Journal of the American Society for Mass Spectrometry

Detecting low-abundance bacterial peptides in samples dominated by human proteins using liquid chromatography tandem mass spectrometry (LC MS/MS) is challenging. Bacterial peptides are typically far less abundant than host-derived peptides, and sequence similarity between bacterial and human peptides can complicate confident attribution to a bacterial origin. To address this problem, we developed and benchmarked an in silico spectral library protein identification workflow for detecting Staphylococcus aureus USA300 peptides against a background of A375 cells. Peptide and protein identifications obtained at a 1% peptide-level false discovery rate were compared between the in silico spectral library search and a conventional sequence database search across a defined bacterial dilution series containing 0, 1%, 2%, 10%, 25%, 50%, and 100% bacterial protein. A human sequence similarity filter was applied to identifications from both search strategies to reduce the likelihood of retaining human derived or human like peptide sequences. Both search strategies showed concentration dependent bacterial detection and shared a practical detection limit of approximately 1 to 2% bacterial protein but exhibited distinct performance profiles. The in silico spectral library search produced fewer bacterial-like background identifications in the human-only control, whereas MS2Rescore substantially increased spectral library identifications at higher bacterial concentrations, exceeding database search at 100% bacterial protein. The partially overlapping peptide sets identified by the two strategies indicate that they provide complementary coverage of the bacterial proteome.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) requires extraction of broad, time-dependent peptide isotope envelopes from noise and overlapping signals. The ExMS/ExMS2 lineage established a retention-time-aware framework for raw-data extraction and integrated HDX-MS analysis. Building on this foundation, msengine is an open-source Python engine that adapts mass-selection windows and retention-time boundaries to the observed data. Spectra are reconstructed as nonnegative combinations of the theoretical undeuterated (all-H) isotope distribution shifted across allowed deuteration states, without prescribing a population shape. The fitted weights, termed deuteration coefficients, describe each state's contribution and guide refinement of mass-selection windows. Spectral matches establish an initial retention interval, which is then expanded using chromatographic intensity to include weaker portions of the elution profile. Interference screening and final quality checks assess the recovered interval. In a 1:1 mixture of undeuterated and deuterated material, interval expansion changed the relative population intensities within the extracted envelope resulting in closer agreement to the 1:1 target, illustrating the importance of boundary selection. Across a four-timepoint experiment with the SET1/SWD2 complex collected in duplicate, msengine accepted an average of 901 peptide-charge targets per measurement versus 636 for ExMS2, a 41.7% increase, while improving replicate-centroid disagreement and retention-window widths between replicates. In a same-workstation benchmark, total wall time decreased from 71.9 to 7.3 min, with higher peak memory use. Together, these results demonstrate adaptive signal extraction across mass and retention time as a means to retain multimodal envelopes while increasing accepted-target yield, persistence, accuracy, and throughput.

Variable-temperature electrospray ionization native mass spectrometry (vT-ESI nMS) provides a means to determine how temperature-dependent changes in protein and solution dynamics influence individual ligand-binding reactions within multiligand protein complexes. Here, the GroEL single-ring mutant (SR1), which binds up to seven nucleotides, serves as a model system due to its sensitivity to solution conditions. Native MS resolves individual ligand-bound populations, while vT-ESI extends these measurements across temperature (3-43 {degrees}C), enabling determination of binding affinities and the associated Gibbs free energy, enthalpy, entropy, heat capacity changes ({Delta}Cp), and enthalpy-entropy compensation (EEC) for each binding reaction. Temperature-dependent changes in van,t Hoff curvature produce distinct {Delta}Cp profiles, indicating changes in the dynamics governing ligand binding that may reflect contributions from protein conformational and protonation microstates. Temperature-dependent shifts in average charge state, consistent with changes in solvent-accessible surface area, indicate corresponding changes in protein conformation. Binding is predominantly enthalpy-driven below ~23 {degrees}C, with increasing entropic contributions at higher temperatures, while the seventh ADP binding reaction exhibits a distinct EEC profile. Comparison of the seventh ADP binding reaction in H2O and D2O reveals pronounced differences in van,t Hoff curvature,{Delta}Cp, and EEC, demonstrating that changes in the hydration environment alter the thermodynamic response. Collectively, these measurements show that temperature-dependent changes in protein and solution dynamics alter the distributions and thermodynamics of individual ligand-bound states and demonstrate the utility of vT-ESI nMS for resolving these effects in complex multiligand systems.

