Intense Ion Research Articles

Advancing carbohydrate quantification in MALDI mass spectrometry by the rapidly freeze-drying droplet (RFDD) method.

Quantitative carbohydrate analysis faces challenges in matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), including insufficient sensitivity and inconsistent spatial distribution of ion intensity. This study introduces an innovative sample preparation approach, the Rapidly Freeze-Drying Droplet (RFDD) method, aimed at overcoming these challenges by enhancing the homogeneity of the sample morphology and signal intensity in MALDI. Compared to conventional preparation methods, the RFDD method reduces the laser energy threshold and demonstrates a remarkable increase in signal intensity for carbohydrates, facilitating the detection of high-molecular-weight polysaccharides (>10 kDa). The RFDD-prepared samples exhibit a uniformly distributed signal intensity that overcomes the 'sweet spot' issue in MALDI. The enhanced signal intensity and reproducibility lead to reliable quantitative analysis of carbohydrates, eliminating the need for expensive isotopic standards in each sample. A straightforward and accessible approach is presented for general laboratories, revolutionizing carbohydrate analysis in MALDI-MS.

Observation of Beam Emittance Reduction due to Gas Sheet Injection for Beam Profile Measurement

In a high intensity ion accelerator, a non-destructive beam monitor is required to realize a continuous measurement of the beam for improving the beam quality in operation for users. We have developed a non-destructive 2-D beam profile monitor detecting photons produced by interaction between a beam and a sheet-shaped gas. Though the developed monitor is a non-destructive type, the injected gas sheet should induce scattering of the beam particles which makes the beam emittance large. We have measured a beam current and a phase space distribution of the J-PARC 3 MeV, 60 mA H− beam with change of the gas sheet flux. It was found that the beam current reduction was in linear relation and the root mean square emittance was constant or decreased by up to 4.5% in y-y′ plane and did not change in x-x′ plane against a rise in the gas sheet flux. These result indicate that the developed monitor can be utilized as a non-destructive one depending on the gas sheet flux condition.

Preparation of polyamine loofah cellulose and its adsorption for reactive brilliant red K–2BP

In this paper, a novel efficient dye adsorbent, functional loofah cellulose (FL) was successfully prepared using loofah cellulose as matrix and hyperbranched polyethylenimine(HBPEI) as functionalizing reagent. Its structure was characterized by various tools. The adsorption behavior of FL to reactive brilliant red K–2BP was further discussed under different pH, different amounts of adsorbent, and interfering ion intensity. The results showed that the adsorption process belonged to Langmuir's isothermal adsorption. When the initial concentration of dye was 500 mg/L, the maximum saturated adsorption capacity was 1552 mg/g. The adsorption process conforms to the quasi-second-order adsorption kinetics and belongs to spontaneous adsorption. In addition, FL has excellent recycling and regeneration ability.

Actinide targets for the synthesis of superheavy nuclei

The use of heavy actinide targets, including 243Am, 240,242,244Pu, 245,248Cm, 249Bk, and 249Cf, irradiated by intense heavy ion beams of 48Ca has resulted in a significant expansion of the periodic table since 2000, including the discovery of five new heaviest elements and more than 50 new isotopes. These actinide materials can only be produced by intense neutron irradiation in very high flux reactors followed by chemical processing and purification in specialized hot cell facilities available in only a few locations worldwide. This paper reviews the reactor production of heavy actinides, the recovery and chemical separation of actinide materials, and the preparation of actinide targets for superheavy element experiments. The focus is on 248Cm, 249Bk, mixed 249−251Cf, and 254Es, including current availabilities and new production processes. The impacts of new facilities, including the Superheavy Element Factory at Dubna, accelerator and separator upgrades at RIKEN, and proposed upgrades to the High Flux Isotope Reactor at Oak Ridge are also described. Examples of recent superheavy element research are discussed as well as future opportunities for superheavy research using actinide targets.

Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19% to 46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

Long-slit spectral study of the unusual post-interacting galaxy Arp 263

ABSTRACT Arp 263 is a single irregularly shaped galaxy with intense star formation. Its short tidal features of low brightness visible in both optics and the H i line, as well as the strong non-circular motion of atomic gas in the outskirts, give pieces of evidence of a recent merging with some intruder, non-observed directly. We carried out spectral observations of this galaxy at the 6 m telescope of the Special Astrophysical Observatory. The spectra for three positions of the spectral slit were obtained: two of them cross the central region of the galaxy and the third one runs along the nearly straight narrow chain of clumps of emission gas with a length of 4–5 kpc. We derived the profiles of velocity, fluxes, and oxygen abundances (O/H) along the slits. We confirm that within the radius of several kpc the galaxy rotates regularly. The maximal value of the rotational velocity allows us to conclude that the dark mass of the galaxy prevails over the mass of its visible components. The observed velocity profile along the peripheral star-forming chain gives pieces of evidence that it is located outside of the plane of the disc, and the gas that formed the chain is probably falling back on to the disc after being ejected from the galaxy. We discuss that the observed chain of clumps is the result of gravitational instability of the gaseous filament.

Preparation and surface nanostructural characterization of multi-emitter sources based on superionic conductor CsAg4Br3-xI2+x (x=0.25)

Since the fabrication of micro-/nano-electronic devices is approaching nanoscales and demanding low energy, high dose ion beam processing with controlled area and dense patterns to meet the current semiconductor industry demands, continuous advancement is needed in terms of material design and ion beam techniques. In this perspective, development of new ion sources may provide advanced technologies useful in the manufacture of high-performance devices. Silver ion beams have salient features including simple generating process, nanoscale beam spot size and high intensity ion current. In this paper, we have developed a solid electrolyte multi emitter ion source (SEIS) with CsAg4Br3-xI2+x (x = 0.25) films deposited on silver tips. Ag+ ion emission is significantly enhanced with the added number of emitters and the ion current of 1.95 μA (with four emitter tips) has been obtained at 168 °C temperature and 20 kV accelerating voltage. The stability and cooling/heating curves of the multi-emitter ion beam are measured, and the interrelation between ion current intensity and mechanism of shape geometry of the emitter tips are discussed. Finally, both the solid electrolyte multi-tip emitters and the collector surfaces are analyzed, with an attempt to precisely control of formation of the nanoparticles (NPs) including their size and height through the control of the ion implantation parameters.

Studying the effect of the argon flow introduced into the arc discharge in the atomic-emission spectrometry of powdered samples using the spillage-injection method

Atomic emission spectral analysis of a powder sample by the method of spillage-injection into the plasma of an arc discharge burning in air is accompanied by the formation of cyanide, nitride and oxide compounds which form molecular bands in the spectrum thus impeding measuring the intensity of the analytical lines of the elements to be determined. Feeding an argon flow into the arc discharge can reduce the amount of spectral interference and increase the plasma temperature, which promotes more a complete evaporation of the analyzed sample injected into the arc discharge and, accordingly, increases the degree of ionization of the elements and the intensity of ion spectral lines. The aim of the study was to develop a device for argon introduction into the arc discharge zone and simultaneous sample delivery by a spillage-injection method, as well as to study the effect of argon consumption on arc discharge parameters and emission spectra of powder samples. The argon input device (up to 2.25 L/min) is developed on the basis on glass funnels, the geometric dimensions of which are selected with the goal of gaining the maximum intensity of spectral lines and reduced intensity of molecular bands at the minimal argon consumption. The study carried out on a «Grand Potok» spectrometer demonstrated that with an increase in argon consumption, the intensity of SiO molecular bands decreases, the plasma temperature increases by 350 – 540 K, and the intensity of ion and atomic lines with the ionization energy above 8 eV increases by more than 4.7 and 2.9 times, respectively. The developed device can be used in analysis of geological materials to improve the metrological characteristics of the results of atomic emission spectral analysis.

Interfacial bond behavior between normal OPC concrete and self-compacting geopolymer concrete enhanced by nano-SiO2

