Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023.

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit.

Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.

Increasing Proteome Coverage Through a Reduction in Analyte Complexity in Single-Cell Equivalent Samples.

The advancement of sophisticated instrumentation in mass spectrometry has catalyzed an in-depth exploration of complex proteomes. This exploration necessitates a nuanced balance in experimental design, particularly between quantitative precision and the enumeration of analytes detected. In bottom-up proteomics, a key challenge is that oversampling of abundant proteins can adversely affect the identification of a diverse array of unique proteins. This issue is especially pronounced in samples with limited analytes, such as small tissue biopsies or single-cell samples. Methods such as depletion and fractionation are suboptimal to reduce oversampling in single cell samples, and other improvements on LC and mass spectrometry technologies and methods have been developed to address the trade-off between precision and enumeration. We demonstrate that by using a monosubstrate protease for proteomic analysis of single-cell equivalent digest samples, an improvement in quantitative accuracy can be achieved, while maintaining high proteome coverage established by trypsin. This improvement is particularly vital for the field of single-cell proteomics, where single-cell samples with limited number of protein copies, especially in the context of low-abundance proteins, can benefit from considering analyte complexity. Considerations about analyte complexity, alongside chromatographic complexity, integration with data acquisition methods, and other factors such as those involving enzyme kinetics, will be crucial in the design of future single-cell workflows.

PIPI2: Sensitive Tag-Based Database Search to Identify Peptides with Multiple Post-translational Modifications.

Peptide identification is important in bottom-up proteomics. Post-translational modifications (PTMs) are crucial in regulating cellular activities. Many database search methods have been developed to identify peptides with PTMs and characterize the PTM patterns. However, the PTMs on peptides hinder the peptide identification rate and the PTM characterization precision, especially for peptides with multiple PTMs. To address this issue, we present a sensitive open search engine, PIPI2, with much better performance on peptides with multiple PTMs than other methods. With a greedy approach, we simplify the PTM characterization problem into a linear one, which enables characterizing multiple PTMs on one peptide. On the simulation data sets with up to four PTMs per peptide, PIPI2 identified over 90% of the spectra, at least 56% more than five other competitors. PIPI2 also characterized these PTM patterns with the highest precision of 77%, demonstrating a significant advantage in handling peptides with multiple PTMs. In the real applications, PIPI2 identified 30% to 88% more peptides with PTMs than its competitors.

Fit for Purpose Approach To Evaluate Detection of Amino Acid Substitutions in Shotgun Proteomics.

Amino acid substitutions (AASs) alter proteins from their genome-expected sequences. Accumulation of substitutions in proteins underlies numerous diseases and antibiotic mechanisms. Accurate global detection of AASs and their frequencies is crucial for understanding these mechanisms. Shotgun proteomics provides an untargeted method for measuring AASs but introduces biases when extrapolating from the genome to identify AASs. To characterize these biases, we created a "ground-truth" approach using the similarities betweenEscherichia coli and Salmonella typhimurium to model the complexity of AAS detection. Shotgun proteomics on mixed lysates generated libraries representing ∼100,000 peptide-spectra and 4161 peptide sequences with a single AAS and defined stoichiometry. Identifying S. typhimurium peptide-spectra with only the E. coli genome resulted in 64.1% correctly identified library peptides. Specific AASs exhibit variable identification efficiencies. There was no inherent bias from the stoichiometry of the substitutions. Short peptides and AASs localized near peptide termini had poor identification efficiency. We identify a new class of "scissor substitutions" that gain or lose protease cleavage sites. Scissor substitutions also had poor identification efficiency. This ground-truth AAS library reveals various sources of bias, which will guide the application of shotgun proteomics to validate AAS hypotheses.

Accelerating Proteomics Using Broad Specificity Proteases.

