Small Domains Research Articles

Photothermal AFM-IR Depth Sensitivity: An Original Pathway to Tomographic Reconstruction.

Photothermal atomic force microscopy-infrared (AFM-IR) enables label-free chemical imaging and spectroscopy with nanometer-scale spatial resolution through the integration of atomic force microscopy (AFM) and infrared radiation. The capability for subsurface and three-dimensional (3D) tomographic material analysis remains, however, largely unexplored. Here, we establish a simple and robust empirical relationship between the probing depth and laser repetition rate for three important modes of AFM-IR operation: resonance-enhanced, tapping, and surface-sensitive AFM-IR. Using this empirical relationship, we demonstrate, based on the example of resonance-enhanced operation, how photothermal AFM-IR of thin surface/subsurface layers of polystyrene domains in the poly(methyl methacrylate) matrix can result in 3D representations revealing the size and thickness of small polystyrene domains in the poly(methyl methacrylate) matrix with nanometer-scale resolution. Experimental findings are confirmed by analytical models.

AMBER: A Modular Model for Tumor Growth, Vasculature and Radiation Response.

Computational models of tumor growth are valuable for simulating the dynamics of cancer progression and treatment responses. In particular, agent-based models (ABMs) tracking individual agents and their interactions are useful for their flexibility and ability to model complex behaviors. However, ABMs have often been confined to small domains or, when scaled up, have neglected crucial aspects like vasculature. Additionally, the integration into tumor ABMs of precise radiation dose calculations using gold-standard Monte Carlo (MC) methods, crucial in contemporary radiotherapy, has been lacking. Here, we introduce AMBER, an Agent-based fraMework for radioBiological Effects in Radiotherapy that computationally models tumor growth and radiation responses. AMBER is based on a voxelized geometry, enabling realistic simulations at relevant pre-clinical scales by tracking temporally discrete states stepwise. Its hybrid approach, combining traditional ABM techniques with continuous spatiotemporal fields of key microenvironmental factors such as oxygen and vascular endothelial growth factor, facilitates the generation of realistic tortuous vascular trees. Moreover, AMBER is integrated with TOPAS, an MC-based particle transport algorithm that simulates heterogeneous radiation doses. The impact of radiation on tumor dynamics considers the microenvironmental factors that alter radiosensitivity, such as oxygen availability, providing a full coupling between the biological and physical aspects. Our results show that simulations with AMBER yield accurate tumor evolution and radiation treatment outcomes, consistent with established volumetric growth laws and radiobiological understanding. Thus, AMBER emerges as a promising tool for replicating essential features of tumor growth and radiation response, offering a modular design for future expansions to incorporate specific biological traits.

MAD2L2 Dimerization Is Not Essential for Mitotic Regulation

MAD2L2 is a small HORMA domain protein that plays a crucial role in DNA repair and mitosis. In both TLS and shieldin, the dimerization of MAD2L2 via its HORMA domain is critical for the stability and function of these complexes. However, in mitosis, the dimerization state of MAD2L2 remains unknown. To assess the importance of MAD2L2’s dimerization during mitosis, we utilized CRISPR/Cas9 to generate MAD2L2 knockout cells, which were subsequently complemented with MAD2L2 species carrying different dimer-disrupting point mutations. We assessed the ability of these MAD2L2 dimer-disrupting mutants to regulate mitosis by evaluating early mitotic events and mitotic fidelity. Our findings indicate that MAD2L2 can function in its monomeric form during mitosis, suggesting that MAD2L2 homodimerization is dispensable for early mitotic regulation. Furthermore, our results suggest that the binding of CDH1 to MAD2L2 is a key regulating factor in mitosis that may actively prevent the formation of MAD2L2 dimers, thereby shifting the cellular balance toward MAD2L2-CDH1 interaction. Thus, the equilibrium between the monomeric and dimeric forms of MAD2L2 is an important cellular factor regulating the MAD2L2-containing complexes.

