Observation of a Near-Threshold Structure in the K + Recoil-Mass Spectra in e + e − → K + ( D s − D * 0 + D s * − D 0 )
Not Available
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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Evidence for a Neutral Near-Threshold Structure in the K S 0 Recoil-Mass Spectra in e + e − → K S 0 D s + D * − and e + e − → K S 0 D s * + D −
Not Available
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Attention-Based Fusion of Infrared and Mass Spectra for Identification of Chemical Threats BY Functional Group Prediction
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Jacobson, Philip Long [Sandia National Laboratorie↗
Peak2Patch: High-Fidelity Functional Group Identification Through Attention-based Fusion of Infrared and Mass Spectra
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Jacobson, Philip Long [Sandia National Laboratorie↗
Eliminating Artifacts in Electrospray Deconvolution with a SoftMax Function
UniDec provides a rapid and robust approach to deconvolving electrospray mass spectra into their corresponding mass and charge components. However, the UniDec algorithm can produce artifacts depending on the quality and complexity of the data. Here, a SoftMax function is applied to the charge state distribution of each data point, which pushes the algorithm towards assigning each data point to one primary charge state. As shown for several data sets of increasing complexity, the SoftMax function significantly reduces deconvolution artifacts, even for data with overlapping charge states. .
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
MolDiscovery: learning mass spectrometry fragmentation of small molecules
Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Development of an inexpensive matrix-assisted laser desorption—time of flight mass spectrometry method for the identification of endophytes and rhizobacteria cultured from the microbiome associated with maize
Many endophytes and rhizobacteria associated with plants support the growth and health of their hosts. The vast majority of these potentially beneficial bacteria have yet to be characterized, in part because of the cost of identifying bacterial isolates. Matrix-assisted laser desorption-time of flight (MALDI-TOF) has enabled culturomic studies of host-associated microbiomes but analysis of mass spectra generated from plant-associated bacteria requires optimization. In this study, we aligned mass spectra generated from endophytes and rhizobacteria isolated from heritage and sweet varieties of Zea mays. Multiple iterations of alignment attempts identified a set of parameters that sorted 114 isolates into 60 coherent MALDI-TOF taxonomic units (MTUs). These MTUs corresponded to strains with practically identical (>99%) 16S rRNA gene sequences. Mass spectra were used to train a machine learning algorithm that classified 100% of the isolates into 60 MTUs. These MTUs provided >70% coverage of aerobic, heterotrophic bacteria readily cultured with nutrient rich media from the maize microbiome and allowed prediction of the total diversity recoverable with that particular cultivation method. Acidovorax sp., Pseudomonas sp. and Cellulosimicrobium sp. dominated the library generated from the rhizoplane. Relative to the sweet variety, the heritage variety contained a high number of MTUs. The ability to detect these differences in libraries, suggests a rapid and inexpensive method of describing the diversity of bacteria cultured from the endosphere and rhizosphere of maize.
dereplication↗
Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection
Generating top-down tandem mass spectra (MS/MS) for complex mixtures of proteoforms has become possible through improvements in fractionation, on-line separation, dissociation, and mass analysis. The algorithms to match tandem mass spectra to sequences have undergone a parallel evolution, with both spectral alignment and peak matching being paired with diverse methods for scoring proteoform-spectral matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification through three distinct challenges. The first is identifying a large yield of PrSMs while controlling false discovery rate (FDR) in identifying thousands of proteoforms from complex cell lysates via four software workflows: ProSight Proteome Discoverer, TopPIC, Informed Proteomics, and pTop. The second is the deconvolution of data from both Thermo Orbitrap-class and Bruker maXis Q-TOF instruments to produce consistent precursor charge and mass determinations while generating fragment mass lists to optimize identification. The third attempts to detect diverse post-translational modifications (PTMs) in proteoforms from cow milk and human ovarian tissue. The data demonstrate that existing software suites produce admirable sensitivity, in some cases identifying a third of collected tandem mass spectra with FDR controlled below 2%; the overlap in these PrSMs, however, illustrates real value in searching data with multiple search engines. Differences among identification workflows seem to result from each search algorithm incorporating its own deconvolution algorithm. By transmitting deconvolution data from multiple deconvolution routes (Thermo Xtract, Bruker Auto MSn, Mascot Distiller, TopFD, and FLASHDeconv) to the downstream TopPIC search algorithm, we were able to detect common causes of deconvolution disagreement. The detection of PTMs was very inconsistent among search algorithms, with some workflows suggesting as little as 1% of PrSMs from cow’s milk were singly-phosphorylated while other workflows found that 18% of PrSMs were singly-phosphorylated. Taken together, these results make a strong argument for top-down researchers to adopt a standard practice of analyzing each MS/MS experiment with at least two different search engines.