Trypsin is the gold-standard protease in bottom-up proteomics, but many sequence stretches of the proteome are inaccessible to trypsin and standard LC-MS approaches. Thus, multienzyme strategies are used to maximize sequence coverage in post-translational modification profiling. We present fast and robust SP3- and STRAP-based protocols for the broad-specificity proteases subtilisin, proteinase K, and thermolysin. All three enzymes are remarkably fast, producing near-complete digests in 1-5 min, and cost 200-1000× less than proteomics-grade trypsin. Using FragPipe resolved a major challenge by drastically reducing the duration of the required "unspecific" searches. In-depth analyses of proteinase K, subtilisin, and thermolysin Jurkat digests identified 7374, 8178, and 8753 unique proteins with average sequence coverages of 21, 29, and 37%, including 10,000s of amino acids not reported in PeptideAtlas' >2400 experiments. While we could not identify distinct cleavage patterns, machine learning could distinguish true protease products from random cleavages, potentially enabling the prediction of cleavage products. Finally, proteinase K, subtilisin, and thermolysin enabled label-free quantitation of 3111, 3659, and 4196 unique Jurkat proteins, which in our hands is comparable to trypsin. Our data demonstrate that broad-specificity proteases enable quantitative proteomics of uncharted areas of the proteome. Their fast kinetics may allow "on-the-fly" digestion of samples in the future.

Full Native timsTOF PASEF-Enabled Quantitative Proteomics with the i2MassChroQ Software Package.

Ion mobility mass spectrometry has become popular in proteomics lately, in particular because the Bruker timsTOF instruments have found significant adoption in proteomics facilities. The Bruker's implementation of the ion mobility dimension generates massive amounts of mass spectrometric data that require carefully designed software both to extract meaningful information and to perform processing tasks at reasonable speed. In a historical move, the Bruker company decided to harness the skills of the scientific software development community by releasing to the public the timsTOF data file format specification. As a proteomics facility that has been developing Free Open Source Software (FOSS) solutions since decades, we took advantage of this opportunity to implement the very first FOSS proteomics complete solution to natively read the timsTOF data, low-level process them, and explore them in an integrated quantitative proteomics software environment. We dubbed our software i2MassChroQ because it implements a (peptide)identification-(protein)inference-mass-chromatogram-quantification processing workflow. The software benchmarking results reported in this paper show that i2MassChroQ performed better than competing software on two critical characteristics: (1) feature extraction capability and (2) protein quantitative dynamic range. Altogether, i2MassChroQ yielded better quantified protein numbers, both in a technical replicate MS runs setting and in a differential protein abundance analysis setting.

Use of Nonhuman Sera as a Highly Cost-Effective Internal Standard for Quantitation of Multiple Human Proteins Using Species-Specific Tryptic Peptides: Applicability in Clinical LC-MS Analyses.

Quantitation of proteins using liquid chromatography-tandem mass spectrometry (LC-MS/MS) is complex, with a multiplicity of options ranging from label-free techniques to chemically and metabolically labeling proteins. Increasingly, for clinically relevant analyses, stable isotope-labeled (SIL) internal standards (ISs) represent the "gold standard" for quantitation due to their similar physiochemical properties to the analyte, wide availability, and ability to multiplex to several peptides. However, the purchase of SIL-ISs is a resource-intensive step in terms of cost and time, particularly for screening putative biomarker panels of hundreds of proteins. We demonstrate an alternative strategy utilizing nonhuman sera as the IS for quantitation of multiple human proteins. We demonstrate the effectiveness of this strategy using two high abundance clinically relevant analytes, vitamin D binding protein [Gc globulin] (DBP) and albumin (ALB). We extend this to three putative risk markers for cardiovascular disease: plasma protease C1 inhibitor (SERPING1), annexin A1 (ANXA1), and protein kinase, DNA-activated catalytic subunit (PRKDC). The results show highly specific, reproducible, and linear measurement of the proteins of interest with comparable precision and accuracy to the gold standard SIL-IS technique. This approach may not be applicable to every protein, but for many proteins it can offer a cost-effective solution to LC-MS/MS protein quantitation.

Collision energies: Optimization strategies for bottom-up proteomics.

