Intrinsic structural dynamics dictate enzymatic activity and inhibition

Enzymes are known to sample various conformations, many of which are critical for their biological function. However, structural characterizations of enzymes predominantly focus on the most populated conformation. As a result, single-point mutations often produce structures that are similar or essentially identical to those of the wild-type enzyme despite large changes in enzymatic activity. Here, we show for mutants of a histone deacetylase enzyme (HDAC8) that reduced enzymatic activities, reduced inhibitor affinities, and reduced residence times are all captured by the rate constants between intrinsically sampled conformations that, in turn, can be obtained independently by solution NMR spectroscopy. Thus, for the HDAC8 enzyme, the dynamic sampling of conformations dictates both enzymatic activity and inhibitor potency. Our analysis also dissects the functional role of the conformations sampled, where specific conformations distinct from those in available structures are responsible for substrate and inhibitor binding, catalysis, and product dissociation. Precise structures alone often do not adequately explain the effect of missense mutations on enzymatic activity and drug potency. Our findings not only assign functional roles to several conformational states of HDAC8 but they also underscore the paramount role of dynamics, which will have general implications for characterizing missense mutations and designing inhibitors

Biomolecular NMR spectroscopy in the era of artificial intelligence

Biomolecular nuclear magnetic resonance (NMR) spectroscopy and artificial intelligence (AI) have a burgeoning synergy. Deep learning-based structural predictors have forever changed structural biology, yet these tools currently face limitations in accurately characterizing protein dynamics, allostery, and conformational heterogeneity. We begin by highlighting the unique abilities of biomolecular NMR spectroscopy to complement AI-based structural predictions toward addressing these knowledge gaps. We then highlight the direct integration of deep learning approaches into biomolecular NMR methods. AI-based tools can dramatically improve the acquisition and analysis of NMR spectra, enhancing the accuracy and reliability of NMR measurements, thus streamlining experimental processes. Additionally, deep learning enables the development of novel types of NMR experiments that were previously unattainable, expanding the scope and potential of biomolecular NMR spectroscopy. Ultimately, a combination of AI and NMR promises to further revolutionize structural biology on several levels, advance our understanding of complex biomolecular systems, and accelerate drug discovery efforts

Solution-state methyl NMR spectroscopy of large non-deuterated proteins enabled by deep neural networks

Abstract Methyl-TROSY nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for characterising large biomolecules in solution. However, preparing samples for these experiments is demanding and entails deuteration, limiting its use. Here we demonstrate that NMR spectra recorded on protonated, uniformly 13C labelled samples can be processed using deep neural networks to yield spectra that are of similar quality to typical deuterated methyl-TROSY spectra, potentially providing information for proteins that cannot be produced in bacterial systems. We validate the methodology experimentally on three proteins with molecular weights in the range 42–360 kDa. We further demonstrate the applicability of our methodology to 3D NOESY spectra of Escherichia coli Malate Synthase G (81 kDa), where observed NOE cross-peaks are in good agreement with the available structure. The method represents an advance in the field of using deep learning to analyse complex magnetic resonance data and could have an impact on the study of large biomolecules in years to come

Optimising curvature of carbon fibre-reinforced polymer composite panel for improved blast resistance: Finite-element analysis

Numerical studies were conducted to investigate the optimum curvature of a carbon fibre-reinforced polymer (CFRP) panel that would provide an improved blast resistance. A dynamic finite-element (FE) model that incorporates fluid-structure interaction was developed to evaluate the response of these panels to blast in commercial finite-element software ABAQUS/Explicit. Previously reported experimental data by authors were utilised to validate a FE model, where a shock-tube apparatus was utilised to apply a controlled shock loading to quasi-isotropic composite panels with different radii of curvature. A three-dimensional digital image correlation (DIC) technique coupled with high-speed photography was employed to measure out-of-plane deflections and velocities, as well as in-plane strains at the back face of panels. Macroscopic post-mortem analysis was performed to compare the deformation in these panels. The numerical results were compared to the experimental data and demonstrated a good agreement. The validated FE model was further used to predict the optimal curvature of CFRP panel with the aim to improve its blast-mitigation characteristics. © 2014 Elsevier Ltd

Effect of plate curvature on blast response of carbon/epoxy composite

Experimental and numerical studies were conducted to understand the effect of plate curvature on the blast response of carbon/epoxy composite panels. A shock-tube system was utilized to impart controlled shock loading to quasi-isotropic composite panels with varying radii of curvature. A 3D digital image correlation (DIC) technique coupled with high-speed photography was used to assess the out-of-plane deflection of composite panels. A finite element (FE) model integrating fluid-structure interaction to represent coupling between the air surrounding composite panels, shock wave and panels, was developed using a general-purpose FE software ABAQUS/Explicit. The numerical results were compared to the experimental data and showed a good correlation. © (2013) Trans Tech Publications

