Cytosolic Factors Controlling PASTA Kinase-Dependent ReoM Phosphorylation.

Bacteria adapt the biosynthesis of their envelopes to specific growth conditions and prevailing stress factors. Peptidoglycan (PG) is the major component of the cell wall in Gram-positive bacteria, where PASTA kinases play a central role in PG biosynthesis regulation. Despite their importance for growth, cell division and antibiotic resistance, the mechanisms of PASTA kinase activation are not fully understood. ReoM, a recently discovered cytosolic phosphoprotein, is one of the main substrates of the PASTA kinase PrkA in the Gram-positive human pathogen Listeria monocytogenes. Depending on its phosphorylation, ReoM controls proteolytic stability of MurA, the first enzyme in the PG biosynthesis pathway. The late cell division protein GpsB has been implicated in PASTA kinase signalling. Consistently, we show that L. monocytogenes prkA and gpsB mutants phenocopied each other. Analysis of in vivo ReoM phosphorylation confirmed GpsB as an activator of PrkA leading to the description of structural features in GpsB that are important for kinase activation. We further show that ReoM phosphorylation is growth phase-dependent and that this kinetic is reliant on the protein phosphatase PrpC. ReoM phosphorylation was inhibited in mutants with defects in MurA degradation, leading to the discovery that MurA overexpression prevented ReoM phosphorylation. Overexpressed MurA must be able to bind its substrates and interact with ReoM to exert this effect, but the extracellular PASTA domains of PrkA or MurJ flippases were not required. Our results indicate that intracellular signals control ReoM phosphorylation and extend current models describing the mechanisms of PASTA kinase activation.

Direct translation of incoming retroviral genomes.

Viruses that carry a positive-sense, single-stranded (+ssRNA) RNA translate their genomes soon after entering the host cell to produce viral proteins, with the exception of retroviruses. A distinguishing feature of retroviruses is reverse transcription, where the +ssRNA genome serves as a template to synthesize a double-stranded DNA copy that subsequently integrates into the host genome. As retroviral RNAs are produced by the host cell transcriptional machinery and are largely indistinguishable from cellular mRNAs, we investigated the potential of incoming retroviral genomes to directly express proteins. Here we show through multiple, complementary methods that retroviral genomes are translated after entry. Our findings challenge the notion that retroviruses require reverse transcription to produce viral proteins. Synthesis of retroviral proteins in the absence of productive infection has significant implications for basic retrovirology, immune responses and gene therapy applications.

Investigation of the potential of Brevibacillus spp. for the biosynthesis of nonribosomally produced bioactive compounds by combination of genome mining with MALDI-TOF mass spectrometry.

The biosynthetic potential of 11 Brevibacillus spp. strains was investigated by combination of genome mining with mass spectrometric analysis using MALDI-TOF mass spectrometry. These endophytic, plant associated Brevibacillus strains were isolated from crop plants, such as coffee and black pepper, in Vietnam. Draft genomes of these strains were available. They were classified (a) by comparison with type strains and a collection of genome-sequenced Brevibacillus spp. deposited in the NCBI data base as well as (b) by construction of a phylogenetic tree from the core sequences of publicly available genomes of Brevibacillus strains. They were identified as Brevibacillus brevis (1 strain); parabrevis (2 strains); porteri (3 strains); and 5 novel Brevibacillus genomospecies. Our work was specifically focused on the detection and characterization of nonribosomal peptides produced by these strains. Structural characterization of these compounds was performed by LIFT-MALDI-TOF/TOF mass spectrometric sequence analysis. The highlights of our work were the demonstration of the tyrocidines, a well-known family of cyclodecapeptides of great structural variability, as the main products of all investigated strains and the identification of a novel class of pentapeptides produced by B. brevis; B. schisleri; and B. porteri which we designate as brevipentins. Our biosynthetic studies demonstrate that knowledge of their biosynthetic capacity can efficiently assist classification of Brevibacillus species.

Phenotypic and genotypic discrimination of Francisella tularensis ssp. holarctica clades.

