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Designing solvent systems is the key to achieving the facile synthesis and separation of desired products from chemical processes. In this regard, many machine-learning models have been developed to predict the solubilities of given solute-solvent pairs. However, breakthroughs in developing predictive models for solubility are needed, which can be accomplished through a remarkable expansion and integration of experimental and computational solubility databases. To maximize predictive accuracy, these two databases should not be separately trained when developing ML models. In addition, they should not be simply combined without reconciling the discrepancies between different magnitudes of errors and uncertainties. Here, we introduce self-evolving solubility databases and graph neural networks developed through semi-supervised self-training approaches. Solubilities from quantum-mechanical calculations are referred to during semi-supervised learning, but they are not directly added to the database. Such methodologies enable the augmentation of databases while correcting the discrepancy between experiments and computation and improving the predictive accuracy against experimental solubilities. The resulting model was successfully applied to two practical examples relevant to solvent selection in organic reactions and separation processes: (i) linear relationship between reaction rates and solvation free energy for three organic reactions, and (ii) partition coefficients for lignin-derived monomers and drug-like molecules.

AQME, Automated Quantum Mechanical Environments, is a free and open-source Python package for the rapid deployment of automated workflows using cheminformatics and quantum chemistry. AQME workflows integrate tasks performed across multiple computational chemistry packages and data formats, preserving all computational protocols, data, and metadata for machine and human users to access and reuse. AQME has a modular structure of independent modules that can be implemented in any sequence, allowing the users to use only the desired parts of the program. The code is intended for researchers with basic familiarity with the Python programming language. The CSEARCH module interfaces to molecular mechanics and semi-empirical QM (SQM) conformer generation tools (e.g., RDKit and Conformer–Rotamer Ensemble Sampling Tool, CREST) starting from various initial structure formats. The CMIN module enables geometry refinement with SQM and neural network potentials, such ANI-1. The QPREP module interfaces with multiple QM programs, such as Gaussian, ORCA, and PySCF. The QCORR module processes QM results, storing structural, energetic, and property data while also enabling automated error handling (i.e., convergence errors, wrong number of imaginary frequencies, isomerization, etc.) and job resubmission. The QDESCP module provides easy access to QM ensemble-averaged molecular descriptors and computed properties, such as NMR spectra. Overall, AQME provides automated, transparent, and reproducible workflows to produce, analyze and archive computational chemistry results. SMILES inputs can be used, and many aspects of tedious human manipulation can be avoided. Installation and execution on Windows, macOS, and Linux platforms has been tested, and the code has been developed to support access through Jupyter Notebooks, the command line, and job submission (e.g., Slurm) scripts. Examples of pre-configured workflows are available in various formats, and hands-on video tutorials illustrate their use.

