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Identification of nanomolar adenosine A2A receptor ligands using reinforcement learning and structure-based drug design
Abstract Generative chemical language models (CLMs) have demonstrated success in learning language-based molecular representations for de novo drug design. Here, we integrate structure-based drug design (SBDD) principles with CLMs to go from protein structure to novel small-molecule ligands, without a priori knowledge of ligand chemistry. Using Augmented Hill-Climb, we successfully optimise multiple objectives within a practical timeframe, including protein-ligand complementarity. Resulting de novo molecules contain known or promising adenosine A 2A receptor ligand chemistry that is not available in commercial vendor libraries, accessing commercially novel areas of chemical space. Experimental validation demonstrates a binding hit rate of 88%, with 50% having confirmed functional activity, including three nanomolar ligands and two novel chemotypes. The two strongest binders are co-crystallised with the A 2A receptor, revealing their binding mechanisms that can be used to inform future iterations of structure-based de novo design, closing the AI SBDD loop.
ADTnorm: robust integration of single-cell protein measurement across CITE-seq datasets
Application effect of supportive psychological nursing combined with continuous nursing in patients with thyroid malignancy undergoing surgery
Risk factors of sentinel lymph node metastasis in early-stage invasive breast cancer
Prediction of thermodynamic properties of aqueous carbohydrates solution using the PHSC and ANN models
Abstract In this work the Artificial Neural Network (ANN) and the Perturbed Hard Sphere Chain (PHSC) equation of state (EoS) have been utilized to estimate the osmotic coefficient, activity coefficient, and water activity of aqueous sugar solutions containing glucose, fructose, fucose, xylose, maltose, mannitol, mannose, sorbitol, xylitol, galactose, lactose, ribose, arabinose, and sucrose. The PHSC model parameters have been adjusted using the osmotic coefficient experimental data. Then, the water activity and sugar activity coefficient were predicted. In the case of the ANN approach, six variables containing critical temperature (Tc), critical volume (Vc), molality, temperature, melting temperature (Tm), and melting enthalpy (∆Hm) of sugars have been considered as input layer. As well, 32 neurons are considered in one hidden layer. The Group Contribution (GC) method was utilized to estimate the critical properties of sugars. The training correlating coefficient (R2), and the Mean Square Error (MSE) have been obtained 0.999 and 2.06 × 10–6, respectively. The average relative deviation (ARD) value of osmotic coefficient, water activity, and sugar activity coefficient using the PHSC EoS and the ANN + GC model have been obtained 0.43%, 0.12%, 0.66%, and 2.1%, 0.89%,1.65%, respectively. The model’s performance has been evaluated using the prediction of sugar solubilities in water. The results show that the ANN + GC and PHSC model can predict the solubility data satisfactory. The ANN + GC method can be used to predict the thermodynamic properties of a new aqueous sugar solution using the molecular structure in the absence of experimental data.
Ultrasound assessment of low type intersphincteric perianal fistulas in Yemen
Family support and its determinants among older patients with chronic diseases in Guangzhou communities: a mixed-methods study
Brain glutamate and gamma-aminobutyric acid levels across COVID-19 lockdowns in patients with recurrent major depressive disorder and healthy individuals
Abstract The coronavirus disease 2019 (COVID-19) led to substantial social restriction measures. Social isolation has been demonstrated to promote psychiatric symptoms and to dysregulate gamma-aminobutyric acid (GABA) and glutamate levels. The aim of this investigation was to observe brain GABA and glutamate concentrations and depressive symptom severity in association to lockdowns in patients with recurrent major depression disorder (rMDD) and healthy individuals (HI). In this longitudinal study, 18 patients with rMDD (11 female: 37.0 ± 10.0years) and 28 HI (16 female, 28.1 ± 5.0years) underwent three magnetic resonance spectroscopy imaging (MRSI) measurements over multiple lockdowns. Ratios of GABA+ (GABA + macromolecules) and glutamate + glutamine (Glx) to total creatinine (tCr) as well as GABA+/Glx ratios were calculated for subcortical regions and the insula. Depressive symptom severity and social support were assessed at each visit. Lockdowns did not significantly change neurotransmitter ratios in individual brain regions (all p corrected > 0.05). Further, no significant changes in Beck’s Depression Inventory II (BDI-II) scores occurred along the lockdowns (all p corrected > 0.05). Our results may be explained by ceiling effects of the beginning of the pandemic and the first lockdown, by good social support during the pandemic in HI and a small sample size. Patients with rMDD reported an insufficient social support, suggesting a special vulnerability to social isolation due to pandemics.