Thousands of planetesimals: Simulating the streaming instability in very large computational domains

The streaming instability is a mechanism whereby pebble-sized particles in protoplanetary discs spontaneously come together in dense filaments, which collapse gravitationally to form planetesimals upon reaching the Roche density. The extent of the filaments along the orbital direction is nevertheless poorly characterised, due to a focus in the literature on small simulation domains where the behaviour of the streaming instability on large scales cannot be determined. We present here computer simulations of the streaming instability in boxes with side lengths up to $6.4$ scale heights in the plane. This is $32$ times larger than typically considered simulation domains and nearly a factor $1,000$ times the volume. We show that the azimuthal extent of filaments in the non-linear state of the streaming instability is limited to approximately one gas scale height. The streaming instability will therefore not transform the pebble density field into axisymmetric rings; rather the non-linear state of the streaming instability appears as a complex structure of loosely connected filaments. Including the self-gravity of the pebbles, our simulations form up to $4,000$ planetesimals. This allows us to probe the high-mass end of the initial mass function of planetesimals with much higher statistical confidence than previously. We find that this end is well-described by a steep exponential tapering. Since the resolution of our simulations is moderate -- a necessary trade-off given the large domains -- the mass distribution is incomplete at the low-mass end. When putting comparatively less weight on the numbers at low masses, at intermediate masses we nevertheless reproduce the power-law shape of the distribution established in previous studies.

The development of single-domain VHH nanobodies that target the Candida albicans cell surface.

Candida albicans causes life-threatening invasive infections that are hard to diagnose and treat, with drug resistance leading to treatment failure. The goal of this study was to develop VHH (single variable domain on a heavy chain) nanobodies to detect drug-resistant infections. Llamas were immunized with a mixture of heat killed and fixed C. albicans cells of different morphologies. Llama lymphocyte RNA was used to generate phage display libraries that were tested for binding to C. albicans cells or cell wall fractions, and single antibody domains were isolated. The libraries were panned against echinocandin-resistant C. albicans isolates and counter-selected against echinocandin-susceptible isolates with the aim of isolating binding domains specific for antigens on drug-resistant cells. Thirty diverse VHH nanobodies were selected, and binding characteristics were assessed via dose-response ELISA. Binding was tested against a variety of C. albicans isolates and other Candida species, indicating that the VHHs were specific for C. albicans. The VHH nanobodies were sorted into four distinct groups based on their binding patterns. Two of the groups bound preferentially to the yeast cell poles and hyphae, respectively. Nanobody binding to C. albicans deletion mutants was tested by fluorescence microscopy and ELISA to identify the antigen targets. VHH19 nanobody, belonging to the largest group, recognized the Als4 adhesin. VHH14 antibody in the hyphae-specific group recognized Als3. None of the isolated VHH nanobodies was selective for drug-resistant clinical isolates. Our data indicate that this approach can generate valuable single-domain antibodies specific to C. albicans proteins.IMPORTANCEThe human fungal pathogen Candida albicans causes a range of diseases from superficial mucosal infections such as oral and vaginal thrush to life-threatening, systemic infections. Accurate and rapid diagnosis of these infections remains challenging, and currently, there are no rapid ways to diagnose drug-resistant infections without performing drug susceptibility testing from blood culture, which can take several days. In this proof-of-concept study, we have generated a diverse set of single domain VHH antibodies (nanobodies) from llamas that recognize and bind specifically to C. albicans cell surface. The nanobodies were classified into four groups based on their binding patterns, for example, cell poles or hyphae. Specific nanobodies were verified as recognizing the important adhesin Als4 or the hyphae associated invasin Als3, respectively. The data validate the approach that small VHH antibody domains hold future promise for diagnostic applications and as probes to study the fungal cell surface.