Objective To analyse the transmission dynamics of the monkeypox outbreak in the UK, declared a Public Health Emergency of International Concern in July 2022. Design Contact tracing study, linking data on case-contact pairs and on probable exposure dates. Setting Case questionnaires from the UK Health Security Agency (UKHSA), United Kingdom. Participants 2746 people with polymerase chain reaction confirmed monkeypox virus in the UK between 6 May and 1 August 2022. Main outcome measures The incubation period and serial interval of a monkeypox infection using two bayesian time delay models—one corrected for interval censoring (ICC—interval censoring corrected) and one corrected for interval censoring, right truncation, and epidemic phase bias (ICRTC—interval censoring right truncation corrected). Growth rates of cases by reporting date, when monkeypox virus was confirmed and reported to UKHSA, were estimated using generalised additive models. Results The mean age of participants was 37.8 years and 95% reported being gay, bisexual, and other men who have sex with men (1160 out of 1213 reporting). The mean incubation period was estimated to be 7.6 days (95% credible interval 6.5 to 9.9) using the ICC model and 7.8 days (6.6 to 9.2) using the ICRTC model. The estimated mean serial interval was 8.0 days (95% credible interval 6.5 to 9.8) using the ICC model and 9.5 days (7.4 to 12.3) using the ICRTC model. Although the mean serial interval was longer than the incubation period for both models, short serial intervals were more common than short incubation periods, with the 25th centile and the median of the serial interval shorter than the incubation period. For the ICC and ICRTC models, the corresponding estimates ranged from 1.8 days (95% credible interval 1.5 to 1.8) to 1.6 days (1.4 to 1.6) shorter at the 25th centile and 1.6 days (1.5 to 1.7) to 0.8 days (0.3 to 1.2) shorter at the median. 10 out of 13 linked patients had documented pre-symptomatic transmission. Doubling times of cases declined from 9.07 days (95% confidence interval 12.63 to 7.08) on the 6 May, when the first case of monkeypox was reported in the UK, to a halving time of 29 days (95% confidence interval 38.02 to 23.44) on 1 August. Conclusions Analysis of the instantaneous growth rate of monkeypox incidence indicates that the epidemic peaked in the UK as of 9 July and then started to decline. Short serial intervals were more common than short incubation periods suggesting considerable pre-symptomatic transmission, which was validated through linked patient level records. For patients who could be linked through personally identifiable data, four days was the maximum time that transmission was detected before symptoms manifested. An isolation period of 16 to 23 days would be required to detect 95% of people with a potential infection. The 95th centile of the serial interval was between 23 and 41 days, suggesting long infectious periods.

Rate coefficients for the reaction of NO 3 radicals with a series of aromatic aldehydes were measured in a 7300 L simulation chamber at ambient temperature and pressure by relative and absolute methods. The rate coefficients for benzaldehyde (BA), ortho-tolualdehyde (O-TA), meta-tolualdehyde (M-TA), para-tolualdehyde (P-TA), 2,4-dimethyl benzaldehyde (2,4-DMBA), 2,5-dimethyl benzaldehyde (2,5-DMBA) and 3,5-dimethyl benzaldehyde (3,5-DMBA) were k1= 2.6 ± 0.3, k2= 8.7 ± 0.8, k3= 4.9 ± 0.5, k4= 4.9 ± 0.4, k5= 15.1 ± 1.3, k6= 12.8 ± 1.2 and k7= 6.2 ± 0.6, respectively, in the units of 10 −15 cm 3 molec. −1 s −1 at 298 ± 2 K. The rate coefficient k13 for the reaction of the NO 3 radical with deuterated benzaldehyde (benzaldehyde-d 1) was found to be half that of k1 . The end product of the reaction in an excess of NO 2 was measured to be C 6 H 5 C(O)O 2 NO 2 . Theoretical calculations of aldehydic bond energies and reaction pathways indicate that the NO 3 radical reacts primarily with aromatic aldehydes through the abstraction of an aldehydic hydrogen atom. The atmospheric implications of the measured rate coefficients are briefly discussed.

The relationship between community prevalence of Plasmodium falciparum and the burden of severe, life-threatening disease remains poorly defined. To examine the three most common severe malaria phenotypes from catchment populations across East Africa, we assembled a dataset of 6506 hospital admissions for malaria in children aged 3 months to 9 years from 2006 to 2020. Admissions were paired with data from community parasite infection surveys. A Bayesian procedure was used to calibrate uncertainties in exposure (parasite prevalence) and outcomes (severe malaria phenotypes). Each 25% increase in prevalence conferred a doubling of severe malaria admission rates. Severe malaria remains a burden predominantly among young children (3 to 59 months) across a wide range of community prevalence typical of East Africa. This study offers a quantitative framework for linking malaria parasite prevalence and severe disease outcomes in children.

