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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Flying Blind, or Just Flying Under the Radar? The Underappreciated Power of De Novo Methods of Mass Spectrometric Peptide Identification

Mass spectrometry-based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo peptide identification infers peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph-based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs.

proteomics, mass spectrometry, forensics, de novo ↗
DOE OSTI2023

OFraMP: a fragment-based tool to facilitate the parametrization of large molecules

Abstract An Online tool for Fragment-based Molecule Parametrization (OFraMP) is described. OFraMP is a web application for assigning atomic interaction parameters to large molecules by matching sub-fragments within the target molecule to equivalent sub-fragments within the Automated Topology Builder (ATB, atb.uq.edu.au) database. OFraMP identifies and compares alternative molecular fragments from the ATB database, which contains over 890,000 pre-parameterized molecules, using a novel hierarchical matching procedure. Atoms are considered within the context of an extended local environment (buffer region) with the degree of similarity between an atom in the target molecule and that in the proposed match controlled by varying the size of the buffer region. Adjacent matching atoms are combined into progressively larger matched sub-structures. The user then selects the most appropriate match. OFraMP also allows users to manually alter interaction parameters and automates the submission of missing substructures to the ATB in order to generate parameters for atoms in environments not represented in the existing database. The utility of OFraMP is illustrated using the anti-cancer agent paclitaxel and a dendrimer used in organic semiconductor devices. Graphical abstract OFraMP applied to paclitaxel (ATB ID 35922).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

An evaluation of methodology to determine algal genome completeness

The advancement of sequencing technologies has resulted in a rapid expansion of genome sequencing. One challenge in processing and analyzing this abundance of genomics data is development and application of tools to accurately assess the quality of novel genomes. The quality of a given genome assembly is commonly assessed using the presence of conserved orthologs. However, the applicability and accuracy of identifying conserved orthologs applied to algal genome assemblies, which are uniquely diverse, is unclear. Here, in this study, we analyze the utility of a genome content assessment tool, Benchmarking Universal Single-Copy Orthologs (BUSCO), to evaluate algal genome assembly completeness. We find that Chlorophyta and Stramenopile BUSCO databases are effective tools to analyze genome completeness of these lineages. For other lineages, the Eukaryota database should be utilized until additional lineage-specific databases are developed. Based on these results, suggested best practices for current usage and necessary future improvements are provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Equilibrium carbon isotope fractionation factors of hydrocarbons: Semi-empirical force-field method

Here, we have calculated the reduced partition function ratios for carbon isotopes (β-factor) of 67 hydrocarbons (alkanes, alkenes, alkynes, cycloalkanes, and aromatics), including their 267 single-substituted isotopomers. The calculations were performed using the harmonic oscillator – rigid rotator model and the Urey / Bigeleisen-Mayer approach. Normal frequencies of molecular vibrations of the isotopologues were calculated from the molecular structures data and valence field force constants stored in the Light-handled Elucidation of Vibrations (LEV) database elaborated by Dr. Gribov and his colleagues in Russia. The LEV database is constructed from experimental spectroscopic and structural data by solving inverse problems of molecular vibrations. The LEV is internally consistent and the most comprehensive database available to date. The β-factor were calculated in the temperature range of 200–800 K with a 10 K step. Our calculations predict that the β-factors increase with an increasing number of C atoms within the same groups of hydrocarbons (e.g., alkanes). Our calculations also show a general descending order of 13 C enrichments among the different groups of hydrocarbons: cycloalkanes, aromatics, alkenes (double bonds) and isoalkanes, alkanes, alkynes (triple bonds). Position-specific, intramolecular isotope effects within hydrocarbons are determined by the β-factors of C in the different functional groups in the order: quaternary (C), tertiary (methine, CH), secondary (methylene, CH 2 ), primary (methyl, CH 3 ) and double bond (C=C), saturated bond (C - C), triple bond (HC≡). Our calculations on bulk and position-specific carbon isotope β-factors of the hydrocarbons, which are generally consistent with very limited ab initio calculations in the literature, are internally consistent and the most comprehensive to date for future applications to position-specific isotope geochemistry of hydrocarbons.