Human IL-22 receptor-targeted small protein antagonist suppress murine DSS-induced colitis

BackgroundHuman interleukin-22 (IL-22) is known as a “dual function” cytokine that acts as a master regulator to maintain homeostasis, structural integrity of the intestinal epithelial barrier, and shielding against bacterial pathogens. On the other hand, the overexpression of IL-22 is associated with hyper-proliferation and recruitment of pathologic effector cells, leading to tissue damage and chronic inflammation in specific diseases including inflammatory bowel disease (IBD). To study a role of IL-22-mediated signaling axis during intestinal inflammation, we generated a set of small protein blockers of IL-22R1 and verified their inhibitory potential on murine model of colitis.MethodsWe used directed evolution of proteins to identify binders of human IL-22 receptor alpha (IL-22R1), designated as ABR ligands. This approach combines the assembly of a highly complex combinatorial protein library derived from small albumin-binding domain scaffold and selection of promising protein variants using ribosome display followed by large-scale ELISA screening. The binding affinity and specificity of ABR variants were analyzed on transfected HEK293T cells by flow cytometry and LigandTracer. Inhibitory function was further verified by competition ELISA, HEK-Blue IL-22 reporter cells, and murine dextran sulfate sodium (DSS)-induced colitis.ResultsWe demonstrate that ABR specifically recognizes transgenic IL-22R1 expressed on HEK293T cells and IL-22R1 on TNFα/IFNγ-activated HaCaT cells. Moreover, some ABR binders compete with the IL-22 cytokine and function as IL-22R1 antagonists in HEK-Blue IL22 reporter cells. In a murine model of DSS-induced acute intestinal inflammation, daily intraperitoneal administration of the best IL-22R1 antagonist, ABR167, suppressed the development of clinical and histological markers of colitis including prevention of mucosal inflammation and architecture deterioration. In addition, ABR167 reduces the DSS-induced increase in mRNA transcript levels of inflammatory cytokines such as IL-1β, IL-6, IL-10, and IL-17A.ConclusionsWe developed small anti-human IL-22R1 blockers with antagonistic properties that ascertain a substantial role of IL-22-mediated signaling in the development of intestinal inflammation. The developed ABR blockers can be useful as a molecular clue for further IBD drug development.

Arc bubble edge detection method based on deep transfer learning in underwater wet welding.

The stability of arc bubble is a crucial indicator of underwater wet welding process. However, limited research exists on detecting arc bubble edges in such environments, and traditional algorithms often produce blurry and discontinuous results. To address these challenges, we propose a novel arc bubble edge detection method based on deep transfer learning for processing underwater wet welding images. The proposed method integrates two training stages: pre-training and fine-tuning. In the pre-training stage, a large source domain dataset is used to train VGG16 as a feature extractor. In the fine-tuning stage, we introduce the Attention-Scale-Semantics (ASS) model, which consists of a Convolutional Block Attention Module (CBAM), a Scale Fusion Module (SCM) and a Semantic Fusion Module (SEM). The ASS model is further trained on a small target domain dataset specific to underwater wet welding to fine-tune the model parameters. The CBAM can adaptively weight the feature maps, focusing on more crucial features to better capture edge information. The SCM training method maximizes feature utilization and simplifies training by combining multi-scale features. Additionally, the skip structure of SEM effectively mitigates semantic loss in the high-level network, enhancing the accuracy of edge detection. On the BSDS500 dataset and a self-constructed underwater wet welding dataset, the ASS model was evaluated against conventional edge detection models-Richer Convolutional Features (RCF), Fully Convolutional Network (FCN), and UNet-as well as state-of-the-art models LDC and TEED. In terms of Mean Absolute Error (MAE), accuracy, and other evaluation metrics, the ASS model consistently outperforms these models, demonstrating edge detection capabilities that are both effective and stable in detecting arc bubble edges in underwater wet welding images.