[3+2] cycloadditions of nitroolefins have emerged as a selective and catalyst-free alternative for the synthesis of 1,2,3-triazoles from azides. We describe mechanistic studies into the cycloaddition/rearomatization reaction sequence. DFT calculations revealed a rate-limiting cycloaddition step proceeding via an asynchronous TS with high kinetic selectivity for the 1,5-triazole. Kinetic studies reveal a second-order rate law, and 13C kinetic isotopic effects at natural abundance were measured with a significant normal effect at the conjugated olefinic centers of 1.0158 and 1.0216 at the α and β-carbons of β-nitrostyrene. Distortion/interaction-activation strain and energy decomposition analyses revealed that the major regioisomeric pathway benefits from an earlier and less-distorted TS, while intermolecular interaction terms dominate the preference for 1,5- over 1,4-cycloadducts. In addition, the major regioisomer also has more favorable electrostatic and dispersion terms. Additionally, while static DFT calculations suggest a concerted but highly asynchronous Ei-type HNO2 elimination mechanism, quasiclassical direct-dynamics calculations reveal the existence of a dynamic intermediate., R.S.P. acknowledges support from the NSF (CHE-1955876) and computational resources from the RMACC Summit supercomputer supported by the National Science Foundation (ACI-1532235 and ACI-1532236), the University of Colorado Boulder and Colorado State University, and the Extreme Science and Engineering Discovery Environment (XSEDE) through allocation TG-CHE180056. J.V.A.-R. acknowledges support from XSEDE through allocation TG-CHE200033. J.V.A.R. acknowledges financial support through a Juan de la Cierva Incorporación contract from the Ministry of Science and Innovation (MCIN) and the State Research Agency (AEI) of Spain, and the European Union (NextGenerationEU/PRTR) under grant reference IJC2020-044217-I. M.A.B.F., C.F.T., and M.Y.K. are grateful to CNPq (405052/2021-9) and FAPESP (17/01754-0, 20/10246-0, and 20/01255-6) for financial support. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001.

Pyridine halogenation reactions are crucial for obtaining the vast array of derivatives required for drug and agrochemical development. Yet, despite more than a century of synthetic endeavors, there are no broadly applicable 3-selective halogenation processes that function directly from pyridine C–H precursors. We have developed a ring-opening, halogenation, ring-closing sequence that temporarily transforms pyridines into a reactive series of alkenes. These Zincke imine intermediates undergo highly regioselective halogenation reactions under mild conditions. Experimental and computational mechanistic studies indicate that the selectivity-determining step changes based on the halogen electrophile. Using this method, we formed a diverse set of 3-halopyridines and demonstrated late-stage halogenation of complex pharmaceuticals and agrochemicals.

Fluoroalkyl groups profoundly affect the physical properties of pharmaceuticals and influence almost all metrics associated with their pharmacokinetic and pharmacodynamic profile1–4. Drug candidates increasingly contain trifluoromethyl (CF3) and difluoromethyl (CF2H) groups, and the same trend in agrochemical development shows that the effect of fluoroalkylation translates across human, insect and plant life5,6. New fluoroalkylation reactions have undoubtedly stimulated this shift; however, methods that directly convert C–H bonds into C–CF2X groups (where X is F or H) in complex drug-like molecules are rare7–13. Pyridines are the most common aromatic heterocycles in pharmaceuticals14, but only one approach—via fluoroalkyl radicals—is viable for achieving pyridyl C–H fluoroalkylation in the elaborate structures encountered during drug development15–17. Here we develop a set of bench-stable fluoroalkylphosphines that directly convert the C–H bonds in pyridine building blocks, drug-like fragments and pharmaceuticals into fluoroalkyl derivatives. No preinstalled functional groups or directing groups are required. The reaction tolerates a variety of sterically and electronically distinct pyridines, and is exclusively selective for the 4-position in most cases. The reaction proceeds through initial formation of phosphonium salts followed by sp2–sp3 coupling of phosphorus ligands—an underdeveloped manifold for forming C–C bonds. Bench-stable fluoroalkylphosphine reagents are used to form C–CF2X bonds in a one-pot process, reliably fluoroalkylating pyridine building blocks and drug and agrochemical intermediates.