Francisella tularensis is the causative agent of tularemia, a zoonotic disease with a wide host range. F. tularensis ssp. holarctica (Fth) is of clinical relevance for European countries, including Germany. Whole genome sequencing methods, including canonical Single Nucleotide Polymorphism (canSNP) typing and whole genome SNP typing, have revealed that European Fth strains belong to a few monophyletic populations. The majority of German Fth isolates belong to two basal phylogenetic clades B.6 (biovar I) and B.12 (biovar II). Strains of B.6 and B.12 seem to differ in their pathogenicity, and it has been shown that strains of biovar II are resistant against erythromycin. In this study, we present data corroborating our previous data demonstrating that basal clade B.12 can be divided into clades B.71 and B.72. By applying phylogenetic whole genome analysis as well as proteome analysis, we could verify that strains of these two clades are distinct from one another. This was confirmed by measuring the intensity of backscatter light on bacteria grown in liquid media. Strains belonging to clades B.6, B.71 or B.72 showed clade-specific backscatter growth curves. Furthermore, we present the whole genome sequence of strain A-1341, as a reference genome of clade B.71, and whole proteomes comparison of Fth strains belonging to clades B.6, B.71 and B.72. Further research is necessary to investigate phenotypes and putative differences in pathogenicity of the investigated different clades of Fth to better understand the relationship between observed phenotypes, pathogenicity and distribution of Fth strains.

Increasing Proteome Depth While Maintaining Quantitative Precision in Short-Gradient Data-Independent Acquisition Proteomics.

The combination of short liquid chromatography (LC) gradients and data-independent acquisition (DIA) by mass spectrometry (MS) has proven its huge potential for high-throughput proteomics. However, the optimization of isolation window schemes resulting in a certain number of data points per peak (DPPP) is understudied, although it is one of the most important parameters for the outcome of this methodology. In this study, we show that substantially reducing the number of DPPP for short-gradient DIA massively increases protein identifications while maintaining quantitative precision. This is due to a large increase in the number of precursors identified, which keeps the number of data points per protein almost constant even at long cycle times. When proteins are inferred from its precursors, quantitative precision is maintained at low DPPP while greatly increasing proteomic depth. This strategy enabled us to quantify 6018 HeLa proteins (>80 000 precursor identifications) with coefficients of variation below 20% in 30 min using a Q Exactive HF, which corresponds to a throughput of 29 samples per day. This indicates that the potential of high-throughput DIA-MS has not been fully exploited yet. Data are available via ProteomeXchange with identifier PXD036451.

Perchlorate-specific proteomic stress responses of Debaryomyces hansenii could enable microbial survival in Martian brines.

If life exists on Mars, it would face several challenges including the presence of perchlorates, which destabilize biomacromolecules by inducing chaotropic stress. However, little is known about perchlorate toxicity for microorganisms on the cellular level. Here, we present the first proteomic investigation on the perchlorate-specific stress responses of the halotolerant yeast Debaryomyces hansenii and compare these to generally known salt stress adaptations. We found that the responses to NaCl and NaClO4 -induced stresses share many common metabolic features, for example, signalling pathways, elevated energy metabolism, or osmolyte biosynthesis. Nevertheless, several new perchlorate-specific stress responses could be identified, such as protein glycosylation and cell wall remodulations, presumably in order to stabilize protein structures and the cell envelope. These stress responses would also be relevant for putative life on Mars, which-given the environmental conditions-likely developed chaotropic defence strategies such as stabilized confirmations of biomacromolecules or the formation of cell clusters.

Recombinant AcnB, NrdR and RibD of Acinetobacter baumannii and their potential interaction with DNA adenine methyltransferase AamA.

