was localized to the cytosol. The present study expands the synthetic biology toolkit and lays a solid foundation for peroxisomal compartmentalization in P. pastoris.

Hydrogel-based 3D cell cultures can recapitulate (patho)physiological phenomena ex vivo. However, due to their complex multifactorial regulation, adapting these tissue and disease models for high-throughput screening workflows remains challenging. In this study, a new precision culture scaling (PCS-X) methodology combines statistical techniques (design of experiment and multiple linear regression) with automated, parallelized experiments and analyses to customize hydrogel-based vasculogenesis cultures using human umbilical vein endothelial cells and retinal microvascular endothelial cells. Variations of cell density, growth factor supplementation, and media composition are systematically explored to induce vasculogenesis in endothelial mono- and cocultures with mesenchymal stromal cells or retinal microvascular pericytes in 384-well plate formats. The developed cultures are shown to respond to vasculogenesis inhibitors in a compound- and dose-dependent manner, demonstrating the scope and power of PCS-X in creating parallelized tissue and disease models for drug discovery and individualized therapies.

A 3D bioprinted neurovascular unit (NVU) model is developed to study glioblastoma (GBM) tumor growth in a brain-like microenvironment. The NVU model includes human primary astrocytes, pericytes and brain microvascular endothelial cells, and patient-derived glioblastoma cells (JHH-520) are used for this study. Fluorescence reporters are used with confocal high content imaging to quantitate real-time microvascular network formation and tumor growth. Extensive validation of the NVU-GBM model includes immunostaining for brain relevant cellular markers and extracellular matrix components; single cell RNA sequencing (scRNAseq) to establish physiologically relevant transcriptomics changes; and secretion of NVU and GBM-relevant cytokines. The scRNAseq reveals changes in gene expression and cytokines secretion associated with wound healing/angiogenesis, including the appearance of an endothelial mesenchymal transition cell population. The NVU-GBM model is used to test 18 chemotherapeutics and anti-cancer drugs to assess the pharmacological relevance of the model and robustness for high throughput screening.

BACKGROUND: Fungal phytopathogens are a significant threat to crops and food security, and there is a constant need to develop safe and effective compounds that antagonize them. In-planta assays are complex and tedious and are thus not suitable for initial high-throughput screening of new candidate antifungal compounds. We propose an in vitro screening pipeline that integrates five rapid quantitative and qualitative methods to estimate the efficacy and mode of action of prospective antifungal compounds.
RESULTS: The pipeline was evaluated using five documented antifungal compounds (benomyl, catechol, cycloheximide, 2,4-diacetylphloroglucinol, and phenylacetic acid) that have different modes of action and efficacy, against the model soilborne fungal pathogen Fusarium oxysporum f. sp. radicis cucumerinum. We initially evaluated the five compounds\' ability to inhibit fungal growth and metabolic activity using green fluorescent protein (GFP)-labeled F. oxysporum and PrestoBlue staining, respectively, in multiwell plate assays. We tested the compounds\' inhibition of both conidial germination and hyphal elongation. We then employed FUN-1 and SYTO9/propidium iodide staining, coupled to confocal microscopy, to differentiate between fungal growth inhibition and death at the cellular level. Finally, using a reactive oxygen species (ROS)-detection assay, we were able to quantify ROS production in response to compound application.
CONCLUSIONS: Collectively, the proposed pipeline provides a wide array of quantitative and qualitative data on the tested compounds that can help pinpoint promising novel compounds; these can then be evaluated more vigorously using in planta screening assays. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

The development of miniaturized high-throughput in situ screening platforms capable of handling the entire process of drug synthesis to final screening is essential for advancing drug discovery in the future. In this study, an approach based on combinatorial solid-phase synthesis, enabling the efficient synthesis of libraries of proteolysis targeting chimeras (PROTACs) in an array format is presented. This on-chip platform allows direct biological screening without the need for transfer steps.  UV-induced release of target molecules into individual droplets facilitates further on-chip experimentation. Utilizing a mitogen-activated protein kinase kinases (MEK1/2) degrader as a template, a series of 132 novel PROTAC-like molecules is synthesized using solid-phase Ugi reaction. These compounds are further characterized using various methods, including matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) imaging, while consuming only a few milligrams of starting materials in total. Furthermore, the feasibility of culturing cancer cells on the modified spots and quantifying the effect of MEK suppression is demonstrated. The miniaturized synthesis platform lays a foundation for high-throughput in situ biological screening of potent PROTACs for potential anticancer activity and offers the potential for accelerating the drug discovery process by integrating miniaturized synthesis and biological steps on the same array.