A strategy for improving piezoelectricity and thermal stability of Na0.25K0.25Bi2.5Nb2O9-based piezoceramics via A/B-site co-doping

Obviously improved piezoelectricity and thermal stability of Na0.25K0.25Bi2.5Nb2O9-based ceramics have been urgently required for high-temperature piezoelectric applications. In this work, Na0.25K0.25Bi2.5-xCexNb2-yWyO9 (NKBCxNWy, x = 0.01, 0.02, 0.03, 0.04; y = 0.02) piezoceramics were developed via a traditional solid-state reaction method. The effect of Ce/W co-doping on the crystal structure and microscopic morphology was systematically investigated, along with the ferroelectric domains and electrical properties. An optimum composition of Na0.25K0.25Bi2.48Ce0.02Nb1.98W0.02O9 was achieved, which shows a high curie temperature of 670 ℃, a good piezoelectric coefficient d33 of 24.4 pC/N. The enhanced d33 stems from small domain sizes and high domain wall density in NKBC0.02NW0.02 ceramics. More importantly, the maximum d33 can remain above 80 % of the d33 (@25 ℃), even if the annealing temperature increases to 500 ℃. This work provides a simple and viable method to obtain high piezoelectric properties and excellent thermal stability in Na0.25K0.25Bi2.5Nb2O9-based ceramics.

Crystal structure of l-threonine-O-3-phosphate decarboxylase CobC from Sinorhizobium meliloti involved in vitamin B12 biosynthesis

Vitamin B12 is involved in many important biochemical reactions for humans, and its deficiency can lead to serious diseases. The industrial production of vitamin B12 is achieved through microbial fermentation. In this work, we determine the crystal structures of the l-threonine-O-3-phosphate (Thr-P) decarboxylase CobC from Sinorhizobium meliloti (SmCobC), an industrial vitamin B12-producing bacterium, in apo form and in complex with a reaction intermediate. Our structures supported the Thr-P decarboxylase activity of SmCobC and revealed that the positively charged substrate-binding pocket between the large and small domains determines its substrate selectivity for Thr-P. Moreover, our results provided evidence for the proposition that the AP-P linker is formed by direct incorporation of AP-P in the biosynthetic pathway of vitamin B12 in S.meliloti.

Pore-scale study of coupled heat and mass transfer during primary freeze-drying using an irregular pore network model

This study presents a pore network (PN) model with transient heat transfer and quasi-steady transitional vapor transport that is for the first time applied to irregular porous structures that are obtained by reconstruction of X-ray tomography image data. In contrast to previous studies, the irregular pores are not approximated by spheres but implemented in their original shape. Secondly, instead of assuming cylindrical throats as pore connections, the actual distance between pore centers as well as the pore cross sections are used for the computation of the vapor transport coefficient. The control volume elements of the computational model are matched with the cells obtained by Voronoi tessellation. The improvements have clear advantages over former approaches where the reconstructed void space is usually strongly simplified by balls and sticks confined in a regular lattice structure. A freeze-dried sample of maltodextrin DE12 with 20% (w/w) solid content is used for benchmarking the new methodology. Its morphological and thermal properties are determined by the novel PN model. The simulation results of primary freeze-drying (FD) are compared to reference cases in two ways. First, the differences in heat and mass transfer kinetics as compared to regular PNs are emphasized. Secondly, the PN simulation results are confronted with a simple literature model that neglects pore size distribution (PSD) and transient heat transfer. It is shown that already in small domains with relatively narrow PSD, the variation of the mass transfer coefficient affects the computed sublimation fluxes and yields a significant deviation from the simpler literature model. Moreover, it is revealed in this study that the structure has a significant impact on the sublimation front temperature. This is demonstrated by the comparison of FD in the irregular PN to a regular PN with almost identical PSD but different porosity. The development, verification, and benchmarking of the new PN model can be seen as an important step for studies of the structure dependence of FD.