This study investigated the interfacial bond performances between ordinary Portland cement concrete (OPCC) and self-compacting geopolymer concrete (SCGC). The studied factors include nano-SiO2, repair material type, curing time, casting direction, and interface pretreatment method. The interfacial bond performances were assessed by Bi-surface shear and drying shrinkage tests. The evolutions in elemental compositions, hydration products, and microstructure in the matrix and interface were verified by BSE/EDS, XRD, and FTIR tests, respectively. ANOVA analysis was conducted to quantitatively analyze the influences of each factor from a statistical approach. The results demonstrated that the bond strength of SCGC increased by 24.5% to 31.7% compared to conventional OPCC. Meanwhile, geopolymers exhibited more compact microstructures but higher drying shrinkage (εsh,u=891 µε). While nanofillers significantly amplified the cracking potential of geopolymers, which was attributed to the higher early-stage autogenous shrinkage instead of drying shrinkage. The SCGC showed a high enrichment degree of calcium at the interface, indicating that more calcium-rich products were formed. In contrast, the use of nano-SiO2 in SCGC caused the enrichment of Na, Al, and Si at the interfacial regions, which provided extra nucleation sites for the polymerization of (C, N)-A-S-H gels. The nano-SiO2 enhanced the intensity of the hydroxide ion and Si-O-T band and promoted the formation of (C, N)-A-S-H gels. The ANOVA analysis proved that the repair material, nano-SiO2, and interface treatment were significant factors to affect the interfacial bond strength. In addition, geopolymer-based repair materials were less sensitive to the casting direction, which differed from the conventional self-compacting OPCC.

Detection and analysis of triacylglycerol regioisomers via electron activated dissociation (EAD) tandem mass spectrometry

Triacylglycerols (TGs) are important components of human diet. The positional distribution of fatty acids (FAs) on the glycerol backbone affects the chemistry and physical properties of fats. Especially for infants, the structure of TGs plays an important role in the growth and development. However, limited by detecting technology, accurately identifying regioisomers of ABA/AAB and BAC/ABC/ACB type TGs is a significant challenge for human milk utilization and the development of infant formula. For this, we exploit a novel method for identifying the regioisomers of ABA/AAB and BAC/ABC/ACB type TGs within complex lipid mixtures, via used electron activated dissociation (EAD) tandem mass spectrometry. The distribution information of acyl chains at the sn-2 and sn-1/3 positions of glycerol backbone and double bonds in unsaturated FAs can be easily obtained by fragmenting TG ions with energetic electrons (15 eV). Then, the standard curve was established by correlating the peak area intensity of sn-2 characteristic product ion with the content of TG regioisomers standard. These analytical methods successfully enabled the identification and quantification of TG regioisomers in human milk, cow milk, infant formula, palm oil, and sunflower oil. Additionally, the distribution of the double-bond positions of unsaturated FAs in these samples was also identified. Compared to traditional methods, this approach eliminates the need for complex processing and analysis procedures, enabling rapid structural characterization of ABA/AAB and BAC/ABC/ACB type TGs within 17 min. Hence, we provide a rapid and convenient methodology for detecting and analyzing ABA/AAB and BAC/ABC/ACB type TG regioisomers, thereby offering valuable assistance in the development of specialized formulations and facilitating effective process control for ensuring the quality of edible oils and fats.

A chemical derivatization-based pseudotargeted liquid chromatography-tandem mass spectrometry method for sensitive and high coverage determination of bile acids in human serum

Global profiling of bile acids (BAs) is imperative for understand their function and disease pathogenesis. But it is still a challenging task, as the collision-induced dissociation (CID) fragment ions of unconjugated BAs showed low ion intensities to insufficient analysis. Herein, we developed a highly sensitive method for pseudotargeted profiling of BAs by chemical derivatization. In the developed method, a labeling reagent, 2-dimethylaminoethylamine (DMED), was adopted to label the carboxyl group of BAs. The results demonstrated that the detection sensitivities of unconjugated BAs were increased by 4–200 folds after DMED-labeling. Moreover, to profile other potential BAs not included in the 91 known targets, diverse survey experiments were performed on Qtrap-MS to search BAs for both precursor and fragment ion species, and retention index (RI) strategy was adopted to facilitate the identification of isomers. Finally, MRM-based LC-MS/MS method was validated for the pseudotargeted profiling of the BAs submetabolome with good linearity (r2 ≥ 0.990 for 89 known BAs) and high sensitivity (0.05–0.5 ng/mL for unconjugated BAs), covering unconjugated, glycine, taurine, sulfuric acid, glucuronic acid, and as well as those doubly-conjugated with above types. With this method, a total of 107 BAs, covering 54 BAs identified by authentic standards and 53 BAs candidates, were successfully determined in human serum of women with intrahepatic cholestasis of pregnancy (ICP). Multivariate analysis revealed deferentially expressed BAs. ICP disease altered the BAs profile with a reduced proportion of unconjugated, sulfate- and doubly-conjugated BAs and an increased proportion of glycine and taurine conjugates. Altered proportion and profile of BAs in ICP groups were gradually recovered during the ursodeoxycholic acid (UDCA) therapy. Overall, the strategy of DMED-labeling technique combined with diverse survey experiments is sufficiently sensitive and robust to comprehensively analysis of metabolic profiling of BAs in human serum.

