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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]

In the context of promoting a circular bioeconomy, the development of green and efficient lignocellulosic biomass pretreatment technologies so as to realize high value-added biomass utilization is of intense interest. We demonstrated the potential of the bio-based green solvent dimethyl isosorbide (DMI) for the fractionation of Eucalyptus biomass with excellent performance. Here, to investigate the mechanisms involved in biomass fractionation, microimaging and microspectroscopic techniques were employed together with molecular dynamics (MD) simulation and COSMO-RS quantum chemical calculations to derive multiscale information. Both the microstructure and regional chemistry of the cell wall vary significantly with the volume ratio of DMI/H2O. The strongest effects were found at DMI/H2O = 9 : 1 and showed visible cell wall tearing cracks and cell wall deformation and collapse as well as the lowest values of cell wall thickness and circularity. From the MD simulations, lignin exhibits collapsed-like structure in pure H2O with low solvent accessibility surface area (SASA) and radius of gyration (Rg). In contrast, lignin in DMI/H2O shows extended structure with high SASA and solvent interactions dominated by van der Waals forces, with maximal contact in the 9 : 1 (v/v) system. Further, the COSMO-RS calculated sigma (σ-) potential suggests the intermolecular interactions in DMI and DMI/H2O co-solvent are weak, leading to stronger interaction with lignin and correspondingly higher lignin dissolution. The radial distribution functions and σ-potential all show that again DMI/H2O at 9 : 1 is an optimal volume ratio for high lignin dissolution. This study provides a solvent-ratio dependent mechanism for the action of polar aprotic solvents in the deconstruction of biomass. [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]

The recalcitrance of plant cell wall lignocellulosic biomass to deconstruction is a major hurdle to sustainable biofuel/bioproduct economy. A multitude of interactions stabilize lignocellulosic biomass structure. Among these, tight packing of hemicellulose-cellulose is partly responsible for biomass recalcitrance. Here, unrestrained molecular dynamics simulations are employed to understand the influence of the nature and pattern of naturally-occuring acetyl decorations of the xylan backbone on interactions with the (100) hydrophobic cellulose surface. Periodically O2-acetylated xylan (2AcX) assume twofold helical screw conformations that are stabilized by a combination of multiple hydrophobic contacts and hydrogen bonds with the hydrophobic cellulose surface. In contrast, acetylation at the O3 position in xylan obstructs interactions, thereby adopting threefold helical screw conformations that potentially preferably interact with lignin rather than cellulose. Fully acetylated xylan desorbs from the surface implying a minimum number of unsubstituted residues on the xylan backbone is required for interaction with the surface. The substituted residues must form ~ 20% fewer contacts than the unsubstituted residues to sustain stable twofold helical screw xylan conformations on the cellulose surface. Thus, specific roles of macromolecular conformations of cellulose and hemicellulose in influencing the supramolecular interactions and function of plant cell walls have been determined. [ABSTRACT FROM AUTHOR]

Molecular mechanisms transducing physical forces in the bone microenvironment to regulate bone mass are poorly understood. Here, we used mouse genetics, mechanical loading, and pharmacological approaches to test the possibility that polycystin-1 and Wwtr1 have interdependent mechanosensing functions in osteoblasts. We created and compared the skeletal phenotypes of control Pkd1flox/+;Wwtr1flox/+, Pkd1Oc-cKO, Wwtr1Oc-cKO, and Pkd1/Wwtr1Oc-cKO mice to investigate genetic interactions. Consistent with an interaction between polycystins and Wwtr1 in bone in vivo, Pkd1/Wwtr1Oc-cKO mice exhibited greater reductions of BMD and periosteal MAR than either Wwtr1Oc-cKO or Pkd1Oc-cKO mice. Micro-CT 3D image analysis indicated that the reduction in bone mass was due to greater loss in both trabecular bone volume and cortical bone thickness in Pkd1/Wwtr1Oc-cKO mice compared to either Pkd1Oc-cKO or Wwtr1Oc-cKO mice. Pkd1/Wwtr1Oc-cKO mice also displayed additive reductions in mechanosensing and osteogenic gene expression profiles in bone compared to Pkd1Oc-cKO or Wwtr1Oc-cKO mice. Moreover, we found that Pkd1/Wwtr1Oc-cKO mice exhibited impaired responses to tibia mechanical loading in vivo and attenuation of load-induced mechanosensing gene expression compared to control mice. Finally, control mice treated with a small molecule mechanomimetic, MS2 that activates the polycystin complex resulted in marked increases in femoral BMD and periosteal MAR compared to vehicle control. In contrast, Pkd1/Wwtr1Oc-cKO mice were resistant to the anabolic effects of MS2. These findings suggest that PC1 and Wwtr1 form an anabolic mechanotransduction signaling complex that mediates mechanical loading responses and serves as a potential novel therapeutic target for treating osteoporosis. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) emissions from fossil fuel combustion are a significant source of greenhouse gas, contributing in a major way to global warming and climate change. Carbon dioxide capture and sequestration is gaining much attention as a potential method for controlling these greenhouse gas emissions. Among the environmentally friendly solvents, deep eutectic solvents (DESs) have demonstrated the potential capability for carbon capture. To establish a theoretical framework for DES activity, thermodynamics modeling and solubility predictions are significant factors to anticipate and understand the system behavior. Here, we combine the COSMO-RS model with machine learning techniques to predict the solubility of CO2 in various deep eutectic solvents. A comprehensive data set was established comprising 1973 CO2 solubility data points in 132 different DESs at a variety of temperatures, pressures, and DES molar ratios. This data set was then utilized for the further verification and development of the COSMO-RS model. The CO2 solubility (ln(xCO2)) in DESs calculated with the COSMO-RS model differs significantly from the experiment with an average absolute relative deviation (AARD) of 23.4%. A multilinear regression model was developed using the COSMO-RS predicted solubility and a temperature-pressure dependent parameter, which improved the AARD to 12%. Finally, a machine learning model using COSMO-RS-derived features was developed based on an artificial neural network algorithm. The results are in excellent agreement with the experimental CO2 solubilities, with an AARD of only 2.72%. The ML model will be a potentially useful tool for the design and selection of DESs for CO2 capture and utilization. [ABSTRACT FROM AUTHOR]