Summary: Mass spectrometry resolution of glycosphingolipids requires advanced separation methods. The virtual-ly identical structures of glycolipid epimers cannot be discriminated by routine multiple reaction monitoring-high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (MRM-LC-ESI-MS/MS). Sep-aration can be achieved using field asymmetric (differential) ion mobility mass spectrometry (FAIMS/DMS). A major bottleneck to MRM-LC-ESI-DMS-MS/MS method development lies in manually optimizing the compound-dependent parameters of separation voltage (SV) and compensation voltage (CoV). To address this challenge, we developed intelligent Differential Mobility Spectrometry (iDMS). iDMS is a supervised deep neural network that evaluates and predicts the optimal ion mobility SV and CoV parameters for any user-requested monoglycosphingolipid from a training set of measured signal intensities composed of 12 lipids (6 stereoisomer pairs). We initially introduced iDMS as a series of python scripts. Here, we share desktop applications of both iDMS and its pre-processing input data assembly modules (DMS Data Extraction Toolkit). Both software assemblies enable users to rapidly deploy a validated machine learning alternative to manual DMS optimization. The applications are user-friendly, operate with-out need for any technical operating environment, make training dataset assembly quick and simple, and return measured, normalized, and predicted ionograms at every user-interrogated SV, evaluation of separability at each SV and CoV combination, and prediction of optimal resolution parameters to accelerate users MRM-LC-ESI-DMS-MS/MS deployment. Availability and implementation: Desktop iDMS and DMS Data Extraction Toolkit are native desktop applications for macOS (Apple Silicon), freely available for download at https://www.neurolipidomics.com/platforms.html under the GNU v3.0 license. The source code is available from GitHub and Zenodo at https://github.com/Neurolipidomics/iDMS, https://zenodo.org/records/22214408, https://github.com/Neurolipidomics/DMSDataExtractionToolkit, and https://zenodo.org/records/22777403. Bench-marking metrics are available at https://www.biorxiv.org/content/10.64898/2026.08.26.747394v1.

Proteolysis can be completed in a single-step, rapid (5-30 minutes) reaction using hyperthermoacidic archaeal (HTA) proteases. Protocol simplicity and stability of HTA proteases in ambient storage conditions removes many logistical barriers to widespread deployment of proteomic analysis. HTA protease cleavage selectivity is complicated but quantitatively reproducible and generates peptides that negligibly overlap with trypsin. Tryptic and HTA protease peptides emphasize different proteins and provide complementary coverage of amino acid sequence and post-translational modification (PTM) sites. Digestion of a sample's separate aliquot with HTA protease is a minor addition to trypsin-based workflows, but LC-MS analysis of both sets of peptides is not practical for large cohorts. This manuscript evaluates the identifications and reproducibility of LC-MS analysis of tryptic and HTA protease peptide mixtures. Adding HTA protease peptides to 20% increased total protein identifications by 24% on average in neat plasma and identified 216 additional non-artifact modifications compared to conventional trypsin-only bottom-up proteomic analysis. This approach is reproducible and does not increase sample preparation or analysis time. Peptides from each protease present independent opportunities to quantify overlapping proteins and a quantification model was developed in which the protease with the most complete data is selected for each protein while the second protease is used to infer missing values. This cross-protease inference is more accurate than the alternative minimum, mean, or nearest neighbor imputations. Finally, biomarkers discovered through HTA protease peptides are amenable to rapid HTA protease only proteomic workflows thus streamlining translation from discovery to fieldable applications.

