Tandem Mass Spectrometry Data Research Articles

Quantitative proteome-wide O-glycoproteomics analysis with FragPipe.

Identification of O-glycopeptides from tandem mass spectrometry data is complicated by the near complete dissociation of O-glycans from the peptide during collisional activation and by the combinatorial explosion of possible glycoforms when glycans are retained intact in electron-based activation. The recent O-Pair search method provides an elegant solution to these problems, using a collisional activation scan to identify the peptide sequence and total glycan mass, and a follow-up electron-based activation scan to localize the glycosite(s) using a graph-based algorithm in a reduced search space. Our previous O-glycoproteomics methods with MSFragger-Glyco allowed for extremely fast and sensitive identification of O-glycopeptides from collisional activation data but had limited support for site localization of glycans and quantification of glycopeptides. Here, we report an improved pipeline for O-glycoproteomics analysis that provides proteome-wide, site-specific, quantitative results by incorporating the O-Pair method as a module within FragPipe. In addition to improved search speed and sensitivity, we add flexible options for oxonium ion-based filtering of glycans and support for a variety of MS acquisition methods and provide a comparison between all software tools currently capable of O-glycosite localization in proteome-wide searches.

Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules.

The application of machine learning (ML) to -omics research is growing at an exponential rate owing to the increasing availability of large amounts of data for model training. Specifically, in metabolomics, ML has enabled the prediction of tandem mass spectrometry and retention time data. More recently, due to the advent of ion mobility, new ML models have been introduced for collision cross-section (CCS) prediction, but those have been trained with different and relatively small data sets covering a few thousands of small molecules, which hampers their systematic comparison. Here, we compared four existing ML-based CCS prediction models and their capacity to predict CCS values using the recently introduced METLIN-CCS data set. We also compared them with simple linear models and with ML models that used fingerprints as regressors. We analyzed the role of structural diversity of the data on which the ML models are trained with and explored the practical application of these models for metabolite annotation using CCS values. Results showed a limited capability of the existing models to achieve the necessary accuracy to be adopted for routine metabolomics analysis. We showed that for a particular molecule, this accuracy could only be improved when models were trained with a large number of structurally similar counterparts. Therefore, we suggest that current annotation capabilities will only be significantly altered with models trained with heterogeneous data sets composed of large homogeneous hubs of structurally similar molecules to those being predicted.

Reinvestigating the Correctness of Decoy-Based False Discovery Rate Control in Proteomics Tandem Mass Spectrometry.

Traditional database search methods for the analysis of bottom-up proteomics tandem mass spectrometry (MS/MS) data are limited in their ability to detect peptides with post-translational modifications (PTMs). Recently, "open modification" database search strategies, in which the requirement that the mass of the database peptide closely matches the observed precursor mass is relaxed, have become popular as ways to find a wider variety of types of PTMs. Indeed, in one study, Kong et al. reported that the open modification search tool MSFragger can achieve higher statistical power to detect peptides than a traditional "narrow window" database search. We investigated this claim empirically and, in the process, uncovered a potential general problem with false discovery rate (FDR) control in the machine learning postprocessors Percolator and PeptideProphet. This problem might have contributed to Kong et al.'s report that their empirical results suggest that false discovery (FDR) control in the narrow window setting might generally be compromised. Indeed, reanalyzing the same data while using a more standard form of target-decoy competition-based FDR control, we found that, after accounting for chimeric spectra as well as for the inherent difference in the number of candidates in open and narrow searches, the data does not provide sufficient evidence that FDR control in proteomics MS/MS database search is inherently problematic.

Analysis of Tandem Mass Spectrometry Data with CONGA: Combining Open and Narrow Searches with Group-Wise Analysis.