Background: In a previous paper, we classified populated HLA class I alleles into supertypes and subtypes based on the similarity of 3D landscape of peptide binding grooves, using newly defined structure distance metric and hierarchical clustering approach. Compared to other approaches, our method achieves higher correlation with peptide binding specificity, intra-cluster similarity (cohesion), and robustness. Here we introduce HLA-Clus, a Python package for clustering HLA Class I alleles using the method we developed recently and describe additional features including a new nearest neighbor clustering method that facilitates clustering based on user-defined criteria. Results: The HLA-Clus pipeline includes three stages: First, HLA Class I structural models are coarse grained and transformed into clouds of labeled points. Second, similarities between alleles are determined using a newly defined structure distance metric that accounts for spatial and physicochemical similarities. Finally, alleles are clustered via hierarchical or nearest-neighbor approaches. We also interfaced HLA-Clus with the peptide:HLA affinity predictor MHCnuggets. By using the nearest neighbor clustering method to select optimal allele-specific deep learning models in MHCnuggets, the average accuracy of peptide binding prediction of rare alleles was improved. Conclusions: The HLA-Clus package offers a solution for characterizing the peptide binding specificities of a large number of HLA alleles. This method can be applied in HLA functional studies, such as the development of peptide affinity predictors, disease association studies, and HLA matching for grafting. HLA-Clus is freely available at our GitHub repository (https://github.com/yshen25/HLA-Clus). [ABSTRACT FROM AUTHOR]

The contrast‐variation method in small‐angle neutron scattering (SANS) is a uniquely powerful technique for determining the structure of individual components in biomolecular systems containing regions of different neutron scattering length density ρ. By altering the ρ of the target solute and the solvent through judicious incorporation of deuterium, the scattering of desired solute features can be highlighted. Most contrast‐variation methods focus on highlighting specific bulk solute elements, but not on how the scattering at specific scattering vectors q, which are associated with specific structural distances, changes with contrast. Indeed, many systems exhibit q‐dependent contrast effects. Here, a method is presented for calculating both bulk contrast‐match points and q‐dependent contrast using 3D models with explicit solute and solvent atoms and SASSENA, an explicit‐atom SANS calculator. The method calculates the bulk contrast‐match points within 2.4% solvent D2O accuracy for test protein–nucleic acid and lipid nanodisc systems. The method incorporates a general model for the incorporation of deuterium at non‐exchangeable sites that was derived by performing mass spectrometry on green fluorescent protein. The method also decomposes the scattering profile into its component parts and identifies structural features that change with contrast. The method is readily applicable to a variety of systems, will expand the understanding of q‐dependent contrast matching and will aid in the optimization of next‐generation neutron scattering experiments. [ABSTRACT FROM AUTHOR]

This dataset contains ligand conformations and docking scores for 1.4 billion molecules docked against 6 structural targets from SARS-CoV2, representing 5 unique proteins: MPro, NSP15, PLPro, RDRP, and the Spike protein. Docking was carried out using the AutoDock-GPU platform on the Summit supercomputer and Google Cloud. The docking procedure employed the Solis Wets search method to generate 20 independent ligand binding poses per compound. Each compound geometry was scored using the AutoDock free energy estimate, and rescored using RFScore v3 and DUD-E machine-learned rescoring models. Input protein structures are included, suitable for use by AutoDock-GPU and other docking programs. As the result of an exceptionally large docking campaign, this dataset represents a valuable resource for discovering trends across small molecule and protein binding sites, training AI models, and comparing to inhibitor compounds targeting SARS-CoV-2. The work also gives an example of how to organize and process data from ultra-large docking screens. Measurement(s) equilibrium association constant (KA) Technology Type(s) molecular docking by scoring function Factor Type(s) Chemical formula and connectivity Sample Characteristic - Organism Severe acute respiratory syndrome-related coronavirus Sample Characteristic - Environment in-silico [ABSTRACT FROM AUTHOR]

The emergence and availability of closely related clinical isolates of SARS-CoV-2 offers a unique opportunity to identify novel nonsynonymous mutations that may impact phenotype. Global sequencing efforts show that SARS-CoV-2 variants have emerged and then been replaced since the beginning of the pandemic, yet we have limited information regarding the breadth of variant-specific host responses. Using primary cell cultures and the K18-hACE2 mouse, we investigated the replication, innate immune response, and pathology of closely related, clinical variants circulating during the first wave of the pandemic. Mathematical modeling of the lung viral replication of four clinical isolates showed a dichotomy between two B.1. isolates with significantly faster and slower infected cell clearance rates, respectively. While isolates induced several common immune host responses to infection, one B.1 isolate was unique in the promotion of eosinophil-associated proteins IL-5 and CCL11. Moreover, its mortality rate was significantly slower. Lung microscopic histopathology suggested further phenotypic divergence among the five isolates showing three distinct sets of phenotypes: (i) consolidation, alveolar hemorrhage, and inflammation, (ii) interstitial inflammation/septal thickening and peribronchiolar/perivascular lymphoid cells, and (iii) consolidation, alveolar involvement, and endothelial hypertrophy/margination. Together these findings show divergence in the phenotypic outcomes of these clinical isolates and reveal the potential importance of nonsynonymous mutations in nsp2 and ORF8. [ABSTRACT FROM AUTHOR]