Silica-supported phosphoric acid and metal phosphate catalyzed 1,3-butadiene (BDE) production from 2,3-butanediol (2,3-BDO) was studied using experimental and computational techniques. The catalyst was initially tested in a continuous flow reactor using commercially available 2,3-BDO, leading to maximum BDE yields of 63 C%. Quantum chemical mechanistic studies revealed 1,2-epoxybutane is a kinetically viable and thermodynamically stable intermediate, supported by experimental demonstration that this epoxide can be converted to BDE under standard reaction conditions. Newly proposed E2 and SN2’ elementary steps were studied to rationalize the formation of BDE and all detected side-products. Additionally, using QM/MM (ONIOM) calculations, we modeled silica-supported phosphate catalysts to study the effect of the alkali metal center. Natural population analysis showed that phosphate oxygen atoms are more negatively charged in CsH2PO4/SiO2 than in H3PO4/SiO2. In combination with temperature-programmed desorption experiments using CO2, the results of this study suggest that the improved selectivity achieved when adding the metal center is related to an increase in the basicity of the catalyst.

Nuclear magnetic resonance (NMR) is one of the primary techniques used to elucidate the chemical structure, bonding, stereochemistry, and conformation of organic compounds. The distinct chemical shifts in an NMR spectrum depend upon each atom's local chemical environment and are influenced by both through-bond and through-space interactions with other atoms and functional groups. The in silico prediction of NMR chemical shifts using quantum mechanical (QM) calculations is now commonplace in aiding organic structural assignment since spectra can be computed for several candidate structures and then compared with experimental values to find the best possible match. However, the computational demands of calculating multiple structural- and stereo-isomers, each of which may typically exist as an ensemble of rapidly-interconverting conformations, are expensive. Additionally, the QM predictions themselves may lack sufficient accuracy to identify a correct structure. In this work, we address both of these shortcomings by developing a rapid machine learning (ML) protocol to predict 1H and 13C chemical shifts through an efficient graph neural network (GNN) using 3D structures as input. Transfer learning with experimental data is used to improve the final prediction accuracy of a model trained using QM calculations. When tested on the CHESHIRE dataset, the proposed model predicts observed 13C chemical shifts with comparable accuracy to the best-performing DFT functionals (1.5 ppm) in around 1/6000 of the CPU time. An automated prediction webserver and graphical interface are accessible online at http://nova.chem.colostate.edu/cascade/. We further demonstrate the model in three applications: first, we use the model to decide the correct organic structure from candidates through experimental spectra, including complex stereoisomers; second, we automatically detect and revise incorrect chemical shift assignments in a popular NMR database, the NMRShiftDB; and third, we use NMR chemical shifts as descriptors for determination of the sites of electrophilic aromatic substitution., From quantum chemical and experimental NMR data, a 3D graph neural network, CASCADE, has been developed to predict carbon and proton chemical shifts. Stereoisomers and conformers of organic molecules can be correctly distinguished.

Yield Sooting Index (YSI) measurements have shown that oxygenated aromatic compounds (OACs) tend to have lower YSI than aromatic hydrocarbon (AHC) compounds. For example, typical AHCs such as toluene and ethyl benzene have a YSI of 170 and 216, respectively, in contrast, OACs such as phenol and anisole have a YSI of 81 and 111, respectively. However, this trend is not always true as was observed for the structural isomers 1-phenylethanol (1PE, YSI=142) and 2-phenylethanol (2PE, YSI=207), where 2PE contains a YSI more representative of AHCs than OACs. We applied flow reactor experiments and density functional theory (DFT) calculations to examine how oxygen functionality present in 1PE and 2PE alters the reaction pathways leading to the observed difference in soot formation. It was determined that the proximity of the oxygen functional group to the aromatic ring determines whether the oxygen remains attached to the primary reacting species (for 1PE) or is eliminated early in the combustion sequence (for 2PE). For these alcohols, preservation of the oxygen in the molecule leads to further OACs, while loss of the oxygen leads to AHCs and benzyl radical. The direct pathways to AHCs and benzyl radical result in the higher YSI observed for 2PE.