58 GEOSCIENCES↗
DOE OSTI2025

CONFLUX: A standardized framework to calculate reactor antineutrino flux

Nuclear fission reactors are abundant sources of antineutrinos for neutrino physics experiments. The flux and spectrum of antineutrinos emitted by a reactor can indicate its activity and composition, suggesting potential applications of neutrino measurements beyond fundamental scientific studies that may be valuable to society. The utility of reactor antineutrinos for applications and fundamental science is dependent on the availability of precise predictions of these emissions. For example, in the last decade, disagreements between reactor antineutrino measurements and models have inspired revision of reactor antineutrino calculations and standard nuclear databases as well as searches for new fundamental particles not predicted by the Standard Model of particle physics. Past predictions and descriptions of the methods used to generate them are documented to varying degrees in the literature, with different modeling teams incorporating a range of methods, input data, and assumptions. The resulting difficulty in accessing or reproducing past models and reconciling results from differing approaches complicates the future study and application of reactor antineutrinos. The CONFLUX (Calculation Of Neutrino FLUX) software framework is a neutrino prediction tool built with the goal of simplifying, standardizing, and democratizing the process of reactor antineutrino flux calculations. CONFLUX includes three primary methods for calculating the antineutrino emissions of nuclear reactors or individual beta decays that incorporate common nuclear data and beta decay theory. The software is prepackaged with the current nuclear databases, including ENDF.B/VIII, JEFF-3.3, and ENSDF, and it includes the capability to predict time-dependent reactor emissions, adjust nuclear database or beta decay inputs/assumptions, and propagate related sources of uncertainty. Here, this paper describes the CONFLUX software structure, details the methods used for flux and spectrum calculations, and provides examples of potential use cases.

Zhang, Xianyi [Lawrence Livermore National Laborat↗
DOE OSTI2025

Mechanically induced thermal runaway severity analysis of Li-ion batteries and continuous energy release monitoring

The large-scale deployment of Li-ion batteries in stationary energy storage and electrical vehicle applications demands a strong focus on safety, particularly on the thermal runaway risk and severity evaluation. A standardized single-side mechanical indentation test protocol was developed to induce an internal short-circuit (ISC) and evaluate cells' thermal runaway severity at different state of charge (SOC). The observed hazard severity (OHS in five categories) and evaluated scores in this work have a comprehensive consideration of each cell's capacity, initial voltage, SOC, temperature and voltage change, allowing a better evaluation of the cells' thermal runaway potential. This method was applied to about 200 Li-ion batteries in order to build an extensive thermal runaway database covering various SOCs, capacities and chemistries. In this study, we monitored the transitions of stored electrochemical energy and applied mechanical energy into both thermal energy and acoustic emissions (AE). The surface temperature and mechanical failures were monitored by infrared imaging and AE to capture critical events within battery cells throughout the mechanical indentation tests. Furthermore, the initial temperature maps can predict two types of follow-up events: thermal runaway or gradual heat release via conduction. Analyzing each cell's severity, AEs, and leveraging the evolving database offer insights into predicting occurrences of thermal runaway. The test method, thermal runaway severity evaluation and prediction, and the corresponding database provide battery designers, manufacturers, and end-users a clear overview of Li-ion batteries' thermal runaway potential under mechanical abuse, advancing the safety design of Li-ion batteries.

Acoustic emission↗
DOE OSTI2020

Multi-device study of temporal characteristics of magnetohydrodynamic modes initiating disruptions

