Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval). METHODS AND FINDINGS: The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths. CONCLUSIONS: COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year.

Recognizing and validating ligands with CheckMyBlob.

Structure-guided drug design depends on the correct identification of ligands in crystal structures of protein complexes. However, the interpretation of the electron density maps is challenging and often burdened with confirmation bias. Ligand identification can be aided by automatic methods such as CheckMyBlob, a machine learning algorithm that learns to generalize ligand descriptions from sets of moieties deposited in the Protein Data Bank. Here, we present the CheckMyBlob web server, a platform that can identify ligands in unmodeled fragments of electron density maps or validate ligands in existing models. The server processes PDB/mmCIF and MTZ files and returns a ranking of 10 most likely ligands for each detected electron density blob along with interactive 3D visualizations. Additionally, for each prediction/validation, a plugin script is generated that enables users to conduct a detailed analysis of the server results in Coot. The CheckMyBlob web server is available at https://checkmyblob.bioreproducibility.org.

Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support.

This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; (ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis; (iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC; (iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences.

Modeling of Future COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Rates and Nonpharmaceutical Intervention Scenarios - United States, April-September 2021.

After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.

Detecting anomalies in X-ray diffraction images using convolutional neural networks.

Our understanding of life is based upon the interpretation of macromolecular structures and their dynamics. Almost 90% of currently known macromolecular models originated from electron density maps constructed using X-ray diffraction images. Even though diffraction images are critical for structure determination, due to their vast amounts and noisy, non-intuitive nature, their quality is rarely inspected. In this paper, we use recent advances in machine learning to automatically detect seven types of anomalies in X-ray diffraction images. For this purpose, we utilize a novel X-ray beam center detection algorithm, propose three different image representations, and compare the predictive performance of general-purpose classifiers and deep convolutional neural networks (CNNs). In benchmark tests on a set of 6,311 X-ray diffraction images, the proposed CNN achieved between 87% and 99% accuracy depending on the type of anomaly. Experimental results show that the proposed anomaly detection system can be considered suitable for early detection of sub-optimal data collection conditions and malfunctions at X-ray experimental stations.

Automatic recognition of ligands in electron density by machine learning.

Motivation: The correct identification of ligands in crystal structures of protein complexes is the cornerstone of structure-guided drug design. However, cognitive bias can sometimes mislead investigators into modeling fictitious compounds without solid support from the electron density maps. Ligand identification can be aided by automatic methods, but existing approaches are based on time-consuming iterative fitting. Results: Here we report a new machine learning algorithm called CheckMyBlob that identifies ligands from experimental electron density maps. In benchmark tests on portfolios of up to 219 931 ligand binding sites containing the 200 most popular ligands found in the Protein Data Bank, CheckMyBlob markedly outperforms the existing automatic methods for ligand identification, in some cases doubling the recognition rates, while requiring significantly less time. Our work shows that machine learning can improve the automation of structure modeling and significantly accelerate the drug screening process of macromolecule-ligand complexes. Availability and implementation: Code and data are available on GitHub at https://github.com/dabrze/CheckMyBlob. Supplementary information: Supplementary data are available at Bioinformatics online.

A Gcn5-Related N-Acetyltransferase (GNAT) Capable of Acetylating Polymyxin B and Colistin Antibiotics in Vitro.

Deeper exploration of uncharacterized Gcn5-related N-acetyltransferases has the potential to expand our knowledge of the types of molecules that can be acylated by this important superfamily of enzymes and may offer new opportunities for biotechnological applications. While determining native or biologically relevant in vivo functions of uncharacterized proteins is ideal, their alternative or promiscuous in vitro capabilities provide insight into key active site interactions. Additionally, this knowledge can be exploited to selectively modify complex molecules and reduce byproducts when synthetic routes become challenging. During our exploration of uncharacterized Gcn5-related N-acetyltransferases from Pseudomonas aeruginosa, we identified such an example. We found that the PA3944 enzyme acetylates both polymyxin B and colistin on a single diaminobutyric acid residue closest to the macrocyclic ring of the antimicrobial peptide and determined the PA3944 crystal structure. This finding is important for several reasons. (1) To the best of our knowledge, this is the first report of enzymatic acylation of polymyxins and thus reveals a new type of substrate that this enzyme family can use. (2) The enzymatic acetylation offers a controlled method for antibiotic modification compared to classical promiscuous chemical methods. (3) The site of acetylation would reduce the overall positive charge of the molecule, which is important for reducing nephrotoxic effects and may be a salvage strategy for this important class of antibiotics. While the physiological substrate for this enzyme remains unknown, our structural and functional characterization of PA3944 offers insight into its unique noncanonical substrate specificity.