In the last decades Acinetobacter baumannii developed into an increasingly challenging nosocomial pathogen. A. baumannii ATCC 17978 harbors a DNA-(adenine N6)-methyltransferase termed AamA. Previous studies revealed a low specific activity of AamA in vitro despite proven folding, which led us to speculate about possible interaction partners assisting AamA in targeting methylation sites. Here, applying a pulldown assay with subsequent mass spectrometry we identified aconitate hydratase 2 (AcnB) as possible interaction partner. In addition, we considered the putative transcriptional regulator gene nrdR (A1S_0220) and the pyrimidine deaminase/reductase gene ribD (A1S_0221) of A. baumannii strain ATCC 17978 to encode additional potential interaction partners due to their vicinity to the aamA gene (A1S_0222). Proteins were recombinantly produced in the milligram scale, purified to near homogeneity, and interactions with AamA were studied applying blue native gel electrophoreses, electrophoretic mobility shift assay, chemical cross-linking and co-immunoprecipitation. These analyses did not provide evidence of interaction between AamA and purified proteins. Solution structures of RibD, NrdR and AcnB were studied by small-angle X-ray scattering (SAXS) alone and in combination with AamA. While in the case of RibD and AcnB no evidence of an interaction with AamA was produced, addition of AamA to NrdR resulted in dissociation of long and rod-shaped polymeric NrdR structures, implying a specific but transient interaction. Moreover, we identified a molecular crowding effect possibly impeding the DNA methyltransferase activity in vivo and a sequence-independent DNA binding activity of AamA calling for continued efforts to identify the interaction network of AamA.

Deep Time Course Proteomics of SARS-CoV- and SARS-CoV-2-Infected Human Lung Epithelial Cells (Calu-3) Reveals Strong Induction of Interferon-Stimulated Gene Expression by SARS-CoV-2 in Contrast to SARS-CoV.

Severe acute respiratory syndrome (SARS)-CoV and SARS-CoV-2 infections are characterized by remarkable differences, including infectivity and case fatality rate. The underlying mechanisms are not well understood, illustrating major knowledge gaps of coronavirus biology. In this study, protein expression of the SARS-CoV- and SARS-CoV-2-infected human lung epithelial cell line Calu-3 was analyzed using data-independent acquisition-mass spectrometry. This resulted in a comprehensive map of infection-related proteome-wide expression changes in human cells covering the quantification of 7478 proteins across four time points. Most notably, the activation of interferon type-I response was observed, which is surprisingly absent in several proteome studies. The data reveal that SARS-CoV-2 triggers interferon-stimulated gene expression much stronger than SARS-CoV, which reflects the already described differences in interferon sensitivity. Potentially, this may be caused by the enhanced abundance of the viral M protein of SARS-CoV in comparison to SARS-CoV-2, which is a known inhibitor of type I interferon expression. This study expands the knowledge on the host response to SARS-CoV-2 infections on a global scale using an infection model, which seems to be well suited to analyze the innate immunity.

Unbiased Antimicrobial Resistance Detection from Clinical Bacterial Isolates Using Proteomics.

Antimicrobial resistance (AMR) poses an increasing challenge for therapy and clinical management of bacterial infections. Currently, antimicrobial resistance detection relies on phenotypic assays, which are performed independently from species identification. Sequencing-based approaches are possible alternatives for AMR detection, although the analysis of proteins should be superior to gene or transcript sequencing for phenotype prediction as the actual resistance to antibiotics is almost exclusively mediated by proteins. In this proof-of-concept study, we present an unbiased proteomics workflow for detecting both bacterial species and AMR-related proteins in the absence of secondary antibiotic cultivation within <4 h from a primary culture. The workflow was designed to meet the needs in clinical microbiology. It introduces a new data analysis concept for bacterial proteomics, and a software (rawDIAtect) for the prediction and reporting of AMR from peptide identifications. The method was validated using a sample cohort of 7 bacterial species and 11 AMR determinants represented by 13 protein isoforms, which resulted in a sensitivity of 98% and a specificity of 100%.

Inactivation of Coronaviruses during Sample Preparation for Proteomics Experiments.

