An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics.

Accurate annotation of all protein-coding sequences (CDSs) is an essential prerequisite to fully exploit the rapidly growing repertoire of completely sequenced prokaryotic genomes. However, large discrepancies among the number of CDSs annotated by different resources, missed functional short open reading frames (sORFs), and overprediction of spurious ORFs represent serious limitations. Our strategy toward accurate and complete genome annotation consolidates CDSs from multiple reference annotation resources, ab initio gene prediction algorithms and in silico ORFs (a modified six-frame translation considering alternative start codons) in an integrated proteogenomics database (iPtgxDB) that covers the entire protein-coding potential of a prokaryotic genome. By extending the PeptideClassifier concept of unambiguous peptides for prokaryotes, close to 95% of the identifiable peptides imply one distinct protein, largely simplifying downstream analysis. Searching a comprehensive Bartonella henselae proteomics data set against such an iPtgxDB allowed us to unambiguously identify novel ORFs uniquely predicted by each resource, including lipoproteins, differentially expressed and membrane-localized proteins, novel start sites and wrongly annotated pseudogenes. Most novelties were confirmed by targeted, parallel reaction monitoring mass spectrometry, including unique ORFs and single amino acid variations (SAAVs) identified in a re-sequenced laboratory strain that are not present in its reference genome. We demonstrate the general applicability of our strategy for genomes with varying GC content and distinct taxonomic origin. We release iPtgxDBs for B. henselae, Bradyrhizobium diazoefficiens and Escherichia coli and the software to generate both proteogenomics search databases and integrated annotation files that can be viewed in a genome browser for any prokaryote.

A Peptidomimetic Antibiotic Targets Outer Membrane Proteins and Disrupts Selectively the Outer Membrane in Escherichia coli.

Increasing antibacterial resistance presents a major challenge in antibiotic discovery. One attractive target in Gram-negative bacteria is the unique asymmetric outer membrane (OM), which acts as a permeability barrier that protects the cell from external stresses, such as the presence of antibiotics. We describe a novel ß-hairpin macrocyclic peptide JB-95 with potent antimicrobial activity against Escherichia coli. This peptide exhibits no cellular lytic activity, but electron microscopy and fluorescence studies reveal an ability to selectively disrupt the OM but not the inner membrane of E. coli. The selective targeting of the OM probably occurs through interactions of JB-95 with selected ß-barrel OM proteins, including BamA and LptD as shown by photolabeling experiments. Membrane proteomic studies reveal rapid depletion of many ß-barrel OM proteins from JB-95-treated E. coli, consistent with induction of a membrane stress response and/or direct inhibition of the Bam folding machine. The results suggest that lethal disruption of the OM by JB-95 occurs through a novel mechanism of action at key interaction sites within clusters of ß-barrel proteins in the OM. These findings open new avenues for developing antibiotics that specifically target ß-barrel proteins and the integrity of the Gram-negative OM.

Genome-wide transcription start site mapping of Bradyrhizobium japonicum grown free-living or in symbiosis - a rich resource to identify new transcripts, proteins and to study gene regulation.

BACKGROUND: Differential RNA-sequencing (dRNA-seq) is indispensable for determination of primary transcriptomes. However, using dRNA-seq data to map transcriptional start sites (TSSs) and promoters genome-wide is a bioinformatics challenge. We performed dRNA-seq of Bradyrhizobium japonicum USDA 110, the nitrogen-fixing symbiont of soybean, and developed algorithms to map TSSs and promoters. RESULTS: A specialized machine learning procedure for TSS recognition allowed us to map 15,923 TSSs: 14,360 in free-living bacteria, 4329 in symbiosis with soybean and 2766 in both conditions. Further, we provide proteomic evidence for 4090 proteins, among them 107 proteins corresponding to new genes and 178 proteins with N-termini different from the existing annotation (72 and 109 of them with TSS support, respectively). Guided by proteomics evidence, previously identified TSSs and TSSs experimentally validated here, we assign a score threshold to flag 14 % of the mapped TSSs as a class of lower confidence. However, this class of lower confidence contains valid TSSs of low-abundant transcripts. Moreover, we developed a de novo algorithm to identify promoter motifs upstream of mapped TSSs, which is publicly available, and found motifs mainly used in symbiosis (similar to RpoN-dependent promoters) or under both conditions (similar to RpoD-dependent promoters). Mapped TSSs and putative promoters, proteomic evidence and updated gene annotation were combined into an annotation file. CONCLUSIONS: The genome-wide TSS and promoter maps along with the extended genome annotation of B. japonicum represent a valuable resource for future systems biology studies and for detailed analyses of individual non-coding transcripts and ORFs. Our data will also provide new insights into bacterial gene regulation during the agriculturally important symbiosis between rhizobia and legumes.