Cooperative folding as a molecular switch in an evolved antibody binder

Designing proteins with tunable activities from easily accessible external cues remains a biotechnological challenge. Here, we set out to create a small antibody-binding domain equipped with a molecular switch inspired by the allosteric response to calcium seen in naturally derived proteins like calmodulin. We have focused on one of the three domains of Protein G that show inherent affinity to antibodies. By combining a semi-rational protein design with directed evolution, we engineered novel variants containing a calcium-binding loop rendering the inherent antibody affinity calcium-dependent. The evolved variants resulted from a designed selection strategy subjecting them to negative and positive selection pressures focused on conditional antibody-binding. Hence, these variants contained molecular “on/off” switches, controlling the target affinity towards antibody fragments simply by the presence or absence of calcium. From NMR spectroscopy we found that the molecular mechanism underlying the evolved switching behavior was a coupled calcium-binding and folding event where the target binding surface was intact and functional only in the presence of bound calcium. Notably, it was observed that the response to the employed selection pressures gave rise to the evolution of a cooperative folding mechanism. This observation illustrates why the cooperative folding reaction is an effective solution seen repeatedly in the natural evolution of fine-tuned macromolecular recognition. Engineering binding moieties to confer conditional target interaction has great potential due to the exquisite interaction control that is tunable to application requirements. Improved understanding of the molecular mechanisms behind regulated interactions is crucial to unlock how to engineer switchable proteins useful in a variety of biotechnological applications.

Model-Based Prediction for Small Domains Using Covariates: A Comparison of Four Methods

Abstract We consider methods for model-based small area estimation when the number of areas with sampled data is a small fraction of the total areas for which estimates are required. Abundant auxiliary information is available from the survey for all the sampled areas. Further, through an external source, there is information for all areas. The goal is to use auxiliary variables to predict the outcome of interest for all areas. We compare areal-level random forests and LASSO approaches to a frequentist forward variable selection approach and a Bayesian shrinkage method using a horseshoe prior. Further, to measure the uncertainty of estimates obtained from random forests and the LASSO, we propose a modification of the split conformal procedure that relaxes the assumption of exchangeable data. We show that the proposed method yields intervals with the correct coverage rate and this is confirmed through a simulation study. This work is motivated by Ghanaian data available from the sixth Ghana Living Standards Survey (GLSS) and the 2010 Population and Housing Census, in the Greater Accra Metropolitan Area (GAMA) region, which comprises eight districts that are further divided into enumeration areas (EAs). We estimate the areal mean household log consumption using both datasets. The outcome variable is measured only in the GLSS for 3 percent of all the EAs (136 out of 5019) and 174 potential covariates are available in both datasets. In the application, among the four modeling methods considered, the Bayesian shrinkage performed the best in terms of bias, mean squared error (MSE), and prediction interval coverages and scores, as assessed through a cross-validation study. We find substantial between-area variation with the estimated log consumption showing a 1.3-fold variation across the GAMA region. The western areas are the poorest while the Accra Metropolitan Area district has the richest areas.

Towards Discriminative Class-Aware Domain Alignment via Coding Rate Reduction for Unsupervised Adversarial Domain Adaptation

Unsupervised domain adaptation (UDA) methods, based on adversarial learning, employ the means of implicit global and class-aware domain alignment to learn the symmetry between source and target domains and facilitate the transfer of knowledge from a labeled source domain to an unlabeled target domain. However, these methods still face misalignment and poor target generalization due to small inter-class domain discrepancy and large intra-class discrepancy of target features. To tackle these challenges, we introduce a novel adversarial learning-based UDA framework named Coding Rate Reduction Adversarial Domain Adaptation (CR2ADA) to better learn the symmetry between source and target domains. Integrating conditional domain adversarial networks with domain-specific batch normalization, CR2ADA learns robust domain-invariant features to implement global domain alignment. For discriminative class-aware domain alignment, we propose the global and local coding rate reduction methods in CR2ADA to maximize inter-class domain discrepancy and minimize intra-class discrepancy of target features. Additionally, CR2ADA combines minimum class confusion and mutual information to further regularize the diversity and discriminability of the learned features. The effectiveness of CR2ADA is demonstrated through experiments on four UDA datasets. The code can be obtained through email or GitHub.