Structure‐based virtual high‐throughput screening involves docking chemical libraries to targets of interest. A parameter pertinent to the accuracy of the resulting pose is the root mean square deviation (RMSD) from a known crystallographic structure, i. e., the 'docking power'. Here, using a popular algorithm, Autodock Vina, as a model program, we evaluate the effects of varying two common docking parameters: the box size (the size of docking search space) and the exhaustiveness of the global search (the number of independent runs starting from random ligand conformations) on the RMSD from the PDBbind v2017 refined dataset of experimental protein‐ligand complexes. Although it is clear that exhaustiveness is an important parameter, there is wide variation in the values used, with variation between 1 and >100. We, therefore, evaluated a combination of cubic boxes of different sizes and five exhaustiveness values (1, 8, 25, 50, 75, 100) within the range of those commonly adopted. The results show that the default exhaustiveness value of 8 performs well overall for most box sizes. In contrast, for all box sizes, but particularly for large boxes, an exhaustiveness value of 1 led to significantly higher median RMSD (mRMSD) values. The docking power was slightly improved with an exhaustiveness of 25, but the mRMSD changes little with values higher than 25. Therefore, although low exhaustiveness is computationally faster, the results are more likely to be far from reality, and, conversely, values >25 led to little improvement at the expense of computational resources. Overall, we recommend users to use at least the default exhaustiveness value of 8 for virtual screening calculations. [ABSTRACT FROM AUTHOR]

Alternation in various properties of n-alkanes (CnH2n+2) as a function of carbon content (n) is termed 'odd–even effect'. Here, we report a comprehensive molecular dynamics simulation study on n-alkane systems carried out with n ranging between 3 (propane) and 8 (octane), examining the odd–even effect in melting point, density, intramolecular conformational ordering, translational and rotational motion. We observe an odd–even alternation in these properties, but with heptane (n = 7) exhibiting anomalous behavior for all except conformational ordering. Our simulations also show the presence of odd–even behavior in rotational and translational dynamics, below and above the melting point, respectively. The results highlight the role of both molecular shape and the variation in density and their interplay in the origins of the odd–even effect. [ABSTRACT FROM AUTHOR]

Amphiphilic co‐solvents can have a significant impact on the structure, organization and physical properties of lipid bilayers. Describing the mutual impact of partitioning and induced structure changes is therefore a crucial consideration for a range of topics such as anesthesia and other pharmacokinetic effects, as well as microbial solvent tolerance in the production of biofuels and other fermentation products, where molecules such as ethanol, butanol or acetic acid might be generated. Small‐angle neutron scattering (SANS) is a key method for studying lipid and polymer bilayer structures, with many models for extracting bilayer structure (thickness, area per lipid etc.) from scattering data in use today. However, the molecular details of co‐solvent partitioning are conflated with induced changes to bilayer structure, making interpretation and modeling of the scattering curves a challenge with the existing set of models. To address this, a model of a bilayer structure is presented which invokes a two‐term partition constant accounting for the localization of the co‐solvent within the bilayer. This model was validated using a series of SANS measurements of lipid vesicles in the presence of the co‐solvent tetrahydrofuran (THF), showing several strategies of how to deploy the two‐parameter partition constant model to describe scattering data and extract both structure and partitioning information from the data. Molecular dynamics simulations are then used to evaluate assumptions of the model, provide additional molecular scale details and illustrate its complementary nature to the data fitting procedure. This approach results in estimates of the partition coefficient for THF in 1,2‐dimyristoyl‐sn‐glycero‐3‐phosphocholine at 35°C, along with an estimate of the fraction of THF residing in the hydrophobic core of the membrane. The authors envision that this model will be applicable to a wide range of other bilayer/amphiphile interactions and provide the associated code needed to implement this model as a fitting algorithm for scattering data in the SasView suite. [ABSTRACT FROM AUTHOR]

The startling diversity in αβ T‐cell receptor (TCR) sequences and structures complicates molecular‐level analyses of the specificity and sensitivity determining T‐cell immunogenicity. A number of three‐dimensional (3D) structures are now available of ternary complexes between TCRs and peptides: major histocompatibility complexes (pMHC). Here, to glean molecular‐level insights we analyze structures of TCRs bound to human class I nonamer peptide–MHC complexes. Residues at peptide positions 4–8 are found to be particularly important for TCR binding. About 90% of the TCRs hydrogen bond with one or both of the peptide residues at positions 4 and 8 presented by MHC allele HLA‐A2, and this number is still ~79% for peptides presented by other MHC alleles. Residue 8, which lies outside the previously‐identified central peptide region, is crucial for TCR recognition of class I MHC‐presented nonamer peptides. The statistics of the interactions also sheds light on the MHC residues important for TCR binding. The present analysis will aid in the structural modeling of TCR:pMHC complexes and has implications for the rational design of peptide‐based vaccines and T‐cell‐based immunotherapies. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic generated a massive amount of clinical data, which potentially hold yet undiscovered answers related to COVID-19 morbidity, mortality, long-term effects, and therapeutic solutions. Objectives: The objectives of this study were (1) to identify novel predictors of COVID-19 any cause mortality by employing artificial intelligence analytics on real-world data through a hypothesis-agnostic approach and (2) to determine if these effects are maintained after adjusting for potential confounders and to what degree they are moderated by other variables. Methods: A Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within the Interrogative Biology® platform was used for Bayesian network learning and hypothesis generation to analyze 16,277 PCR+ patients from a database of 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods during the first pandemic year in Central Florida. This approach generated Bayesian networks that enabled unbiased identification of significant predictors of any cause mortality for specific COVID-19 patient populations. These findings were further analyzed by logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping. Results: We found that in the COVID-19 PCR+ patient cohort, early use of the antiemetic agent ondansetron was associated with decreased any cause mortality 30 days post-PCR+ testing in mechanically ventilated patients. Conclusions: The results demonstrate how a real-world COVID-19-focused data analysis using artificial intelligence can generate unexpected yet valid insights that could possibly support clinical decision making and minimize the future loss of lives and resources. [ABSTRACT FROM AUTHOR]