MRI detection of posterior urethral diverticulum following surgical repair of anorectal malformations

Aim: To identify and to assess imaging and clinical features of Posterior urethral diverticula (PUD) in a single-centre series and include a brief review of literature. Materials and method: Post operative MRI of 140 children from north India were retrospectively reviewed who underwent surgical repair for anorectal malformation (ARM) along with the Hospital records. Results: Ten cases had MRI features of posterior urethral diverticulum. All of these patients had undergone primary abdominoperineal pull through (APPT) procedure. The lesions ranged between 6 mm and 38 mm in size. Two of these lesions were missed in the post operative MRI report. Only one of these patients was symptomatic and presented with dribbling of urine and gross bilateral vesicoureteric reflux in which the diverticulum was excised surgically. Conclusion: PUD is an under-recognised entity and can be identified in preclinical stage on MRI. Careful assessment of urethra and periurethral structures should be a mandatory step in MRI evaluation of post repair ARM cases. An observational conservative approach in selected asymptomatic patients can be an effective management strategy. Keywords: Posterior urethral diverticulum, MRI, Anorectal malformatio

Density, Viscosity, and Surface Tension of Aqueous 1‑Methylpiperazine and Its Carbonated Solvents for the CO₂ Capture Process

Physicochemical properties of amine solutions like density, viscosity, and surface tension results are indispensable for designing carbon dioxide (CO2) absorption and regeneration columns, and they are also crucial for modeling and simulation for CO2 capture applications using the postcombustion capture method. In the present work, the density and viscosity of 1-methylpiperazine (1-MPZ) solution are studied for the temperature range of 298.15 to 348.15 K. Surface tension measurements for temperatures ranging from 303.15 to 348.15 K are reported for various concentrations of 1-MPZ. To validate the instrumental accuracy and procedure, properties of aqueous 0.3 weight fraction (w) monoethanolamine (MEA) were first measured and compared with reported results before the study of 1-MPZ. The weight fraction of 1-MPZ was kept at 0.1, 0.2, 0.3, and 0.4 for the physical property study of unloaded aqueous 1-MPZ, and 0.3w was considered for CO2-loaded properties. The 1-MPZ solution was loaded with CO2 up to 0.45 mol CO2/mol amine. The Redlich–Kister equation for excess molar volume was used to correlate the measured density of the fresh and CO2-loaded solvents. The viscosity data of unloaded aqueous 1-MPZ and CO2-loaded aqueous 1-MPZ were correlated using the Grunberg–Nissan and modified Weiland models, respectively. Surface tension results of fresh and CO2-loaded 1-MPZ were fitted by a polynomial function. These new data and models are helpful for the design of postcombustion CO2 capture using 1-MPZ-based solvents and their blends

A Comprehensive Transcriptomic Analysis of Arsenic-Induced Bladder Carcinogenesis

Arsenic (sodium arsenite: NaAsO2) is a potent carcinogen and a known risk factor for the onset of bladder carcinogenesis. The molecular mechanisms that govern arsenic-induced bladder carcinogenesis remain unclear. We used a physiological concentration of NaAsO2 (250 nM: 33 µg/L) for the malignant transformation of normal bladder epithelial cells (TRT-HU1), exposed for over 12 months. The increased proliferation and colony-forming abilities of arsenic-exposed cells were seen after arsenic exposure from 4 months onwards. Differential gene expression (DEG) analysis revealed that a total of 1558 and 1943 (padj < 0.05) genes were deregulated in 6-month and 12-month arsenic-exposed TRT-HU1 cells. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that cell proliferation and survival pathways, such as the MAPK, PI3K/AKT, and Hippo signaling pathways, were significantly altered. Pathway analysis revealed that the enrichment of stem cell activators such as ALDH1A1, HNF1b, MAL, NR1H4, and CDH1 (p < 0.001) was significantly induced during the transformation compared to respective vehicle controls. Further, these results were validated by qPCR analysis, which corroborated the transcriptomic analysis. Overall, the results suggested that stem cell activators may play a significant role in facilitating the arsenic-exposed cells to gain a survival advantage, enabling the healthy epithelial cells to reprogram into a cancer stem cell phenotype, leading to malignant transformation