Production and characterization of nano-magnetic material through different red mud reduction processes

Red mud (RM), is an industrial waste deriving from the production of primary aluminium, with about 200 Mtons generated annually. In this work, the reduction of RM for obtaining magnetic products with particular chemical-physical characteristics has been explored. RM reduction was carried out in a semi-closed reducing environment at a high processing temperature (750–1100 °C) in the presence of carbon-rich ash (CA) resulting from a commercial pyro-gasification plant and by using an H2 atmosphere. All the materials were characterized by elemental analysis, XRD diffraction technique, SEM/EDX microscopy, thermogravimetry and infrared spectroscopy. The materials obtained under the different conditions have in common the presence of small iron-magnetic domains (with different iron oxidation states), in the order of hundreds of nanometres, and by the ferro-magnetic or paramagnetic response to an external magnetic field. In particular, the material pre-treated with HCl (enriched in iron) and reduced with H2 showed very good magnetic responses, more iron in reduced oxidative states showing high ferro-magnetic responses to the external field. The synthesised materials are quite differents each others and appear to have several potential fields of application, as sorbents for specific wastewater pollutants, as materials for CO2 capture, for catalysis and (bio)sensing applications. Nevertheless, the high interconnection between the magnetic and non-magnetic phases limit the direct separation or the iron enrichment. Further efforts should be focused on the functional studies and subsequent LCA and LCC analyses.

TFCP2L1 drives stemness and enhances their resistance to Sorafenib treatment by modulating the NANOG/STAT3 pathway in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a prevalent and aggressive malignancy associated with high risks of recurrence and metastasis. Liver cancer stem cells (CSCs) are increasingly recognized as pivotal drivers of these processes. In our previous research, we demonstrated the involvement of TFCP2L1 in maintaining the pluripotency of embryonic stem cells. However, its relevance to liver CSCs remains unexplored. In this study, we report an inverse correlation between TFCP2L1 protein levels in HCC tissue and patient outcomes. The knockdown of TFCP2L1 significantly reduced HCC cell proliferation, invasion, metastasis, clonal formation, and sphere-forming capacity, while its overexpression enhanced these functions. In addition, experiments using a nude mouse model confirmed TFCP2L1’s essential role in liver CSCs’ function and tumorigenic potential. Mechanistically, we showed that TFCP2L1 promotes the stemness of CSCs by upregulating NANOG, which subsequently activates the JAK/STAT3 pathway, thereby contributing to HCC pathogenesis. Importantly, we identified a specific small molecule targeting TFCP2L1’s active domain, which, in combination with Sorafenib, sensitizes hepatoma cells to treatment. Together, these findings underscore TFCP2L1’s pathological significance in HCC progression, supporting its potential as a prognostic biomarker and therapeutic target in this disease.

Nanoengineered pure Fe in a citrate matrix (Fe–CIT) with significant and tunable magnetic properties

This work demonstrates the synthesis and characterization of Fe nanoparticles surrounded by a citrate (CIT) matrix prepared at various temperatures and concentrations of metal, capping agent and reducing agent at standard conditions. We study the effect of reactant ratio and reaction temperature on the magnetization of the produced nanoparticles and their crystal structure. We found that for optimal metal concentrations, magnetic saturation increases with increase in the concentration of capping and reducing agents but decreases as the temperature of the reaction increases. Synthesis conditions were tailored to reveal nucleation of particles with average sizes ranging from 24 to 105 nm and a spherical shape. The ultra-high saturation magnetization of 228 emu g-1obtained for samples prepared at a metal precursor concentration of 27.8 mol l-1was attributed to the formation of small magnetic domains. Energy band gap measurements revealed a band gap energy for the Fe nanoparticles in the CIT matrix which is associated with CIT concentration and/or possible formation of a few thin layers of iron oxide shell and does not have a significant effect on the magnetic properties of the samples. Herein, we demonstrate that the synthesis parameters are crucial for the nucleation of Fe-CIT nanoparticles tailoring their magnetizatic properties as well as their potential for different applications.

