Search

Bacterial binding to host receptors underlies both commensalism and pathogenesis. Many streptococci adhere to protein-attached carbohydrates expressed on cell surfaces using Siglec-like binding regions (SLBRs). The precise glycan repertoire recognized may dictate whether the organism is a strict commensal versus a pathogen. However, it is currently not clear what drives receptor selectivity. Here, we use five representative SLBRs and identify regions of the receptor binding site that are hypervariable in sequence and structure. We show that these regions control the identity of the preferred carbohydrate ligand using chimeragenesis and single amino acid substitutions. We further evaluate how the identity of the preferred ligand affects the interaction with glycoprotein receptors in human saliva and plasma samples. As point mutations can change the preferred human receptor, these studies suggest how streptococci may adapt to changes in the environmental glycan repertoire.

The outstanding mechanical properties, light weight, and biodegradability of cellulose nanofibrils (CNFs) make them promising components of renewable and sustainable next-generation reinforced composite biomaterials and bioplastics. Manufacturing CNFs at a pilot scale requires disc-refining fibrillation of dilute cellulose fibers in aqueous pulp suspensions to shear the fibers apart into their nanodimensional forms, which is, however, an energy-intensive process. Here, we used atomistic molecular dynamics (MD) simulation to examine media that might facilitate the reduction of interactions between cellulose fibers, thereby reducing energy consumption in fibrillation. The most suitable medium found by the simulations was an aqueous solution with 0.007:0.012 wt.% NaOH:urea, and indeed this was found in pilot-scale experiments to reduce the fibrillation energy by ~21% on average relative to water alone. The NaOH:urea-mediated CNFs have similar crystallinity, morphology, and mechanical strength to those formed in water. The NaOH and urea act synergistically on CNFs to aid fibrillation but at different length scales. NaOH deprotonates hydroxyl groups leading to mesoscale electrostatic repulsion between fibrils, whereas urea forms hydrogen bonds with protonated hydroxyl groups thus disrupting interfibril hydrogen bonds. This suggests a general mechanism in which an aqueous medium that contains a strong base and a small organic molecule acting as a hydrogen-bond acceptor and/or donor may be effectively employed in materials processes where dispersion of deprotonable polymers is required. The study demonstrates how atomic-detail computer simulation can be integrated with pilot-scale experiments in the rational design of materials processes for the circular bioeconomy. [ABSTRACT FROM AUTHOR]

GPRC6A, a member of the Family C G‐protein coupled receptors, regulates energy metabolism and sex hormone production and is activated by diverse ligands, including cations, L‐amino acids, the osteocalcin (Ocn) peptide and the steroid hormone testosterone. We sought a structural framework for the ability of multiple distinct classes of ligands to active GPRC6A. We created a structural model of GPRC6A using Alphafold2. Using this model we explored a putative orthosteric ligand binding site in the bilobed Venus fly trap (VFT) domain of GPRC6A and two positive allosteric modulator (PAM) sites, one in the VFT and the other in the 7 transmembrane (7TM) domain. We provide evidence that Ocn peptides act as a PAM for GPRC6A by binding to a site in the VFT that is distinct from the orthosteric site for calcium and L‐amino acids. In agreement with this prediction, alternatively spliced GPRC6A isoforms 2 and 3, which lack regions of the VFT, and mutations in the computationally predicted Ocn binding site, K352E and H355P, prevent Ocn activation of GPRC6A. These observations explain how dissimilar ligands activate GPRC6A and set the stage to develop novel molecules to activate and inhibit this previously poorly understood receptor. [ABSTRACT FROM AUTHOR]

