In this Perspective, the authors discuss recent advances in multi-omics microbiome research, outlining key obstacles in data integration and metadata harmonization, and proposing a roadmap for scalabl...

Abstract. Peptide-spectrum match (PSM) rescoring has become standard in proteomics workflows, improving peptide identification accuracy across diverse sear

In mass spectrometry (MS)-based proteomics, computational tools match acquired tandem MS spectra to peptides from a sequence database. Machine learning increasingly supports this task through peptide-spectrum match (PSM) rescoring, in which a classifier, typically a linear semi-supervised model, refines the initial matching score. However, Mokapot allows the user to choose among different machine learning algorithms of increasing complexity, from the default linear support vector machine (LSVM) to random forest and XGBoost. Here, we use an entrapment approach to assess the effect of this increasing complexity on PSM identification and the accuracy of the estimated false discovery rate (FDR). We show that, while more complex models increase the number of identified PSMs at a fixed FDR threshold, this gain reflects a bias towards random matches from the target proteome database rather than genuine identifications. Indeed, for the most complex model, the entrapment FDR reaches 6.3% instead of the estimated 1% decoy FDR. This bias thus yields overly optimistic FDR estimates, indicating that model complexity in PSM rescoring must be carefully balanced against this overfitting risk.

AbstractMotivation. Accurate prediction of peptide detectability in mass spectrometry–based proteomics is critical for improving both protein identificatio

AbstractMotivation. Mass spectrometry-based proteomics allows studying all proteins of a sample on a molecular level. However, mass spectra are noisy and c

Metaproteomics is the investigation of the protein composition of multi-organism samples. While metagenomics answers the question which organisms are present in a sample, metaproteomics additionally a...

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Deep learning models have advanced de novo peptide sequencing, but their predictions may reflect both physics-based spectral evidence and learned peptide-sequence priors. Systematically measuring such...

Scientific Reports - Metaproteomic profiling reveals viral proteins and associated host proteomic alterations in glioblastoma

A spatially resolved map of the human proteome across a variety of healthy tissues and cancers provides wide-ranging insights in developmental biology and oncology, and could aid the identification of therapeutic targets and development of treatments for cancer.

Nature - Four antibiotic compounds produced in Streptomyces bacteria attack multiple parts of an essential metabolic pathway.

In BriefImmunopeptidomics enables untargeted discovery of peptides presented by MHC molecules, informing vaccine and immunotherapy development. Existing workflows focus either on throughput or sensiti...

Metaproteomics, the large-scale study of proteins from microbial communities, faces challenges in identifying species due to similarities in protein sequences across different organisms. Current methods often rely on simple counting of matches between proteins and taxa, which can lead to low accurac...

Metaproteomic peptide identification is constrained by the structure and size of the protein search space. Pooled gene catalogues provide coverage but obscure genome-level evidence, and current workfl...

Data-independent acquisition (DIA) mass spectrometry has become increasingly prevalent in proteomics as advances in instrumentation, chromatography, and computational analysis have enabled robust prot...

De novo peptide sequencing from tandem mass spectrometry is pivotal in proteomics, enabling identification of novel peptides without reference databases. While recent Transformer-based encoder-decoder...

Metabolomics software development has accelerated rapidly, yet no recent systematic analysis has quantified how the landscape is evolving across computational methods, geographies, and the research co...

Bariatric surgery is widely recognized as the most effective and durable intervention for severe obesity; however, its long-term molecular effects on gut microbiota-host interactions remain poorly understood. By applying shotgun metaproteomics to fecal samples collected before and 2 years after surgery, our study provides novel insights into the functional consequences of bariatric bypass procedures. We demonstrate sustained alterations in both microbial and host protein profiles, including metabolic enzymes, outer membrane proteins, and immune-related factors, revealing a long-lasting remodeling of gut ecosystem functions. These findings underscore the value of metaproteomics in uncovering molecular mechanisms underlying bariatric surgery outcomes and may ultimately guide the development of microbiome- or host-targeted strategies to optimize therapy and long-term patient care.