The rapid expansion of mRNA-based medicines has driven demand for robust, high-resolution analytical methods capable of characterising critical quality attributes including sequence identity, 5[prime] capping efficiency, and poly(A) tail length and heterogeneity. Here, we present two complementary liquid chromatography-mass spectrometry based workflows for mRNA characterisation, both based on site-specific digestion with the endoribonuclease MazF from Escherichia coli. A middle-up approach, optimised via specific 5[prime]-ACA cleavage enables rapid characterisation via oligoribonucleotide mass mapping providing complete sequence coverage and simultaneous assessment of 5[prime]-capping efficiency and 3[prime]-poly(A) tail length and heterogeneity. This workflow was successfully applied to three different mRNA constructs, NLuc, eGFP and FLuc mRNAs, achieving sequence coverages of 100%, 87%, and 74%, respectively. A complementary bottom-up approach, employing less specific 5[prime]-ACX cleavage enables tandem mass spectrometry-based sequencing of shorter oligoribonucleotides and detailed characterisation via mRNA sequence mapping. Applied to SARS-CoV-2 Spike Protein mRNA, this method yielded 50% sequence coverage based on unique oligoribonucleotides with MazF alone, extended to 90% by combining MazF, partial RNase T1, and partial RNase U2 digests. Together, these workflows provide a flexible, orthogonal platform for both high-throughput quality assessment and in-depth primary sequence characterisation of mRNA vaccines and therapeutics.

This paper predicts antimicrobial resistance (AMR) from matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectra using DRIAMS, evaluated across 13 species-antibiotic datasets spanning sensitive/resistant imbalance ratios of 2.95:1 to 141.21:1 and raw spectral similarities of 0.9166-0.9946. We propose HetSAGE, a heterogeneous graph neural network (GNN) that combines full-spectrum "raw" edges with consensus "biomarker" edges derived from a 4-method feature-selection vote and applies per-edge-type dropout to regularize the two views separately. HetSAGE outperforms a plain multilayer perceptron (MLP) only when spectral similarity is high, consistent with oversmoothing effects reported in the broader GNN literature; the biomarker view converges on features close to independently established clinical markers: within {+/-}1 Da in Staphylococcus aureus, Oxacillin, and within range in Klebsiella pneumoniae, Meropenem, confirming it is not a black box. At the same time, per-edge-type dropout protects this smaller signal from dilution by regularization tuned for the noisier raw spectrum. Together, these results indicate that heterogeneous graph structure is not a universal win for MALDI-TOF AMR prediction but a conditional one, tied to a measurable property of the data, spectral similarity, rather than to architecture alone. This caveat is largely unaddressed in current DRIAMS-based GNN work. It provides a concrete signal for practitioners deciding when a graph model is worth the added complexity over a plain MLP.

Raman spectroscopy (RS) has been shown to differentiate various disease states in tissue samples, yet the impact of sample substrate on spectral quality and downstream machine-learning (ML) performance is under-explored. We collected 960 spectra from 36 formalin-fixed, paraffin-embedded colonic specimens mounted in parallel on calcium fluoride (CaF2) and mirrored stainless-steel (SS) slides. Raw SS spectra were more intense than CaF2, and routine dewaxing left prominent paraffin peaks on CaF2 that were largely absent on SS. Extending CaF2 xylene immersion to 48 h reduced key paraffin peaks by 11 to 23%, aligning its biochemical profile with SS. Nested cross-validated classifiers were built to distinguish true disease (T1) from epithelial misplacement (EM). The highest accuracies were observed on SS (0.65 - 0.66), while CaF2 improved from 0.41 - 0.57 to 0.65 - 0.69 after extended dewaxing, chiefly via gains in recall. Although optimal dewaxing renders CaF2 diagnostically non-inferior, SS still offers superior accuracy, faster preparation, at approximately 4% of the cost, supporting its integration into scalable, AI-enabled RS pathology workflows.

Mass spectrometry generates data faster than it can be read, and the exchange standard, mzML, is text-based and must be parsed in full before any spectrum is accessible. Binary alternatives are smaller but depend on storage engines such as HDF5, so access is dictated by the engine, not the file. We present Ionic (.ion), an open-source, compact, streamable binary format, and Quant{middle dot}ion, a processing toolkit built on the former. Ionic stores spectra, chromatograms and metadata as independently compressed, indexed blocks, so a reader retrieves only the bytes it needs, even inside a web browser, and converts losslessly to and from mzML. Ionic was smaller than compressed mzMLb on every acquisition type tested, and extracting one compound took under 40 ms, 35 to 90 times faster than an mzML reader. Quant{middle dot}ion exposes one core to R, Python, and JavaScript with identical results, and recovered 97% of true features on a ground-truth benchmark.