Artificial-intelligence and machine-learning (AI/ML) approaches to predicting Tcell receptor (TCR)-epitope specificity achieve high performance metrics on test datasets which include sequences that are also part of the training set but fail to generalize to test sets consisting of epitopes and TCRs that are absent from the training set, i.e., are 'unseen' during training of the MLmodel.We present TCR-H, a supervised classification Support Vector Machines model using physicochemical features trained on the largest dataset available to date using only experimentally validated non-binders as negative datapoints. TCR-H exhibits an area under the curve of the receiver-operator characteristic (AUC of ROC) of 0.87 for epitope 'hard splitting' (i.e., on test sets with all epitopes unseen duringML training), 0.92 for TCR hard splitting and 0.89 for 'strict splitting' in which neither the epitopes nor the TCRs in the test set are seen in the training data. Furthermore,we employ the SHAP (Shapley additive explanations) eXplainable AI (XAI) method for post hoc interrogation to interpret the models trained with different hard splits, shedding light on the key physiochemical features driving model predictions. TCR-H thus represents a significant step towards general applicability and explainability of epitope:TCR specificity prediction. [ABSTRACT FROM AUTHOR]

Pretreatment facilitates cost-effective operations on lignocellulosic biomass ranging from densification to deconstruction and bioproduct development. However, determining molecular-level mechanisms behind pretreatment and their effects has remained elusive. Here, we combine computational simulation and experiment to investigate the effects on wheat straw agricultural residue densification of an emerging pretreatment solvent, namely, a deep eutectic solvent (DES) consisting of choline chloride (ChCl) and oxalic acid (OA). Ab initio molecular dynamics indicates that dissociation of lignin from cellulose in lignin–carbohydrate complexes, which does not occur to a significant extent in aqueous solution, is favorable in the DES and occurs via cleavage of the guaiacyl : xylose ether bond linkage by OA. The ensuing hemicellulose removal exposes lignin to the DES which, molecular dynamics simulation indicates, leads to lignin expansion. The resulting changes in wheat straw fiber structure, lignin distribution, and functional group modifications upon DES treatment by scanning electron and fluorescence microscopy along with Fourier-transform infrared spectroscopy. The molecular expansion of lignin enhances inter-particle binding in wheat straw, leading to denser structures under pelletization. The resulting high mechanical stability and combustion properties make the wheat straw a suitable precursor of high-quality densified solids (e.g., solid biofuel). Overall, we shed light on the molecular-level mechanisms involved in DES pretreatment for biomass densification, demonstrated here in the development of a solid biofuel. The approach here illuminates the rational design from first chemical principles of methods to convert lignocellulosic resources into advanced materials. [ABSTRACT FROM AUTHOR]

Knowledge of the physical properties of ionic liquids (ILs), such as the surface tension and speed of sound, is important for both industrial and research applications. Unfortunately, technical challenges and costs limit exhaustive experimental screening efforts of ILs for these critical properties. Previous work has demonstrated that the use of quantum-mechanics-based thermochemical property prediction tools, such as the conductor-like screening model for real solvents, when combined with machine learning (ML) approaches, may provide an alternative pathway to guide the rapid screening and design of ILs for desired physiochemical properties. However, the question of which machine-learning approaches are most appropriate remains. In the present study, we examine how different ML architectures, ranging from tree-based approaches to feed-forward artificial neural networks, perform in generating nonlinear multivariate quantitative structure–property relationship models for the prediction of the temperature- and pressure-dependent surface tension of and speed of sound in ILs over a wide range of surface tensions (16.9–76.2 mN/m) and speeds of sound (1009.7–1992 m/s). The ML models are further interrogated using the powerful interpretation method, shapley additive explanations. We find that several different ML models provide high accuracy, according to traditional statistical metrics. The decision tree-based approaches appear to be the most accurate and precise, with extreme gradient-boosting trees and gradient-boosting trees being the best performers. However, our results also indicate that the promise of using machine-learning to gain deep insights into the underlying physics driving structure–property relationships in ILs may still be somewhat premature. [ABSTRACT FROM AUTHOR]