Searching for tandem mass spectrometry proteomics data against a database is a well-established method for assigning peptide sequences to observed spectra but typically cannot identify peptides harboring unexpected post-translational modifications (PTMs). Open modification searching aims to address this problem by allowing a spectrum to match a peptide even if the spectrum's precursor mass differs from the peptide mass. However, expanding the search space in this way can lead to a loss of statistical power to detect peptides. We therefore developed a method, called CONGA (combining open and narrow searches with group-wise analysis), that takes into account results from both types of searches─a traditional "narrow window" search and an open modification search─while carrying out rigorous false discovery rate control. The result is an algorithm that provides the best of both worlds: the ability to detect unexpected PTMs without a concomitant loss of power to detect unmodified peptides.

Combinatory data-independent acquisition and parallel reaction monitoring method for revealing the lipid metabolism biomarkers of coronary heart disease and its comorbidities.

Disorders of lipid metabolism are a common cause of coronary heart disease (CHD) and its comorbidities. In this study, ultra-performance liquid chromatography-high-resolution mass spectrometry in data-independent acquisition (DIA) mode was applied to collect abundant tandem mass spectrometry data, which provided valuable information for lipid annotation. For the lipid isomers that could not be completely separated by chromatography, parallel reaction monitoring (PRM) mode was used for quantification. A total of 223 plasma lipid metabolites were annotated, and 116 of them were identified for their fatty acyl chain composition and location. In addition, 152 plasma lipids in patients with CHD and its comorbidities were quantitatively analyzed. Multivariate statistical analysis and metabolic pathway analysis demonstrated that glycerophospholipid and sphingolipid metabolism deserved more attention for CHD. This study proposed a method combining DIA and PRM for high-throughput characterization of plasma lipids. The results also improved our understanding of metabolic disorders of CHD and its comorbidities, which can provide valuable suggestions for medical intervention.

MS2Rescore 3.0 Is a Modular, Flexible, and User-Friendly Platform to Boost Peptide Identifications, as Showcased with MS Amanda 3.0.

Rescoring of peptide-spectrum matches (PSMs) has emerged as a standard procedure for the analysis of tandem mass spectrometry data. This emphasizes the need for software maintenance and continuous improvement for such algorithms. We introduce MS2Rescore 3.0, a versatile, modular, and user-friendly platform designed to increase peptide identifications. Researchers can install MS2Rescore across various platforms with minimal effort and benefit from a graphical user interface, a modular Python API, and extensive documentation. To showcase this new version, we connected MS2Rescore 3.0 with MS Amanda 3.0, a new release of the well-established search engine, addressing previous limitations on automatic rescoring. Among new features, MS Amanda now contains additional output columns that can be used for rescoring. The full potential of rescoring is best revealed when applied on challenging data sets. We therefore evaluated the performance of these two tools on publicly available single-cell data sets, where the number of PSMs was substantially increased, thereby demonstrating that MS2Rescore offers a powerful solution to boost peptide identifications. MS2Rescore's modular design and user-friendly interface make data-driven rescoring easily accessible, even for inexperienced users. We therefore expect the MS2Rescore to be a valuable tool for the wider proteomics community. MS2Rescore is available at https://github.com/compomics/ms2rescore.

The astounding exhaustiveness and speed of the Astral mass analyzer for highly complex samples is a quantum leap in the functional analysis of microbiomes

BackgroundBy analyzing the proteins which are the workhorses of biological systems, metaproteomics allows us to list the taxa present in any microbiota, monitor their relative biomass, and characterize the functioning of complex biological systems.ResultsHere, we present a new strategy for rapidly determining the microbial community structure of a given sample and designing a customized protein sequence database to optimally exploit extensive tandem mass spectrometry data. This approach leverages the capabilities of the first generation of Quadrupole Orbitrap mass spectrometer incorporating an asymmetric track lossless (Astral) analyzer, offering rapid MS/MS scan speed and sensitivity. We took advantage of data-dependent acquisition and data-independent acquisition strategies using a peptide extract from a human fecal sample spiked with precise amounts of peptides from two reference bacteria.ConclusionsOur approach, which combines both acquisition methods, proves to be time-efficient while processing extensive generic databases and massive datasets, achieving a coverage of more than 122,000 unique peptides and 38,000 protein groups within a 30-min DIA run. This marks a significant departure from current state-of-the-art metaproteomics methodologies, resulting in broader coverage of the metabolic pathways governing the biological system. In combination, our strategy and the Astral mass analyzer represent a quantum leap in the functional analysis of microbiomes.DtQfodvXmxPWcwvjCbxEj1Video