Mass spectrometry-based proteomics is applied in SARS-CoV-2 research and is, moreover, being discussed as a novel method for SARS-CoV-2 diagnostics. However, the safe inactivation of coronaviruses by proteomics lysis buffers has not been systematically analyzed yet. Hence, for safety reasons a heating step prior to sample preparation is often performed. This step could be omitted once the safe inactivation with the typical buffers is proven. Here we test five different proteomics lysis buffers-4% SDS, 1% SDC, TFA, 6 M GdmCl, and 8 M urea-for their inactivation capacity of coronaviruses. Two representative human coronaviruses, namely HCoV-229E and HCoV-OC43, were used as surrogate for SARS-CoV-2. Lysis was performed at room temperature and at 95 °C for 5 min. Inactivation was confirmed by the absence of a cytopathic effect in MRC-5 cells, and equivocal results were further confirmed by serial passaging and quantitative real-time PCR. While at room temperature SDS, SDC, and TFA inactivated both coronaviruses, and GdmCl and urea resulted in partially incomplete inactivation. This demonstrates that care should be taken when choosing lysis buffers for proteomics analysis of coronaviruses, because some buffers do not ensure inactivation and, hence, biosafety during the further sample preparation.

Application of spectral library prediction for parallel reaction monitoring of viral peptides.

A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here, we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Viral peptides were chosen as an example, because virology is an area where in silico library generation could significantly improve PRM assay design. With both libraries a total of 1174 precursors were identified. Notably, compared to the DDA-derived library, we could identify 101 more precursors by using the Prosit-derived library. Additionally, we show that Prosit can be applied to predict tandem mass spectra of synthetic viral peptides with different collision energies. Finally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab demonstrating that both libraries are suited for peptide identification by PRM. Summarized, Prosit-derived viral spectral libraries predicted in silico can be used for PRM data analysis, making DDA analysis for library generation partially redundant in the future.

Evaluation of MALDI-ToF Mass Spectrometry for Rapid Detection of Cereulide From Bacillus cereus Cultures.

Bacillus cereus plays an often unrecognized role in food borne diseases. Food poisoning caused by this pathogen is manifested by either diarrhea or emesis. Due to the relatively high prevalence of emetic toxin cereulide associated food poisoning, methods for simple and reliable detection of cereulide producing strains are of utmost importance. Recently, two different studies reported on the application of MALDI-ToF MS for either the differentiation of emetic and non-emetic strains of B. cereus or for direct detection of cereulide from bacterial colony smears. However, for implementation of cereulide detection using MALDI-ToF MS in routine microbiological diagnostics additional investigations on the sensitivity and specificity as well as on the fitting into common workflows for bacterial identification are needed. These aspects prompted us to investigate open issues and to test sample preparation methods, commonly used for microbial identification for their suitability to detect the emetic toxin from bacteria. Based on our experimental findings we propose a workflow that allows identification of B. cereus and sensitive detection of cereulide in parallel, using linear-mode MALDI-ToF MS equipment. The protocol was validated in a blinded study and is based on the well-established ethanol/formic acid extraction method. Cereulide is detected in the ethanol wash solution of samples identified as B. cereus as peaks at m/z 1175 and 1191. Peak position difference of 16 Th (Thomson) indicates detection of the sodium and potassium adducts of cereulide. This sample treatment offers possibilities for further characterization by more sophisticated LC-MS-based methods. In summary, the ease of use and the achieved level of analytical sensitivity as well as the wide-spread availability of MALDI-ToF MS equipment in clinical microbiological laboratories provides a promising tool to improve and to facilitate routine diagnostics of B. cereus associated food intoxications.

Perspective on Proteomics for Virus Detection in Clinical Samples.

One of the most widely used methods to detect an acute viral infection in clinical specimens is diagnostic real-time polymerase chain reaction. However, because of the COVID-19 pandemic, mass-spectrometry-based proteomics is currently being discussed as a potential diagnostic method for viral infections. Because proteomics is not yet applied in routine virus diagnostics, here we discuss its potential to detect viral infections. Apart from theoretical considerations, the current status and technical limitations are considered. Finally, the challenges that have to be overcome to establish proteomics in routine virus diagnostics are highlighted.

Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in Silico Peptide Mass Libraries.

