Spurred into action by the COVID-19 pandemic, the global scientific community has, in a short of period of time, made astonishing progress in understanding and combating COVID-19. Given the known human protein machinery for (a) SARS-CoV-2 entry, (b) the host innate immune response, and (c) virus-host interactions (protein-protein and RNA-protein), the potential effects of human genetic variation in this machinery, which may contribute to clinical differences in SARS-CoV-2 pathogenesis and help determine individual risk for COVID-19 infection, are explored. The Genome Aggregation Database (gnomAD) was used to show that several rare germline exome variants of proteins in these pathways occur in the human population, suggesting that carriers of these rare variants (especially for proteins of innate immunity pathways) are at risk for severe symptoms (like the severe symptoms in patients who are known to be rare variant carriers), whereas carriers of other variants could have a protective advantage against infection. The occurrence of genetic variation is thus expected to motivate the experimental probing of natural variants to understand the mechanistic differences in SARS-CoV-2 pathogenesis from one individual to another.

Networks and network analyses are fundamental tools of systems biology. Networks are built by inferring pair-wise relationships among biological entities from a large number of samples such that subject-specific information is lost. The possibility of constructing these sample (individual)-specific networks from single molecular profiles might offer new insights in systems and personalized medicine and as a consequence is attracting more and more research interest. In this study, we evaluated and compared LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) and ssPCC (single sample network based on Pearson correlation) in the metabolomics context of metabolite-metabolite association networks. We illustrated and explored the characteristics of these two methods on (i) simulated data, (ii) data generated from a dynamic metabolic model to simulate real-life observed metabolite concentration profiles, and (iii) 22 metabolomic data sets and (iv) we applied single sample network inference to a study case pertaining to the investigation of necrotizing soft tissue infections to show how these methods can be applied in metabolomics. We also proposed some adaptations of the methods that can be used for data exploration. Overall, despite some limitations, we found single sample networks to be a promising tool for the analysis of metabolomics data.

Busulfan-based conditioning is the most commonly used high-dose conditioning regimen for allogeneic hematopoietic cell transplant (HCT). The alkylating agent busulfan has a narrow therapeutic index, with busulfan doses personalized to a target plasma exposure (targeted busulfan). Using a global pharmacometabonomics approach, we sought to identify novel biomarkers of relapse or acute graft versus host disease (GVHD) in a cohort of 84 patients receiving targeted busulfan before allogeneic HCT. A total of 763 endogenous metabolomic compounds (EMCs) were quantitated in 230 longitudinal blood samples before, during, and shortly after intravenous busulfan administration. We performed both univariate linear regression and pathway enrichment analyses using global testing. The cysteine/methionine pathway and the glycine, serine, and threonine metabolism pathway were most associated with relapse. The latter be explained by the fact that glutathione S-transferases conjugate both busulfan and glutathione, which contains glycine as a component. The d-arginine and d-ornithine metabolism pathway and arginine and proline metabolism pathway were most associated with acute GVHD. None of these associations were significant after correcting for false discovery rate (FDR) with a strict cutoff of FDR-adjusted p < 0.1. Although larger studies are needed to substantiate these findings, the results show that EMCs may be used as predictive biomarkers in HCT patients.

Bacteria of the genus Dehalogenimonas respire with vicinally halogenated alkanes via dihaloelimination. We aimed to describe involved proteins and their supermolecular organization. Metagenomic sequencing of a Dehalogenimonas-containing culture resulted in a 1.65 Mbp draft genome of Dehalogenimonas alkenigignens strain BRE15M. It contained 31 full-length reductive dehalogenase homologous genes (rdhA), but only eight had cognate rdhB gene coding for membrane-anchoring proteins. Shotgun proteomics of cells grown with 1,2-dichloropropane as an electron acceptor identified 1152 proteins representing more than 60% of the total proteome. Ten RdhA proteins were detected, including a DcpA ortholog, which was the strongest expressed RdhA. Blue native gel electrophoresis (BNE) demonstrating maximum activity was localized in a protein complex of 146-242 kDa. Protein mass spectrometry revealed the presence of DcpA, its membrane-anchoring protein DcpB, two hydrogen uptake hydrogenase subunits (HupL and HupS), an iron-sulfur protein (HupX), and subunits of a redox protein with a molybdopterin-binding motif (OmeA and OmeB) in the complex. BNE after protein solubilization with different detergent concentrations revealed no evidence for an interaction between the putative respiratory electron input module (HupLS) and the OmeA/OmeB/HupX module. All detected RdhAs comigrated with the organohalide respiration complex. Based on genomic and proteomic analysis, we propose quinone-independent respiration in Dehalogenimonas.