Two membrane cell envelopes act as selective permeability barriers in Gram-negative bacteria, protecting cells against antibiotics and other small molecules. Significant efforts are being directed toward understanding how small molecules permeate these barriers. In this study, we developed an approach to analyze the permeation of compounds into Gram-negative bacteria and applied it to Pseudomonas aeruginosa, an important human pathogen notorious for resistance to multiple antibiotics. The approach uses mass spectrometric measurements of accumulation of a library of structurally diverse compounds in four isogenic strains of P. aeruginosa with varied permeability barriers. We further developed a machine learning algorithm that generates a deterministic classification model with minimal synonymity between the descriptors. This model predicted good permeators into P. aeruginosa with an accuracy of 89% and precision above 58%. The good permeators are broadly distributed in the property space and can be mapped to six distinct regions representing diverse chemical scaffolds. We posit that this approach can be used for more detailed mapping of the property space and for rational design of compounds with high Gram-negative permeability. [ABSTRACT FROM AUTHOR]

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. Streptococcal siglec-like binding regions (SLBRs) selectively bind cell surface glycans, but the basis for this selectivity is not understood. Here, the authors identify selectivity-modulating SLBR regions and study how changes in SLBR glycan selectivity affect interactions with human glycoprotein receptors. [ABSTRACT FROM AUTHOR]

As a field of significant activity for historical sociologists in recent decades, civilizational analysis has produced extensive and incisive works examining Japan as a historical formation of Eurasia. However, the same cannot be said of Japan's Pacific relationship with the United States, which is neglected in the major historical sociologies of Japanese modernity. This essay seeks to address that unnecessary oversight by putting that relationship into focus as an international dimension of the institution of both states. It would be tempting to elucidate the entanglement of the two as an encounter of civilizations, but the author instead casts it as intercivilizational engagement, that is a deeper set of connections generated by routine contacts and migratory movements, trade in commerce and culture, and selective appropriation of models of statehood. Delineating the lines of exchange in all four domains of connectivity between Japan and the US, the essay profiles the international and imperial extensions of both states. In altering the perspective on Japan's relations with the world, the author outlines a larger potential historical sociology of intercivilizational engagement between two Pacific-edge civilizational constellations. [ABSTRACT FROM AUTHOR]

The interaction of xylan, an abundant plant polysaccharide, with cellulose microfibrils is essential for secondary cell wall strength. A deeper understanding of these interactions is crucial both to improve our understanding of plant cell wall architecture and to design alternate strategies to overcome cellulose recalcitrance for the production of biofuels and sustainable biomaterials. Naturally occurring acetate or glucuronic acid substitutions on xylan have been shown to influence xylan-cellulose interactions. Here, we use unrestrained molecular dynamics simulations to determine the interactions with the (110) hydrophilic face of cellulose fibers of four different xylans. In the absence of cellulose, all xylans, independent of the substitution pattern, adopt a highly flexible threefold helical screw conformation. However, when xylan is spatially close to a cellulose surface 1,2 linked acetyl xylans (2AcX) adopt rigid twofold helical screw conformations. The 2AcX conformations are primarily stabilized by interactions between the acetylated oxygen and the glycosidic linkage with C-O6 of cellulose. In contrast, the glycosidic oxygens and acetyl decorations for 1,3 linked acetyl groups (3AcX) are oriented away from the cellulose surface and the 3AcX xylans maintain threefold helical screw conformations on the cellulose surface. Our results show that evenly spaced chemical functionalization (with acetyl groups) and the position of substitution (1,2) on xylan backbone play key roles in tuning the xylan-cellulose interactions to stabilize the twofold helical screw conformations of xylan on the cellulose surface. A comparison with previous experimental findings further suggests that 1,2 substitutions induce twofold helical screw conformations of xylan on the cellulose surface irrespective of the chemical nature of the substituent, while 1,3 substitutions primarily bind lignin in threefold helical screw conformations rather than cellulose in plant cell walls. [ABSTRACT FROM AUTHOR]

Sequence-specific targeting of double-stranded DNA and non-coding RNA via triple-helix-forming peptide nucleic acids (PNAs) has attracted considerable attention in therapeutic, diagnostic and nanotechnological fields. An E-base (3-oxo-2,3-dihydropyridazine), attached to the polyamide backbone of a PNA Hoogsteen strand by a side-chain linker molecule, is typically used in the hydrogen bond recognition of the 4-oxo group of thymine and uracil nucleic acid bases in the major groove. We report on the application of quantum chemical computational methods, in conjunction with spatial constraints derived from the experimental structure of a homopyrimidine PNA·DNA-PNA hetero-triplex, to investigate the influence of linker flexibility on binding interactions of the E-base with thymine and uracil bases in geometry-optimised model systems. Hydrogen bond formation between the N2 E-base atom and target pyrimidine base 4-oxo groups in model systems containing a β-alanine linker (J Am Chem Soc 119:11116, 1997) was found to incur significant internal strain energy and the potential disruption of intra-stand aromatic base stacking interactions in an oligomeric context. In geometry-optimised model systems containing a 3-trans olefin linker (Bioorg Med Chem Lett 14:1551, 2004) the E-base swung out away from the target pyrimidine bases into the solvent. These findings are in qualitative agreement with calorimetric measurements in hybridisation experiments at T–A and U–A inversion sites. In contrast, calculations on a novel 2-cis olefin linker design indicate that it could permit simultaneous E-base hydrogen bonding with the thymine 4-oxo group, circumvention and solvent screening of the thymine 5-methyl group, and maintenance of triplex intra-stand base stacking interactions. [ABSTRACT FROM AUTHOR]