Crystal structure of thebaine 6-O-demethylase from the morphine biosynthesis pathway.

Thebaine 6-O-demethylase (T6ODM) from Papaver somniferum (opium poppy), which belongs to the non-heme 2-oxoglutarate/Fe(II)-dependent dioxygenases (ODD) family, is a key enzyme in the morphine biosynthesis pathway. Initially, T6ODM was characterized as an enzyme catalyzing O-demethylation of thebaine to neopinone and oripavine to morphinone. However, the substrate range of T6ODM was recently expanded to a number of various benzylisoquinoline alkaloids. Here, we present crystal structures of T6ODM in complexes with 2-oxoglutarate (T6ODM:2OG, PDB: 5O9W) and succinate (T6ODM:SIN, PDB: 5O7Y). Both metal and 2OG binding sites display similarity to other proteins from the ODD family, but T6ODM is characterized by an exceptionally large substrate binding cavity, whose volume can partially explain the promiscuity of this enzyme. Moreover, the size of the cavity allows for binding of multiple molecules at once, posing a question about the substrate-driven specificity of the enzyme.

Refining the macromolecular model - achieving the best agreement with the data from X-ray diffraction experiment.

Refinement of macromolecular X-ray crystal structures involves using complex software with hundreds of different settings. The complexity of underlying concepts and the sheer amount sof instructions may make it difficult for less experienced crystallographers to achieve optimal results in their refinements. This tutorial review offers guidelines for choosing the best settings for the reciprocal-space refinement of macromolecular models and provides practical tips for manual model correction. To help aspiring crystallographers navigate the process, some of the most practically important concepts of protein structure refinement are described. Among the topics covered are the use and purpose of R-free, geometrical restraints, restraints on atomic displacement parameters (ADPs), refinement weights, various parametrizations of ADPs (full anisotropic refinement and TLS), and omit maps. We also give practical tips for manual model correction in Coot, modelling of side-chains with poor or missing density, and ligand identification, fitting, and refinement.

Serum albumins-unusual allergens.

BACKGROUND: Albumins are multifunctional proteins present in the blood serum of animals. They can bind and transport a wide variety of ligands which they accommodate due to their conformational flexibility. Serum albumins are highly conserved both in amino acid sequence and three-dimensional structure. Several mammalian and avian serum albumins (SAs) are also allergens. Sensitization to one of the SAs coupled with the high degree of conservation between SAs may result in cross-reactive antibodies in allergic individuals. Sensitivity to SA generally begins with exposure to an aeroallergen, which can then lead to cross-sensitization to serum albumins present in food. SCOPE OF REVIEW: This review focuses on the allergenicity of SAs presented in a structural context. MAJOR CONCLUSIONS: SA allergenicity is unusual taking into account the high sequence identity and similarity between SA from different species and human serum albumin. Cross-reactivity of human antibodies towards different SAs is one of the most important characteristics of these allergens. GENERAL SIGNIFICANCE: Establishing a relationship between sequence and structure of different SAs and their interactions with antibodies is crucial for understanding the mechanisms of cross-sensitization of atopic individuals. Structural information can also lead to better design and production of recombinant SAs to replace natural proteins in allergy testing and desensitization. Therefore, structural analyses are important for diagnostic and treatment purposes. This article is part of a Special Issue entitled Serum Albumin.

Data-driven mechanistic framework with stratified immunity and effective transmissibility for COVID-19 scenario projections.