Randomization is used in experimental design to reduce the prevalence of unanticipated confounders. Complete randomization can however create imbalanced designs, for example, grouping all samples of the same condition in the same batch. Block randomization is an approach that can prevent severe imbalances in sample allocation with respect to both known and unknown confounders. This feature provides the reader with an introduction to blocking and randomization, and insights into how to effectively organize samples during experimental design, with special considerations with respect to proteomics.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) expresses a multifunctional papain-like proteinase (PLpro), which mediates the processing of the viral replicase polyprotein. Inhibition of PLpro has been shown to suppress the viral replication. This study aimed to explore new anti-PLpro candidates by applying virtual screening based on GRL0617, a known PLpro inhibitor of SARS coronavirus (SARS-CoV). The three-dimensional (3D) structure of SARS-CoV-2 PLpro was built by homology modeling, using SARS-CoV PLpro as the template. The model was refined and studied through molecular dynamic simulation. AutoDock Vina was then used to perform virtual screening where 50 chemicals with at least 65% similarity to GRL0617 were docked with the optimized SARS-CoV-2 PLpro. In this screening, 5-(aminomethyl)-2-methyl-N-[(1R)-1-naphthalen-1-ylethyl]benzamide outperformed GRL0617 in terms of binding affinity (-9.7 kcal/mol). Furthermore, 2-(4-fluorobenzyl)-5-nitro-1H-isoindole-1,3(2H)-dione (previously introduced as an inhibitor of cyclooxygenase-2), 3-nitro-N-[(1r)-1-phenylethyl]-5-(trifluoromethyl)benzamide (inhibitor against Mycobacterium tuberculosis), as well as the recently introduced SARS-CoV-2 PLpro inhibitor 5-acetamido-2-methyl-N-[(1S)-1-naphthalen-1-ylethyl]benzamide showed promising affinity for the viral proteinase. All of the identified compounds demonstrated an acceptable pharmacokinetic profile. In conclusion, our findings represent rediscovery of analgesic, anti-inflammatory, antibacterial, or antiviral drugs as promising pharmaceutical candidates against the ongoing coronavirus.

Deisotoping, or the process of removing peaks in a mass spectrum resulting from the incorporation of naturally occurring heavy isotopes, has long been used to reduce complexity and improve the effectiveness of spectral annotation methods in proteomics. We have previously described MSFragger, an ultrafast search engine for proteomics, that did not utilize deisotoping in processing input spectra. Here, we present a new, high-speed parallelized deisotoping algorithm, based on elements of several existing methods, that we have incorporated into the MSFragger search engine. Applying deisotoping with MSFragger reveals substantial improvements to database search speed and performance, particularly for complex methods like open or nonspecific searches. Finally, we evaluate our deisotoping method on data from several instrument types and vendors, revealing a wide range in performance and offering an updated perspective on deisotoping in the modern proteomics environment.

Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest cancers. Dissecting the tumor cell proteome from that of the non-tumor cells in the PDAC tumor bulk is critical for tumorigenesis studies, biomarker discovery, and development of therapeutics. However, investigating the tumor cell proteome has proven evasive due to the tumor's extremely complex cellular composition. To circumvent this technical barrier, we have combined bioorthogonal noncanonical amino acid tagging (BONCAT) and data-independent acquisition mass spectrometry (DIA-MS) in an orthotopic PDAC model to specifically identify the tumor cell proteome in vivo. Utilizing the tumor cell-specific expression of a mutant tRNA synthetase transgene, this approach provides tumor cells with the exclusive ability to incorporate an azide-bearing methionine analogue into newly synthesized proteins. The azide-tagged tumor cell proteome is subsequently enriched and purified via a bioorthogonal reaction and then identified and quantified using DIA-MS. Applying this workflow to the orthotopic PDAC model, we have identified thousands of proteins expressed by the tumor cells. Furthermore, by comparing the tumor cell and tumor bulk proteomes, we showed that the approach can distinctly differentiate proteins produced by tumor cells from those of non-tumor cells within the tumor microenvironment. Our study, for the first time, reveals the tumor cell proteome of PDAC under physiological conditions, providing broad applications for tumorigenesis, therapeutics, and biomarker studies in various human cancers.