Directed shotgun proteomics guided by saturated RNA-seq identifies a complete expressed prokaryotic proteome.

Prokaryotes, due to their moderate complexity, are particularly amenable to the comprehensive identification of the protein repertoire expressed under different conditions. We applied a generic strategy to identify a complete expressed prokaryotic proteome, which is based on the analysis of RNA and proteins extracted from matched samples. Saturated transcriptome profiling by RNA-seq provided an endpoint estimate of the protein-coding genes expressed under two conditions which mimic the interaction of Bartonella henselae with its mammalian host. Directed shotgun proteomics experiments were carried out on four subcellular fractions. By specifically targeting proteins which are short, basic, low abundant, and membrane localized, we could eliminate their initial underrepresentation compared to the estimated endpoint. A total of 1250 proteins were identified with an estimated false discovery rate below 1%. This represents 85% of all distinct annotated proteins and ∼90% of the expressed protein-coding genes. Genes that were detected at the transcript but not protein level, were found to be highly enriched in several genomic islands. Furthermore, genes that lacked an ortholog and a functional annotation were not detected at the protein level; these may represent examples of overprediction in genome annotations. A dramatic membrane proteome reorganization was observed, including differential regulation of autotransporters, adhesins, and hemin binding proteins. Particularly noteworthy was the complete membrane proteome coverage, which included expression of all members of the VirB/D4 type IV secretion system, a key virulence factor.

Quantitative proteomic analysis of Shigella flexneri and Shigella sonnei Generalized Modules for Membrane Antigens (GMMA) reveals highly pure preparations.

Outer membrane blebs are naturally shed by Gram-negative bacteria and are candidates of interest for vaccines development. Genetic modification of bacteria to induce hyperblebbing greatly increases the yield of blebs, called Generalized Modules for Membrane Antigens (GMMA). The composition of the GMMA from hyperblebbing mutants of Shigella flexneri 2a and Shigella sonnei were quantitatively analyzed using high-sensitivity mass spectrometry with the label-free iBAQ procedure and compared to the composition of the solubilized cells of the GMMA-producing strains. There were 2306 proteins identified, 659 in GMMA and 2239 in bacteria, of which 290 (GMMA) and 1696 (bacteria) were common to both S. flexneri 2a and S. sonnei. Predicted outer membrane and periplasmic proteins constituted 95.7% and 98.7% of the protein mass of S. flexneri 2a and S. sonnei GMMA, respectively. Among the remaining proteins, small quantities of ribosomal proteins collectively accounted for more than half of the predicted cytoplasmic protein impurities in the GMMA. In GMMA, the outer membrane and periplasmic proteins were enriched 13.3-fold (S. flexneri 2a) and 8.3-fold (S. sonnei) compared to their abundance in the parent bacteria. Both periplasmic and outer membrane proteins were enriched similarly, suggesting that GMMA have a similar surface to volume ratio as the surface to periplasmic volume ratio in these mutant bacteria. Results in S. flexneri 2a and S. sonnei showed high reproducibility indicating a robust GMMA-producing process and the low contamination by cytoplasmic proteins support the use of GMMA for vaccines. Data are available via ProteomeXchange with identifier PXD002517.

Protter: interactive protein feature visualization and integration with experimental proteomic data.

SUMMARY: The ability to integrate and visualize experimental proteomic evidence in the context of rich protein feature annotations represents an unmet need of the proteomics community. Here we present Protter, a web-based tool that supports interactive protein data analysis and hypothesis generation by visualizing both annotated sequence features and experimental proteomic data in the context of protein topology. Protter supports numerous proteomic file formats and automatically integrates a variety of reference protein annotation sources, which can be readily extended via modular plug-ins. A built-in export function produces publication-quality customized protein illustrations, also for large datasets. Visualizations of surfaceome datasets show the specific utility of Protter for the integrated visual analysis of membrane proteins and peptide selection for targeted proteomics. AVAILABILITY AND IMPLEMENTATION: The Protter web application is available at http://wlab.ethz.ch/protter. Source code and installation instructions are available at http://ulo.github.io/Protter/. CONTACT: wbernd@ethz.ch SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

σ54-Dependent Response to Nitrogen Limitation and Virulence in Burkholderia cenocepacia Strain H111.