A metabolomics approach for monitoring thermal processing temperature of bovine milk using ultra-performance liquid chromatography tandem Q-Exactive mass spectrometry and multivariate data analysis

A metabolomics approach for monitoring thermal processing temperature of bovine milk using ultra-performance liquid chromatography tandem Q-Exactive mass spectrometry and multivariate data analysis

IsoForma: An R Package for Quantifying and Visualizing Positional Isomers in Top-Down LC-MS/MS Data.

Proteoforms, the different forms of a protein with sequence variations including post-translational modifications (PTMs), execute vital functions in biological systems, such as cell signaling and epigenetic regulation. Advances in top-down mass spectrometry (MS) technology have permitted the direct characterization of intact proteoforms and their exact number of modification sites, allowing for the relative quantification of positional isomers (PI). Protein positional isomers refer to a set of proteoforms with identical total mass and set of modifications, but varying PTM site combinations. The relative abundance of PI can be estimated by matching proteoform-specific fragment ions to top-down tandem MS (MS2) data to localize and quantify modifications. However, the current approaches heavily rely on manual annotation. Here, we present IsoForma, an open-source R package for the relative quantification of PI within a single tool. Benchmarking IsoForma's performance against two existing workflows produced comparable results and improvements in speed. Overall, IsoForma provides a streamlined process for quantifying PI, reduces the analysis time, and offers an essential framework for developing customized proteoform analysis workflows. The software is open source and available at https://github.com/EMSL-Computing/isoforma-lib.

A MassQL-Integrated Molecular Networking Approach for the Discovery and Substructure Annotation of Bioactive Cyclic Peptides.

The marine sponge-derived fungus Stachylidium bicolor 293 K04 is a prolific producer of specialized metabolites, including certain cyclic tetrapeptides called endolides, which are characterized by the presence of the unusual amino acid N-methyl-3-(3-furyl)-alanine. This rare feature can be used as bait to detect new endolide-like analogs through customized fragment pattern searches of tandem mass spectrometry data using the Mass Spec Query Language (MassQL). Here, we integrate endolide-specific MassQL queries with molecular networking to obtain substructural information guiding the targeted isolation and structure elucidation of the new proline-containing endolides E (1) and F (2). We showed that endolide F (but not E) is a moderate antagonist of the arginine vasopressin V1A receptor, a member of the G protein-coupled receptor superfamily.

Disease spectrum and pathogenic genes of inherited metabolic disorder in Gansu Province of China

To investigate the disease spectrum and pathogenic genes of inherited metabolic disorder (IMD) among neonates in Gansu Province of China. A retrospective analysis was conducted on the tandem mass spectrometry data of 286 682 neonates who received IMD screening in Gansu Provincial Maternal and Child Health Hospital from January 2018 to December 2021. A genetic analysis was conducted on the neonates with positive results in tandem mass spectrometry during primary screening and reexamination. A total of 23 types of IMD caused by 28 pathogenic genes were found in the 286 682 neonates, and the overall prevalence rate of IMD was 0.63 (1/1 593), among which phenylketonuria showed the highest prevalence rate of 0.32 (1/3 083), followed by methylmalonic acidemia (0.11, 1/8 959) and tetrahydrobiopterin deficiency (0.06, 1/15 927). In this study, 166 variants were identified in the 28 pathogenic genes, with 13 novel variants found in 9 genes. According to American College of Medical Genetics and Genomics guidelines, 5 novel variants were classified as pathogenic variants, 7 were classified as likely pathogenic variants, and 1 was classified as the variant of uncertain significance. This study enriches the database of pathogenic gene variants for IMD and provides basic data for establishing an accurate screening and diagnosis system for IMD in this region.