There is mounting evidence that subclinical nonpathological high blood pressure and heart rate during youth and adulthood steadily increase the risk of developing a cardiovascular disease at a later stage. For this reason, it is important to understand the mechanisms underlying the subclinical elevation of blood pressure and heart rate in healthy, relatively young individuals. In the present study, we present a network-based metabolomic study of blood plasma metabolites and lipids measured using nuclear magnetic resonance spectroscopy on 841 adult healthy blood donor volunteers, which were stratified for subclinical low and high blood pressure (systolic and diastolic) and heart rate. Our results indicate a rewiring of metabolic pathways active in high and low groups, indicating that the subjects with subclinical high blood pressure and heart rate could present latent cardiometabolic dysregulations.

Saw-scaled or carpet vipers (genus Echis) are considered to cause a higher global snakebite mortality than any other snake. Echis carinatus sochureki (ECS) is a widely distributed snake species, also found across the thirteen provinces of Iran, where it is assumed to be responsible for the most snakebite envenomings. Here, we collected the Iranian specimens of ECS from three different geographically distinct populations, investigated food habits, and performed toxicity assessment and venom proteome profiling to better understand saw-scaled viper life. Our results show that the prey items most commonly found in all populations were arthropods, with scorpions from the family Buthidae particularly well represented. LD50 (median lethal dose) values of the crude venom demonstrate highly comparable venom toxicities in mammals. Consistent with this finding, venom characterization via top-down and bottom-up proteomics, applied to both crude venoms and size-exclusion chromatographic fractions, revealed highly comparable venom compositions among the different populations. By combining all proteomics data, we identified 22 protein families from 102 liquid chromatography and tandem mass spectrometry (LC-MS/MS) raw files, including the most abundant snake venom metalloproteinases (SVMPs, 29-34%); phospholipase A2 (PLA2s, 26-31%); snake venom serine proteinases (SVSPs, 11-12%); l-amino acid oxidases (LAOs, 8-11%), C-type lectins/lectin-like (CTLs, 7-9%) protein families, and many newly detected ones, e.g., renin-like aspartic proteases (RLAPs), fibroblast growth factors (FGFs), peptidyl-prolyl cis-trans isomerases (PPIs), and venom vasodilator peptides (VVPs). Furthermore, we identified and characterized methylated, acetylated, and oxidized proteoforms relating to the PLA2 and disintegrin toxin families and the site of their modifications. It thus seems that post-translational modifications (PTMs) of toxins, particularly target lysine residues, may play an essential role in the structural and functional properties of venom proteins and might be able to influence the therapeutic response of antivenoms, to be investigated in future studies.

Tandem mass tag (TMT)-based mass spectrometry (MS) enables deep proteomic profiling of more than 10,000 proteins in complex biological samples but requires up to 100 μg protein in starting materials during a standard analysis. Here, we present a streamlined protocol to quantify more than 9000 proteins with 0.5 μg protein per sample by 16-plex TMT coupled with two-dimensional liquid chromatography and tandem mass spectrometry (LC/LC-MS/MS). In this protocol, we optimized multiple conditions to reduce sample loss, including processing each sample in a single tube to minimize surface adsorption, increasing digestion enzymes to shorten proteolysis and function as carriers, eliminating a desalting step between digestion and TMT labeling, and developing miniaturized basic pH LC for prefractionation. By profiling 16 identical human brain tissue samples of Alzheimer's disease (AD), vascular dementia (VaD), and non-dementia controls, we directly compared this new microgram-scale protocol to the standard-scale protocol, quantifying 9116 and 10,869 proteins, respectively. Importantly, bioinformatics analysis indicated that the microgram-scale protocol had adequate sensitivity and reproducibility to detect differentially expressed proteins in disease-related pathways. Thus, this newly developed protocol is of general application for deep proteomics analysis of biological and clinical samples at sub-microgram levels.

Cross-linking mass spectrometry (XL-MS) has matured into a potent tool to identify protein-protein interactions or to uncover protein structures in living cells, tissues, or organelles. The unique ability to investigate the interplay of proteins within their native environment delivers valuable complementary information to other advanced structural biology techniques. This Review gives a comprehensive overview of the current possible applications as well as the remaining limitations of the technique, focusing on cross-linking in highly complex biological systems like cells, organelles, or tissues. Thanks to the commercial availability of most reagents and advances in user-friendly data analysis, validation, and visualization tools, studies using XL-MS can, in theory, now also be utilized by nonexpert laboratories.

