Holcombvendelbo0959

Crystallization and/or incubation of EstD11 at high temperature provided unique information on cap dynamics and a first glimpse of enzymatic activity in vivo. Very interestingly, we have discovered a unique Met zipper lining the active site and the cap domains that could be essential in pivotal aspects as thermo-stability and substrate promiscuity in EstD11.Wheat is one of the world's most important crops, but its production relies heavily on agrochemical inputs which can be harmful to the environment when used excessively. It is well known that a multitude of microbes interact with eukaryotic organisms, including plants, and the sum of microbes and their functions associated with a given host is termed the microbiome. Plant-microbe interactions can be beneficial, neutral or harmful to the host plant. Over the last decade, with the development of next generation DNA sequencing technology, our understanding of the plant microbiome structure has dramatically increased. Considering that defining the wheat microbiome is key to leverage crop production in a sustainable way, here we describe how different factors drive microbiome assembly in wheat, including crop management, edaphic-environmental conditions and host selection. In addition, we highlight the benefits to take a multidisciplinary approach to define and explore the wheat core microbiome to generate solutions based on microbial (synthetic) communities or single inoculants. Advances in plant microbiome research will facilitate the development of microbial strategies to guarantee a sustainable intensification of crop production.Ras undergoes interconversion between the active GTP-bound state and the inactive GDP-bound state. This GTPase cycle, which controls the activities of Ras, is accelerated by Ras GTPase-activating proteins (GAPs) and guanine nucleotide exchange factors (SOS). Oncogenic Ras mutations could affect the GTPase cycle and impair Ras functions. Additionally, Src-induced K-Ras Y32/64 dual phosphorylation has been reported to disrupt GTPase cycle and hinder Ras downstream signaling. However, the underlying mechanisms remain unclear. To address this, we performed molecular dynamics simulations (~30 μs in total) on unphosphorylated and phosphorylated K-Ras4B in GTP- and GDP-bound states, and on their complexes with GTPase cycle regulators (GAP and SOS) and the effector protein Raf. We found that K-Ras4B dual phosphorylation mainly alters the conformation at the nucleotide binding site and creates disorder at the catalytic site, resulting in the enlargement of GDP binding pocket and the retard of Ras-GTP intrinsic hydrolysis. selleck compound We observed phosphorylation-induced shift in the distribution of Ras-GTP inactive-active sub-states and recognized potential druggable pockets in the phosphorylated Ras-GTP. Moreover, decreased catalytic competence or signal delivery abilities due to reduced binding affinities and/or distorted catalytic conformations of GAP, SOS and Raf were observed. In addition, the allosteric pathway from Ras/Raf interface to the distal Raf L4 loop was compromised by Ras phosphorylation. These results reveal the mechanisms by which phosphorylation influences the intrinsic or GAP/SOS catalyzed transformations between GTP- and GDP-bound states of Ras and its signal transduction to Raf. Our findings project Ras phosphorylation as a target for cancer drug discovery.Extrachromosomal circular DNA (eccDNA) is independent of the chromosome and exists in many eukaryotes. However, the nature and origin of eccDNA in plants remains unclear. In this study, we sequenced 12 samples from four tissues (leaf, flower, stem and root) with three biological replicates. In total, we found 743 eccDNAs found in at least two samples. Most of eccDNA have inverted repeats ranging from 4 to 12 bp in the boundaries. Interestingly, eccDNA is not only related to transposon activity, but also hosts tRNA genes, suggesting that the eccDNAs may be associated with tRNA abundance which controls protein synthesis under conditions of stress. Our results provide an unprecedented view of eccDNA, which is still naïve in scope.Advanced sequencing technologies such as RNASeq provide the means for production of massive amounts of data, including transcriptome-wide expression levels of coding RNAs (mRNAs) and non-coding RNAs such as miRNAs, lncRNAs, piRNAs and many other RNA species. In silico analysis of datasets, representing only one RNA species is well established and a variety of tools and pipelines are available. However, attaining a more systematic view of how different players come together to regulate the expression of a gene or a group of genes requires a more intricate approach to data analysis. To fully understand complex transcriptional networks, datasets representing different RNA species need to be integrated. In this review, we will focus on miRNAs as key post-transcriptional regulators summarizing current computational approaches for miRNAtarget gene prediction as well as new data-driven methods to tackle the problem of comprehensively and accurately dissecting miRNome-targetome interactions.Protein domains are the basic units of proteins that can fold, function, and evolve independently. Knowledge of protein domains is critical for protein classification, understanding their biological functions, annotating their evolutionary mechanisms and protein design. Thus, over the past two decades, a number of protein domain identification approaches have been developed, and a variety of protein domain databases have also been constructed. This review divides protein domain prediction methods into two categories, namely sequence-based and structure-based. These methods are introduced in detail, and their advantages and limitations are compared. Furthermore, this review also provides a comprehensive overview of popular online protein domain sequence and structure databases. Finally, we discuss potential improvements of these prediction methods.Despite the scientific and economic importance of maize, little is known about its specialized metabolism. Here, five maize organs were profiled using different reversed-phase liquid chromatography-mass spectrometry methods. The resulting spectral metadata, combined with candidate substrate-product pair (CSPP) networks, allowed the structural characterization of 427 of the 5,420 profiled compounds, including phenylpropanoids, flavonoids, benzoxazinoids, and auxin-related compounds, among others. Only 75 of the 427 compounds were already described in maize. Analysis of the CSPP networks showed that phenylpropanoids are present in all organs, whereas other metabolic classes are rather organ-enriched. Frequently occurring CSPP mass differences often corresponded with glycosyl- and acyltransferase reactions. The interplay of glycosylations and acylations yields a wide variety of mixed glycosides, bearing substructures corresponding to the different biochemical classes. For example, in the tassel, many phenylpropanoid and flavonoid-bearing glycosides also contain auxin-derived moieties. 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.