Chemical and microbial diversity of a tropical intertidal ascidian holobiont

The tropical ascidian Eudistoma vannamei, endemic to the northeastern coast of Brazil, is considered a prolific source of secondary metabolites and hosts Actinomycetota that produce bioactive compounds. Herein, we used an omics approach to study the ascidian as a holobiont, including the microbial diversity through 16S rRNA gene sequencing and metabolite production using mass spectrometry-based metabolomics. Gene sequencing analysis revealed all samples of E. vannamei shared about 50% of the observed ASVs, and Pseudomonadota (50.7%), Planctomycetota (9.58%), Actinomycetota (10.34%), Bacteroidota (12.05%) were the most abundant bacterial phyla. Analysis of tandem mass spectrometry (MS/MS) data allowed annotation of compounds, including phospholipids, amino acids, and pyrimidine alkaloids, such as staurosporine, a member of a well-known chemical class recognized as a microbial metabolite. Isolated bacteria, mainly belonging to Streptomyces and Micromonospora genera, were cultivated and extracted with ethyl acetate. MS/MS analysis of bacterial extracts allowed annotation of compounds not detected in the ascidian tissue, including marineosin and dihydroergotamine, yielding about 30% overlapped ions between host and isolated bacteria. This study reveals E. vannamei as a rich source of microbial and chemical diversity and, furthermore, highlights the importance of omic tools for a comprehensive investigation of holobiont systems.

Determining the parent and associated fragment formulae in mass spectrometry via the parent subformula graph

BackgroundIdentifying the molecular formula and fragmentation reactions of an unknown compound from its mass spectrum is crucial in areas such as natural product chemistry and metabolomics. We propose a method for identifying the correct candidate formula of an unidentified natural product from its mass spectrum. The method involves scoring the plausibility of parent candidate formulae based on a parent subformula graph (PSG), and two possible metrics relating to the number of edges in the PSG. This method is applicable to both electron-impact mass spectrometry (EI-MS) and tandem mass spectrometry (MS/MS) data. Additionally, this work introduces the two-dimensional fragmentation plot (2DFP) for visualizing PSGs.ResultsOur results suggest that incorporating information regarding the edges of the PSG results in enhanced performance in correctly identifying parent formulae, in comparison to the more well-accepted “MS/MS score”, on the 2016 Computational Assessment of Small Molecule Identification (CASMI 2016) data set (76.3 vs 58.9% correct formula identification) and the Research Centre for Toxic Compounds in the Environment (RECETOX) data set (66.2% vs 59.4% correct formula identification). In the extension of our method to identify the correct candidate formula from complex EI-MS data of semiochemicals, our method again performed better (correct formula appearing in the top 4 candidates in 20/23 vs 7/23 cases) than the MS/MS score, and enables the rapid identification of both the correct parent ion mass and the correct parent formula with minimal expert intervention.ConclusionOur method reliably identifies the correct parent formula even when the mass information is ambiguous. Furthermore, should parent formula identification be successful, the majority of associated fragment formulae can also be correctly identified. Our method can also identify the parent ion and its associated fragments in EI-MS spectra where the identity of the parent ion is unclear due to low quantities and overlapping compounds. Finally, our method does not inherently require empirical fitting of parameters or statistical learning, meaning it is easy to implement and extend upon.Scientific contributionDeveloped, implemented and tested new metrics for assessing plausibility of candidate molecular formulae obtained from HR-MS data.

Thirteen new peptaibols with antimicrobial activities from Trichoderma sp.

From the fungus Trichoderma sp., we isolated seven novel 18-residue peptaibols, neoatroviridins E-K (1−7), and six new 14-residue peptaibols, harzianins NPDG J-O (8−13). Additionally, four previously characterized 18-residue peptaibols neoatroviridins A-D (14−17) were also identified. The structural configurations of the newly identified peptaibols (1−13) were determined by comprehensive nuclear magnetic resonance (NMR) and high-resolution electrospray ionization tandem mass spectrometry (HR-ESI-MS/MS) data. Their absolute configurations were further determined using Marfey’s method. Notably, compounds 12 and 13 represent the first 14-residue peptaibols containing an acidic amino acid residue. In antimicrobial assessments, all 18-residue peptaibols (1−7, 14−17) exhibited moderate inhibitory activities against Staphylococcus aureus 209P, with minimum inhibitory concentration (MIC) values ranging from 8−32 μg·mL−1. Moreover, compound 9 exhibited moderate inhibitory effect on Candida albicans FIM709, with a MIC value of 16 μg·mL−1.