Scenario-based modeling frameworks have been widely used to support policy-making at state and federal levels in the United States during the COVID-19 response. While custom-built models can be used to support one-off studies, sustained updates to projections under changing pandemic conditions requires a robust, integrated, and adaptive framework. In this paper, we describe one such framework, UVA-adaptive, that was built to support the CDC-aligned Scenario Modeling Hub (SMH) across multiple rounds, as well as weekly/biweekly projections to Virginia Department of Health (VDH) and US Department of Defense during the COVID-19 response. Building upon an existing metapopulation framework, PatchSim, UVA-adaptive uses a calibration mechanism relying on adjustable effective transmissibility as a basis for scenario definition while also incorporating real-time datasets on case incidence, seroprevalence, variant characteristics, and vaccine uptake. Through the pandemic, our framework evolved by incorporating available data sources and was extended to capture complexities of multiple strains and heterogeneous immunity of the population. Here we present the version of the model that was used for the recent projections for SMH and VDH, describe the calibration and projection framework, and demonstrate that the calibrated transmissibility correlates with the evolution of the pathogen as well as associated societal dynamics.

Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread.

We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of "what-if" scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity. As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10K CPU cores. Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4K to 28.2K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4% improvement) and total averted deaths could be increased from 28.2K to 29.9K (a 6% improvement) nationwide.

Molecular determinants for antibody binding on group 1 house dust mite allergens.

House dust mites produce potent allergens, Der p 1 and Der f 1, that cause allergic sensitization and asthma. Der p 1 and Der f 1 are cysteine proteases that elicit IgE responses in 80% of mite-allergic subjects and have proinflammatory properties. Their antigenic structure is unknown. Here, we present crystal structures of natural Der p 1 and Der f 1 in complex with a monoclonal antibody, 4C1, which binds to a unique cross-reactive epitope on both allergens associated with IgE recognition. The 4C1 epitope is formed by almost identical amino acid sequences and contact residues. Mutations of the contact residues abrogate mAb 4C1 binding and reduce IgE antibody binding. These surface-exposed residues are molecular targets that can be exploited for development of recombinant allergen vaccines.

Alternaria alternata allergen Alt a 1: a unique ß-barrel protein dimer found exclusively in fungi.

BACKGROUND: Alternaria species is one of the most common molds associated with allergic diseases, and 80% of Alternaria species-sensitive patients produce IgE antibodies to a major protein allergen, Alt a 1. The structure and function of Alt a 1 is unknown. OBJECTIVE: We sought to obtain a high-resolution structure of Alt a 1 using x-ray crystallography and to investigate structural relationships between Alt a 1 and other allergens and proteins reported in the Protein Data Bank. METHODS: X-ray crystallography was used to determine the structure of Alt a 1 by using a custom-designed set of crystallization conditions. An initial Alt a 1 model was determined by the application of a Ta(6)Br(12)(2+) cluster and single-wavelength anomalous diffraction. Bioinformatic analyses were used to compare the Alt a 1 sequence and structure with that of other proteins. RESULTS: Alt a 1 is a unique ß-barrel comprising 11 ß-strands and forms a "butterfly-like" dimer linked by a single disulfide bond with a large (1345 Å(2)) dimer interface. Intramolecular disulfide bonds are conserved among Alt a 1 homologs. Currently, the Alt a 1 structure has no equivalent in the Protein Data Bank. Bioinformatics analyses suggest that the structure is found exclusively in fungi. Four previously reported putative IgE-binding peptides have been located on the Alt a 1 structure. CONCLUSIONS: Alt a 1 has a unique, dimeric ß-barrel structure that appears to define a new protein family with unknown function found exclusively in fungi. The location of IgE antibody-binding epitopes is in agreement with the structural analysis of Alt a 1. The Alt a 1 structure will allow mechanistic structure/function studies and immunologic studies directed toward new forms of immunotherapy for Alternaria species-sensitive allergic patients.

Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: A multi-model study.