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.1.529 (Omicron) variant caused international concern due to its rapid spread in Southern Africa. It was unknown whether this variant would replace or coexist with (either transiently or long term) the then-dominant Delta variant on its introduction to England. We developed a set of hierarchical logistic growth models to describe changes in the frequency of S gene target failure (SGTF) PCR tests, a proxy for Omicron. The doubling time of SGTF cases peaked at 1.56 days (95% CI: 1.49 to 1.63) on 5 December, whereas triple-positive cases were halving every 5.82 days (95% CI: 5.11 to 6.67) going into Christmas 2021. We were unable to characterize the replacement of Delta by Omicron with a single rate. The replacement rate decreased by 53.56% (95% CrI: 45.38 to 61.01) between 14 and 15 December, meaning the competitive advantage of Omicron approximately halved. Preceding the changepoint, Omicron was replacing Delta 16.24% (95% CrI: 9.72 to 23.41) faster in those with two or more vaccine doses, indicative of vaccine escape being a substantial component of competitive advantage. Despite the slowdown, Delta was almost entirely replaced in England within a month of the first sequenced domestic case. The synchrony of changepoints across regions at various stages of Omicron epidemics suggests that the growth rate advantage was not attenuated because of biological mechanisms related to strain competition. The step change in replacement could have resulted from behavioral changes, potentially elicited by public health messaging or policies, that differentially affected Omicron.

The understanding of biomolecular recognition of posttranslationally modified histone proteins is centrally important to the histone code hypothesis. Despite extensive binding and structural studies on the readout of histones, the molecular language by which posttranslational modifications on histone proteins are read remains poorly understood. Here we report physical-organic chemistry studies on the recognition of the positively charged trimethyllysine by the electron-rich aromatic cage containing PHD3 finger of KDM5A. The aromatic character of two tryptophan residues that solely constitute the aromatic cage of KDM5A was fine-tuned by the incorporation of fluorine substituents. Our thermodynamic analyses reveal that the wild-type and fluorinated KDM5A PHD3 fingers associate equally well with trimethyllysine. This work demonstrates that the biomolecular recognition of trimethyllysine by fluorinated aromatic cages is associated with weaker cation–π interactions that are compensated by the energetically more favourable trimethyllysine-mediated release of high-energy water molecules that occupy the aromatic cage. Lysine trimethylation is a key post-translational modification, which some epigenetic reader proteins detect through binding to aromatic cages involving cation-π interactions. Here systematic modification of the aromatic cage reveals that weaker cation-π interactions do not correlate with weaker binding owing to a compensating release of bound water molecules.

The [2+2] photocycloaddition is the most valuable and intensively investigated photochemical process. Here we demonstrate that irradiation of N‐acryloyl heterocycles with blue LED light (440 nm) in the presence of an IrIII complex leads to efficient and high yielding fused γ‐lactam formation across a range of substituted heterocycles. Quantum calculations show that the reaction proceeds via cyclization in the triplet excited state to yield a 1,4‐diradical; intersystem crossing leads preferentially to the closed shell singlet zwitterion. This is geometrically restricted from undergoing recombination to yield a cyclobutane by the planarity of the amide substituent. A prototropic shift leads to the observed bicyclic products in what can be viewed as an interrupted [2+2] cycloaddition., Irradiation of N‐acryloyl heterocycles with blue LED light and an IrIII complex leads to fused γ‐lactam formation. The reaction proceeds via cyclization in the triplet excited state to yield a 1,4‐diradical; intersystem crossing leads to a singlet zwitterion that is geometrically restricted from yielding a cyclobutane. Proton transfer leads to the products in an interrupted [2+2] cycloaddition.