Molecular dynamics (MD) simulation is widely used to complement ensemble-averaged experiments of intrinsically disordered proteins (IDPs). However, MD often suffers from limitations of inaccuracy. Here, we show that enhancing the sampling using Hamiltonian replica-exchange MD (HREMD) led to unbiased and accurate ensembles, reproducing small-angle scattering and NMR chemical shift experiments, for three IDPs of varying sequence properties using two recently optimized force fields, indicating the general applicability of HREMD for IDPs. We further demonstrate that, unlike HREMD, standard MD can reproduce experimental NMR chemical shifts, but not small-angle scattering data, suggesting chemical shifts are insufficient for testing the validity of IDP ensembles. Surprisingly, we reveal that despite differences in their sequence, the inter-chain statistics of all three IDPs are similar for short contour lengths (< 10 residues). The results suggest that the major hurdle of generating an accurate unbiased ensemble for IDPs has now been largely overcome. Shrestha et al. show that enhancing the sampling using Hamiltonian replica exchange molecular simulation (HREMD) leads to accurate unbiased ensembles of intrinsically disordered proteins. They find that standard molecular simulation cannot reproduce small-angle scattering data as well as HREMD, highlighting the utility of the suggested approach. [ABSTRACT FROM AUTHOR]

Human interleukin‐6 (hIL‐6) is a multifunctional cytokine that regulates immune and inflammatory responses in addition to metabolic and regenerative processes and cancer. hIL‐6 binding to the IL‐6 receptor (IL‐6Rα) induces homodimerization and recruitment of the glycoprotein (gp130) to form a hexameric signaling complex. Anti‐IL‐6 and IL‐6R antibodies are clinically approved inhibitors of IL‐6 signaling pathway for treating rheumatoid arthritis and Castleman's disease, respectively. There is a potential to develop novel small molecule IL‐6 antagonists derived from understanding the structural basis for IL‐6/IL‐6Rα interactions. Here, we combine homology modeling with extensive molecular dynamics (MD) simulations to examine the association of hIL‐6 with IL‐6Rα. A comparison with MD of apo hIL‐6 reveals that the binding of hIL‐6 to IL‐6Rα induces structural and dynamic rearrangements in the AB loop region of hIL‐6, disrupting intraprotein contacts and increasing the flexibility of residues 48 to 58 of the AB loop. In contrast, due to the involvement of residues 59 to 78 in forming contacts with the receptor, these residues of the AB loop are observed to rigidify in the presence of the receptor. The binary complex is primarily stabilized by two pairs of salt bridges, Arg181 (hIL‐6)‐ Glu182 (IL‐6Rα) and Arg184 (hIL‐6)‐ Glu183 (IL‐6Rα) as well as hydrophobic and aromatic stacking interactions mediated essentially by Phe residues in both proteins. An interplay of electrostatic, hydrophobic, hydrogen bonding, and aromatic stacking interactions facilitates the formation of the hIL‐6/IL‐6Rα complex. [ABSTRACT FROM AUTHOR]

The urgent search for drugs to combat SARS-CoV-2 has included the use of supercomputers. The use of general-purpose graphical processing units (GPUs), massive parallelism, and new software for high-performance computing (HPC) has allowed researchers to search the vast chemical space of potential drugs faster than ever before. We developed a new drug discovery pipeline using the Summit supercomputer at Oak Ridge National Laboratory to help pioneer this effort, with new platforms that incorporate GPU-accelerated simulation and allow for the virtual screening of billions of potential drug compounds in days compared to weeks or months for their ability to inhibit SARS-COV-2 proteins. This effort will accelerate the process of developing drugs to combat the current COVID-19 pandemic and other diseases. [ABSTRACT FROM AUTHOR]

The disruptive effect of organic solvents on microbial membranes represents a significant challenge to the economical production of green fuels and value-added chemicals from lignocellulosic feedstocks. One route to overcoming this challenge is to engineer microbes with membranes capable of resisting organic solvent stresses. In this regard, it is useful to understand the mechanisms by which organic solvents disrupt typical biomembranes. Here, molecular dynamics (MD) simulation, complemented by small-angle X-ray and neutron scattering (SANS/SAXS), provide a molecular-scale view of the disruption of a microbial model membrane by 1-butanol and tetrahydrofuran (THF), two common water–organic cosolvent mixtures of importance in biofuel production. Solvent interactions at the interface between the head-group and fatty acid tail regions lead to more dramatic membrane changes than interactions solely at the head-groups or tails. Although both organic solvents are found to partition into the membrane, the depth of solvent penetration into the membrane is quite different. Specifically, 1-butanol localizes near the interface between the lipid heads and tails at low concentrations, but partitions into both the head and tail regions at high concentrations. In contrast, THF, overall, partitions less than 1-butanol and prefers the lipid tail regions. Importantly, the presence of 1-butanol near the head/tail interface introduces drastic membrane changes not seen with THF. The organic solvent interactions with the lipids lead to membrane thinning and fluidization, but more so for 1-butanol than for THF. These results suggest that an aim for the future engineering of robust membranes could be to design lipid head groups that reduce the accumulation of organic solvents at the head–tail interface and that rational designs need also be cognizant of the different solvent-specific mechanisms responsible for membrane disruption. [ABSTRACT FROM AUTHOR]