Members of the genus Burkholderia are versatile bacteria capable of colonizing highly diverse environmental niches. In this study, we investigated the global response of the opportunistic pathogen Burkholderia cenocepacia H111 to nitrogen limitation at the transcript and protein expression levels. In addition to a classical response to nitrogen starvation, including the activation of glutamine synthetase, PII proteins, and the two-component regulatory system NtrBC, B. cenocepacia H111 also upregulated polyhydroxybutyrate (PHB) accumulation and exopolysaccharide (EPS) production in response to nitrogen shortage. A search for consensus sequences in promoter regions of nitrogen-responsive genes identified a σ(54) consensus sequence. The mapping of the σ(54) regulon as well as the characterization of a σ(54) mutant suggests an important role of σ(54) not only in control of nitrogen metabolism but also in the virulence of this organism.

Combine and conquer: surfactants, solvents, and chaotropes for robust mass spectrometry based analyses of membrane proteins.

Mass spectrometry (MS) based proteomic technologies enable the identification and quantification of membrane proteins as well as their post-translational modifications. A prerequisite for their quantitative and reliable MS-based bottom-up analysis is the efficient digestion into peptides by proteases, though digestion of membrane proteins is typically challenging due to their inherent properties such as hydrophobicity. Here, we investigated the effect of eight commercially available MS-compatible surfactants, two organic solvents, and two chaotropes on the enzymatic digestion efficiency of membrane protein-enriched complex mixtures in a multiphase study using a gelfree approach. Multiple parameters, including the number of peptides and proteins identified, total protein sequence coverage, and digestion specificity were used to evaluate transmembrane protein digestion performance. A new open-source software tool was developed to allow for the specific assessment of transmembrane domain sequence coverage. Results demonstrate that while Progenta anionic surfactants outperform other surfactants when tested alone, combinations of guanidine and acetonitrile improve performance of all surfactants to near similar levels as well as enhance trypsin specificity to >90%, which has critical implications for future quantitative and qualitative proteomic studies.

A link between arabinose utilization and oxalotrophy in Bradyrhizobium japonicum.

Rhizobia have a versatile catabolism that allows them to compete successfully with other microorganisms for nutrients in the soil and in the rhizosphere of their respective host plants. In this study, Bradyrhizobium japonicum USDA 110 was found to be able to utilize oxalate as the sole carbon source. A proteome analysis of cells grown in minimal medium containing arabinose suggested that oxalate oxidation extends the arabinose degradation branch via glycolaldehyde. A mutant of the key pathway genes oxc (for oxalyl-coenzyme A decarboxylase) and frc (for formyl-coenzyme A transferase) was constructed and shown to be (i) impaired in growth on arabinose and (ii) unable to grow on oxalate. Oxalate was detected in roots and, at elevated levels, in root nodules of four different B. japonicum host plants. Mixed-inoculation experiments with wild-type and oxc-frc mutant cells revealed that oxalotrophy might be a beneficial trait of B. japonicum at some stage during legume root nodule colonization.

Proteomics analysis of Psychotria leaf nodule symbiosis: improved genome annotation and metabolic predictions.

Several plant species of the genus Psychotria (Rubiaceae) harbor Burkholderia sp. bacteria within specialized leaf nodules. The bacteria are transmitted vertically between plant generations and have not yet been cultured outside of their host. This symbiosis is considered to be obligatory because plants devoid of symbionts fail to develop into mature individuals. The genome of 'Candidatus Burkholderia kirkii' has been sequenced recently and has revealed evidence of reductive genome evolution, as shown by the proliferation of insertion sequences and the presence of numerous pseudogenes. We employed shotgun proteomics to investigate the expression of 'Ca. B. kirkii' proteins in the leaf nodule. Drawing from this dataset and refined comparative genomics analyses, we designed a new pseudogene prediction algorithm and improved the genome annotation. We also found conclusive evidence that nodule bacteria allocate vast resources to synthesis of secondary metabolites, possibly of the C7N aminocyclitol family. Expression of a putative 2-epi-5-valiolone synthase, a key enzyme of the C7N aminocyclitol synthesis, is high in the nodule population but downregulated in bacteria residing in the shoot apex, suggesting that production of secondary metabolites is particularly important in the leaf nodule.

Proteomic analysis reveals drug accessible cell surface N-glycoproteins of primary and established glioblastoma cell lines.