59 BASIC BIOLOGICAL SCIENCES↗
Measurement report: The Fifth International Workshop on Ice Nucleation phase 1 (FIN-01): intercomparison of single-particle mass spectrometers
Knowledge of the chemical composition and mixing state of aerosols at a single-particle level is critical for gaining insights into atmospheric processes. One common tool to make these measurements is single-particle mass spectrometry. There remains a need to compare the performance of different single-particle mass spectrometers (SPMSs). An intercomparison of SPMSs was conducted at the Aerosol Interaction and Dynamics in the Atmosphere (AIDA) chamber at the Karlsruhe Institute of Technology (KIT) in November 2014, as part of the first phase of the Fifth International Workshop on Ice Nucleation (FIN-01). In this paper we compare size distributions and mass spectra of atmospherically relevant particle types measured by five SPMSs. These include different minerals, desert and soil dusts, soot, bioaerosol (Snomax; protein granule), secondary organic aerosol (SOA), and SOA-coated mineral particles. Most SPMSs reported similar vacuum aerodynamic diameter (d va ) within typical instrumental ranges from ~100–200 nm (lower limit) to ~2–3 µm (upper limit). In general, all SPMSs exhibited a wide dynamic range (up to ~10 3 ) and high signal-to-noise ratio (up to ~10 4 ) in mass spectra. Common spectral features with small diversities in mass spectra were found with high average Pearson's correlation coefficients, i.e., for average positive spectra r avg-pos =0.74 ± 0.12 and average negative spectra r avg-neg =0.67 ± 0.22. We found that instrument-specific detection efficiency (DE) was more dependent on particle size than particle type, and particle identification favored the use of bipolar, rather than monopolar, instruments. Particle classification from “blind experiments” showed that all instruments differentiated SOA, soot, and soil dust and detected subtle changes in the particle internal mixing but had difficulties differentiating among specific mineral types and dusts. This study helps to further understand the capabilities and limitations of the single-particle mass spectrometry technique in general and the specific performance of the instrument in characterizing atmospheric aerosol particles.
54 ENVIRONMENTAL SCIENCES↗
Matrix-Assisted Ionization of Molecular Uranium Species
Matrix-assisted ionization (MAI) demonstrates high sensitivity for a variety of organic compounds; however, few studies have reported the application of MAI for the detection and characterization of inorganic analytes. Trace-level uranium analysis is important in the realms of nuclear forensics, nuclear safeguards, and environmental monitoring. Traditional mass spectrometry methods employed in these fields require combinations of extensive laboratory chemistry sample preparation and destructive ionization methods. There has been recent interest in exploring ambient mass spectrometry methods that enable timely sample analysis and higher sensitivity than what is attainable by field-portable radiation detectors. Rapid characterization of uranium at nanogram levels is demonstrated in this study using MAI techniques. Mass spectra were collected on an atmospheric pressure mass spectrometer for solutions of uranyl nitrate, uranyl chloride, uranyl acetate, and uranyl oxalate utilizing 3-nibrobenzonitrile as the ionization matrix. The uranyl complexes investigated were detectable, and the chemical speciation was preserved. Sample analysis was accomplished in a matter of seconds, and limits of detection of 5 ng of uranyl nitrate, 10 ng of uranyl oxalate, 100 ng of uranyl chloride, and 200 ng of uranyl acetate were achieved. The observed gas-phase speciation was similar to negative-ion electrospray ionization of uranyl compounds with notable differences. Six matrix-derived ions were detected in all negative-ion mass spectra, and some of these ions formed adducts with the uranyl analyte. Subsequent analysis of the matrix suggests that these molecules are not matrix contaminants and are instead created during the ionization process.