It is increasingly recognized that the ‘omic analysis of whole blood has applications for precision medicine and disease phenotyping. Despite this realization, whole blood is generally viewed as a challenging analytical matrix in comparison to plasma or serum. Moreover, proteomic analyses of whole blood have almost exclusively focused on (non)targeted analyses of protein abundances and much less on post-translational modifications (PTMs). Here, we developed a streamlined workflow for processing 20 microliters of venous blood collected by volumetric absorptive microsampling that incorporates serial trypsinization and N-glycopeptide and phosphopeptide enrichment and avoids laborious sample dry-down or cleanup steps. As many as 10,000 analytes (reported as protein groups, glycopeptidoforms, and phosphosites) can be quantified by liquid chromatography-tandem mass spectrometry in under 2 h of MS acquisition time. Using these methods, we explored the stability of “dried” and “wet” blood proteomes, as well as the effects of ex vivo inflammatory stimulus or phosphatase inhibition. Multiomics factor analysis enabled facile identification of analytes that contributed to interindividual variability of the blood proteomes, including N-glycopeptides that distinguish immunoglobulin heavy constant alpha 2 allotypes. Collectively, our results help to establish feasibility and best practices for the integrated MS-based quantification of proteins and PTMs from dried blood.

Data-dependent acquisition mass spectrometry (DDA-MS) and data-independent acquisition mass spectrometry (DIA-MS) have historically offered complementary strengths in bottom-up proteomics, with DDA providing high-selectivity spectra for post-translational modification (PTM) analysis and DIA enabling more systematic peptide sampling. Here, we asked if this is still the case for the Orbitrap Astral platform that offers high-speed DDA and (ultra-) narrow-window DIA (nDIA) capabilities across proteome and phosphoproteome applications. When DDA and DIA measurements were parameter-matched (to the extent possible), the differences in analytical performance diminished markedly. Across extensive replicate analyses, both methods continued to identify new peptides and proteins without reaching saturation, indicating that the molecular complexity of biological samples still overwhelms even the fastest liquid chromatography-MS (LC-MS) methods. Incomplete sampling also contributed to substantial peptide-level non-overlap between DDA and nDIA and data completeness was only modestly better for nDIA than DDA across many replicates. Quantitatively, DDA and nDIA showed broadly similar precision and accuracy, with nDIA offering slightly higher precision and DDA slightly better accuracy in controlled mixture experiments. MS1-based quantification outperformed MS2-based quantification, particularly for short gradients, supporting MS1 quantification as a robust and general strategy for high-throughput proteomics. In phosphoproteomic samples, DDA and nDIA identified similar numbers of phosphopeptides, but DDA retained a small edge for phosphorylation site localisation. Together, the results show that advances in acquisition speed and sensitivity are narrowing the historical gap between DDA and DIA, while also revealing that current LC-MS workflows remain far from providing comprehensive proteome coverage. Going forward, further gains in dynamic range, scan speed, sensitivity, and transparent software tools will be required to reach systematic, comprehensive and reliable measurements of complex proteomes in a single shot.

Liquid chromatography-tandem mass spectrometry (LC–MS/MS)-based proteomics, particularly data-independent acquisition (DIA), has become widely adopted across One Health approaches for biological and clinical research for quantitative protein characterization. Among the many computational tools available, DIA-NN has demonstrated superior performance; however, the primary output of the current versions is provided as a compact, compressed PARQUET file that can be difficult to interrogate without programming expertise. To address this limitation, we developed DIA-NN EasyFilter (DEF), a fast, user-friendly, KNIME-based workflow for comprehensive protein filtering and visualization. DEF integrates chromatographic peak-based filtering, curated contaminant libraries, and quantity-quality assessment along with interactive modules for qualitative and quantitative data exploration. The workflow is optimized for efficient execution within the KNIME local desktop environment and is designed to support end-users in improving accuracy and interpretability without requiring coding skills. We provide a detailed description on how to run DEF and demonstrate the utility and robustness of DEF using published large-scale proteomics data sets, showing high comparability across studies regardless of instrument platform or data set size.

Recent high-throughput applications to shotgun proteomics have shown great benefits of coupling ion mobility spectrometry (IMS) to mass spectrometry. IMS adds a separation dimension by differentiating biomolecules from their size and shape. We (and others) find that the distribution of the peptide collision cross section (CCS) is often bimodal, which limits the utility of current machine learning predictions for peptide identification. Molecular dynamics simulations indicate that the peptides in the drift tube can adopt multiple stable conformations and that the two modes correspond to predominantly extended (mostly helical) and more compact (globular and less ordered) conformations. Most peptides have a charge-dependent strong preference for one of the two conformations, while some can adapt to both, as evidenced by a simple geometric model of the CCS data. We suggest a novel two-valued CCS predictor that allows for multiple peptide conformations. Its integration into data-independent acquisition proteomics increases identification rates of peptides compared with single-value predictors.

Hair fibers are a common form of biological trace evidence. Taxonomic classification can provide valuable forensic intelligence and aid in evidence prioritization. Traditional methods of classificatio...

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Accurately identifying metabolites i.e. small molecules from mass spectrometry data remains a core challenge in metabolomics, with broad applications in drug discovery, environmental analysis, and cli...