Disruptions in tokamaks are often preceded by magnetohydrodynamic (MHD) instabilities that can rotate or become locked to the wall. Measurements from magnetic diagnostics in the presence of MHD mode precursors to disruptions can yield potentially valuable input to the plasma control system, with a view to disruption avoidance, prediction and mitigation. This report presents an exploratory analysis of the growth of MHD modes and corresponding time scales on the basis of magnetic measurements in multiple tokamaks. To this end, a database was compiled using disruptive discharges from COMPASS, ASDEX Upgrade, DIII-D and JET, manually classified according to disruption root cause, and characterized by a great diversity of operational conditions and mode dynamics. The typical time during which a mode can be detected using saddle coils and the duration of the locked mode phase in the database both extend over several orders of magnitude, but generally the time scales increase with plasma size. Several additional factors are discussed that can influence these durations, including the disruption root cause. A scaling law for the locked phase duration was estimated, yielding predictions toward ITER of the order of hundreds of milliseconds or even seconds. In addition, a scaling law for the mode amplitude at the disruption onset, proposed earlier by de Vries et al. (2016), is applied to the database, and its predictive capabilities are assessed. Despite significant uncertainty on the predictions from both scaling laws, encouraging trends are observed of the fraction of disruptions that may be detected with sufficient warning time to allow mitigation or even avoidance, based solely on observations of MHD mode dynamics. When combined with similar analysis of measurements from diagnostics that are sensitive to other disruption precursors, our analysis methods and results may contribute to the reliability, robustness and generalization of disruption warning schemes for ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

A discrete integral transform for rapid spectral synthesis

Accurate synthetic spectra that rely on large Line-By-Line (LBL)-databases are used in a wide range of applications such as high temperature combustion, atmospheric re-entry, planetary surveillance and laboratory plasmas. Conventionally synthetic spectra are calculated by computing a lineshape for every spectral line in the database and adding those together, which may take multiple hours for large databases. In this paper we propose a new approach for spectral synthesis based on an integral transform: the synthetic spectrum is calculated as the integral over the product of a Voigt profile and a newly proposed three-dimensional “lineshape distribution function”, which is a function of spectral position and Gaussian- & Lorentzian width coordinates. A fast discrete version of this transform based on the Fast Fourier Transform (FFT) is proposed, which improves performance compared to the conventional approach by several orders of magnitude while maintaining accuracy. Strategies that minimize the discretization error are discussed. A Python implementation of the method is compared against state-of-the-art spectral code RADIS, and is since adopted as RADIS's default synthesis method. The synthesis of a benchmark CO2 spectrum consisting of 1.8 M spectral lines and 200k spectral points took only 3.1 s using the proposed method (1011 lines × spectral points/s), a factor ~300 improvement over the state-of-the-art, with the relative improvement generally increasing for higher number of lines and/or number of spectral points. Finally, an experimental GPU-implementation of the method was also benchmarked, which demonstrated another 2~3 orders performance increase, achieving up to 5 ∙ 10 14 lines × spectral points/s.

42 ENGINEERING↗
DOE OSTI2021

MiR-205–5p suppresses angiogenesis in gastric cancer by downregulating the expression of VEGFA and FGF1