Background: The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5-11 years on COVID-19 burden and resilience against variant strains. Methods: Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5-11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. Findings: Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5-11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. Interpretation: Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5-11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Funding: Various (see acknowledgments).

Potential impact of annual vaccination with reformulated COVID-19 vaccines: lessons from the U.S. COVID-19 Scenario Modeling Hub.

Importance: COVID-19 continues to cause significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Objective: To project COVID-19 hospitalizations and deaths from April 2023-April 2025 under two plausible assumptions about immune escape (20% per year and 50% per year) and three possible CDC recommendations for the use of annually reformulated vaccines (no vaccine recommendation, vaccination for those aged 65+, vaccination for all eligible groups). Design: The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023-April 15, 2025 under six scenarios representing the intersection of considered levels of immune escape and vaccination. State and national projections from eight modeling teams were ensembled to produce projections for each scenario. Setting: The entire United States. Participants: None. Exposure: Annually reformulated vaccines assumed to be 65% effective against strains circulating on June 15 of each year and to become available on September 1. Age and state specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. Main outcomes and measures: Ensemble estimates of weekly and cumulative COVID-19 hospitalizations and deaths. Expected relative and absolute reductions in hospitalizations and deaths due to vaccination over the projection period. Results: From April 15, 2023-April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November-January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% PI: 1,438,000-4,270,000) hospitalizations and 209,000 (90% PI: 139,000-461,000) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% CI: 104,000-355,000) fewer hospitalizations and 33,000 (95% CI: 12,000-54,000) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000-598,000) fewer hospitalizations and 49,000 (95% CI: 29,000-69,000) fewer deaths. Conclusion and Relevance: COVID-19 is projected to be a significant public health threat over the coming two years. Broad vaccination has the potential to substantially reduce the burden of this disease.

Crystal structures of putative phosphoglycerate kinases from B. anthracis and C. jejuni.

Phosphoglycerate kinase (PGK) is indispensable during glycolysis for anaerobic glucose degradation and energy generation. Here we present comprehensive structure analysis of two putative PGKs from Bacillus anthracis str. Sterne and Campylobacter jejuni in the context of their structural homologs. They are the first PGKs from pathogenic bacteria reported in the Protein Data Bank. The crystal structure of PGK from Bacillus anthracis str. Sterne (BaPGK) has been determined at 1.68 Å while the structure of PGK from Campylobacter jejuni (CjPGK) has been determined at 2.14 Å resolution. The proteins' monomers are composed of two domains, each containing a Rossmann fold, hinged together by a helix which can be used to adjust the relative position between two domains. It is also shown that apo-forms of both BaPGK and CjPGK adopt open conformations as compared to the substrate and ATP bound forms of PGK from other species.

Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination.

In Spring 2021, the highly transmissible SARS-CoV-2 Delta variant began to cause increases in cases, hospitalizations, and deaths in parts of the United States. At the time, with slowed vaccination uptake, this novel variant was expected to increase the risk of pandemic resurgence in the US in summer and fall 2021. As part of the COVID-19 Scenario Modeling Hub, an ensemble of nine mechanistic models produced 6-month scenario projections for July-December 2021 for the United States. These projections estimated substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant, projected to occur across most of the US, coinciding with school and business reopening. The scenarios revealed that reaching higher vaccine coverage in July-December 2021 reduced the size and duration of the projected resurgence substantially, with the expected impacts was largely concentrated in a subset of states with lower vaccination coverage. Despite accurate projection of COVID-19 surges occurring and timing, the magnitude was substantially underestimated 2021 by the models compared with the of the reported cases, hospitalizations, and deaths occurring during July-December, highlighting the continued challenges to predict the evolving COVID-19 pandemic. Vaccination uptake remains critical to limiting transmission and disease, particularly in states with lower vaccination coverage. Higher vaccination goals at the onset of the surge of the new variant were estimated to avert over 1.5 million cases and 21,000 deaths, although may have had even greater impacts, considering the underestimated resurgence magnitude from the model.