The modeling of aroma release in food science is a complex, multidisciplinary field integrating mass transfer theory and principles of physical chemistry. While mechanistic models have long been used to understand volatile release, their practical adoption has been hindered because existing implementations are often private, "in-house" tools that lack reproducibility and extensibility. This paper introduces digimouth, an open-source Python framework designed to provide a unified, modular, and reproducible environment for simulating and fitting aroma release kinetics. A key innovation of digimouth is its handling of events and stages. Events represent discrete, instantaneous changes in the system (such as opening a bottle or swallowing), while stages allow for multi-phase simulations where experimental conditions or physical dynamics change over time. To demonstrate the tool's utility, the authors implemented established in vitro convection models and performed sensitivity analyses on parameters such as air-water partition coefficients and mass transfer coefficients. The package also includes an optimization module based on the least-squares method to fit parameters to experimental data. Validation against real-time data from Proton Transfer Reaction-Time of Flight- Mass Spectrometry (PTR-ToF-MS) shows a satisfactory fit, with an average error of approximately 2%. Ultimately, digimouth facilitates robust model development and hypothesis testing, offering the food science community a collaborative tool to better understand the mechanisms of flavor release.

Understanding the pathological significance of protein glycosylation and translating disease-associated glycan alterations into diagnostic and therapeutic opportunities require integrated analysis of glycans, their carrier glycoproteins, and spatial context. Here, we present an expanded multimodal glycoprotein analysis framework incorporating lectin-assisted imaging mass cytometry (Lectin-IMC) for stepwise discovery and spatial validation of disease-associated glyco-niches. Disease-associated glycans identified by laser microdissection-assisted lectin microarray (LMD-LMA) tissue glycome mapping are spatially evaluated by Lectin-IMC in relation to cell types and tissue microenvironments, and candidate carrier glycoproteins identified by MS-based glycoproteomics are subsequently incorporated for higher-order evaluation of glycan-protein-cell type relationships. Multiplex panel design is guided by LMA-based assessment of lectin-lectin interactions and biologically informed probe selection. Using a dilated cardiomyopathy model, we constructed a five-lectin panel centered on Wisteria floribunda agglutinin (WFA), which recognizes fibrosis-associated asialo N-glycans identified previously. Glycan-dependent WFA detection was validated by competitive inhibition and PNGase F treatment, and WFA-reactive glycans were spatially associated with fibrotic regions containing ACTA2VIM myofibroblast-like cells. Among six extracellular matrix glycoprotein candidates identified in WFA-binding fractions, periostin showed the strongest spatial correspondence with WFA-positive regions by pixel-based quantitative analysis and was further associated with ACTA2VIMWFA fibrotic microenvironments. The optimized Lectin-IMC panel was also transferable to a human FFPE cardiomyopathy specimen. Collectively, Lectin-IMC provides intuitive spatial visualization and interpretability of glycan-protein-cell type relationships and serves as an on-tissue spatial validation and prioritization layer within an iterative multimodal framework in which spatially defined glyco-niches can guide subsequent proteomic or glycoproteomic discovery.

Mass spectrometry based immunopeptidomics is limited by peptide identification. HLA class I ligands are non-tryptic, frequently carry low charge states and often fragment incompletely, yet nearly all workflows rely on a single activation method, higher-energy collisional dissociation (HCD). Unlike proteomics, where a protein is inferred from several peptides, in immunopeptidomics the peptide is itself the analyte and the candidate therapeutic target, so a ligand that is not identified is a target that is not found. Here we evaluate whether two alternative activation chemistries, ion trap collision-induced dissociation (CID) and infrared multiphoton dissociation (IRMPD), recover ligands that HCD does not. To our knowledge this is the first application of either method to HLA class I immunopeptidomics, made practical by the integrated infrared laser of the Orbitrap Tribrid Apex MultiOmics. Four HLA-typed cell lines were analysed in technical triplicate against the canonical human proteome and a Ribo-seq-derived cryptic ORF database. Because fragment-intensity predictors are trained almost exclusively on HCD spectra, AlphaPeptDeep was fine-tuned separately for each method; fine-tuning and Percolator rescoring increased unique peptide identifications in every cell line and method combination (median +17.1% canonical, +54.5% cryptic), and fine-tuned models transferred across cell lines with a median loss of 0.4%, placing the three methods on a common analytical footing. On that footing the three methods yielded immunopeptidomes of similar size and composition, but each recovered peptides the others missed. HCD produced the most complete fragment-ion ladders and recovered more peptides from the basic-anchor alleles A*11:01 and A*33:03, both members of the A3 supertype. CID and IRMPD recovered a larger share of alleles without a basic C-terminal anchor and, for peptides containing no lysine, arginine or histidine, produced higher search scores and more matched b/y ion current in three of four cell lines. Each method therefore contributed exclusive peptides, and at a fixed budget of three LC-MS/MS runs, one run per method yielded on average more unique peptides than triplicate runs of a single method in all four cell lines (+0.4% to +10.9%). Of 239 novel canonical predicted HLA binders in the two cancer cell lines (HCT116 and MDA-MB-231) absent from the Immune Epitope Database, 55% were detected by only one method; nine derive from cancer-associated antigens. CID and IRMPD can therefore provide sequences that may not be identified by HCD alone, or with better spectral quality for a chemically defined subset of ligands. Their value lies alongside HCD rather than in its place, and realising it will require fragment-intensity models and acquisition schemes built for these chemistries rather than adapted from HCD.