Spectral fingerprint of laser emission from rhodamine 6g infused male Indian Peafowl tail feathers
Synthesis, in vitro evaluation and computational modelling of benzene sulfonamide derivatives as Dickkopf 1 inhibitors for anticancer drug development
SARS-CoV-2 nucleocapsid protein directly prevents cGAS–DNA recognition through competitive binding
A hallmark of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is the delayed interferon response. Interferons are typically produced upon host recognition of pathogen- or damage-associated molecular patterns, such as nucleic acids. While the mechanisms by which SARS-CoV-2 evades host recognition of its RNA are well studied, how it evades immune responses to cytosolic DNA—leaked from mitochondria or nuclei during infection—remains poorly understood. Here, we demonstrate that the SARS-CoV-2 nucleocapsid protein directly suppresses DNA sensing by cyclic guanosine monophosphate–adenosine monophosphate synthase (cGAS). Although primarily known for packaging the viral RNA genome, we uncover that the SARS-CoV-2 nucleocapsid protein also binds DNA with high affinity and competitively blocks cGAS activation. Using cell-free biochemical and biophysical approaches, including single-molecule optical tweezers, we show that the nucleocapsid protein binds to DNA at nanomolar concentrations and cocondenses with DNA at micromolar concentrations, thereby impeding stable cGAS-DNA interactions required for signal propagation. Hyperphosphorylation of the nucleocapsid protein diminishes its competitive binding capacity. Our findings reveal an unexpected role of the SARS-CoV-2 nucleocapsid protein in directly suppressing the cGAS-STING pathway, strongly suggesting that this contributes to the delayed interferon response during infection. This study raises the possibility that nucleocapsid proteins of other RNA viruses may also exhibit moonlighting functions by antagonizing host nucleic acid–sensing pathways.
Bipartite reweight-annealing algorithm of quantum Monte Carlo to extract large-scale data of entanglement entropy and its derivative
A skin-interfaced three-dimensional closed-loop sensing and therapeutic electronic wound bandage
Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning
Synergistic Ru Species on Poly(heptazine imide) Enabling Efficient Photocatalytic CO <sub>2</sub> Reduction with H <sub>2</sub> O beyond 800 nm
Abstract Photocatalytic CO 2 conversion with H 2 O to carbonaceous fuels is a desirable strategy for CO 2 management and solar utilization, yet its efficiency remains suboptimal. Herein, efficient and durable CO 2 photoreduction is realized over a Ru NPs /Ru‐PHI catalyst assembled by anchoring Ru single atoms (SAs) and nanoparticles (NPs) onto poly(heptazine imide) (PHI) via the in‐plane Ru‐N 4 coordination and interfacial Ru‐N bonds, respectively. This catalyst shows an unsurpassed CO production (32.8 µmol h −1 ), a record‐high apparent quantum efficiency (0.26%) beyond 800 nm, and the formation of the valuable H 2 O 2 . Ru SAs tune PHI's electronic structure to promote in‐plane charge transfer to Ru NPs, forming a built‐in electron field at the interface, which directs electron‐hole separation and rushes excited electron movement from Ru‐PHI to Ru NPs. Simultaneously, Ru SAs introduce an impurity level in PHI to endow long‐wavelength photoabsorption, while Ru NPs strengthen CO 2 adsorption/activation and expedite CO desorption. These effects of Ru species together effectively ensure CO 2 ‐to‐CO conversion. The CO 2 reduction on the catalyst is revealed to follow the pathway CO 2 → *CO 2 → *COOH→ *CO→ CO, based on the intermediates identified by in situ diffuse reflectance infrared Fourier transform spectroscopy and further supported by density functional theory calculations.
Investigation of predictors of air pollution-reducing behaviors among taxi drivers in Tehran based on the health belief model in 2024
Development of a triangular Fermatean fuzzy EDAS model for remote patient monitoring applications
Sensitive RP-HPLC method with fluorimetric detection for concurrent quantification of emtricitabine, Daclatasvir and Ledipasvir in human urine
Abstract Co-infection with hepatitis C virus (HCV) in human immunodeficiency virus (HIV) patients is common and has a poor prognosis leading to many complications. The anti-HIV drug emtricitabine (FTC) is co-administered with two direct acting anti-HCV drugs; daclatasvir (DAC) and ledipasvir (LDV). The three drugs are simultaneously determined in human urine for the first time by a validated, simple and sensitive RP-HPLC with programmed fluorescence detection. The column used is Exsil 100 ODS C18 column (250 × 4.6 mm, 5 μm). The used mobile phase is acetonitrile: methanol: 0.01 M ammonium acetate buffer, pH 4.5 in ratio (20: 60: 20) in isocratic mode pumped at flow rate 1 mL/min. The proposed method is successfully validated according to FDA bioanalytical validation guidelines. The calibration curves are linear over the ranges (500-15000, 1–50 and 10–100 ng/mL) with average recoveries (97.9-99.54%, 98.78-104.17% and 98.49–100.96%) for FTC, DAC and LDV, respectively. The intraday and inter-day accuracy and precision results are within the acceptable limits. Stability assays reveal that the three studied drugs were stable during preparation, injection and storage. The method can be applied for the quantification of the three drugs co-administered to HIV/HCV co-infected patients’ urine which aids in therapeutic drug monitoring and dosage adjustment for chronic patients.