Inhibiting Protein Aggregation Using Cellulose Nanocrystal in MALDI-TOF MS Analysis: Improving the Sensitivity and Repeatability of Intact Protein in Pueraria.

Protein aggregation can induce low sensitivity and poor repeatability of matrix-assisted laser desorption/ionization time-of-fight mass spectrometry (MALDI-TOF MS) analysis for intact protein. Herein, we introduced a strategy to decrease protein aggregation in the sample solution by using cellulose nanocrystal (CNC). The results indicated that protein granule size was effectively reduced by adding CNC to the sample solution. Through MALDI-TOF MS analysis, the signal-to-noise ratio of [M + H]+ peak increased 2-fold, and the detection of limit was <10 μg/mL for intact protein. The CNC also contributed to excellent point-to-point repeatability for MALDI-TOF MS analysis with the coefficient of variation (CV) of 10.0% with CNC vs 48.9% without CNC in Hb solution. Also, the repeatability of Pueraria protein ion signals was improved by using CNC, and the CV with and without CNC was 16.1% and 39.6%, respectively. Moreover, protein ion intensity exhibited great linear relationship (y = 53.04x - 3.474, R2 = 0.9936) with the concentrations (ranging from 0.1 to 10 mg/mL) when using CNC. Further investigation revealed that m/z 19,000 and m/z 21,000 peaks of Pueraria could be used for the adulteration analysis and post-translational modification research, demonstrating our method has the potential for broad applications.

Secondary Ion Mass Spectrometry of Single Giant Unilamellar Vesicles Reveals Compositional Variability.

Giant unilamellar vesicles (GUVs) are a widely used model system to interrogate lipid phase behavior, study biomembrane mechanics, reconstitute membrane proteins, and provide a chassis for synthetic cells. It is generally assumed that the composition of individual GUVs is the same as the nominal stock composition; however, there may be significant compositional variability between individual GUVs. Although this compositional heterogeneity likely impacts phase behavior, the function and incorporation of membrane proteins, and the encapsulation of biochemical reactions, it has yet to be directly quantified. To assess heterogeneity, we use secondary ion mass spectrometry (SIMS) to probe the composition of individual GUVs using non-perturbing isotopic labels. Both 13C- and 2H-labeled lipids are incorporated into a ternary mixture, which is then used to produce GUVs via gentle hydration or electroformation. Simultaneous detection of seven different ion species via SIMS allows for the concentration of 13C- and 2H-labeled lipids in single GUVs to be quantified using calibration curves, which correlate ion intensity to composition. Additionally, the relative concentration of 13C- and 2H-labeled lipids is assessed for each GUV via the ion ratio 2H-/13C-, which is highly sensitive to compositional differences between individual GUVs and circumvents the need for calibration by using standards. Both quantification methods suggest that gentle hydration produces GUVs with greater compositional variability than those formed by electroformation. However, both gentle hydration and electroformation display standard deviations in composition (n = 30 GUVs) on the order of 1-4 mol %, consistent with variability seen in previous indirect measurements.

Color tunability studies of samarium ions/silver nanoparticles doped titanosilicate glass matrix

The current study aims at synthesizing a titanosilicate glass composite doped with rare earth/plasmonic nanoparticles using a cost effective and easier method of non-hydrolytic sol gel method. The rare earth employed is samarium ions while the plasmonic nanoparticle used is Silver. For densification samples were annealed at 800 0C and further characterized by FTIR, XRD, TEM. Matrix confirmation is done by FTIR spectroscopic studies which confirm the presence of Si-O-Si, Si-O, and Ti-O-Ti bonds. Both XRD and TEM analysis affirm the presence and morphology of Ag and TiO2 nanoparticles respectively. In the photoluminescence analysis the trivalent samarium ions show strong reddish-orange luminescence in the 550–670 nm range. The major peaks are obtained at 581 nm, 662 nm, 605 nm. Each of the spectra obtained show three prominent characteristic emission bands correlated to the transitions 4G5/2 to 6H5/2, corresponding to 581 nm, 4G5/2 to 6H7/2 corresponding to 605 nm, and 4G5/2 to 6H9/2 corresponding to 662 nm. We successfully plotted the CIE colorimetry diagram and it is observed that the effect of plasmonic nanoparticles can tune the relative emission intensity of rare earth ions in CIE colorimetry. Our study highlights the significance of incorporating the rare earth ions doping with plasmonic nanoparticles in the color tunability studies, exhibiting their broad applications in LED devices.