Highlights: • MiR-205-5p was commonly downregulated in GC tissues from TCGA database and our hospital, and these events were associated with a worse TNM stagedistant metastasis and poor survival. • MiR-205-5p and CD31 expressions were negatively inter-related within GC tissues from TCGA database and our hospital. • GC cell lines expressed lower level of miR-205-5p significantly as compared with the normal gastric cell line. • MiR-205-5p upregulation significantly impaired proliferation and colony formation of GC cells and neovascularization induced by GC cells in vitro and in vivo. • The angiogenesis and ERK-signaling were depressed by VEGFA and FGF1 downregulation induced by miR-205-5p overexpression. The miR-205-5p inhibitor promoted malignant phenotypes by enhancing VEGFA and FGF1 expressions, as well as the activation of ERK-signaling. Anti-angiogenic therapy represents one of the most promising treatment modalities for human cancers. However, the response to antiangiogenic therapy in gastric cancer (GC) remains dismal. To help identify new strategies for antiangiogenic therapy in GC, we evaluated miR-205–5p expression in GC tissues from TCGA database and our hospital, and its functions in angiogenesis were explored in vitro and in vivo. We investigated miR-205–5p expression and microvessel densities (MVDs) in GC tissues and liver metastases from patients. The function and mechanisms of miR-205–5p were examined in human cell lines and in xenograft mouse models. Associations between miR-205–5p expression and clinical characteristics were analyzed using either Pearson's χ{sup 2} test or Fisher's exact test. Differences in overall survival (OS) distributions were evaluated using the log-rank test. Differences in measurement data were compared using Student's t-test and one-way ANOVA. We found that miR-205–5p expression was downregulated in GC tissues and was negatively correlated with CD31 expression in both TCGA and our clinical samples. GC cell lines expressed low levels of miR-205–5p, and miR-205–5p upregulation significantly impaired the proliferation and angiogenesis of GC cells. Moreover, vascular endothelial growth factor A (VEGFA) and fibroblast growth factor 1 (FGF1) expression and activation of extracellular-related kinase (ERK) signaling were suppressed by miR-205–5p. MiR-205–5p inhibition promoted malignant phenotypes by enhancing VEGFA and FGF1 expression, as well as the activation of ERK signaling. Angiogenesis and ERK signaling were decreased in response to VEGFA and FGF1 downregulation induced by miR-205–5p overexpression. The dual-luciferase reporter assay showed that VEGFA and FGF1 were direct targets of miR-205–5p. Xenograft mouse models revealed that miR-205–5p suppressed tumor growth by inhibiting neovascularization. Altogether, these results demonstrate that miR-205–5p suppresses angiogenesis in GC by attenuating the expression of VEGFA and FGF1, indicating that upregulation of miR-205–5p may represent as an antiangiogenic therapy for GC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Thermochemical Data Fusion Using Graph Representation Learning

Large databases are required for “Big Data” applications in catalysis and materials science. Thermochemical databases can be created by combining data from various sources and by correcting low-fidelity datasets to higher accuracy with minimal computation. To achieve this “data fusion”, thermochemical quantities of interest, calculated at various levels of density functional theory (DFT), need to be mapped to the same, high levels of theory. In this work, a graph theoretical, statistical framework is proposed for such tasks. Subgraph frequencies are shown to provide a natural representation for learning these fusion maps. The maps are linear and are learnt with automated descriptor selection. Using a dataset of as few as ~1% from the QM9 database of 133,885 molecules, these models can predict multiple thermochemical quantities at a higher level of theory with an accuracy of 1 kcal/mol. Here, the method is explainable, generalizable, and provides a diagnostic tool for outlier identification

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Development of Porous Crystalline Materials for Selective Binding of O 2 from Air

Porous materials that take advantage of different chemisorptive behaviors of O 2 and N 2 could be an effective alternative to approaches based on physisorption for this important separation. Recently, the tetra-nuclear cobalt complex [(Co(III) 2 (bpbp)O 2 ) 2 bdc](PF 6 ) 4 (CSD code: GAMVIB; bpbp – = 2,6-bis(N,N-bis(2-pyridylmethyl)aminomethyl)-4-tert-butylphenolato; bdc 2– = 1,4-benzenedicarboxylato) was shown to have potential for O 2 /N 2 separations based on this concept. This observation raises the question of what other known materials have similar properties. Here, we combine structure screening with a high-throughput periodic density functional theory (DFT) workflow to investigate O 2 and N 2 adsorption in materials from validated crystal structure databases (e.g., CoRE-MOF database and CSD database). These calculations identify multiple materials that have similar di-Co clusters to GAMVIB that are predicted to selectively bind O 2 over N 2 , and suggest design rules that can be used to tune the O 2 and N 2 affinities in materials of this kind.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Accurate Identification of Deamidation and Citrullination from Global Shotgun Proteomics Data Using a Dual-Search Delta Score Strategy