Mass-spectrometry coupled to liquid chromatography is an indispensable tool in the field of proteomics. In the last decades, more and more complex and diverse biochemical and biomedical questions have arisen. Problems to be solved involve protein identification, quantitative analysis, screening of low abundance modifications, handling matrix effect, and concentrations differing by orders of magnitude. This led the development of more tailored protocols and problem centered proteomics workflows, including advanced choice of experimental parameters. In the most widespread bottom-up approach, the choice of collision energy in tandem mass spectrometric experiments has outstanding role. This review presents the collision energy optimization strategies in the field of proteomics which can help fully exploit the potential of MS based proteomics techniques. A systematic collection of use case studies is then presented to serve as a starting point for related further scientific work. Finally, this article discusses the issue of comparing results from different studies or obtained on different instruments, and it gives some hints on methodology transfer between laboratories based on measurement of reference species.

The utility of proteases in proteomics, from sequence profiling to structure and function analysis.

In mass spectrometry (MS)-based bottom-up proteomics, protease digestion plays an essential role in profiling both proteome sequences and post-translational modifications (PTMs). Trypsin is the gold standard in digesting intact proteins into small-size peptides, which are more suitable for high-performance liquid chromatography (HPLC) separation and tandem MS (MS/MS) characterization. However, protein sequences lacking Lys and Arg cannot be cleaved by trypsin and may be missed in conventional proteomic analysis. Proteases with cleavage sites complementary to trypsin are widely applied in proteomic analysis to greatly improve the coverage of proteome sequences and PTM sites. In this review, we survey the common and newly emerging proteases used in proteomics analysis mainly in the last 5 years, focusing on their unique cleavage features and specific proteomics applications such as missing protein characterization, new PTM discovery, and de novo sequencing. In addition, we summarize the applications of proteases in structural proteomics and protein function analysis in recent years. Finally, we discuss the future development directions of new proteases and applications in proteomics.

Small proteins in bacteria - Big challenges in prediction and identification.

Proteins with up to 100 amino acids have been largely overlooked due to the challenges associated with predicting and identifying them using traditional methods. Recent advances in bioinformatics and machine learning, DNA sequencing, RNA and Ribo-seq technologies, and mass spectrometry (MS) have greatly facilitated the detection and characterisation of these elusive proteins in recent years. This has revealed their crucial role in various cellular processes including regulation, signalling and transport, as toxins and as folding helpers for protein complexes. Consequently, the systematic identification and characterisation of these proteins in bacteria have emerged as a prominent field of interest within the microbial research community. This review provides an overview of different strategies for predicting and identifying these proteins on a large scale, leveraging the power of these advanced technologies. Furthermore, the review offers insights into the future developments that may be expected in this field.

Mass Spectrometry-Based Multiomics Identifies Metabolic Signatures of Sarcopenia in Rhesus Monkey Skeletal Muscle.

Sarcopenia is a progressive disorder characterized by age-related loss of skeletal muscle mass and function. Although significant progress has been made over the years to identify the molecular determinants of sarcopenia, the precise mechanisms underlying the age-related loss of contractile function remains unclear. Advances in "omics" technologies, including mass spectrometry-based proteomic and metabolomic analyses, offer great opportunities to better understand sarcopenia. Herein, we performed mass spectrometry-based analyses of the vastus lateralis from young, middle-aged, and older rhesus monkeys to identify molecular signatures of sarcopenia. In our proteomic analysis, we identified proteins that change with age, including those involved in adenosine triphosphate and adenosine monophosphate metabolism as well as fatty acid beta oxidation. In our untargeted metabolomic analysis, we identified metabolites that changed with age largely related to energy metabolism including fatty acid beta oxidation. Pathway analysis of age-responsive proteins and metabolites revealed changes in muscle structure and contraction as well as lipid, carbohydrate, and purine metabolism. Together, this study discovers new metabolic signatures and offers new insights into the molecular mechanisms underlying sarcopenia for the evaluation and monitoring of a therapeutic treatment of sarcopenia.

Proteome Coverage after Simultaneous Proteo-Metabolome Liquid-Liquid Extraction.