Fine‐Scale Structures of STEVE Revealed by 4K Imaging

AbstractWe utilized a 4K imaging to examine properties of fine‐scale structures of Strong Thermal Emission Velocity Enhancement (STEVE) near the magnetic zenith. Its high spatial (0.09 km at 200 km altitude) and temporal (24 Hz) resolution provided unprecedented details of fine‐scale structures in the subauroral ionosphere. Although the STEVE emission was seen as a homogeneous purple/mauve arc in the all‐sky images, the high‐speed imaging revealed that STEVE contained substantial multi‐scale structures. The characteristic wavelength and period were 12.4 ± 7.4 km and 1.4 ± 0.8 s, and they drifted westward at 8.9 ± 0.7 km/s. The speed is comparable to the reported magnitude of the intense subauroral ion drifts (SAID), suggesting that the fine‐scale structures are an optical manifestation of the E × B drift in the intense SAID. A spectral analysis identified multiple peaks at &gt;10, 4, 2, 1.1, and &lt;1/5 s period (&gt;83, 33, 16, 9, and &lt;1.7 km wavelength). Although most of the fine‐scale structures were stable during the drift across the field of view, some of the structures dynamically evolved within a few tens of km. The fine‐scale structures have a power law spectrum with a slope of −1, indicating that shear flow turbulence cascade structures to smaller scales. The fine‐scale structures pose a challenge to the subauroral ionosphere‐thermosphere interaction about how the ionosphere creates such fine‐scale structures and how the thermosphere reacts much faster than expected from a typical chemical reaction time.

Longitudinal Tomography of Ion Bunches in the JINR Superconducting Booster Synchrotron

Methods for processing a digital signal proportional to the longitudinal intensity of ions in bunches during their acceleration in a synchrotron are discussed. Computer tomography is employed to reconstruct the longitudinal 2D ion distribution function in a bunch using data on its longitudinal profiles that depend on the bunch length and the synchrotron motion of particles. Presented are examples of tomographic reconstruction of the longitudinal distribution function of xenon ions in the JINR superconducting booster synchrotron prior to extraction (after acceleration) and on the injection plateau also with the activated electron cooling system.

Oligomer formation from cross-reaction of Criegee intermediates in the styrene-isoprene-O3 mixed system

Alkene ozonolysis can produce stabilized Criegee intermediates (SCIs), which play a key role in oligomers' formation. Though styrene and isoprene coexist in the ambient atmosphere as important anthropogenic and biogenic secondary organic aerosol (SOA) precursors, respectively, their cross-reactions have not received attention. This study investigated the interactions of SCIs from styrene and isoprene ozonolysis for the first time. The high-resolution Orbitrap mass spectrometer was used to determine the unique ion mass spectra of the isoprene-styrene-O3 mixture. The results show that the signal intensities of new ions account for >8.4% of total ions in the mass spectra of the styrene-isoprene-O3 mixed system. Styrene and isoprene ozonolysis can produce characteristic C7–SCI and C4–SCI, respectively. C7–SCI and C4–SCI can be involved in the cross-reactions, and the results of tandem mass spectra directly confirmed both C7–SCI and C4–SCI as chain units. The O/C and H/C ratios of cross-products are in the range of 0.38–1.07 and 1.00–1.50, respectively, which are consistent with cross-reaction products. Adding a C7–SCI unit reduces the oligomer's volatility by 1.3–1.4 orders of magnitude lower than adding a C4–SCI unit. Thus, C4–SCI can compete with C7–SCI to react with styrene-derived RO2/RC(O)OH to produce more volatile cross-products, while the less volatile cross-products can be formed when isoprene-derived RO2/RC(O)OH reacted with C7–SCI instead of C4–SCI. The SOA yield of the mixed system is lower than that of the single styrene-O3 system but higher than that of the single isoprene-O3 system. Ambient particles were also collected, and 5 possible SCI-related cross-products were identified. This study illustrates the effects of SCI-related cross-reactions on SOA components and physicochemical properties, providing a basis for future research on SCI-related cross-reactions that frequently occur in the ambient atmosphere.