While proteins with deamidated/citrullinated amino acids play critical roles in the pathogenesis of many human diseases, identifying these modifications in complex biological samples has been an ongoing challenge. Herein we present a method to accurately identify these modifications from shotgun proteomics data from a deep proteome profiling study of human pancreatic islets obtained by laser capture microdissection. All MS/MS spectra were searched against database by MSGF+ twice with or without a +0.9840 Da mass shift on amino acids asparagine, glutamine, and arginine (NQR) as a dynamic modification. Consequently, for each spectrum the resulting two peptide-to-spectrum matches (PSM) with their respective MSGF+ scores were used for Delta Score calculation. It was observed that all PSMs with positive Delta Score values were clustered with mass errors around 0 ppm, while PSMs with negative Delta Score values were distributed nearly equally within the defined mass error range (20 ppm) for database searching. To estimate false discovery rate (FDR), a “pseudo-decoy” approach was applied whether datasets were searched against a database with a “real modification” mass shift (+0.9840 Da) and a “mock modification” mass shift (+1.0227 Da). FDR was controlled to ~2% with a Delta Score filter greater than zero. Manual inspection of spectra showed that PSMs with positive Delta Score value contained deamidated/citrullinated fragments in their MS/MS spectra. Finally, the results demonstrated that in-situ deamidated/citrullinated peptides can be accurately identified from shotgun tissue proteomics data by the Dual-search Delta Score Strategy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

On the Morphodynamics of a Wide Class of Large‐Scale Meandering Rivers: Insights Gained by Coupling LES With Sediment‐Dynamics

Abstract In meandering rivers, interactions between flow, sediment transport, and bed topography affect diverse processes, including bedform development and channel migration. Predicting how these interactions affect the spatial patterns and magnitudes of bed deformation in meandering rivers is essential for various river engineering and geoscience problems. Computational fluid dynamics simulations can predict river morphodynamics at fine temporal and spatial scales but have traditionally been challenged by the large scale of natural rivers. We conducted coupled large‐eddy simulation and bed morphodynamics simulations to create a unique database of hydro‐morphodynamic data sets for 42 meandering rivers with a variety of planform shapes and large‐scale geometrical features that mimic natural meanders. For each simulated river, the database includes (a) bed morphology, (b) three‐dimensional mean velocity field, and (c) bed shear stress distribution under bankfull flow conditions. The calculated morphodynamics results at dynamic equilibrium revealed the formation of scour and deposition patterns near the outer and inner banks, respectively, while the location of point bars and scour regions around the apexes of the meander bends is found to vary as a function of the radius of curvature of the bends to the width ratio. A new mechanism is proposed that explains this seemingly paradoxical finding. The high‐fidelity simulation results generated in this work provide researchers and scientists with a rich numerical database for morphodynamics and bed shear stress distributions in large‐scale meandering rivers to enable systematic investigation of the underlying phenomena and support a range of river engineering applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Forest expansion dominates China’s land carbon sink since 1980

Carbon budget accounting relies heavily on Food and Agriculture Organization land-use data reported by governments. Here we develop a new land-use and cover-change database for China, finding that differing historical survey methods biased China’s reported data causing large errors in Food and Agriculture Organization databases. Land ecosystem model simulations driven with the new data reveal a strong carbon sink of 8.9 ± 0.8 Pg carbon from 1980 to 2019 in China, which was not captured in Food and Agriculture Organization data-based estimations due to biased land-use and cover-change signals. The land-use and cover-change in China, characterized by a rapid forest expansion from 1980 to 2019, contributed to nearly 44% of the national terrestrial carbon sink. In contrast, climate changes (22.3%), increasing nitrogen deposition (12.9%), and rising carbon dioxide (8.1%) are less important contributors. This indicates that previous studies have greatly underestimated the impact of land-use and cover-change on the terrestrial carbon balance of China. This study underlines the importance of reliable land-use and cover-change databases in global carbon budget accounting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Identification of high-dielectric constant compounds from statistical design

Abstract The discovery of high-dielectric materials is crucial to increasing the efficiency of electronic devices and batteries. Here, we report three previously unexplored materials with very high dielectric constants (69 < ϵ < 101) and large band gaps (2.9 < E g (eV) < 5.5) obtained by screening materials databases using statistical optimization algorithms aided by artificial neural networks (ANN). Two of these new dielectrics are mixed-anion compounds (Eu 5 SiCl 6 O 4 and HoClO) and are shown to be thermodynamically stable against common semiconductors via phase diagram analysis. We also uncovered four other materials with relatively large dielectric constants (20 < ϵ < 40) and band gaps (2.3 < E g (eV) < 2.7). While the ANN training-data are obtained from the Materials Project, the search-space consists of materials from the Open Quantum Materials Database (OQMD)—demonstrating a successful implementation of cross-database materials design. Overall, we report the dielectric properties of 17 materials calculated using ab initio calculations, that were selected in our design workflow. The dielectric materials with high-dielectric properties predicted in this work open up further experimental research opportunities.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Machine learning-based discovery of vibrationally stable materials