Glioblastoma is the most common primary brain tumor in adults with low average survival time after diagnosis. In order to improve glioblastoma treatment, new drug-accessible targets need to be identified. Cell surface glycoproteins are prime drug targets due to their accessibility at the surface of cancer cells. To overcome the limited availability of suitable antibodies for cell surface protein detection, we performed a comprehensive mass spectrometric investigation of the glioblastoma surfaceome. Our combined cell surface capturing analysis of primary ex vivo glioblastoma cell lines in combination with established glioblastoma cell lines revealed 633 N-glycoproteins, which vastly extends the known data of surfaceome drug targets at subcellular resolution. We provide direct evidence of common glioblastoma cell surface glycoproteins and an approximate estimate of their abundances, information that could not be derived from genomic and/or transcriptomic glioblastoma studies. Apart from our pharmaceutically valuable repertoire of already and potentially drug-accessible cell surface glycoproteins, we built a mass-spectrometry-based toolbox enabling directed, sensitive, and repetitive glycoprotein measurements for clinical follow-up studies. The included Skyline Glioblastoma SRM assay library provides an elevated starting point for parallel testing of the abundance level of the detected glioblastoma surfaceome members in future drug perturbation experiments.

vmdICE: a plug-in for rapid evaluation of molecular dynamics simulations using VMD.

Molecular dynamics (MD) is a powerful in silico method to investigate the interactions between biomolecules. It solves Newton's equations of motion for atoms over a specified period of time and yields a trajectory file, containing the different spatial arrangements of atoms during the simulation. The movements and energies of each single atom are recorded. For evaluating of these simulation trajectories with regard to biomedical implications, several methods are available. Three well-known ones are the root mean square deviation (RMSD), the root mean square fluctuation (RMSF) and solvent accessible surface area (SASA). Herein, we present a novel plug-in for the software "visual molecular dynamics" (VMD) that allows an interactive 3D representation of RMSD, RMSF, and SASA, directly on the molecule. On the one hand, our plug-in is easy to handle for inexperienced users, and on the other hand, it provides a fast and flexible graphical impression of the spatial dynamics of a system for experts in the field.

jSimMacs for GROMACS: a Java application for advanced molecular dynamics simulations with remote access capability.

Molecular dynamics (MD) is a technique to simulate movements of molecular structures to understand their functional behavior. GROMACS is a software package primarily developed for biological MD and offers a huge amount of possible options and settings for tailoring the simulations. This makes it powerful but also complicated to handle. We introduce jSimMacs, a Java application for creating molecular dynamics projects in GROMACS. It simplifies the handling of files and options via an intuitive user interface. Users unexperienced in MD can work along prepared lines, while experts may enjoy a significant relief from the tedium of typing and scripting. Furthermore, jSimMacs supports 3D interactivity and the launch of remote projects on other computers accessible via networks. Thus, jSimMacs not only opens GROMACS to a broader public but also eases the burden of performing series of MD runs, as necessary in parameter studies.

A critical cross-validation of high throughput structural binding prediction methods for pMHC.

T-cells recognize antigens via their T-cell receptors. The major histocompatibility complex (MHC) binds antigens in a specific way, transports them to the surface and presents the peptides to the TCR. Many in silico approaches have been developed to predict the binding characteristics of potential T-cell epitopes (peptides), with most of them being based solely on the amino acid sequence. We present a structural approach which provides insights into the spatial binding geometry. We combine different tools for side chain substitution (threading), energy minimization, as well as scoring methods for protein/peptide interfaces. The focus of this study is on high data throughput in combination with accurate results. These methods are not meant to predict the accurate binding free energy but to give a certain direction for the classification of peptides into peptides that are potential binders and peptides that definitely do not bind to a given MHC structure. In total we performed approximately 83,000 binding affinity prediction runs to evaluate interactions between peptides and MHCs, using different combinations of tools. Depending on the tools used, the prediction quality ranged from almost random to around 75% of accuracy for correctly predicting a peptide to be either a binder or a non-binder. The prediction quality strongly depends on all three evaluation steps, namely, the threading of the peptide, energy minimization and scoring.

Proteotype profiling unmasks a viral signalling network essential for poxvirus assembly and transcriptional competence.