To understand and treat immunology-related diseases, a comprehensive, unbiased characterization of major histocompatibility complex (MHC) peptide ligands is of key importance. Preceding the analysis by mass spectrometry, MHC class I peptide ligands are typically isolated by MHC immunoaffinity chromatography (MHC-IAC) and less often by mild acid elution (MAE). MAE may provide a cheap alternative to MHC-IAC for suspension cells but has been hampered by the high number of contaminating, MHC-unrelated peptides. Here, we optimized MAE, yielding MHC peptide ligand purities of more than 80%. When compared with MHC-IAC, obtained peptides were similar in numbers, identities, and to a large extent intensities, while the percentage of cysteinylated peptides was 5 times higher in MAE. The latter benefitted the discovery of MHC-allotype-specific, distinct cysteinylation frequencies at individual positions of MHC peptide ligands. MAE revealed many MHC ligands with unmodified, N-terminal cysteine residues which get lost in MHC-IAC workflows. The results support the idea that MAE might be particularly valuable for the high-confidence analysis of post-translational modifications by avoiding the exposure of the investigated peptides to enzymes and reactive molecules in the cell lysate. Our improved and carefully documented MAE workflow represents a high-quality, cost-effective alternative to MHC-IAC for suspension cells.

Mitochondrial respiration in mammalian cells not only generates ATP to meet their own energy needs but also couples with biosynthetic pathways to produce metabolites that can be exported to support neighboring cells. However, how defects in mitochondrial respiration influence these biosynthetic and exporting pathways remains poorly understood. Mitochondrial dysfunction in retinal pigment epithelium (RPE) cells is an emerging contributor to the death of their neighboring photoreceptors in degenerative retinal diseases including age-related macular degeneration. In this study, we used targeted-metabolomics and 13C tracing to investigate how inhibition of mitochondrial respiration influences the intracellular and extracellular metabolome. We found inhibition of mitochondrial respiration strikingly influenced both the intracellular and extracellular metabolome in primary RPE cells. Intriguingly, the extracellular metabolic changes sensitively reflected the intracellular changes. These changes included substantially enhanced glucose consumption and lactate production; reduced release of pyruvate, citrate, and ketone bodies; and massive accumulation of multiple amino acids and nucleosides. In conclusion, these findings reveal a metabolic signature of nutrient consumption and release in mitochondrial dysfunction in RPE cells. Testing medium metabolites provides a sensitive and noninvasive method to assess mitochondrial function in nutrient utilization and transport.

Acetylation was initially discovered as a post-translational modification (PTM) on the unstructured, highly basic N-terminal tails of eukaryotic histones in the 1960s. Histone acetylation constitutes part of the "histone code", which regulates chromosome compaction and various DNA processes such as gene expression, recombination, and DNA replication. In bacteria, nucleoid-associated proteins (NAPs) are responsible these functions in that they organize and compact the chromosome and regulate some DNA processes. The highly conserved DNABII family of proteins are considered functional homologues of eukaryotic histones despite having no sequence or structural conservation. Within the past decade, a growing interest in Nε-lysine acetylation led to the discovery that hundreds of bacterial proteins are acetylated with diverse cellular functions, in direct contrast to the original thought that this was a rare phenomenon. Similarly, other previously undiscovered bacterial PTMs, like serine, threonine, and tyrosine phosphorylation, have also been characterized. In this review, the various PTMs that were discovered among DNABII family proteins, specifically histone-like protein (HU) orthologues, from large-scale proteomic studies are discussed. The functional significance of these modifications and the enzymes involved are also addressed. The discovery of novel PTMs on these proteins begs this question: is there a histone-like code in bacteria?

Identification of metal-binding sites in proteins and understanding metal-coupled protein folding mechanisms are aspects of high importance for the structure-to-function relationship. Mass spectrometry (MS) has brought a powerful adjunct perspective to structural biology, obtaining from metal-to-protein stoichiometry to quaternary structure information. Currently, the different experimental and/or instrumental setups usually require the use of multiple data analysis software, and in some cases, they lack some of the main data analysis steps (MS processing, scoring, identification). Here, we present a comprehensive data analysis pipeline that addresses charge-state deconvolution, statistical scoring, and mass assignment for native MS, bottom-up, and native top-down with emphasis on metal-protein complexes. We have evaluated all of the approaches using assemblies of increasing complexity, including free and chemically labeled proteins, from low- to high-resolution MS. In all cases, the results have been compared with common software and proved how MetaOdysseus outperformed them.