Infective endocarditis (IE) is a cardiovascular disease often caused by bacteria of the viridans group of streptococci, which includes Streptococcus gordonii and Streptococcus sanguinis. Previous research has found that serine-rich repeat (SRR) proteins on the S. gordonii bacterial surface play a critical role in pathogenesis by facilitating bacterial attachment to sialylated glycans displayed on human platelets. Despite their important role in disease progression, there are currently no anti-adhesive drugs available on the market. Here, we performed structure-based virtual screening using an ensemble docking approach followed by consensus scoring to identify novel small molecule effectors against the sialoglycan binding domain of the SRR adhesin protein Hsa from the S. gordonii strain DL1. The screening successfully predicted nine compounds which were able to displace the native ligand (sialyl-T antigen) in an in vitro assay and bind competitively to Hsa. Furthermore, hierarchical clustering based on the MACCS fingerprints showed that eight of these small molecules do not share a common scaffold with the native ligand. This study indicates that SRR family of adhesin proteins can be inhibited by diverse small molecules and thus prevent the interaction of the protein with the sialoglycans. This opens new avenues for discovering potential drugs against IE. [ABSTRACT FROM AUTHOR]

A particularly promising approach to deconstructing and fractionating lignocellulosic biomass to produce green renewable fuels and high-value chemicals pretreats the biomass with organic solvents in aqueous solution. Here, neutron scattering and moleculardynamics simulations reveal the temperature-dependent morphological changes in poplar wood biomass during tetrahydrofuran (THF):water pretreatment and provide a mechanism by which the solvent components drive efficient biomass breakdown. Whereas lignin dissociates over a wide temperature range (>25 °C) cellulose disruption occurs only above 150 °C. Neutron scattering with contrast variation provides direct evidence for the formation of THF-rich nanoclusters (Rg ~ 0.5 nm) on the nonpolar cellulose surfaces and on hydrophobic lignin, and equivalent waterrich nanoclusters on polar cellulose surfaces. The disassembly of the amphiphilic biomass is thus enabled through the local demixing of highly functional cosolvents, THF and water, which preferentially solvate specific biomass surfaces so as to match the local solute polarity. A multiscale description of the efficiency of THF:water pretreatment is provided: matching polarity at the atomic scale prevents lignin aggregation and disrupts cellulose, leading to improvements in deconstruction at the macroscopic scale. [ABSTRACT FROM AUTHOR]

Lignin solubilization is key to a viable biorefinery because its removal leads to facile deconstruction of biomass and because the isolated lignin can serve to derive precursors of novel high-value materials. The mixing of organic solvents with water has been shown to improve biomass fractionation and lignin conversion reactions. However, generally-applicable solubilization strategies are lacking because of the remarkable variability of lignin across plant feedstocks. Here, to obtain a predictive understanding of lignin solvation, we perform molecular dynamics simulations of model lignin polymers in two mixtures of water with polar aprotic solvents: tetrahydrofuran (THF) : water and γ-valerolactone (GVL) : water. The model lignins include H-, G- and S-only homopolymers and lignins with an S : G 1 : 1 ratio. We find that a well-established theory of self-avoiding polymers in a "good" solvent describes accurately the physical conformations of all types of lignin in both solvents. As the degree of methoxy substitution increases in the homopolymers, the distributions of the lignin radius of gyration and the Flory exponent ν, which describes the lignin-solvent interactions, do not change in THF : water, while ν shifts to slightly higher values in GVL : water. We attribute this increase to the interaction between the methyl group of GVL with the lignin methoxy groups. We also find that the reduction in the lignin radius of gyration due to branching is accurately described by the Zimm–Stockmayer theory for both THF : water and GVL : water. The above findings validate the applicability of polymer physics concepts to lignin and suggest that GVL : water may have the most favorable interaction with S-lignin, whereas the interactions of THF : water with lignin are independent of lignin monomeric content. [ABSTRACT FROM AUTHOR]

Carotenoids are pigment molecules that protect biomembranes against degradation and may be involved in the formation of functional bacterial membrane microdomains. Little is known on whether different types of carotenoids have different effects on the membrane or if there is any concentration dependence of these effects. In this work, we present results from molecular dynamics simulations of phospholipid bilayers containing different amounts of either β-carotene or zeaxanthin. Both β-carotene and zeaxanthin show the ability to laterally condense the membrane lipids and reduce their inter-leaflet interactions. With increasing concentrations, both carotenoids increase the bilayer thickness and rigidity. The results reveal that carotenoids have similar effects to cholesterol on regulating the behavior of fluid-phase membranes, suggesting that they could function as sterol substitutes and confirming their potential role in the formation of functional membrane domains. [ABSTRACT FROM AUTHOR]