To orchestrate context-dependent signalling programmes, poxviruses encode two dual-specificity enzymes, the F10 kinase and the H1 phosphatase. These signalling mediators are essential for poxvirus production, yet their substrate profiles and systems-level functions remain enigmatic. Using a phosphoproteomic screen of cells infected with wild-type, F10 and H1 mutant vaccinia viruses, we systematically defined the viral signalling network controlled by these enzymes. Quantitative cross-comparison revealed 33 F10 and/or H1 phosphosites within 17 viral proteins. Using this proteotype dataset to inform genotype-phenotype relationships, we found that H1-deficient virions harbour a hidden hypercleavage phenotype driven by reversible phosphorylation of the virus protease I7 (S134). Quantitative phosphoproteomic profiling further revealed that the phosphorylation-dependent activity of the viral early transcription factor, A7 (Y367), underlies the transcription-deficient phenotype of H1 mutant virions. Together, these results highlight the utility of combining quantitative proteotype screens with mutant viruses to uncover proteotype-phenotype-genotype relationships that are masked by classical genetic studies.

Explosive cell lysis as a mechanism for the biogenesis of bacterial membrane vesicles and biofilms.

Many bacteria produce extracellular and surface-associated components such as membrane vesicles (MVs), extracellular DNA and moonlighting cytosolic proteins for which the biogenesis and export pathways are not fully understood. Here we show that the explosive cell lysis of a sub-population of cells accounts for the liberation of cytosolic content in Pseudomonas aeruginosa biofilms. Super-resolution microscopy reveals that explosive cell lysis also produces shattered membrane fragments that rapidly form MVs. A prophage endolysin encoded within the R- and F-pyocin gene cluster is essential for explosive cell lysis. Endolysin-deficient mutants are defective in MV production and biofilm development, consistent with a crucial role in the biogenesis of MVs and liberation of extracellular DNA and other biofilm matrix components. Our findings reveal that explosive cell lysis, mediated through the activity of a cryptic prophage endolysin, acts as a mechanism for the production of bacterial MVs.

Surfaceome of classical Hodgkin and non-Hodgkin lymphoma.

PURPOSE: Classical Hodgkin lymphoma (cHL) is characterized by a low percentage of tumor cells in a background of diverse, reactive immune cells. cHL cells commonly derive from preapoptotic germinal-center B cells and are characterized by the loss of B-cell markers and the varying expression of other hematopoietic lineage markers. This phenotypic variability and the scarcity of currently available cHL-specific cell surface markers can prevent clear distinction of cHL from related lymphomas. EXPERIMENTAL DESIGN: We applied the cell surface capture technology to directly measure the pool of cell surface exposed proteins in four cHL and four non-Hodgkin lymphoma (NHL) cell lines. RESULTS: More than 1000 membrane proteins, including 178 cluster of differentiation annotated proteins, were identified and allowed the generation of lymphoma surfaceome maps. The functional properties of identified cell surface proteins enable, but also limit the information exchange of lymphoma cells with their microenvironment. CONCLUSION AND CLINICAL RELEVANCE: Selected candidate proteins with potential diagnostic value were evaluated on a tissue microarray (TMA). Primary lymphoma tissues of 126 different B cell-derived lymphoma cases were included in the TMA analysis. The TMA analysis indicated gamma-glutamyltranspeptidase 1 as a potential additional marker that can be included in a panel of markers for differential diagnosis of cHL versus NHL.

A mass spectrometric-derived cell surface protein atlas.

Cell surface proteins are major targets of biomedical research due to their utility as cellular markers and their extracellular accessibility for pharmacological intervention. However, information about the cell surface protein repertoire (the surfaceome) of individual cells is only sparsely available. Here, we applied the Cell Surface Capture (CSC) technology to 41 human and 31 mouse cell types to generate a mass-spectrometry derived Cell Surface Protein Atlas (CSPA) providing cellular surfaceome snapshots at high resolution. The CSPA is presented in form of an easy-to-navigate interactive database, a downloadable data matrix and with tools for targeted surfaceome rediscovery (http://wlab.ethz.ch/cspa). The cellular surfaceome snapshots of different cell types, including cancer cells, resulted in a combined dataset of 1492 human and 1296 mouse cell surface glycoproteins, providing experimental evidence for their cell surface expression on different cell types, including 136 G-protein coupled receptors and 75 membrane receptor tyrosine-protein kinases. Integrated analysis of the CSPA reveals that the concerted biological function of individual cell types is mainly guided by quantitative rather than qualitative surfaceome differences. The CSPA will be useful for the evaluation of drug targets, for the improved classification of cell types and for a better understanding of the surfaceome and its concerted biological functions in complex signaling microenvironments.

Proteome-wide identification of predominant subcellular protein localizations in a bacterial model organism.