Skeletal muscle myofibers have differential protein expression resulting in functionally distinct slow- and fast-twitch types. While certain protein classes are well-characterized, the depth of all proteins involved in this process is unknown. We utilized the Human Protein Atlas (HPA) and the HPASubC tool to classify mosaic expression patterns of staining across 49,600 unique tissue microarray (TMA) images using a visual proteomic approach. We identified 2164 proteins with potential mosaic expression, of which 1605 were categorized as "likely" or "real." This list included both well-known fiber-type-specific and novel proteins. A comparison of the 1605 mosaic proteins with a mass spectrometry (MS)-derived proteomic dataset of single human muscle fibers led to the assignment of 111 proteins to fiber types. We additionally used a multiplexed immunohistochemistry approach, a multiplexed RNA-ISH approach, and STRING v11 to further assign or suggest fiber types of newly characterized mosaic proteins. This visual proteomic analysis of mature skeletal muscle myofibers greatly expands the known repertoire of twitch-type-specific proteins.

Since the novel coronavirus pandemic, people around the world have been touched in varying degrees, and this pandemic has raised a major global health concern. As there is no effective drug or vaccine, it is urgent to find therapeutic drugs that can serve to deal with the current epidemic situation in all countries and regions. We searched drugs and response measures for SARS-CoV-2 in the PubMed database, and then updated the potential targets and therapeutic drugs from the perspective of the viral replication cycle. The drug research studies of the viral replication cycle are predominantly focused on the process of the virus entering cells, proteases, and RdRp. The inhibitors of the virus entry to cells and RdRp, such as Arbidol, remdesivir, favipiravir, EIDD-2081, and ribavirin, are in clinical trials, while most of the protease inhibitors are mainly calculated by molecular docking technology, which needs in vivo and in vitro experiments to prove the effect for SARS-CoV-2. This review summarizes the drugs targeting the viral replication process and provides a basis and directions for future drug development and reuse on the protein level of COVID-19.

Identification of proteoforms, the different forms of a protein, is important to understand biological processes. A proteoform family is the set of different proteoforms from the same gene. We previously developed the software program Proteoform Suite, which constructs proteoform families and identifies proteoforms by intact-mass analysis. Here, we have applied this approach to top-down proteomic data acquired at the National High Magnetic Field Laboratory 21 tesla Fourier transform ion cyclotron resonance mass spectrometer (data available on the MassIVE platform with identifier MSV000085978). We explored the ability to construct proteoform families and identify proteoforms from the high mass accuracy data that this instrument provides for a complex cell lysate sample from the MCF-7 human breast cancer cell line. There were 2830 observed experimental proteforms, of which 932 were identified, 44 were ambiguous, and 1854 were unidentified. Of the 932 unique identified proteoforms, 766 were identified by top-down MS2 analysis at 1% false discovery rate (FDR) using TDPortal, and 166 were additional intact-mass identifications (∼4.7% calculated global FDR) made using Proteoform Suite. We recently published a proteoform level schema to represent ambiguity in proteoform identifications. We implemented this proteoform level classification in Proteoform Suite for intact-mass identifications, which enables users to determine the ambiguity levels and sources of ambiguity for each intact-mass proteoform identification.

With ever-increasing amounts of data produced by mass spectrometry (MS) proteomics and metabolomics, and the sheer volume of samples now analyzed, the need for a common open format possessing both file size efficiency and faster read/write speeds has become paramount to drive the next generation of data analysis pipelines. The Proteomics Standards Initiative (PSI) has established a clear and precise extensible markup language (XML) representation for data interchange, mzML, receiving substantial uptake; nevertheless, storage and file access efficiency has not been the main focus. We propose an HDF5 file format "mzMLb" that is optimized for both read/write speed and storage of the raw mass spectrometry data. We provide an extensive validation of the write speed, random read speed, and storage size, demonstrating a flexible format that with or without compression is faster than all existing approaches in virtually all cases, while with compression is comparable in size to proprietary vendor file formats. Since our approach uniquely preserves the XML encoding of the metadata, the format implicitly supports future versions of mzML and is straightforward to implement: mzMLb's design adheres to both HDF5 and NetCDF4 standard implementations, which allows it to be easily utilized by third parties due to their widespread programming language support. A reference implementation within the established ProteoWizard toolkit is provided.