The mer operon in bacteria encodes a set of proteins and enzymes that impart resistance to environmental mercury toxicity by importing Hg2+ and reducing it to volatile Hg(0). Because the reduction occurs in the cytoplasm, mercuric ions must first be transported across the cytoplasmic membrane by one of a few known transporters. MerF is the smallest of these, containing only two transmembrane helices and two pairs of vicinal cysteines that coordinate mercuric ions. In this work, we use molecular dynamics simulations to characterize the dynamics of MerF in its apo and Hg2+‐bound states. We find that the apo state positions one of the cysteine pairs closer to the periplasmic side of the membrane, while in the bound state the same pair approaches the cytoplasmic side. This finding is consistent with the functional requirement of accepting Hg2+ from the periplasmic space, sequestering it on acceptance, and transferring it to the cytoplasm. Conformational changes in the TM helices facilitate the functional interaction of the two cysteine pairs. Free‐energy calculations provide a barrier of 16 kcal/mol for the association of the periplasmic Hg2+‐bound protein MerP with MerF and 7 kcal/mol for the subsequent association of MerF's two cysteine pairs. Despite the significant conformational changes required to move the binding site across the membrane, coarse‐grained simulations of multiple copies of MerF support the expectation that it functions as a monomer. Our results demonstrate how conformational changes and binding thermodynamics could lead to such a small membrane protein acting as an ion transporter. Published 2019. This article is a U.S. Government work and is in the public domain in the USA. [ABSTRACT FROM AUTHOR]

To assess the chemical reactivity, toxicity, and mobility of pollutants in the environment, knowledge of their species distributions is critical. Because their direct measurement is often infeasible, speciation modeling is widely adopted. Mercury (Hg) is a representative pollutant for which study of its speciation benefits from modeling. However, Hg speciation modeling is often hindered by a lack of reliable thermodynamic constants. Although computational chemistry (e.g., density functional theory [DFT]) can generate these constants, methods for directly coupling DFT and speciation modeling are not available. Here, we combine computational chemistry and continuum‐scale modeling with curated online databases to ameliorate the problem of unreliable inputs to Hg speciation modeling. Our AQUA‐MER databases and web server (https://aquamer.ornl.gov) provides direct speciation results by combining web‐based interfaces to a speciation calculator, databases of thermodynamic constants, and a computational chemistry toolkit to estimate missing constants. Although Hg is presented as a concrete use case, AQUA‐MER can also be readily applied to other elements. © 2019 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Summary: A microbe's ecological niche and biotechnological utility are determined by its specific set of co‐evolved metabolic pathways. The acquisition of new pathways, through horizontal gene transfer or genetic engineering, can have unpredictable consequences. Here we show that two different pathways for coumarate catabolism failed to function when initially transferred into Escherichia coli. Using laboratory evolution, we elucidated the factors limiting activity of the newly acquired pathways and the modifications required to overcome these limitations. Both pathways required host mutations to enable effective growth with coumarate, but the necessary mutations differed. In one case, a pathway intermediate inhibited purine nucleotide biosynthesis, and this inhibition was relieved by single amino acid replacements in IMP dehydrogenase. A strain that natively contains this coumarate catabolism pathway, Acinetobacter baumannii, is resistant to inhibition by the relevant intermediate, suggesting that natural pathway transfers have faced and overcome similar challenges. Molecular dynamics simulation of the wild type and a representative single‐residue mutant provide insight into the structural and dynamic changes that relieve inhibition. These results demonstrate how deleterious interactions can limit pathway transfer, that these interactions can be traced to specific molecular interactions between host and pathway, and how evolution or engineering can alleviate these limitations. [ABSTRACT FROM AUTHOR]

The electric field dependence of the structure and dynamics of water at 77 K, i.e., below the glass transition temperature (136 K), is investigated using molecular dynamics simulations. Transitions are found at two critical field strengths, denoted E1 and E2. The transition around E1≈3.5 V/nm is characterized by the onset of significant structural disorder, a rapid increase in the orientational polarization, and a maximum in the dynamical fluctuations. At E2≈40 V/nm, the system crystallizes in discrete steps into a body-centered-cubic unit cell that minimizes the potential energy by simultaneous superpolarization of the water molecular dipoles and maximization of the intermolecular hydrogen bonds. The stepwise and discontinuous increase of the orientational polarization with the increasing electric field indicates that the dipole relaxation in the electric field is highly cooperative. [ABSTRACT FROM AUTHOR]

Cellulosic biomass has the potential to serve as a major renewable energy source. However, its strong recalcitrance to degradation hampers its large-scale use in biofuel production. To overcome this problem, a detailed understanding of the origins of the recalcitrance is required. One main biophysical phenomenon leading to the recalcitrance is the high structural ordering of natural cellulose fibrils, that arises largely from an extensive hydrogen-bond network between and within cellulose polymers. Here, we present a lattice-based model of cellulose Iα, one of the two major natural forms, at the resolution of explicit hydrogen bonds. The partition function and thermodynamic properties are evaluated using the transfer matrix method. Two competing hydrogen-bond patterns are found. This plasticity of the hydrogen-bond network leads to an entropic contribution stabilizing the crystalline fibril at intermediate temperatures. At these temperatures, an enhanced probability of bonding between the individual cellulose chains gives rise to increased resistance of the entire cellulose fibril to degradation, before the final disassembly temperature is reached. The results are consistent with the available crystallographic and IR spectroscopic experiments on the thermostability of cellulose Iα. [ABSTRACT FROM AUTHOR]

Parallel tempering (PT) molecular dynamics simulations have been extensively investigated as a means of efficient sampling of the configurations of biomolecular systems. Recent work has demonstrated how the short physical trajectories generated in PT simulations of biomolecules can be used to construct the Markov models describing biomolecular dynamics at each simulated temperature. While this approach describes the temperature-dependent kinetics, it does not make optimal use of all available PT data, instead estimating the rates at a given temperature using only data from that temperature. This can be problematic, as some relevant transitions or states may not be sufficiently sampled at the temperature of interest, but might be readily sampled at nearby temperatures. Further, the comparison of temperature-dependent properties can suffer from the false assumption that data collected from different temperatures are uncorrelated. We propose here a strategy in which, by a simple modification of the PT protocol, the harvested trajectories can be reweighted, permitting data from all temperatures to contribute to the estimated kinetic model. The method reduces the statistical uncertainty in the kinetic model relative to the single temperature approach and provides estimates of transition probabilities even for transitions not observed at the temperature of interest. Further, the method allows the kinetics to be estimated at temperatures other than those at which simulations were run. We illustrate this method by applying it to the generation of a Markov model of the conformational dynamics of the solvated terminally blocked alanine peptide. [ABSTRACT FROM AUTHOR]