1. Density dependence is the effect of density on population growth. Density dependence is an aggregate term for a suite of complex interactions between animals and their environment. 2. Mechanistic studies of density dependence in mosquito ecology are sparse, and the role of environmental factors is poorly understood. 3. Two empirical study designs were compared to consider the interaction between nutritional availability and density in Aedes aegypti. First, larvae were fed per capita. Second, larvae were fed a fixed amount of food unadjusted for the number of individuals; therefore, at higher densities, individuals received less per capita. 4. Survivorship, wing length, and development rate were lower at high densities when larvae were fed a fixed, unadjusted amount of food. The opposite was observed when food was adjusted per capita, suggesting that high densities may be beneficial for larval development when per capita nutrition is held constant 5. These results demonstrate that negative associations between Ae. aegypti larval density and larval development are a manifestation of decreased per capita nutrient uptake at high densities. 6. Population regulation is a proportional response to environmental variability in Ae. aegypti. Increased survivorship at high densities when larvae were fed per capita demonstrates that nutritional availability is not the only mechanism of density dependence in mosquitoes. Further studies should characterise density dependence in mosquitoes by using mechanistic study designs across diverse environmental conditions.

Halopyridines are key building blocks for synthesizing pharmaceuticals, agrochemicals, and ligands for metal complexes, but strategies to selectively halogenate pyridine C–H precursors are lacking. We designed a set of heterocyclic phosphines that are installed at the 4-position of pyridines as phosphonium salts and then displaced with halide nucleophiles. A broad range of unactivated pyridines can be halogenated, and the method is viable for late-stage halogenation of complex pharmaceuticals. Computational studies indicate that C–halogen bond formation occurs via an S(N)Ar pathway, and phosphine elimination is the rate-determining step. Steric interactions during C–P bond cleavage account for differences in reactivity between 2- and 3-substituted pyridines.

Biaryl scaffolds are privileged templates used in the discovery and design of therapeutics with high affinity and specificity for a broad range of protein targets. Biaryls are found in the structures of therapeutics, including antibiotics, anti-inflammatory, analgesic, neurological and antihypertensive drugs. However, existing synthetic routes to biphenyls rely on traditional coupling approaches that require both arenes to be prefunctionalized with halides or pseudohalides with the desired regiochemistry. Therefore, the coupling of drug fragments may be challenging via conventional approaches. As an attractive alternative, directed C-H activation has the potential to be a versatile tool to form para-substituted biphenyl motifs selectively. However, existing C-H arylation protocols are not suitable for drug entities as they are hindered by catalyst deactivation by polar and delicate functionalities present alongside the instability of macrocyclic intermediates required for para-C-H activation. To address this challenge, we have developed a robust catalytic system that displays unique efficacy towards para-arylation of highly functionalized substrates such as drug entities, giving access to structurally diversified biaryl scaffolds. This diversification process provides access to an expanded chemical space for further exploration in drug discovery. Further, the applicability of the transformation is realized through the synthesis of drug molecules bearing a biphenyl fragment. Computational and experimental mechanistic studies further provide insight into the catalytic cycle operative in this versatile C-H arylation protocol.