Differential Gene Expression Analysis of Human Ovarian Follicular Cumulus and Mural Granulosa Cells Under the Influence of Insulin in IVF Ovulatory Women and Polycystic Ovary Syndrome Patients Through Network Analysis

ABSTRACT Background Polycystic ovarian syndrome (PCOS) is a commonly occurring reproductive disorder among the reproductive-aged women. Its global occurrence varies based on diagnostic guidelines, ethnicities, and locations of concern. Insulin resistance (IR) is commonly observed around 65–70% of women diagnosed with PCOS, representing a prevalent association. Consequently, the study was designed with an objective of illustrating the effect of insulin on mural and cumulus granulosa cells (GCs) of PCOS patients in comparison to normal ovulating women. Methodology This study is a case–control design, wherein a total of 80 participants were recruited meeting criterion of inclusion and exclusion, divided into 8 groups with each group consisting of 10 samples. The process involves the isolation and culturing of mural granulosa cells (MGC) and cumulus granulosa cells (CGC) with and without exposure to insulin. The proteins released by untreated GCs and insulin-treated GCs were extracted, and complex protein mixtures were digested with trypsin, followed by tandem mass spectrometry analysis and data processing using bioinformatics. Results We found 595 proteins in both control and PCOS samples, of which 310 were contributed by MGCs and 285 by CGCs. The PCOS MGCs expressed 20%, both the normal MGCs and CGCs have equal representation of 16% by each, whereas the PCOS CGCs proteins contributed 15% of the total of the proteomic expression. However, the poor expression observed with the Insulin exposure, the Insulin treated PCOS CGCs contributes 13%, PCOS MGCs contributes 8%. The normal MGCs upon the Insulin treatment give 8% then and there only 4% of proteins expressed by normal CGCs after Insulin treatment. The Venn analysis widened on their precise expression topographies. The examination of strings exhibited important protein–protein interaction pathways. Conclusion This is a pioneering investigation aimed to establish the link between hyperinsulinemia in localized follicular GCs and PCOS mechanisms by comparing them to control group. The examination of various attributes, mechanisms, and traits shown by genes and proteins in individuals with PCOS compared to control populations, alongside the investigation of the dynamics of these genes and proteins following exposure to insulin, holds promise for the formulation of novel hypotheses and strategies in the identification of new biomarkers.

MIST-CF: Chemical Formula Inference from Tandem Mass Spectra.

Chemical formula annotation for tandem mass spectrometry (MS/MS) data is the first step toward structurally elucidating unknown metabolites. While great strides have been made toward solving this problem, the current state-of-the-art method depends on time-intensive, proprietary, and expert-parametrized fragmentation tree construction and scoring. In this work, we extend our previous spectrum Transformer methodology into an energy-based modeling framework, MIST-CF: Metabolite Inference with Spectrum Transformers for Chemical Formula prediction, for learning to rank chemical formula and adduct assignments given an unannotated MS/MS spectrum. Importantly, MIST-CF learns in a data-dependent fashion using a Formula Transformer neural network architecture and circumvents the need for fragmentation tree construction. We train and evaluate our model on a large open-access database, showing an absolute improvement of 10% top 1 accuracy over other neural network architectures. We further validate our approach on the CASMI2022 challenge data set, achieving nearly equivalent performance to the winning entry within the positive mode category without any manual curation or postprocessing of our results. These results demonstrate an exciting strategy to more powerfully leverage MS2 fragment peaks for predicting MS1 precursor chemical formulas with data-driven learning.