Molecular dynamics simulation generates large quantities of data that must be interpreted using physically meaningful analysis. A common approach is to describe the system dynamics in terms of transitions between coarse partitions of conformational space. In contrast to previous work that partitions the space according to geometric proximity, the authors examine here clustering based on kinetics, merging configurational microstates together so as to identify long-lived, i.e., dynamically metastable, states. As test systems microsecond molecular dynamics simulations of the polyalanines Ala8 and Ala12 are analyzed. Both systems clearly exhibit metastability, with some kinetically distinct metastable states being geometrically very similar. Using the backbone torsion rotamer pattern to define the microstates, a definition is obtained of metastable states whose lifetimes considerably exceed the memory associated with interstate dynamics, thus allowing the kinetics to be described by a Markov model. This model is shown to be valid by comparison of its predictions with the kinetics obtained directly from the molecular dynamics simulations. In contrast, clustering based on the hydrogen-bonding pattern fails to identify long-lived metastable states or a reliable Markov model. Finally, an approach is proposed to generate a hierarchical model of networks, each having a different number of metastable states. The model hierarchy yields a qualitative understanding of the multiple time and length scales in the dynamics of biomolecules. [ABSTRACT FROM AUTHOR]

Intrinsically disordered proteins (IDPs) are abundant in eukaryotic proteomes, play a major role in cell signaling, and are associated with human diseases. To understand IDP function it is critical to determine their configurational ensemble, i.e., the collection of 3-dimensional structures they adopt, and this remains an immense challenge in structural biology. Attempts to determine this ensemble computationally have been hitherto hampered by the necessity of reweighting molecular dynamics (MD) results or biasing simulation in order to match ensemble-averaged experimental observables, operations that reduce the precision of the generated model because different structural ensembles may yield the same experimental observable. Here, by employing enhanced sampling MD we reproduce the experimental small-angle neutron and X-ray scattering profiles and the NMR chemical shifts of the disordered N terminal (SH4UD) of c-Src kinase without reweighting or constraining the simulations. The unbiased simulation results reveal a weakly funneled and rugged free energy landscape of SH4UD, which gives rise to a heterogeneous ensemble of structures that cannot be described by simple polymer theory. SH4UD adopts transient helices, which are found away from known phosphorylation sites and could play a key role in the stabilization of structural regions necessary for phosphorylation. Our findings indicate that adequately sampledmolecular simulations can be performed to provide accurate physical models of flexible biosystems, thus rationalizing their biological function. [ABSTRACT FROM AUTHOR]

The collective behaviour of individual lipid molecules determines the properties of phospholipid membranes. However, the collective molecular motions often remain challenging to characterise at the desired spatial and temporal resolution. Here we study collective vibrational motion on picosecond time scales in dioleoylphosphatidylcholine lipid bilayers with varying cholesterol content using all-atom molecular dynamics simulations. Cholesterol is found to not only laterally compact the lipid bilayer, but also to change the velocity of longitudinal density fluctuations propagating in the plane of the membrane. Cholesterol-induced reduction of the area per lipid alters the collective dynamics of the lipid headgroups, but not of the lipid tails. The introduction of cholesterol reduces the number of water molecules interacting with the lipid headgroups, leading to a decrease in the velocity of the laterally-propagating sound mode. Thus, the stiffening effect of cholesterol is found to be indirect: decreasing the area per lipid weakens the interactions between the lipid headgroups and water. The collective modes characterised in this work can enable the membrane to dissipate excess energy and thus maintain its structural integrity, e.g., under mechanical stress. [ABSTRACT FROM AUTHOR]

The making of Euro-Australia occurred against the backdrop of two dimensions of its historical constitution. First, it occurred on the back of Britain's entry into the Oceanian world and its intercivilizational encounters with Pacific cultures. The second dimension was the appropriation of the land of a complex and internally diverse Aboriginal civilization and suppression of its social world view. This was vital to a lasting sense of ambivalence in Australian identity and in the relations of the Commonwealth of Australia with island states in the Pacific. After Federation (1901), Australia became more independent in the context of devolution of the Commonwealth. Engagement in the Pacific War heralded a turn from allegiance to Britain to alliance with the United States. A new orientation to the Asia-Pacific was not a chosen course, but one compelled by geo-political conditions and a growing dynamism in this multicivilizational world region. From the 1970s to the end of the twentieth century, engagement in Asia accelerated with the onset of a policy regime of multiculturalism and a process of neo-liberal modernization. This essay argues that Euro-Australia emerged out of complex intercivilizational interactions entailing colonialism, diverse migratory and cultural flows, and the creation of a homogenizing collective memory. I contend that Australian modernity, due in part to its suppression of its indigenous civilization and accompanying denial of that suppression, has borne considerable cultural and political ambivalence about its place in the region -- an ambivalence which structures its economic and political relations with neighbouring countries. In this essay, I focus on Pacific relations. I compare developments and turns in Australian foreign policy with patterns of cultural engagement since the 1970s. Towards the end, I raise the Australian regime of refugee detention in relation to climate refugees. The essay concludes with notes on the merits of civilizational analysis in understanding the Oceanian constellation and its potential futures and points for further research on Australia in a multi-civilizational context. [ABSTRACT FROM AUTHOR]