Proteomics and metabolomics are essential in systems biology, and simultaneous proteo-metabolome liquid-liquid extraction (SPM-LLE) allows isolation of the metabolome and proteome from the same sample. Since the proteome is present as a pellet in SPM-LLE, it must be solubilized for quantitative proteomics. Solubilization and proteome extraction are critical factors in the information obtained at the proteome level. In this study, we investigated the performance of two surfactants (sodium deoxycholate (SDC), sodium dodecyl sulfate (SDS)) and urea in terms of proteome coverage and extraction efficiency of an interphase proteome pellet generated by methanol-chloroform based SPM-LLE. We also investigated how the performance differs when the proteome is extracted from the interphase pellet or by direct cell lysis. We quantified 12 lipids covering triglycerides and various phospholipid classes, and 25 polar metabolites covering central energy metabolism in chloroform and methanol extracts. Our study reveals that the proteome coverages between the two surfactants and urea for the SPM-LLE interphase pellet were similar, but the extraction efficiencies differed significantly. While SDS led to enrichment of basic proteins, which were mainly ribosomal and ribonuclear proteins, urea was the most efficient extraction agent for simultaneous proteo-metabolome analysis. The results of our study also show that the performance of surfactants for quantitative proteomics is better when the proteome is extracted through direct cell lysis rather than an interphase pellet. In contrast, the performance of urea for quantitative proteomics was significantly better when the proteome was extracted from an interphase pellet than by direct cell lysis. We demonstrated that urea is superior to surfactants for proteome extraction from SPM-LLE interphase pellets, with a particularly good performance for the extraction of proteins associated with metabolic pathways. Data are available via ProteomeXchange with identifier PXD027338.

Extending the Range of SLIM-Labeling Applications: From Human Cell Lines in Culture to Caenorhabditis elegans Whole-Organism Labeling.

The simple light isotope metabolic-labeling technique relies on the in vivo biosynthesis of amino acids from U-[12C]-labeled molecules provided as the sole carbon source. The incorporation of the resulting U-[12C]-amino acids into proteins presents several key advantages for mass-spectrometry-based proteomics analysis, as it results in more intense monoisotopic ions, with a better signal-to-noise ratio in bottom-up analysis. In our initial studies, we developed the simple light isotope metabolic (SLIM)-labeling strategy using prototrophic eukaryotic microorganisms, the yeasts Candida albicans and Saccharomyces cerevisiae, as well as strains with genetic markers that lead to amino-acid auxotrophy. To extend the range of SLIM-labeling applications, we evaluated (i) the incorporation of U-[12C]-glucose into proteins of human cells grown in a complex RPMI-based medium containing the labeled molecule, considering that human cell lines require a large number of essential amino-acids to support their growth, and (ii) an indirect labeling strategy in which the nematode Caenorhabditis elegans grown on plates was fed U-[12C]-labeled bacteria (Escherichia coli) and the worm proteome analyzed for 12C incorporation into proteins. In both cases, we were able to demonstrate efficient incorporation of 12C into the newly synthesized proteins, opening the way for original approaches in quantitative proteomics.

Formalin Fixation Followed by Paraffin Embedding Allows Long-Term Storage of Proteins for Liquid Chromatography-Tandem Mass Spectrometry Analysis.