Proteomics data provide unique insights into biological systems, including the predominant subcellular localization (SCL) of proteins, which can reveal important clues about their functions. Here we analyzed data of a complete prokaryotic proteome expressed under two conditions mimicking interaction of the emerging pathogen Bartonella henselae with its mammalian host. Normalized spectral count data from cytoplasmic, total membrane, inner and outer membrane fractions allowed us to identify the predominant SCL for 82% of the identified proteins. The spectral count proportion of total membrane versus cytoplasmic fractions indicated the propensity of cytoplasmic proteins to co-fractionate with the inner membrane, and enabled us to distinguish cytoplasmic, peripheral inner membrane and bona fide inner membrane proteins. Principal component analysis and k-nearest neighbor classification training on selected marker proteins or predominantly localized proteins, allowed us to determine an extensive catalog of at least 74 expressed outer membrane proteins, and to extend the SCL assignment to 94% of the identified proteins, including 18% where in silico methods gave no prediction. Suitable experimental proteomics data combined with straightforward computational approaches can thus identify the predominant SCL on a proteome-wide scale. Finally, we present a conceptual approach to identify proteins potentially changing their SCL in a condition-dependent fashion. BIOLOGICAL SIGNIFICANCE: The work presented here describes the first prokaryotic proteome-wide subcellular localization (SCL) dataset for the emerging pathogen B. henselae (Bhen). The study indicates that suitable subcellular fractionation experiments combined with straight-forward computational analysis approaches assessing the proportion of spectral counts observed in different subcellular fractions are powerful for determining the predominant SCL of a large percentage of the experimentally observed proteins. This includes numerous cases where in silico prediction methods do not provide any prediction. Avoiding a treatment with harsh conditions, cytoplasmic proteins tend to co-fractionate with proteins of the inner membrane fraction, indicative of close functional interactions. The spectral count proportion (SCP) of total membrane versus cytoplasmic fractions allowed us to obtain a good indication about the relative proximity of individual protein complex members to the inner membrane. Using principal component analysis and k-nearest neighbor approaches, we were able to extend the percentage of proteins with a predominant experimental localization to over 90% of all expressed proteins and identified a set of at least 74 outer membrane (OM) proteins. In general, OM proteins represent a rich source of candidates for the development of urgently needed new therapeutics in combat of resurgence of infectious disease and multi-drug resistant bacteria. Finally, by comparing the data from two infection biology relevant conditions, we conceptually explore methods to identify and visualize potential candidates that may partially change their SCL in these different conditions. The data are made available to researchers as a SCL compendium for Bhen and as an assistance in further improving in silico SCL prediction algorithms.

Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data.

The in silico prediction of the best-observable "proteotypic" peptides in mass spectrometry-based workflows is a challenging problem. Being able to accurately predict such peptides would enable the informed selection of proteotypic peptides for targeted quantification of previously observed and non-observed proteins for any organism, with a significant impact for clinical proteomics and systems biology studies. Current prediction algorithms rely on physicochemical parameters in combination with positive and negative training sets to identify those peptide properties that most profoundly affect their general detectability. Here we present PeptideRank, an approach that uses learning to rank algorithm for peptide detectability prediction from shotgun proteomics data, and that eliminates the need to select a negative dataset for the training step. A large number of different peptide properties are used to train ranking models in order to predict a ranking of the best-observable peptides within a protein. Empirical evaluation with rank accuracy metrics showed that PeptideRank complements existing prediction algorithms. Our results indicate that the best performance is achieved when it is trained on organism-specific shotgun proteomics data, and that PeptideRank is most accurate for short to medium-sized and abundant proteins, without any loss in prediction accuracy for the important class of membrane proteins. BIOLOGICAL SIGNIFICANCE: Targeted proteomics approaches have been gaining a lot of momentum and hold immense potential for systems biology studies and clinical proteomics. However, since only very few complete proteomes have been reported to date, for a considerable fraction of a proteome there is no experimental proteomics evidence that would allow to guide the selection of the best-suited proteotypic peptides (PTPs), i.e. peptides that are specific to a given proteoform and that are repeatedly observed in a mass spectrometer. We describe a novel, rank-based approach for the prediction of the best-suited PTPs for targeted proteomics applications. By building on methods developed in the field of information retrieval (e.g. web search engines like Google's PageRank), we circumvent the delicate step of selecting positive and negative training sets and at the same time also more closely reflect the experimentalist´s need for selecting e.g. the 5 most promising peptides for targeting a protein of interest. This approach allows to predict PTPs for not yet observed proteins or for organisms without prior experimental proteomics data such as many non-model organisms.