The identification of the ground state phases of a chemical space in the convex hull analysis is a key determinant of the synthesizability of materials. Online material databases have been instrumental in exploring one aspect of the synthesizability of many materials, namely thermodynamic stability. However, the vibrational stability, which is another aspect of synthesizability, of new materials is not known. Applying first principles approaches to calculate the vibrational spectra of materials in online material databases is computationally intractable. Here, a dataset of vibrational stability for ~3100 materials is used to train a machine learning classifier that can accurately distinguish between vibrationally stable and unstable materials. This classifier has the potential to be further developed as an essential filtering tool for online material databases that can inform the material science community of the vibrational stability or instability of the materials queried in convex hulls.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Candidate ferroelectrics via ab initio high-throughput screening of polar materials

Ferroelectrics are a class of polar and switchable functional materials with diverse applications, from microelectronics to energy conversion. Computational searches for new ferroelectric materials have been constrained by accurate prediction of the polarization and switchability with electric field, properties that, in principle, require a comparison with a nonpolar phase whose atomic-scale unit cell is continuously deformable from the polar ground state. For most polar materials, such a higher-symmetry nonpolar phase does not exist or is unknown. Here, we introduce a general high-throughput workflow that screens polar materials as potential ferroelectrics. We demonstrate our workflow on 1978 polar structures in the Materials Project database, for which we automatically generate a nonpolar reference structure using pseudosymmetries, and then compute the polarization difference and energy barrier between polar and nonpolar phases, comparing the predicted values to known ferroelectrics. Focusing on a subset of 182 potential ferroelectrics, we implement a systematic ranking strategy that prioritizes candidates with large polarization and small polar-nonpolar energy differences. To assess stability and synthesizability, we combine information including the computed formation energy above the convex hull, the Inorganic Crystal Structure Database id number, a previously reported machine learning-based synthesizability score, and ab initio phonon band structures. To distinguish between previously reported ferroelectrics, materials known for alternative applications, and lesser-known materials, we combine this ranking with a survey of the existing literature on these candidates through Google Scholar and Scopus databases, revealing ~130 promising materials uninvestigated as ferroelectric. Our workflow and large-scale high-throughput screening lays the groundwork for the discovery of novel ferroelectrics, revealing numerous candidates materials for future experimental and theoretical endeavors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Birth of protein folds and functions in the virome

The rapid evolution of viruses generates proteins that are essential for infectivity and replication but with unknown functions, due to extreme sequence divergence. Here, using a database of 67,715 newly predicted protein structures from 4,463 eukaryotic viral species, we found that 62% of viral proteins are structurally distinct and lack homologues in the AlphaFold database. Among the remaining 38% of viral proteins, many have non-viral structural analogues that revealed surprising similarities between human pathogens and their eukaryotic hosts. Structural comparisons suggested putative functions for up to 25% of unannotated viral proteins, including those with roles in the evasion of innate immunity. In particular, RNA ligase T-like phosphodiesterases were found to resemble phage-encoded proteins that hydrolyse the host immune-activating cyclic dinucleotides 3',3'- and 2',3'-cyclic GMP-AMP (cGAMP). Experimental analysis showed that RNA ligase T homologues encoded by avian poxviruses similarly hydrolyse cGAMP, showing that RNA ligase T-mediated targeting of cGAMP is an evolutionarily conserved mechanism of immune evasion that is present in both bacteriophage and eukaryotic viruses. Together, the viral protein structural database and analyses presented here afford new opportunities to identify mechanisms of virus–host interactions that are common across the virome.

59 BASIC BIOLOGICAL SCIENCES↗