Over the past decade, modern methods of MS (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. Although MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem MS (LC-MS2) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be laborious and time-consuming, especially if the experimental LC-MS2 data are tested against sequence databases covering a broad panel of different microbiological taxa. In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. Peptide masses are then extracted from MS1 data and systematically tested against an in silico library of all possible peptide mass data compiled in-house. The library has been computed from the UniProt Knowledgebase covering Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from correlation coefficients between experimental and strain-specific in silico peptide mass profiles and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient - less than 2 mins per sample - and has been successfully tested by a test set of 39 LC-MS1 peak lists obtained from 19 different microbial pathogens. The proposed method is rapid, simple and automatable and we foresee wide application potential for future microbiological applications.

Silence as a way of niche adaptation: mecC-MRSA with variations in the accessory gene regulator (agr) functionality express kaleidoscopic phenotypes.

Functionality of the accessory gene regulator (agr) quorum sensing system is an important factor promoting either acute or chronic infections by the notorious opportunistic human and veterinary pathogen Staphylococcus aureus. Spontaneous alterations of the agr system are known to frequently occur in human healthcare-associated S. aureus lineages. However, data on agr integrity and function are sparse regarding other major clonal lineages. Here we report on the agr system functionality and activity level in mecC-carrying methicillin resistant S. aureus (MRSA) of various animal origins (n = 33) obtained in Europe as well as in closely related human isolates (n = 12). Whole genome analysis assigned all isolates to four clonal complexes (CC) with distinct agr types (CC599 agr I, CC49 agr II, CC130 agr III and CC1943 agr IV). Agr functionality was assessed by a combination of phenotypic assays and proteome analysis. In each CC, isolates with varying agr activity levels were detected, including the presence of completely non-functional variants. Genomic comparison of the agr I-IV encoding regions associated these phenotypic differences with variations in the agrA and agrC genes. The genomic changes were detected independently in divergent lineages, suggesting that agr variation might foster viability and adaptation of emerging MRSA lineages to distinct ecological niches.

Isolation Window Optimization of Data-Independent Acquisition Using Predicted Libraries for Deep and Accurate Proteome Profiling.

In silico spectral library prediction of all possible peptides from whole organisms has a great potential for improving proteome profiling by data-independent acquisition (DIA) and extending its scope of application. In combination with other recent improvements in the field of mass spectrometry (MS)-based proteomics, including sample preparation, peptide separation, and data analysis, we aimed to uncover the full potential of such an advanced DIA strategy by optimization of the data acquisition. The results demonstrate that the combination of high-quality in silico libraries, reproducible and high-resolution peptide separation using micropillar array columns, as well as neural network supported data analysis enables the use of long MS scan cycles without impairing the quantification performance. The study demonstrates that mean coefficient of variations of 4% were obtained even at only 1.5 data points per peak (full width at half-maximum) across different gradient lengths, which in turn improved proteome coverage up to more than 8000 proteins from HeLa cells using empirically corrected libraries and more than 7000 proteins using a whole human in silico predicted library. These data were obtained using a Q Exactive orbitrap mass spectrometer with moderate scanning speed (12 Hz) and perform very well in comparison to recent studies using more advanced MS instruments, which underline the high potential of this optimization strategy for various applications in clinical proteomics, microbiology, and molecular biology.

Stable Isotope-Triggered Offset Fragmentation Allows Massively Multiplexed Target Profiling on Quadrupole-Orbitrap Mass Spectrometers.

Parallel-reaction monitoring (PRM) using high resolution, accurate mass (HR/AM) analysis on quadrupole-Orbitrap mass spectrometers, like the Q Exactive, is one of the most promising approaches for targeted protein analysis. However, PRM has a limited multiplexing capacity, which depends heavily on the reproducibility of peptide retention times. To overcome these limitations, we aimed to establish an easily applicable data acquisition mode that allows retention-time-independent massive multiplexing on Q Exactive mass spectrometers. The presented method is based on data-dependent acquisition and is called pseudo-PRM. In principle, high-intensity stable isotope-labeled peptides are used to trigger the repeated fragmentation of the corresponding light peptides. In this way, pseudo-PRM data can be analyzed like normal PRM data. We tested pseudo-PRM for the target detection from yeast, human cells, and serum, showing good reproducibility and sensitivities comparable to normal PRM. We demonstrated further that pseudo-PRM can be used for accurate and precise quantification of target peptides, using both precursor and fragment ion areas. Moreover, we showed multiplexing of more than 1000 targets in a single run. Finally, we applied pseudo-PRM to quantify vaccinia virus proteins during infection, verifying that pseudo-PRM presents an alternative method for multiplexed target profiling on Q Exactive mass spectrometers.