Impact of SARS-CoV-2 vaccination of children ages 5-11 years on COVID-19 disease burden and resilience to new variants in the United States, November 2021-March 2022: a multi-model study.

Background: SARS-CoV-2 vaccination of persons aged 12 years and older has reduced disease burden in the United States. The COVID-19 Scenario Modeling Hub convened multiple modeling teams in September 2021 to project the impact of expanding vaccine administration to children 5-11 years old on anticipated COVID-19 burden and resilience against variant strains. Methods: Nine modeling teams contributed state- and national-level projections for weekly counts of cases, hospitalizations, and deaths in the United States for the period September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of: 1) presence vs. absence of vaccination of children ages 5-11 years starting on November 1, 2021; and 2) continued dominance of the Delta variant vs. emergence of a hypothetical more transmissible variant on November 15, 2021. Individual team projections were combined using linear pooling. The effect of childhood vaccination on overall and age-specific outcomes was estimated by meta-analysis approaches. Findings: Absent a new variant, COVID-19 cases, hospitalizations, and deaths among all ages were projected to decrease nationally through mid-March 2022. Under a set of specific assumptions, models projected that vaccination of children 5-11 years old was associated with reductions in all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880-0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834-0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797-1.020) compared with scenarios where children were not vaccinated. This projected effect of vaccinating children 5-11 years old increased in the presence of a more transmissible variant, assuming no change in vaccine effectiveness by variant. Larger relative reductions in cumulative cases, hospitalizations, and deaths were observed for children than for the entire U.S. population. Substantial state-level variation was projected in epidemic trajectories, vaccine benefits, and variant impacts. Conclusions: Results from this multi-model aggregation study suggest that, under a specific set of scenario assumptions, expanding vaccination to children 5-11 years old would provide measurable direct benefits to this age group and indirect benefits to the all-age U.S. population, including resilience to more transmissible variants.

A study on the structure, mechanism, and biochemistry of kanamycin B dioxygenase (KanJ)-an enzyme with a broad range of substrates.

Kanamycin A is an aminoglycoside antibiotic isolated from Streptomyces kanamyceticus and used against a wide spectrum of bacteria, including Mycobacterium tuberculosis. Biosynthesis of kanamycin involves an oxidative deamination step catalyzed by kanamycin B dioxygenase (KanJ), thereby the C2' position of kanamycin B is transformed into a keto group upon release of ammonia. Here, we present for the first time, structural models of KanJ with several ligands, which along with the results of ITC binding assays and HPLC activity tests explain substrate specificity of the enzyme. The large size of the binding pocket suggests that KanJ can accept a broad range of substrates, which was confirmed by activity tests. Specificity of the enzyme with respect to its substrate is determined by the hydrogen bond interactions between the methylamino group of the antibiotic and highly conserved Asp134 and Cys150 as well as between hydroxyl groups of the substrate and Asn120 and Gln80. Upon antibiotic binding, the C terminus loop is significantly rearranged and Gln80 and Asn120, which are directly involved in substrate recognition, change their conformations. Based on reaction energy profiles obtained by density functional theory (DFT) simulations, we propose a mechanism of ketone formation involving the reactive FeIV  = O and proceeding either via OH rebound, which yields a hemiaminal intermediate or by abstraction of two hydrogen atoms, which leads to an imine species. At acidic pH, the latter involves a lower barrier than the OH rebound, whereas at basic pH, the barrier leading to an imine vanishes completely. DATABASES: Structural data are available in PDB database under the accession numbers: 6S0R, 6S0T, 6S0U, 6S0W, 6S0V, 6S0S. Diffraction images are available at the Integrated Resource for Reproducibility in Macromolecular Crystallography at http://proteindiffraction.org under DOIs: 10.18430/m36s0t, 10.18430/m36s0u, 10.18430/m36s0r, 10.18430/m36s0s, 10.18430/m36s0v, 10.18430/m36s0w. A data set collection of computational results is available in the Mendeley Data database under DOI: 10.17632/sbyzssjmp3.1 and in the ioChem-BD database under DOI: 10.19061/iochem-bd-4-18.