Prior studies have demonstrated that immunologic dysfunction underpins severe illness in COVID-19 patients, but have lacked an in-depth analysis of the immunologic drivers of death in the most critically ill patients. We performed immunophenotyping of viral antigen-specific and unconventional T cell responses, neutralizing antibodies, and serum proteins in critically ill patients with SARS-CoV-2 infection, using influenza infection, SARS-CoV-2-convalescent health care workers, and healthy adults as controls. We identify mucosal-associated invariant T (MAIT) cell activation as an independent and significant predictor of death in COVID-19 (HR = 5.92, 95% CI = 2.49–14.1). MAIT cell activation correlates with several other mortality-associated immunologic measures including broad activation of CD8+ T cells and non-Vδ2 γδT cells, and elevated levels of cytokines and chemokines, including GM-CSF, CXCL10, CCL2, and IL-6. MAIT cell activation is also a predictor of disease severity in influenza (ECMO/death HR = 4.43, 95% CI = 1.08–18.2). Single-cell RNA-sequencing reveals a shift from focused IFNα-driven signals in COVID-19 ICU patients who survive to broad pro-inflammatory responses in fatal COVID-19 –a feature not observed in severe influenza. We conclude that fatal COVID-19 infection is driven by uncoordinated inflammatory responses that drive a hierarchy of T cell activation, elements of which can serve as prognostic indicators and potential targets for immune intervention., Author summary We examined the immune abnormalities linked to critical illness and death in COVID-19 patients on ICU, performing immunophenotyping of viral antigen-specific and unconventional T cell responses, together with studies of neutralizing antibodies, and serum proteins. We compared these findings to a parallel set of patients with severe influenza. From this screen we identified mucosal-associated invariant T (MAIT) cell activation as an independent and significant predictor of death in COVID-19. MAIT cell activation correlated with several other mortality-associated immunologic measures including elevated levels of cytokines and chemokines, such as GM-CSF and CXCL10. MAIT cell activation is also a predictor of disease severity in influenza. Single-cell RNA-sequencing revealed a shift from focused IFNα-driven signals in COVID-19 ICU patients who survive to broad pro-inflammatory responses in fatal COVID-19 –a feature not observed in severe influenza. Overall we observed key potential biomarkers and targetable pathways in critical viral illness, many shared between influenza and COVID-19 and some unique to each infection.

The role of immune responses to previously seen endemic coronavirus epitopes in severe acute respiratory coronavirus 2 (SARS-CoV-2) infection and disease progression has not yet been determined. Here, we show that a key characteristic of fatal outcomes with coronavirus disease 2019 (COVID-19) is that the immune response to the SARS-CoV-2 spike protein is enriched for antibodies directed against epitopes shared with endemic beta-coronaviruses and has a lower proportion of antibodies targeting the more protective variable regions of the spike. The magnitude of antibody responses to the SARS-CoV-2 full-length spike protein, its domains and subunits, and the SARS-CoV-2 nucleocapsid also correlated strongly with responses to the endemic beta-coronavirus spike proteins in individuals admitted to an intensive care unit (ICU) with fatal COVID-19 outcomes, but not in individuals with nonfatal outcomes. This correlation was found to be due to the antibody response directed at the S2 subunit of the SARS-CoV-2 spike protein, which has the highest degree of conservation between the beta-coronavirus spike proteins. Intriguingly, antibody responses to the less cross-reactive SARS-CoV-2 nucleocapsid were not significantly different in individuals who were admitted to an ICU with fatal and nonfatal outcomes, suggesting an antibody profile in individuals with fatal outcomes consistent with an "original antigenic sin" type response. ispartof: JCI INSIGHT vol:7 issue:13 ispartof: location:United States status: published

Greater understanding of enzymatic mechanisms aids the discovery of new targets for biologics, the development of biocatalytic transformations, and de novo enzyme design. Methods using quantum mechanical (QM) potentials, such as Density Functional Theory (DFT), have enabled complex multistep enzymatic mechanisms to be studied, often in quantitative detail. Nevertheless, the dynamic interconversion of enzyme conformations between active and inactive catalytic forms, involving length- and timescales inaccessible to QM treatments, presents a formidable challenge for the development of computational models for allosterically modulated enzymes. We present an overview of the key concepts underlying multistate models of enzyme catalysis, enzyme allostery, and the challenge that large-scale conformational changes pose for methods using QM, QM/MM, and MM potentials. Structural clustering is highlighted as a valuable approach to bridge molecular dynamics conformational sampling of MM potentials and quantum chemical cluster models of catalysis. Particularly relevant to this discussion is structural allostery, which serves as the exemplar of conformational consequences. Here, a well-characterized allosteric enzyme, Imidazole Glycerol Phosphate Synthase (IGPS), is used to showcase the importance of multiple conformations and guide a new direction for qualitative understanding and quantitative modeling in enzyme catalysis.