In bottom-up proteomics, protease specificity determines peptide length and LC/MS/MS detectability; in N-terminomics, this is critical because each protein N-terminal peptide is unique and cannot be substituted by other peptides. CHromatographic AMplification of Protein N-terminal peptides (CHAMP-N) combines LysargiNase digestion with StageTip-based strong cation exchange chromatography (SCX), removing internal peptides by exploiting their newly generated N-terminal Lys/Arg residues and thereby selectively isolating protein N-terminal peptides. However, LysargiNase cleavage near the protein N-terminus can generate excessively short peptides. Here, we developed two complementary CHAMP-N approaches that modurate N-terminal peptide length while retaining the charge-based SCX separation principle. LysN digestion avoided cleavage at N-terminal-proximal Arg residues, whereas D3-acetylation blocked Lys cleavage by LysargiNase to produce ArgN-like specificity. Both approaches extended otherwise short N-terminal peptides while preserving N-terminal basic residues on internal peptides. Conventional LysargiNase, LysN, and ArgN-like CHAMP-N generated complementary sets of N-terminal peptides. Integration of the three workflows identified 2,552 protein N-termini, approximately twice the number identified by conventional CHAMP-N alone. The median relative standard deviations of N-terminal peptide intensities were approximately 15% for all three workflows, indicating comparable technical reproducibility. These results demonstrate that coordinating protease cleavage specificity with SCX selectivity reduces sequence-dependent losses and expands N-terminome coverage while preserving the simplicity and reproducibility of CHAMP-N.

Central carbon metabolism coordinates energy production and carbon skeleton allocation across photosynthetic and heterotrophic organisms. Here, we established an optimized targeted metabolomics workflow for Chlamydomonas reinhardtii and Arabidopsis thaliana using a single-run HILIC-MS/MS assay with alternating positive and negative ionization. Our method provides robust multi-class coverage across 21 amino acids, 11 carboxylic acids, 4 sugars, 3 adenine nucleotides, 2 nucleotide sugars, 18 phosphorylated compounds in CCM, and Acetyl-CoA in a 20-minute run using both ESI modes. An optimal concentration of medronic acid with a BEH Amide column at high pH prevented metal adsorption, and low quantification limits (10 to 50 nM) for labile intermediates such as PEP, ATP and RUBP were achieved. Retention time variations remained below 1.3%, and over 90% of analytes showed high linearity (R2 > 0.990) across dynamic ranges spanning two to three orders of magnitude (typically 0.01-0.5 uM up to 15-20 uM). For sample preparation, glass-fiber filters (GF/C) outperformed nitrocellulose for harvesting C. reinhardtii. For A. thaliana, it was proved that no further grinding with micro pestle and extended cooling were necessary. A two-phase chloroform/methanol/water extraction was essential, as a single-phase extraction co-extracted green chlorophyll, which heavily suppressed phosphorylated signals. In Arabidopsis, glutamate, fumaric acid and citric acid were the dominant primary pools, indicating the importance of dilution and column washing for preventing carryover, and sensitive intermediates were reliably quantified at physiological levels. Together, this unified analytical and extraction strategy enables robust, artifact-free, high-throughput profiling of central metabolism in photosynthetic organisms.