Histone deacetylases (HDACs) are important cancer drug targets. Existing FDA-approved drugs target the catalytic pocket of HDACs, which is conserved across subfamilies (classes) of HDAC. However, engineering specificity is an important goal. Herein, we use molecular modeling approaches to identify and target potential novel pockets specific to Class IIA HDAC-HDAC4 at the interface between HDAC4 and the transcriptional corepressor component protein NCoR. These pockets were screened using an ensemble docking approach combined with consensus scoring to identify compounds with a different binding mechanism than the currently known HDAC modulators. Binding was compared in experimental assays between HDAC4 and HDAC3, which belong to a different family of HDACs. HDAC4 was significantly inhibited by compound 88402 but not HDAC3. Two other compounds (67436 and 134199) had IC50 values in the low micromolar range for both HDACs, which is comparable to the known inhibitor of HDAC4, SAHA (Vorinostat). However, both of these compounds were significantly weaker inhibitors of HDAC3 than SAHA and thus more selective, albeit to a limited extent. Five compounds exhibited activity on human breast carcinoma and/or urothelial carcinoma cell lines. The present result suggests potential mechanistic and chemical approaches for developing selective HDAC4 modulators. [ABSTRACT FROM AUTHOR]

Contemporary research into community music programmes highlights diverse contexts for collaborative music making. This article contributes a case of the collaboration in music making to a growing literature on creative practice in community music initiatives led by major orchestras. The authors reflect on field research involving an after-school programme that provides free, classical instrumental music instruction to primary school-age students who would not normally have an opportunity due to their socio-economic circumstances. The Pizzicato Effect, run by the Melbourne Symphony Orchestra (MSO) in the City of Hume, Australia has operated in the northern suburbs of Melbourne since 2009. Through focus group interviews with students, family members and teaching artists, the researchers examined community experiences of accruing cultural capital in conditions of social inequality and multicultural practice. Using qualitative methods, the case study aimed to position respondents as experts in their own lives, who own their knowledge of their worlds. Collaboration was central to a model of improvisation developed locally by teaching artists and applied on site. Furthermore, improvisation took the programme in different directions to the El Sistema model of youth orchestra training in Venezuela, which had been the original source of inspiration for the programme. [ABSTRACT FROM AUTHOR]

Classical and contemporary civilizational analysis has not sat comfortably with theoretical constructions of the intercultural or their empirical applications. A 'classical era' of civilizations analysis generated a program of research problems that was productive in critical and multidisciplinary ways and limited in scope and vision in others, but this failed to generate a provisional notion of the intercultural. Contemporary civilizational analysis improves on this position significantly in respect of the intercultural, particularly in the development of a current around 'intercivilizational encounters'. This essay examines this current especially in the work of Benjamin Nelson, Marshall Hodgson and Johann P. Arnason. Arguing that this approach represents vital advances for theoretical constructions of the intercultural in civilizational analysis and more generally in the human sciences, the essay also identifies limitations in latter-day approaches. [ABSTRACT FROM AUTHOR]

A recently proposed wide-angle velocity selector (WAVES) device for choosing the velocity of detected neutrons after they have been scattered by the sample paves the way for inverted geometry neutron spectrometers with continuously adjustable final neutron wavelength. BWAVES broadband inverted geometry spectrometer proposed for the Second Target Station at the Spallation Neutron Source at Oak Ridge National Laboratory is designed using WAVES to simultaneously probe dynamic processes spanning 4.5 decades in time (energy transfer). This makes BWAVES a uniquely flexible instrument which can be viewed as either a quasielasitc neutron scattering (QENS) spectrometer with a practically unlimited (overlapping with the vibrational excitations) range of energy transfers, or a broadband inelastic vibrational neutron spectrometer with QENS capabilities, including a range of accessible momentum transfer (Q) and a sufficiently high energy resolution at the elastic line. The new capabilities offered by BWAVES will expand the application of neutron scattering in ways not possible with existing neutron spectrometers.