In an anatomical pathology laboratory, liquid chromatography-tandem mass spectrometry (LC-MS/MS) is used to characterize amyloid deposits identified in formalin-fixed paraffin-embedded tissue (FFPET). However, the development of additional tests is partially limited by the lack of information the passage of time has on the proteins in FFPET. To investigate the reliability of LC-MS/MS in the analysis of old FFPET specimens, 1 bone marrow aspirate clot was analyzed by LC-MS/MS yearly from 2014 to 2018, in 3 consecutive months. Peptide-spectrum match, number of peptides identified, and percentage of the proteins covered were the parameters collected for the hemoglobin subunits alpha (HbA), beta (HbB), delta (HbD), and gamma (HbG). These proteins are constant components of the peripheral blood and are present in high and low abundance, allowing the monitorization of the performance of the test across varying protein concentrations. The hemoglobin subunits were stable over the years studied; 71% to 74% of HbA, 77% to 80% of HbB, 69% to 77% of HbD, and 57% to 63% of HbG were covered, with no statistical difference between 2014 and 2018. The number of peptides identified was also constant, 11 to 13 for HbA, 13 to 15 for HbB, 11 to 14 for HbD, and 7 to 9 for HbG. Peptide spectrum match was only slightly more variable: 209 to 327 for HbA, 569 to 1052 for HbB, 286 to 533 HbD, and 142 to 292 for HbG. In conclusion, high abundance hemoglobins, HbA and HbB, and relatively low abundance ones, HbD and HbG, are preserved in FFPET and confidently identified by LC-MS/MS for at least 5 years.

Limited proteolysis of mycobacterial DNA-binding protein 1 with an extended, lysine-rich, intrinsically disordered region to unveil posttranslational modifications.

The basic, intrinsically disordered regions of eukaryotic histones and their bacterial counterparts are presumed to act as signaling hubs to regulate the compaction of chromosomes or nucleoids and various DNA processes such as gene expression, recombination, and DNA replication. Posttranslational modifications (PTMs) on these regions are pivotal in regulating chromosomal or nucleoid compaction and DNA processes. However, the low sequence complexity and the presence of short lysine-rich repeats in the regions have hindered the accurate determination of types and locations of PTMs using conventional proteomic procedures. We described a limited proteolysis protocol using trypsin to analyze PTMs on mycobacterial DNA-binding protein 1 (MDP1), a nucleoid-associated protein in mycobacterial species that possesses an extended, lysine-rich, intrinsically disordered region in its C-terminal domain. This limited proteolysis approach successfully revealed significant methylation on many lysine residues in the C-terminal domain of MDP1 purified from Mycobacterium tuberculosis, which was lacking in the corresponding region of recombinant MDP1 expressed in Escherichia coli.

Identification evidence unraveled by strict proteomics rules toward forensic samples.

Snake venom is a complex mixture of proteins and peptides secreted by venomous snakes from their poison glands. Although proteomics for snake venom composition, interspecific differences, and developmental evolution has been developed for a decade, current diagnosis or identification techniques of snake venom in clinical intoxication and forensic science applications are mainly dependent on morphological and immunoassay. It could be expected that the proteomics techniques directly offer great help. This work applied a bottom-up proteomics method to identify proteins' types and species attribution in suspected snake venom samples using ultrahigh-performance liquid chromatography-quadrupole-electrostatic field Orbitrap tandem mass spectrometric technique, and cytotoxicity assay was amended to provide a direct evidence of toxicity. Toward the suspicious samples seized in the security control, sample pretreatment (in-sol and in-gel digestion) and data acquisition (nontargeted and targeted screening) modes complemented and validated each other. We have implemented two consequent approaches in identifying the species source of proteins in the samples via the points of venom proteomics and strict forensic identification. First, we completed a workflow consisting of a proteomics database match toward an entire SWISS-PROT (date 2018-11-22) database and a result-directed specific taxonomy database. The latter was a helpful hint to compare master protein kinds and reveal the insufficiency of specific venom proteomics characterization rules. Second, we suggested strict rules for protein identification to meet the requirements of forensic science on improved identification correctness, that is, (1) peptide spectrum matches confidence, peptide confidence, and protein confidence were both high (with the false-discovery ratio less than 1%); (2) the number of unique peptides was more than or equal to two in one protein, and (3) within unique peptides, which at least 75% of the ∆m/z of the matched y and b ions were less than 5 ppm. We identified these samples as cobra venom containing 10 highly abundant proteins (P00597, P82463, P60770, Q9YGI4, P62375, P49123, P80245, P60302, P01442, and P60304) from two snake venom protein families (acid phospholipase A2 and three-finger toxins), and the most abundant proteins were cytotoxins.