An engineered ubiquitin binding coiled coil peptide.

Recognition of ubiquitin (Ub) is often mediated by small Ub binding domains such as the Ubiquitin Interacting Motif (UIM). Most Ub binding events are low affinity interactions, and designing stronger binders for Ub can be challenging. We here report the design of a short crosslinked coiled coil (CC) which is conformationally and chemically stable, and which can act as a scaffold to present the key binding residues from known UIM sequences. Doing so gives rise to a hybrid CC peptide that reconciles the important features of both UIM and CC sequences. We show by fluorescence polarization assays that this crosslinked 'CC-UIM' peptide exhibits enhanced binding to Ub compared to the original UIM sequence. Furthermore, we report a crystal structure of this peptide in complex with Ub. These studies show that preorganization of a small number of important binding residues onto a stable helical scaffold can be a successful strategy for binder design.

Imperative role of adaptor proteins in macrophage toll-like receptor signaling pathways.

Macrophages are integral part of the body's defense against pathogens and serve as vital regulators of inflammation. Adaptor molecules, featuring diverse domains, intricately orchestrate the recruitment and transmission of inflammatory responses through signaling cascades. Key domains involved in macrophage polarization include Toll-like receptors (TLRs), Src Homology2 (SH2) and other small domains, alongside receptor tyrosine kinases, crucial for pathway activation. This review aims to elucidate the enigmatic role of macrophage adaptor molecules in modulating macrophage activation, emphasizing their diverse roles and potential therapeutic and investigative avenues for further exploration.

Structural and biochemical insights into the molecular mechanism of N-acetylglucosamine/N-Acetylmuramic acid kinase MurK from Clostridium acetobutylicum

MurK is a MurNAc- and GlcNAc-specific amino sugar kinase, phosphorylates MurNAc and GlcNAc at the 6-hydroxyl group in an ATP-dependent manner, and contributes to the recovery of both amino sugars during the cell wall turnover in Clostridium acetobutylicum. Herein, we determined the crystal structures of MurK in complex with MurNAc, GlcNAc, and glucose, respectively. MurK represents the V-shaped fold, which is divided into a small N-terminal domain and a large C-terminal domain. The catalytic pocket is located within the deep cavity between the two domains of the MurK monomer. We mapped the significant enzyme-substrate interactions, identified key residues involved in the catalytic activity of MurK, and found that residues Asp77 and Arg78 from the β4-α2-loop confer structural flexibilities to specifically accommodate GlcNAc and MurNAc, respectively. Moreover, structural comparison revealed that MurK adopts closed-active conformation induced by the N-acetyl moiety from GlcNAc/MurNAc, rather than closed-inactive conformation induced by glucose, to carry out its catalytic reaction. Taken together, our study provides structural and functional insights into the molecular mechanism of MurK for the phosphorylation of both MurNAc and GlcNAc, sugar substrate specificity, and conformational changes upon sugar substrate binding.

Spatiotemporal patterns in the active cyclic Potts model

Abstract The nonequilibrium dynamics of a cycling three-state Potts model is studied on a square lattice using Monte Carlo simulations and continuum theory. This model is relevant to chemical reactions on a catalytic surface and to molecular transport across a membrane. Several characteristic modes are formed depending on the flipping energies between successive states and the contact energies between neighboring sites. Under cyclic symmetry conditions, cycling homogeneous phases and spiral waves (SW) form at low and high flipping energies, respectively. In the intermediate flipping energy regime, these two modes coexist temporally in small systems and/or at low contact energies. Under asymmetric conditions, we observed small biphasic domains exhibiting amoeba-like locomotion and temporal coexistence of SW and a dominant non-cyclic one-state phase. An increase in the flipping energy between two successive states, say state 0 and state 1, while keeping the other flipping energies constant, induces the formation of the third phase (state 2), owing to the suppression of the nucleation of state 0 domains. Under asymmetric conditions regarding the contact energies, two different modes can appear depending on the initial state, due to a hysteresis phenomenon.