The characterized compounds and mass differences are an important step forward in metabolic pathway discovery and systems biology research. The spectral metadata of the 5,420 compounds is publicly available (DynLib spectral database, https//bioit3.irc.ugent.be/dynlib/).Broad-spectrum amino acid racemases (Bsrs) enable bacteria to generate non-canonical D-amino acids (NCDAAs), whose roles and impact on microbial physiology, including modulation of cell wall structure and dissolution of biofilms, are just beginning to be appreciated. Here we used a diverse array of structural, biochemical and molecular simulation studies to define and characterize how BsrV is post-translationally regulated. We discovered that contrary to Vibrio cholerae alanine racemase AlrV highly compacted active site, BsrV's is broader and can be occupied by cell wall stem peptides. We found that peptidoglycan peptides modified with NCDAAs are better stabilized by BsrV's catalytic cavity and show better inhibitory capacity than canonical muropeptides. Notably, BsrV binding and inhibition can be recapitulated by undigested peptidoglycan sacculi as it exists in the cell. Docking simulations of BsrV binding the peptidoglycan polymer generate a model where the peptide stems are perfectly accommodated and stabilized within each of the dimeŕs active sites. Taking these biochemical and structural data together, we propose that inhibition of BsrV by peptidoglycan peptides underlies a negative regulatory mechanism to avoid excessive NCDAA production. Our results collectively open the door to use "à la carte" synthetic peptides as a tool to modulate DAAs production of Bsr enzymes.Effective use of plant biomass as an abundant and renewable feedstock for biofuel production and biorefinery requires efficient enzymatic mobilization of cell wall polymers. Knowledge of plant cell wall composition and architecture has been exploited to develop novel multifunctional enzymes with improved activity against lignocellulose, where a left-handed β-3-prism synthetic scaffold (BeSS) was designed for insertion of multiple protein domains at the prism vertices. This allowed construction of a series of chimeras fusing variable numbers of a GH11 β-endo-1,4-xylanase and the CipA-CBM3 with defined distances and constrained relative orientations between catalytic domains. The cellulose binding and endoxylanase activities of all chimeras were maintained. Activity against lignocellulose substrates revealed a rapid 1.6- to 3-fold increase in total reducing saccharide release and increased levels of all major oligosaccharides as measured by polysaccharide analysis using carbohydrate gel electrophoresis (PACE). A construct with CBM3 and GH11 domains inserted in the same prism vertex showed highest activity, demonstrating interdomain geometry rather than number of catalytic sites is important for optimized chimera design. These results confirm that the BeSS concept is robust and can be successfully applied to the construction of multifunctional chimeras, which expands the possibilities for knowledge-based protein design.Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities.Diet is one of the main sources of exposure to toxic chemicals with carcinogenic potential, some of which are generated during food processing, depending on the type of food (primarily meat, fish, bread and potatoes), cooking methods and temperature. Although demonstrated in animal models at high doses, an unequivocal link between dietary exposure to these compounds with disease has not been proven in humans. A major difficulty in assessing the actual intake of these toxic compounds is the lack of standardised and harmonised protocols for collecting and analysing dietary information. The intestinal microbiota (IM) has a great influence on health and is altered in some diseases such as colorectal cancer (CRC). Diet influences the composition and activity of the IM, and the net exposure to genotoxicity of potential dietary carcinogens in the gut depends on the interaction among these compounds, IM and diet. This review analyses critically the difficulties and challenges in the study of interactions among these three actors on the onset of CRC. Machine Learning (ML) of data obtained in subclinical and precancerous stages would help to establish risk thresholds for the intake of toxic compounds generated during food processing as related to diet and IM profiles, whereas Semantic Web could improve data accessibility and usability from different studies, as well as helping to elucidate novel interactions among those chemicals, IM and diet.As a recent global health emergency, the quick and reliable diagnosis of COVID-19 is urgently needed. Thus, many artificial intelligence (AI)-base methods are proposed for COVID-19 chest CT (computed tomography) image analysis. However, there are very limited COVID-19 chest CT images publicly available to evaluate those deep neural networks. On the other hand, a huge amount of CT images from lung cancer are publicly available. To build a reliable deep learning model trained and tested with a larger scale dataset, the proposed model builds a public COVID-19 CT dataset, containing 1186 CT images synthesized from lung cancer CT images using CycleGAN. Additionally, various deep learning models are tested with synthesized or real chest CT images for COVID-19 and Non-COVID-19 classification. selleck chemicals llc In comparison, all models achieve excellent results in accuracy, precision, recall and F1 score for both synthesized and real COVID-19 CT images, demonstrating the reliable of the synthesized dataset. The public dataset and deep learning models can facilitate the development of accurate and efficient diagnostic testing for COVID-19.