Evaluation of affinity-purification coupled to mass spectrometry approaches for capture of short linear motif-based interactions.

Low affinity and transient protein-protein interactions, such as short linear motif (SLiM)-based interactions, require dedicated experimental tools for discovery and validation. Here, we evaluated and compared biotinylated peptide pulldown and protein interaction screen on peptide matrix (PRISMA) coupled to mass-spectrometry (MS) using a set of peptides containing interaction motifs. Eight different peptide sequences that engage in interactions with three distinct protein domains (KEAP1 Kelch, MDM2 SWIB, and TSG101 UEV) with a wide range of affinities were tested. We found that peptide pulldown can be an effective approach for SLiM validation, however, parameters such as protein abundance and competitive interactions can prevent the capture of known interactors. The use of tandem peptide repeats improved the capture and preservation of some interactions. When testing PRISMA, it failed to provide comparable results for model peptides that successfully pulled down known interactors using biotinylated peptide pulldown. Overall, in our hands, we find that albeit more laborious, biotin-peptide pulldown was more successful in terms of validation of known interactions. Our results highlight that the tested affinity-capture MS-based methods for validation of SLiM-based interactions from cell lysates are suboptimal, and we identified parameters for consideration for method development.

Trimethylacetic Anhydride-Based Derivatization Facilitates Quantification of Histone Marks at the MS1 Level.

Histone post-translational modifications (hPTMs) are epigenetic marks that strongly affect numerous processes, including cell cycling and protein interactions. They have been studied by both antibody- and MS-based methods for years, but the analyses are still challenging, mainly because of the diversity of histones and their modifications arising from high contents of reactive amine groups in their amino acid sequences. Here, we introduce use of trimethylacetic anhydride (TMA) as a new reagent for efficient histone derivatization, which is a requirement for bottom-up proteomic hPTM analysis. TMA can derivatize unmodified amine groups of lysine residues and amine groups generated at peptide N-termini by trypsin digestion. The derivatization is facilitated by microwave irradiation, which also reduces incubation times to minutes. We demonstrate that histone derivatization with TMA reliably provides high yields of fully derivatized peptides and thus is an effective alternative to conventional methods. TMA afforded more than 98% and 99% labeling efficiencies for histones H4 and H3, respectively, thereby enabling accurate quantification of peptide forms. Trimethylacetylation substantially improves chromatographic separation of peptide forms, which is essential for direct quantification based on signals extracted from MS1 data. For this purpose, software widely applied by the proteomics community can be used without additional computational development. Thorough comparison with widely applied propionylation highlights the advantages of TMA-based histone derivatization for monitoring hPTMs in biological samples.

GroEL-Proteotyping of Bacterial Communities Using Tandem Mass Spectrometry.

Profiling bacterial populations in mixed communities is a common task in microbiology. Sequencing of 16S small subunit ribosomal-RNA (16S rRNA) gene amplicons is a widely accepted and functional approach but relies on amplification primers and cannot quantify isotope incorporation. Tandem mass spectrometry proteotyping is an effective alternative for taxonomically profiling microorganisms. We suggest that targeted proteotyping approaches can complement traditional population analyses. Therefore, we describe an approach to assess bacterial community compositions at the family level using the taxonomic marker protein GroEL, which is ubiquitously found in bacteria, except a few obligate intracellular species. We refer to our method as GroEL-proteotyping. GroEL-proteotyping is based on high-resolution tandem mass spectrometry of GroEL peptides and identification of GroEL-derived taxa via a Galaxy workflow and a subsequent Python-based analysis script. Its advantage is that it can be performed with a curated and extendable sample-independent database and that GroEL can be pre-separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) to reduce sample complexity, improving GroEL identification while simultaneously decreasing the instrument time. GroEL-proteotyping was validated by employing it on a comprehensive raw dataset obtained through a metaproteome approach from synthetic microbial communities as well as real human gut samples. Our data show that GroEL-proteotyping enables fast and straightforward profiling of highly abundant taxa in bacterial communities at reasonable taxonomic resolution.