Machine learning-based peptide-spectrum match rescoring opens up the immunopeptidome.

Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and vaccine development. However, identifying immunopeptides remains challenging due to their non-tryptic nature, which results in distinct spectral characteristics. Moreover, the absence of strict digestion rules leads to extensive search spaces, further amplified by the incorporation of somatic mutations, pathogen genomes, unannotated open reading frames, and post-translational modifications. This inflation in search space leads to an increase in random high-scoring matches, resulting in fewer identifications at a given false discovery rate. Peptide-spectrum match rescoring has emerged as a machine learning-based solution to address challenges in mass spectrometry-based immunopeptidomics data analysis. It involves post-processing unfiltered spectrum annotations to better distinguish between correct and incorrect peptide-spectrum matches. Recently, features based on predicted peptidoform properties, including fragment ion intensities, retention time, and collisional cross section, have been used to improve the accuracy and sensitivity of immunopeptide identification. In this review, we describe the diverse bioinformatics pipelines that are currently available for peptide-spectrum match rescoring and discuss how they can be used for the analysis of immunopeptidomics data. Finally, we provide insights into current and future machine learning solutions to boost immunopeptide identification.

Label-free quantification of host cell protein impurity in recombinant hemoglobin materials

Quantitative analysis relies on pure-substance primary calibrators with known mass fractions of impurity. Here, label-free quantification (LFQ) is being evaluated as a readily available, reliable method for determining the mass fraction of host cell proteins (HCPs) in bioengineered proteins which are intended for use as protein calibration standards. In this study a purified hemoglobin-A2 (HbA2) protein, obtained through its overexpression in E. coli, was used. Two different materials were produced: natural and U15N-labeled HbA2. For the quantification of impurities, precursor ion (MS1-) intensities were integrated over all E. coli proteins identified and divided by the intensities obtained for HbA2. This ratio was calibrated against the corresponding results for an E. coli cell lysate, which had been spiked at known mass ratios to pure HbA2. To demonstrate the universal applicability of LFQ, further proteomes (yeast and human K562) were then alternatively used for calibration and found to produce comparable results. Valid results were also obtained when the complexity of the calibrator was reduced to a mix of just nine proteins, and a minimum of five proteins was estimated to be sufficient to keep the sampling error below 15%. For the studied materials, HbA2 mass fractions (or purities) of 923 and 928 mg(HbA2)/g(total protein) were found with expanded uncertainties (U) of 2.8 and 1.3%, resp. Value assignment by LFQ thus contributes up to about 3% of the overall uncertainty of HbA2 quantification when these materials are used as calibrators. Further purification of the natural HbA2 yielded a mass fraction of 999.1 mg/g, with a negligible uncertainty (U = 0.02%), though at a significant loss of material. If an overall uncertainty of 5% is acceptable for protein quantification, working with the original materials would therefore definitely be viable, circumventing the need of further purification.

Merging Full-Spectrum and Fragment Ion Intensity Predictions from Deep Learning for High-Quality Spectral Libraries.

Spectral libraries are useful resources in proteomic data analysis. Recent advances in deep learning allow tandem mass spectra of peptides to be predicted from their amino acid sequences. This enables predicted spectral libraries to be compiled, and searching against such libraries has been shown to improve the sensitivity in peptide identification over conventional sequence database searching. However, current prediction models lack support for longer peptides, and thus far, predicted library searching has only been demonstrated for backbone ion-only spectrum prediction methods. Here, we propose a deep learning-based full-spectrum prediction method to generate predicted spectral libraries for peptide identification. We demonstrated the superiority of using full-spectrum libraries over backbone ion-only prediction approaches in spectral library searching. Furthermore, merging spectra from different prediction models, as a form of ensemble learning, can produce improved spectral libraries, in terms of identification sensitivity. We also show that a hybrid library combining predicted and experimental spectra can lead to 20% more confident identifications over experimental library searching or sequence database searching.