38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
Effect of Basicity and Structure on the Hydration of Protonated Molecules, Proton-Bound Dimer and Cluster Formation: An Ion Mobility-Time of Flight Mass Spectrometry and Theoretical Study
Protonation, hydration, and cluster formation of ammonia, formaldehyde, formic acid, acetone, butanone, 2-ocatanone, 2-nonanone, acetophenone, ethanol, pyridine, and its derivatives were studied by IMS-TOFMS technique equipped with a corona discharge ion source. It was found that tendency of the protonated molecules, MH{sup +}, to participate in hydration or cluster formation depends on the basicity of M. The molecules with higher basicity were hydrated less than those with lower basicity. The mass spectra of the low basic molecules such as formaldehyde exhibited larger clusters of M{sub n}H{sup +}(H{sub 2}O){sub n}, while for compounds with high basicity such as pyridine, only MH{sup +} and MH{sup +}M peaks were observed. The results of DFT calculations show that enthalpies of hydrations and cluster formation decrease as basicities of the molecules increases. Using comparison of mass spectra of formic acid, formaldehyde, and ethanol, effect of structure on the cluster formation was also investigated. Formation of symmetric (MH{sup +}M) and asymmetric proton-bound dimers (MH{sup +}N) was studied by ion mobility and mass spectrometry techniques. Both theoretical and experimental results show that asymmetric dimers are formed more easily between molecules (M and N) with comparable basicity. As the basicity difference between M and N increases, the enthalpy of MH{sup +}N formation decreases. .
74 ATOMIC AND MOLECULAR PHYSICS↗
Electron-Impact-Induced Fragmentation of a Glutamine Molecule
The formation of ionized products upon single and dissociative ionization of a glutamine (C{sub 5}H{sub 10}N{sub 2}O{sub 3}) molecule by electron impact at low (70 eV) and high (11.5 MeV) energies has been studied by method of mass spectrometry. The mass spectra of glutamine molecules have been obtained upon electron irradiation to various doses (0, 5, 10, and 20 kGy) and for various threshold functions of ion fragment yield. The absolute values of ion fragment appearance energies are determined. Electron beams were generated by a three-electrode gun and electron accelerator (microtron). Comparative analysis of the measured mass spectra of unirradiated and irradiated glutamine molecules showed that high-energy irradiation produced irreversible changes in the molecular structure.
74 ATOMIC AND MOLECULAR PHYSICS↗
Towards increasing the performance of FTICR-MS with signal detection at frequency multiples: Signal theory and numerical study
Not Available
Wu, Qinghao (ORCID:000000031665567X)↗
HypoRiPPAtlas as an Atlas of hypothetical natural products for mass spectrometry database search
Abstract Recent analyses of public microbial genomes have found over a million biosynthetic gene clusters, the natural products of the majority of which remain unknown. Additionally, GNPS harbors billions of mass spectra of natural products without known structures and biosynthetic genes. We bridge the gap between large-scale genome mining and mass spectral datasets for natural product discovery by developing HypoRiPPAtlas, an Atlas of hypothetical natural product structures, which is ready-to-use for in silico database search of tandem mass spectra. HypoRiPPAtlas is constructed by mining genomes using seq2ripp, a machine-learning tool for the prediction of ribosomally synthesized and post-translationally modified peptides (RiPPs). In HypoRiPPAtlas, we identify RiPPs in microbes and plants. HypoRiPPAtlas could be extended to other natural product classes in the future by implementing corresponding biosynthetic logic. This study paves the way for large-scale explorations of biosynthetic pathways and chemical structures of microbial and plant RiPP classes.
60 APPLIED LIFE SCIENCES↗
UV–Vis Photodissociation Action Spectroscopy on Thermo LTQ-XL ETD and Bruker amaZon Ion Trap Mass Spectrometers: a Practical Guide
We report automated procedures for multiple tandem mass spectra acquisition allowing UV–Vis photodissociation action spectroscopy measurements of ions and radicals. The procedures were developed for two commercial ion trap mass spectrometers and applied to collision-induced and electron–transfer dissociation tandem mass spectrometry modes of ion generation. .