Proteolysis-targeting chimeras (PROTACs) promote targeted protein degradation by inducing a transient ternary complex between a protein of interest and an E3 ubiquitin ligase. Despite the clinical promise of these event-driven therapeutics, defining the conformational landscape and interfacial organization of these complexes remains challenging. Here, we use an integrated native mass spectrometry (nMS) approach combining ion mobility, surface-induced dissociation (SID), and variable-temperature electrospray ionization (vT-ESI) to determine how small-molecule architecture shapes the structural and thermodynamic landscapes of bromodomain-containing protein 4 (BRD4) with the VCB E3 ligase complex. Using a set of PROTACs that differ in targeting ligand identity (JQ1 versus I-BET726) and PEG linker length, ion mobility measurements show that shorter linkers constrain the ternary complex into more extended topologies, whereas longer, more flexible linkers allow dynamic sampling of relaxed, native-like conformations. SID directly probes noncovalent interfaces and shows that JQ1 complexes favor prompt PROTAC ejection, whereas tighter-binding I-BET726 complexes are more likely to retain the PROTAC at the interface. Complementing these kinetically-controlled gas-phase measurements, vT-ESI reveals a monophasic melting transition for a JQ1-mediated complex and a distinct biphasic melting profile for an I-BET726-mediated complex. The gas-phase activation measurements revealed targeting-ligand-driven differences in PROTAC ejection and retention, whereas vT-ESI uncovered distinct temperature-dependent disassembly pathways for the matched-linker-length JQ10L and IBET10L complexes. Together, these complementary techniques show how PROTAC architecture influences ternary-complex conformation, interfacial connectivity, and disassembly.

Peptides containing noncanonical amino acids (ncAAs) are becoming increasingly important in drug discovery, but their molecular dynamics (MD) simulation remains limited by labor-intensive residue parameterization and topology preparation. Here, we present TopFor (Topology and Residue Force-Field Parameter Preparation), an open-source, peptide-oriented workflow that automates the generation of ncAA residue templates and force-field parameter files for MD simulations with AMBER using established charge-derivation methods. TopFor supports single-residue, whole-peptide, and batch parameterization workflows. The workflow was validated using the MDM2-p53 complex by comparing native AMBER residue definitions with residues processed by TopFor using three charge-derivation methods, each evaluated in three independent 100 ns MD simulations. TopFor-generated residue definitions showed structural and conformational behavior comparable to the native AMBER representation. Applicability to more complex peptide architectures was demonstrated using the head-to-tail cyclic peptide cyclosporin A and the cyclic depsipeptide cyclohexylgriselimycin. At library scale, 1,138 of 1,214 cyclohexylgriselimycin derivatives (93.7%) successfully completed TopFor parameterization and the subsequent initial AMBER-based computational workflow across two derivatization campaigns. TopFor was further integrated into a structure-based peptide discovery workflow based on the DnaN-griselimycin complex. Six GM-derived peptides were computationally prioritized and experimentally evaluated, of which four produced SPR responses against Mycobacterium smegmatis (M. smegmatis) DnaN and three showed concentration-dependent responses. TopFor reduces the manual effort required to prepare ncAA-containing peptides for MD simulations and enables their integration into both library-scale computational screening and structure-based peptide discovery workflows. Keywords: noncanonical amino acids; peptide parameterization; molecular dynamics; AMBER; AmberTools; peptide design; high-throughput virtual screening

The perception of flavor during food consumption is a complex process governed by the dynamic release of aroma compounds, which is influenced by the interaction between the food matrix and human physiology. While mechanistic models have been developed to simulate these phenomena, their practical application has often been limited by the lack of flexible software tools capable of integrating diverse food structures and real-time physiological data. This study addresses this gap by incorporating in vivo mechanistic models for both liquid foods and melting solid matrices in a modular Python-based framework: digimouth. The modeling approach treats the oral, pharyngeal, and nasal cavities as a series of interconnected reactors with mass transfer governed by ordinary differential equations (ODEs). These models explicitly integrate key physiological parameters, including salivary flow, bidirectional respiratory airflow, and discrete events such as mastication and swallowing. For solid foods, the framework incorporates additional mechanisms for matrix dissolution, temperature-dependent melting, and dynamic surface area change. The models were validated using experimental data from 30 subjects who consumed aroma solutions and flavored gelatin gummy discs while monitored by PTR-MS (Proton Transfer Reaction - Mass Spectrometry). The simulations successfully reproduced the main kinetic features and release profiles observed experimentally for both types of matrices. Despite some limitations related to the self-reporting of physiological events and the non-inclusion of initial orthonasal mass transfer in the models, the developed software package proved to be a robust and evolutive tool. It offers a foundation for exploring inter-individual variability and the impact of complex food structures on sensory perception.