CREBBP (CBP/KAT3A) and its paralogue EP300 (KAT3B) are lysine acetyltransferases (KATs) that are essential for human development. They each comprise 10 domains through which they interact with >400 proteins, making them important transcriptional co-activators and key nodes in the human protein-protein interactome. The bromodomains of CREBBP and EP300 enable the binding of acetylated lysine residues from histones and a number of other important proteins, including p53, p73, E2F, and GATA1. Here, we report a work to develop a high-affinity, small-molecule ligand for the CREBBP and EP300 bromodomains [(-)-OXFBD05] that shows >100-fold selectivity over a representative member of the BET bromodomains, BRD4(1). Cellular studies using this ligand demonstrate that the inhibition of the CREBBP/EP300 bromodomain in HCT116 colon cancer cells results in lowered levels of c-Myc and a reduction in H3K18 and H3K27 acetylation. In hypoxia (

Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity and selectivity. However, BDE computations at sufficiently high levels of quantum mechanical theory require substantial computing resources. In this paper, we develop a machine learning model capable of accurately predicting BDEs for organic molecules in a fraction of a second. We perform automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory for 42,577 small organic molecules, resulting in 290,664 BDEs. A graph neural network trained on a subset of these results achieves a mean absolute error of 0.58 kcal mol

The development of new catalytic enantioselective methods is routinely carried out using easily accessible and prototypical substrates. This approach to reaction development often yields asymmetric methods that perform poorly using substrates sterically or electronically dissimilar to those used during the reaction optimization campaign. Consequently, expanding the scope of previously optimized catalytic asymmetric reactions to include new substrates is decidedly non-trivial. Here we address this challenge through the development of a systematic workflow to broaden the applicability and reliability of asymmetric conjugate additions to substrates conventionally regarded as sterically and electronically challenging. The copper-catalyzed asymmetric conjugate addition of alkylzirconium nucleophiles to form tertiary centers, although successful for linear alkyl chains, fails for more sterically demanding linear α,β-unsaturated ketones. Key to adapting this method to obtain high enantioselectivity was the discovery of new phosphoramidite ligands, designed using quantitative structure-selectivity relationships (QSSR). Iterative rounds of model construction and ligand synthesis were executed in parallel to evaluate the performance of twenty chiral ligands. The copper-catalyzed asymmetric addition is now more broadly applicable, even tolerating linear enones bearing tert-butyl β-substituents. The presence of common functional groups is tolerated in both nucleophiles and electrophiles, giving up to 99% yield and 95% ee across twenty examples.

[Image: see text] Despite numerous advances in spectroscopic methods through the latter part of the 20th century, the unequivocal structure determination of natural products can remain challenging, and inevitably, incorrect structures appear in the literature. Computational methods that allow the accurate prediction of NMR chemical shifts have emerged as a powerful addition to the toolbox of methods available for the structure determination of small organic molecules. Herein, we report the structure determination of a small, stereochemically rich natural product from Laurencia majuscula using the powerful combination of computational methods and total synthesis, along with the structure confirmation of notoryne, using the same approach. Additionally, we synthesized three further diastereomers of the L. majuscula enyne and have demonstrated that computations are able to distinguish each of the four synthetic diastereomers from the 32 possible diastereomers of the natural product. Key to the success of this work is to analyze the computational data to provide the greatest distinction between each diastereomer, by identifying chemical shifts that are most sensitive to changes in relative stereochemistry. The success of the computational methods in the structure determination of stereochemically rich, flexible organic molecules will allow all involved in structure determination to use these methods with confidence.