TaxIt: An Iterative Computational Pipeline for Untargeted Strain-Level Identification Using MS/MS Spectra from Pathogenic Single-Organism Samples.

Untargeted accurate strain-level classification of a priori unidentified organisms using tandem mass spectrometry is a challenging task. Reference databases often lack taxonomic depth, limiting peptide assignments to the species level. However, the extension with detailed strain information increases runtime and decreases statistical power. In addition, larger databases contain a higher number of similar proteomes. We present TaxIt, an iterative workflow to address the increasing search space required for MS/MS-based strain-level classification of samples with unknown taxonomic origin. TaxIt first applies reference sequence data for initial identification of species candidates, followed by automated acquisition of relevant strain sequences for low level classification. Furthermore, proteome similarities resulting in ambiguous taxonomic assignments are addressed with an abundance weighting strategy to increase the confidence in candidate taxa. For benchmarking the performance of our method, we apply our iterative workflow on several samples of bacterial and viral origin. In comparison to noniterative approaches using unique peptides or advanced abundance correction, TaxIt identifies microbial strains correctly in all examples presented (with one tie), thereby demonstrating the potential for untargeted and deeper taxonomic classification. TaxIt makes extensive use of public, unrestricted, and continuously growing sequence resources such as the NCBI databases and is available under open-source BSD license at https://gitlab.com/rki_bioinformatics/TaxIt.

Sample Preparation by Easy Extraction and Digestion (SPEED) - A Universal, Rapid, and Detergent-free Protocol for Proteomics Based on Acid Extraction.

The main challenge of bottom-up proteomic sample preparation is to extract proteomes in a manner that enables efficient protein digestion for subsequent mass spectrometric analysis. Today's sample preparation strategies are commonly conceptualized around the removal of detergents, which are essential for extraction but strongly interfere with digestion and LC-MS. These multi-step preparations contribute to a lack of reproducibility as they are prone to losses, biases and contaminations, while being time-consuming and labor-intensive. We report a detergent-free method, named Sample Preparation by Easy Extraction and Digestion (SPEED), which consists of three mandatory steps, acidification, neutralization and digestion. SPEED is a universal method for peptide generation from various sources and is easily applicable even for lysis-resistant sample types as pure trifluoroacetic acid (TFA) is used for highly efficient protein extraction by complete sample dissolution. The protocol is highly reproducible, virtually loss-less, enables very rapid sample processing and is superior to the detergent/chaotropic agent-based methods FASP, ISD-Urea and SP3 for quantitative proteomics. SPEED holds the potential to dramatically simplify and standardize sample preparation while improving the depth of proteome coverage especially for challenging samples.

Purple: A Computational Workflow for Strategic Selection of Peptides for Viral Diagnostics Using MS-Based Targeted Proteomics.

Emerging virus diseases present a global threat to public health. To detect viral pathogens in time-critical scenarios, accurate and fast diagnostic assays are required. Such assays can now be established using mass spectrometry-based targeted proteomics, by which viral proteins can be rapidly detected from complex samples down to the strain-level with high sensitivity and reproducibility. Developing such targeted assays involves tedious steps of peptide candidate selection, peptide synthesis, and assay optimization. Peptide selection requires extensive preprocessing by comparing candidate peptides against a large search space of background proteins. Here we present Purple (Picking unique relevant peptides for viral experiments), a software tool for selecting target-specific peptide candidates directly from given proteome sequence data. It comes with an intuitive graphical user interface, various parameter options and a threshold-based filtering strategy for homologous sequences. Purple enables peptide candidate selection across various taxonomic levels and filtering against backgrounds of varying complexity. Its functionality is demonstrated using data from different virus species and strains. Our software enables to build taxon-specific targeted assays and paves the way to time-efficient and robust viral diagnostics using targeted proteomics.