Bottom-up proteomics relies on identification of peptides from tandem mass spectra, usually via matching against sequence databases. Confidence in a peptide-spectrum match can be characterized by a score value given by the database search engines, and it depends on the information content and the quality of the spectrum. The latter are influenced by experimental parameters, of which the collision energy is the most important one in the case of collision-induced dissociation. We examined how the identification score of the Byonic and Andromeda (MaxQuant) engines varies with collision energy for more than a thousand individual peptides from a HeLa tryptic digest on a QTof instrument. We thereby extended our earlier study on Mascot scores and corroborated its findings on the potential bimodal nature of this energy dependence. Optimal energies as a function of m/z show comparable linear trends for the three engines. On the basis of peptide-level results, we designed methods with one or two liquid chromatography-tandem mass spectrometry (LC-MS/MS) runs and various collision energy settings and assessed their practical performance in peptide and protein identification from the HeLa standard sample. A 10-40% gain in various measures, such as the number of identified proteins or sequence coverage, was obtained over the factory default settings. Best performing methods differ for the three engines, suggesting that the experimental parameters should be fine-tuned to the choice of the engine. We also recommend a simple approach and provide reference data to ease the transfer of the optimized methods to other mass spectrometers relevant for proteomics. We demonstrate the utility of this approach on an Orbitrap instrument. Data sets can be accessed via the MassIVE repository (MSV000086379).

The isobaric carrier approach, which combines small isobarically labeled samples with a larger isobarically labeled carrier sample, finds diverse applications in ultrasensitive mass spectrometry analysis of very small samples, such as single cells. To enhance the growing use of isobaric carriers, we characterized the trade-offs of using isobaric carriers in controlled experiments with complex human proteomes. The data indicate that isobaric carriers directly enhance peptide sequence identification without simultaneously increasing the number of protein copies sampled from small samples. The results also indicate strategies for optimizing the amount of isobaric carrier and analytical parameters, such as ion accumulation time, for different priorities such as improved quantification or an increased number of identified proteins. Balancing these trade-offs enables adapting isobaric carrier experiments to different applications, such as quantifying proteins from limited biopsies or organoids, building single-cell atlases, or modeling protein networks in single cells. In all cases, the reliability of protein quantification should be estimated and incorporated in all subsequent analyses. We expect that these guidelines will aid in explicit incorporation of the characterized trade-offs in experimental designs and transparent error propagation in data analysis.

The throughput efficiency and increased depth of coverage provided by isobaric-labeled proteomics measurements have led to increased usage of these techniques. However, the structure of missing data is different than unlabeled studies, which prompts the need for this review to compare the efficacy of nine imputation methods on large isobaric-labeled proteomics data sets to guide researchers on the appropriateness of various imputation methods. Imputation methods were evaluated by accuracy, statistical hypothesis test inference, and run time. In general, expectation maximization and random forest imputation methods yielded the best performance, and constant-based methods consistently performed poorly across all data set sizes and percentages of missing values. For data sets with small sample sizes and higher percentages of missing data, results indicate that statistical inference with no imputation may be preferable. On the basis of the findings in this review, there are core imputation methods that perform better for isobaric-labeled proteomics data, but great care and consideration as to whether imputation is the optimal strategy should be given for data sets comprised of a small number of samples.

Liquid chromatography-tandem mass spectrometry is an increasingly powerful tool for studying proteins in the context of disease. As technological advances in instrumentation and data analysis have enabled deeper profiling of proteomes and peptidomes, the need for a rigorous, standardized approach to validate individual peptide-spectrum matches (PSMs) has emerged. To address this need, we developed a novel and broadly applicable workflow: PSM validation with internal standards (P-VIS). In this approach, the fragmentation spectrum and chromatographic retention time of a peptide within a biological sample are compared with those of a synthetic version of the putative peptide sequence match. Similarity measurements obtained for a panel of internal standard peptides are then used to calculate a prediction interval for valid matches. If the observed degree of similarity between the biological and the synthetic peptide falls within this prediction interval, then the match is considered valid. P-VIS enables systematic and objective assessment of the validity of individual PSMs, providing a measurable degree of confidence when identifying peptides by mass spectrometry.