Reliable quality control (QC) is essential for reproducible biological mass spectrometry, yet QC information is still typically stored in tool-specific, poorly interoperable formats that hinder comparison, reuse, and transparent reporting. We present mzQC, a community-developed standard from the HUPO Proteomics Standards Initiative for the compact, machine-readable representation of QC metrics and their associated metadata across mass spectrometry workflows. mzQC is implemented in JSON, organized to support both individual mass spectrometry acquisitions and collections thereof, with formal QC metrics definitions supported by controlled vocabularies to ensure unambiguous metric interpretation and semantic consistency. The format is supported by validation tools and reference libraries in Python, R, and Java, facilitating integration into existing software ecosystems. We demonstrate the utility of mzQC across diverse analytical modalities and QC settings, spanning longitudinal instrument monitoring, acquisition-method evaluation, study-level quality assessment, repository-scale data characterization, and spatially resolved mass spectrometry imaging. Together, these use cases show that mzQC provides not merely a file format, but a shared foundation for interoperable QC reporting, large-scale data assessment, and more rigorous, reproducible, and reusable mass spectrometry-based research.

Peptide hormones are important signaling molecules that regulate diverse physiological processes and have substantial therapeutic relevance. However, their experimental identification can be challenging because of low abundance, limited stability, and complex post-translational processing, highlighting the need for reliable computational approaches. Here, we present StackHPpred, a stacking-based ensemble framework for accurate identification of hormone peptides (HPs) from amino acid sequences. We systematically evaluated 56 feature representations, comprising 35 conventional sequence descriptors and 21 PLM/NLP-based embeddings, across 10 machine-learning classifiers, generating 560 baseline models. Prediction-level correlation analysis was subsequently employed to identify 14 complementary, non-redundant feature representations, which were integrated through feature- and classifier-level stacking strategies using out-of-fold predicted probabilities. The final StackHPpred model, based on the top three feature representations CTDD, KSCTriad, and PTAB, achieved an AUC of 0.9965, MCC of 0.9506, ACC of 0.9752, and F1-score of 0.9749 on the independent dataset. StackHPpred was further evaluated under increasingly imbalanced independent datasets with positive-to-negative ratios ranging from 1:10 to 1:50, demonstrating robust predictive performance under challenging screening conditions. t-SNE visualization and SHAP analysis further showed improved class separation and highlighted the contributions of multiple classifier-feature combinations to the final prediction. Overall, StackHPpred provides a robust and interpretable framework for distinguishing HPs from non-HPs and is freely available as a web server at https://jayasreekirthipati.org/StackHPpred/, with a standalone implementation available at https://github.com/JayasreeKirthipati04/StackHPpred.

The acquisition of sufficient nitrogen for growth, as well as the disposal of highly toxic nitrogen-containing degradation products, represent serious metabolic challenges in all multicellular organisms and microbial ecosystems. Understanding these processes requires a broadly applicable system to accurately quantify the concentration of a wide spectrum of biological amines and inorganic nitrogen-containing compounds. To date, this has been challenging due to limited specificity, selectivity, breadth and/or sensitivity of assays. Here we present a simple, optimized method based on liquid chromatography and high-resolution mass spectrometry, with an accompanying analysis pipeline, for the accurate quantification of more than 30 of these compounds, including ammonia. We demonstrate the effectiveness of this method in quantifying relevant nitrogen-containing compounds in mouse and human gut content, bacterial cultures, algal symbiosis in a cnidarian and freshwater ecosystem samples. As a proof-of-concept, we applied the method to study the influence of the gut microbiome on cecal nitrogen distribution in mice with different gnotobiotic microbiomes. This revealed a profound influence of gut microbiota composition on amino acid, ammonia, and amine concentrations in the upper large intestine. Overall, we provide a broadly applicable approach that can be used across microbial ecology fields to generate novel insights into the biology of nitrogen assimilation, exchange, and disposal.