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
ChemEcho v1.0
ChemEcho is a tool that converts tandem mass spectra into embeddings used to build machine learning (ML) models with fully explainable predictions. It provides an API for transforming raw tandem mass spectral data into embeddings, along with functions for training and validating ML models. Additionally, it includes utilities for retrieving and cleaning training data. ChemEcho is broadly applicable in ML pipelines that use tandem mass spectra for a variety of tasks, such as chemical classification or bioactivity mining. While there are existing methods to generate embeddings from fragmentation data, ChemEcho's approach ensures that predictions remain interpretable, enabling experts to evaluate results and generate hypotheses about the underlying data.
Harwood, Thomas [Lawrence Berkeley National Labora↗
A robust clustering algorithm for analysis of composition-dependent organic aerosol thermal desorption measurements
Abstract. One of the challenges of understanding atmospheric organic aerosol (OA) particles stems from its complex composition. Mass spectrometry is commonly used to characterize the compositional variability of OA. Clustering of a mass spectral dataset helps identify components that exhibit similar behavior or have similar properties, facilitating understanding of sources and processes that govern compositional variability. Here, we developed an algorithm for clustering mass spectra, the noise-sorted scanning clustering (NSSC), appropriate for application to thermal desorption measurements of collected OA particles from the Filter Inlet for Gases and AEROsols coupled to a chemical ionization mass spectrometer (FIGAERO-CIMS). NSSC, which extends the common density-based special clustering of applications with noise (DBSCAN) algorithm, provides a robust, reproducible analysis of the FIGAERO temperature-dependent mass spectral data. The NSSC allows for the determination of thermal profiles for compositionally distinct clusters of mass spectra, increasing the accessibility and enhancing the interpretation of FIGAERO data. Applications of NSSC to several laboratory biogenic secondary organic aerosol (BSOA) systems demonstrate the ability of NSSC to distinguish different types of thermal behaviors for the components comprising the particles along with the relative mass contributions and chemical properties (e.g., average molecular formula) of each mass spectral cluster. For each of the systems examined, more than 80 % of the total mass is clustered into 9–13 mass spectral clusters. Comparison of the average thermograms of the mass spectral clusters between systems indicates some commonality in terms of the thermal properties of different BSOA, although with some system-specific behavior. Application of NSSC to sets of experiments in which one experimental parameter, such as the concentration of NO, is varied demonstrates the potential for mass spectral clustering to elucidate the chemical factors that drive changes in the thermal properties of OA particles. Further quantitative interpretation of the thermograms of the mass spectral clusters will allow for a more comprehensive understanding of the thermochemical properties of OA particles.
54 ENVIRONMENTAL SCIENCES↗
Array-Based Machine Learning for Functional Group Detection in Electron Ionization Mass Spectrometry
Mass spectrometry is a ubiquitous technique capable of complex chemical analysis. The fragmentation patterns that appear in mass spectrometry are an excellent target for artificial intelligence methods to automate and expedite the analysis of data to identify targets such as functional groups. To develop this approach, we trained models on electron ionization (a reproducible hard fragmentation technique) mass spectra so that not only the final model accuracies but also the reasoning behind model assignments could be evaluated. The convolutional neural network (CNN) models were trained on 2D images of the spectra using transfer learning of Inception V3, and the logistic regression models were trained using array-based data and Scikit Learn implementation in Python. Our training dataset consisted of 21,166 mass spectra from the United States’ National Institute of Standards and Technology (NIST) Webbook. The data was used to train models to identify functional groups, both specific (e.g., amines, esters) and generalized classifications (aromatics, oxygen-containing functional groups, and nitrogen-containing functional groups). We found that the highest final accuracies on identifying new data were observed using logistic regression rather than transfer learning on CNN models. It was also determined that the mass range most beneficial for functional group analysis is 0–100 m/z. We also found success in correctly identifying functional groups of example molecules selected from both the NIST database and experimental data. Beyond functional group analysis, we also have developed a methodology to identify impactful fragments for the accurate detection of the models’ targets. The results demonstrate a potential pathway for analyzing and screening substantial amounts of mass spectral data.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