In Search of Disentanglement in Tandem Mass Spectrometry Datasets.

Generative modeling and representation learning of tandem mass spectrometry data aim to learn an interpretable and instrument-agnostic digital representation of metabolites directly from MS/MS spectra. Interpretable and instrument-agnostic digital representations would facilitate comparisons of MS/MS spectra between instrument vendors and enable better and more accurate queries of large MS/MS spectra databases for metabolite identification. In this study, we apply generative modeling and representation learning using variational autoencoders to understand the extent to which tandem mass spectra can be disentangled into their factors of generation (e.g., collision energy, ionization mode, instrument type, etc.) with minimal prior knowledge of the factors. We find that variational autoencoders can disentangle tandem mass spectra data with the proper choice of hyperparameters into meaningful latent representations aligned with known factors of variation. We develop a two-step approach to facilitate the selection of models that are disentangled, which could be applied to other complex and high-dimensional data sets.

Identification of organic micro-pollutants in surface water using MS-based infrared ion spectroscopy

Comprehensive monitoring of organic micro-pollutants (OMPs) in drinking water sources relies on non-target screening (NTS) using liquid-chromatography and high-resolution mass spectrometry (LC-HRMS). Identification of OMPs is typically based on accurate mass and tandem mass spectrometry (MS/MS) data by matching against entries in compound databases and MS/MS spectral libraries. MS/MS spectra are, however, not always diagnostic for the full molecular structure and, moreover, emerging OMPs or OMP transformation products may not be present in libraries. Here we demonstrate how infrared ion spectroscopy (IRIS), an emerging MS-based method for structural elucidation, can aid in the identification of OMPs. IRIS measures the IR spectrum of an m/z-isolated ion in a mass spectrometer, providing an orthogonal diagnostic for molecular identification. Here, we demonstrate the workflow for identification of OMPs in river water and show how quantum-chemically predicted IR spectra can be used to screen potential candidates and suggest structural assignments. A crucial step herein is to define a set of candidate structures, presumably including the actual OMP, for which we present several strategies based on domain knowledge, the IR spectrum and MS/MS spectrum.

Targeted Workflow Investigating Variations in the Tear Proteome by Liquid Chromatography Tandem Mass Spectrometry.

Proteins in tears have an important role in eye health and have been shown as a promising source of disease biomarkers. The goal of this study was to develop a robust, sensitive, and targeted method for profiling tear proteins to examine the variability within a group of healthy volunteers over three days. Inter-individual and inter-day variabilities were examined to contribute to understanding the normal variations in the tear proteome, as well as to establish which proteins may be better candidates as eventual biomarkers of specific diseases. Tear samples collected on Schirmer strips were subjected to bottom-up proteomics, and resulting peptides were analyzed using an optimized targeted method measuring 226 proteins by liquid chromatography-scheduled multiple reaction monitoring. This method was developed using an in-house database of identified proteins from tears compiled from high-resolution data-dependent liquid chromatography tandem mass spectrometry data. The measurement of unique peptide signals can help better understand the dynamics of each of these proteins in tears. Some interesting trends were seen in specific pathways or protein classes, including higher variabilities for those involved in glycolysis, glutathione metabolism, and cytoskeleton proteins and lower variation for those involving the degradation of the extracellular matrix. The overall aim of this study was to contribute to the field of tear proteomics with the development of a novel and targeted method that is highly amenable to the clinical laboratory using high flow LC and commonly used triple quadrupole mass spectrometry while ensuring that protein quantitation was reported based on unique peptides for each protein and robust peak areas with data normalization. These results report on variabilities on over 200 proteins that are robustly detected in tear samples from healthy volunteers with a simple sample preparation procedure.

MetaNovo: An open-source pipeline for probabilistic peptide discovery in complex metaproteomic datasets.

Microbiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focused search sequence databases based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing only targets the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. Here we describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines. We compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome sequence database-but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic sequence database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying an experimental sample contaminant without prior expectation. By estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence databases to search. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic sequence database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself.