Peptide-spectrum Matches Research Articles

SPACEPro: A Software Tool for Analysis of Protein Sample Cleavage for Tandem Mass Spectrometry.

The efficiency of shotgun proteomic analysis is dependent on the reproducibility of the peptide cleavage process during sample preparation. To generate rapid and useful metrics for peptide cleavage efficiency, as in enzymatic or chemical cleavage, SPACEPro was developed to evaluate efficiency and reproducibility of protein cleavage in peptide samples following data-dependent analysis by mass spectrometry. SPACEPro analyzes samples at the peptide-spectrum match (PSM), peptide, and protein levels to provide a comprehensive representation of the overall sample processing to peptides. All output is provided in human-readable text and JSON files that can be further processed to assess the cleavage efficiency on proteins within the sample. SPACEPro provides a snapshot of the protein cleavage efficiency through very minimal effort so that the user is informed of the quality of the sample processing efficiency and can accordingly develop protocols to improve the initial sample preparation for subsequent analyses.

Benefits of Iterative Searches of Large Databases to Interpret Large Human Gut Metaproteomic Data Sets.

The gut microbiota are increasingly considered as a main partner of human health. Metaproteomics enables us to move from the functional potential revealed by metagenomics to the functions actually operating in the microbiome. However, metaproteome deciphering remains challenging. In particular, confident interpretation of a myriad of MS/MS spectra can only be pursued with smart database searches. Here, we compare the interpretation of MS/MS data sets from 48 individual human gut microbiomes using three interrogation strategies of the dedicated Integrated nonredundant Gene Catalog (IGC 9.9 million genes from 1267 individual fecal samples) together with the Homo sapiens database: the classical single-step interrogation strategy and two iterative strategies (in either two or three steps) aimed at preselecting a reduced-sized, more targeted search space for the final peptide spectrum matching. Both iterative searches outperformed the single-step classical search in terms of the number of peptides and protein clusters identified and the depth of taxonomic and functional knowledge, and this was the most convincing with the three-step approach. However, iterative searches do not help in reducing variability of repeated analyses, which is inherent to the traditional data-dependent acquisition mode, but this variability did not affect the hierarchical relationship between replicates and all other samples.

Enhancing Open Modification Searches via a Combined Approach Facilitated by Ursgal.

The identification of peptide sequences and their post-translational modifications (PTMs) is a crucial step in the analysis of bottom-up proteomics data. The recent development of open modification search (OMS) engines allows virtually all PTMs to be searched for. This not only increases the number of spectra that can be matched to peptides but also greatly advances the understanding of the biological roles of PTMs through the identification, and the thereby facilitated quantification, of peptidoforms (peptide sequences and their potential PTMs). Whereas the benefits of combining results from multiple protein database search engines have been previously established, similar approaches for OMS results have been missing so far. Here we compare and combine results from three different OMS engines, demonstrating an increase in peptide spectrum matches of 8-18%. The unification of search results furthermore allows for the combined downstream processing of search results, including the mapping to potential PTMs. Finally, we test for the ability of OMS engines to identify glycosylated peptides. The implementation of these engines in the Python framework Ursgal facilitates the straightforward application of the OMS with unified parameters and results files, thereby enabling yet unmatched high-throughput, large-scale data analysis.

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep-learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT, and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson’s correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep-learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

New mixture models for decoy-free false discovery rate estimation in mass spectrometry proteomics.

Accurate estimation of false discovery rate (FDR) of spectral identification is a central problem in mass spectrometry-based proteomics. Over the past two decades, target-decoy approaches (TDAs) and decoy-free approaches (DFAs) have been widely used to estimate FDR. TDAs use a database of decoy species to faithfully model score distributions of incorrect peptide-spectrum matches (PSMs). DFAs, on the other hand, fit two-component mixture models to learn the parameters of correct and incorrect PSM score distributions. While conceptually straightforward, both approaches lead to problems in practice, particularly in experiments that push instrumentation to the limit and generate low fragmentation-efficiency and low signal-to-noise-ratio spectra. We introduce a new decoy-free framework for FDR estimation that generalizes present DFAs while exploiting more search data in a manner similar to TDAs. Our approach relies on multi-component mixtures, in which score distributions corresponding to the correct PSMs, best incorrect PSMs and second-best incorrect PSMs are modeled by the skew normal family. We derive EM algorithms to estimate parameters of these distributions from the scores of best and second-best PSMs associated with each experimental spectrum. We evaluate our models on multiple proteomics datasets and a HeLa cell digest case study consisting of more than a million spectra in total. We provide evidence of improved performance over existing DFAs and improved stability and speed over TDAs without any performance degradation. We propose that the new strategy has the potential to extend beyond peptide identification and reduce the need for TDA on all analytical platforms. https://github.com/shawn-peng/FDR-estimation. Supplementary data are available at Bioinformatics online.

Tailoring to Search Engines: Bottom-Up Proteomics with Collision Energies Optimized for Identification Confidence.

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).

Open-pFind Verified Four Missing Proteins from Multi-Tissues.

The Chromosome-Centric Human Proteome Project (C-HPP) was launched in 2012 to perfect the annotation of human protein existence by identifying stronger evidence of the expression of missing proteins (MPs) at the protein level. After an 8 year effort all over the world, the number of MPs in the neXtProt database significantly decreased from 5511 (2012-02-24) to 1899 (2020-01-17). It is now more difficult to provide confident evidence of the remaining MPs because of their specific characteristics, including low abundance, low molecular weight, unexpected modifications, transmembrane structure, tissue-expression specificity, and so on. A higher resolution mass spectrometry (MS) interpretation engine might provide an opportunity to identify these buried MPs in complex samples by the combination with multi-tissue large-scale proteomics. In this study, open-pFind was used to dig MPs from 20 pairs of healthy human tissues by Wang et al. ( Mol. Syst. Biol. 2019, 15 (2), e8503) combined with our large-scale testis data set digested by three enzymes (Glu-C, Lys-C, and trypsin) with specificity for different amino acid residues ( J. Proteme Res. 2019, 18 (12), 4189-4196). A total of 1 535 536 peptides with 17 283 477 peptide-spectrum matches (PSMs) were mapped to 14 279 protein entries at a false discovery rate of <1% at the PSM, peptide, and protein levels. A total of 103 MP candidates were identified, among which 86 candidates had more unique peptide numbers compared with our single testis tissue. After rigorous screening, manual checks, peptide synthesis, and matching with documented peptides from PeptideAtlas, we validated four MPs, P0C7T8 (duodenum and small intestine), Q8WWZ4 (stomach and rectum), Q8IV35 (fallopian tube), and O14921 (tonsil), at the protein level. All MS raw files have been deposited to the ProteomeXchange with identifier PXD021391.

Characterizing Proteins That Mediate CALM-AF10 Leukemogenesis

Background: Leukemia is the most common type of childhood cancer. Although the prognosis for many pediatric leukemias has improved, leukemias associated with the t(10;11) CALM-AF10 translocation remain difficult to treat. CALM-AF10 leukemias account for ~5-10% of childhood T-cell acute lymphoid leukemia (T-ALL) as well as a subset of acute myeloid leukemia (AML). CALM-AF10 leukemias exhibit increased expression of proleukemic HOXA genes, but relatively little is known about the cellular mechanisms that drive CALM-AF10 leukemogenesis. Our laboratory has demonstrated that the CALM protein contains a nuclear export signal (NES) that is critical for CALM-AF10-dependent leukemogenesis. The NES interacts with the CRM1/XPO1 nuclear export receptor, which shuttles proteins from the nucleus to the cytoplasm through the nuclear pore complex. We have shown that transcriptional activation of HOXA genes by CALM-AF10 is critically dependent on its interaction with CRM1. Importantly, CRM1 does not contain a recognized DNA binding domain, and it is not currently understood how the CALM-AF10/CRM1 complex interacts with regulatory regions of HOXAgenes. In order to identify proteins that mediate the interaction between the CALM-AF10/CRM1 complex and DNA, we took advantage of a proximity-based labeling approach using BioID2, a second-generation biotin ligase. When fused to a protein of interest and in the presence of biotin, BioID2 biotinylates proteins in close proximity to the ligase. These biotinylated proteins can then be identified by mass spectrometry (MS). Methods: We prepared an expression plasmid in which BioID2 was cloned in-frame with CALM-AF10. We then transiently transfected Human Embryonic Kidney 293 (HEK293) cells with the BioID2-CALM-AF10 plasmid, grew them in the presence or absence of biotin, and performed streptavidin-pulldown followed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) to identify candidate interacting proteins. Proteins were considered candidates if they had a peptide spectrum match (PSM) score &amp;gt; 10 and at least a two-fold greater PSM score versus negative control. We validated direct interactions of candidate proteins with CALM-AF10 by performing co-immunoprecipitation experiments. Results: We first confirmed that the addition of BioID2 to CALM-AF10 does not affect the transcriptional activation of HOXA genes or CALM-AF10 mediated immortalization of hematopoietic stem cells. We carried out three independent transfections/LC-MS/MS experiments, which identified 71, 95 and 61 proteins, respectively. Of the proteins identified, 11 candidates were common to all three experiments.Of particular interest, we identified Disruptor Of Telomeric silencing 1-Like (DOT1L), a protein known to interact with AF10, and Nuclear pore complex protein 214 (NUP214), a protein that has been identified in leukemogenic translocations. The nine additional candidate proteins included: EPS15, DVL2, DVL3, and DDX3X -all known to play a role in leukemogenesis. We performed initial validation of direct interactions via co-immunoprecipitation and found that Epidermal Growth Factor Receptor Substrate 15(EPS15) co-precipitates with CALM-AF10. Conclusion: We used biotin ligase-dependent proximity-based labeling to identify candidate proteins that potentially interact with the CALM-AF10 fusion protein. Our identification of DOT1L validates the approach, since DOT1L is known to interact with CALM-AF10. We have started to investigate other candidate proteins, focusing on known translocation partners in various leukemias. Our screen identified EPS15, a protein involved in receptor-mediated endocytosis of epidermal growth factor and a known translocation partner for MLL/KMT2A. KMT2A-EPS15 translocations (t(1;11)(p32;q23)) have been identified in both AML and ALL, and KMT2A-EPS15 is among the eight most common KMT2A rearrangements. We have shown that EPS15 co-immunoprecipitates with CALM-AF10, suggesting that EPS15 may also play a role in CALM-AF10 leukemogenesis. Further evaluation of this interaction is underway, and may lead to identification of novel pathways involved in CALM-AF10 leukemogenesis. Disclosures No relevant conflicts of interest to declare.

Maximized quantitative phosphoproteomics allows high confidence dissection of the DNA damage signaling network

The maintenance of genomic stability relies on DNA damage sensor kinases that detect DNA lesions and phosphorylate an extensive network of substrates. The Mec1/ATR kinase is one of the primary sensor kinases responsible for orchestrating DNA damage responses. Despite the importance of Mec1/ATR, the current network of its identified substrates remains incomplete due, in part, to limitations in mass spectrometry-based quantitative phosphoproteomics. Phosphoproteomics suffers from lack of redundancy and statistical power for generating high confidence datasets, since information about phosphopeptide identity, site-localization, and quantitation must often be gleaned from a single peptide-spectrum match (PSM). Here we carefully analyzed the isotope label swapping strategy for phosphoproteomics, using data consistency among reciprocal labeling experiments as a central filtering rule for maximizing phosphopeptide identification and quantitation. We demonstrate that the approach allows drastic reduction of false positive quantitations and identifications even from phosphopeptides with a low number of spectral matches. Application of this approach identifies new Mec1/ATR-dependent signaling events, expanding our understanding of the DNA damage signaling network. Overall, the proposed quantitative phosphoproteomic approach should be generally applicable for investigating kinase signaling networks with high confidence and depth.

Urinary proteins detected using modern proteomics intervene in early type 2 diabetic kidney disease - a pilot study.

Aim: An advanced proteomics platform for protein biomarker discovery in diabetic chronic kidney disease (DKD) was developed, validated and implemented. Materials & methods: Three Type 2 diabetes mellitus patients and three control subjects were enrolled. Urinary peptides were extracted, samples were analyzed on a hybrid LTQ-Orbitrap Velos Pro instrument. Raw data were searched using the SEQUEST algorithm and integrated into Proteome Discoverer platform. Results & discussion: Unique peptide sequences, resulted sequence coverage, scoring of peptide spectrum matches were reported to albuminuria and databases. Five proteins that can be associated with early DKD were found: apolipoprotein AI, neutrophil gelatinase-associated lipocalin, cytidine deaminase, S100-A8 and hemoglobin subunit delta. Conclusion: Urinary proteome analysis could be used to evaluate mechanisms of pathogenesis of DKD.

DNMSO; an ontology for representing de novo sequencing results from Tandem-MS data.

For the identification and sequencing of proteins, mass spectrometry (MS) has become the tool of choice and, as such, drives proteomics. MS/MS spectra need to be assigned a peptide sequence for which two strategies exist. Either database search or de novo sequencing can be employed to establish peptide spectrum matches. For database search, mzIdentML is the current community standard for data representation. There is no community standard for representing de novo sequencing results, but we previously proposed the de novo markup language (DNML). At the moment, each de novo sequencing solution uses different data representation, complicating downstream data integration, which is crucial since ensemble predictions may be more useful than predictions of a single tool. We here propose the de novo MS Ontology (DNMSO), which can, for example, provide many-to-many mappings between spectra and peptide predictions. Additionally, an application programming interface (API) that supports any file operation necessary for de novo sequencing from spectra input to reading, writing, creating, of the DNMSO format, as well as conversion from many other file formats, has been implemented. This API removes all overhead from the production of de novo sequencing tools and allows developers to concentrate on algorithm development completely. We make the API and formal descriptions of the format freely available at https://github.com/savastakan/dnmso.

Improved Sensitivity of Ultralow Flow LC–MS-Based Proteomic Profiling of Limited Samples Using Monolithic Capillary Columns and FAIMS Technology

In this work, we pioneered a combination of ultralow flow (ULF) high-efficiency ultranarrow bore monolithic LC columns coupled to MS via a high-field asymmetric waveform ion mobility spectrometry (FAIMS) interface to evaluate the potential applicability for high sensitivity, robust, and reproducible proteomic profiling of low nanogram-level complex biological samples. As a result, ULF LC-FAIMS-MS brought unprecedented sensitivity levels and high reproducibility in bottom-up proteomic profiling. In addition, FAIMS improved the dynamic range, signal-to-noise ratios, and detection limits in ULF LC-MS-based measurements by significantly reducing chemical noise in comparison to the conventional nanoESI interface used with the same ULF LC-MS setup. Two, three, or four compensation voltages separated by at least 15 V were tested within a single LC-MS run using the FAIMS interface. The optimized ULF LC-ESI-FAIMS-MS/MS conditions resulted in identification of 2,348 ± 42 protein groups, 10,062 ± 285 peptide groups, and 15,734 ± 350 peptide-spectrum matches for 1 ng of a HeLa digest, using a 1 h gradient at the flow rate of 12 nL/min, which represents an increase by 38%, 91%, and 131% in respective identifications, as compared to the control experiment (without FAIMS). To evaluate the practical utility of the ULF LC-ESI-FAIMS-MS platform in proteomic profiling of limited samples, approximately 100, 1,000, and 10,000 U937 myeloid leukemia cells were processed, and a one-tenth of each sample was analyzed. Using the optimized conditions, we were able to reliably identify 251 ± 54, 1,135 ± 80, and 2,234 ± 25 protein groups from injected aliquots corresponding to ∼10, 100, and 1,000 processed cells.

Identification of Molecular Network Associated with Neuroprotective Effects of Yashtimadhu (Glycyrrhiza glabra L.) by Quantitative Proteomics of Rotenone-Induced Parkinson's Disease Model.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder, whose treatment with modern therapeutics leads to a plethora of side effects with prolonged usage. Therefore, the management of PD with complementary and alternative medicine is often pursued. In the Ayurveda system of alternative medicine, Yashtimadhu choorna, a Medhya Rasayana (nootropic), prepared from the dried roots of Glycyrrhiza glabra L. (licorice), is prescribed for the management of PD with a favorable outcome. We pursued to understand the neuroprotective effects of Yashtimadhu choorna against a rotenone-induced cellular model of PD using differentiated IMR-32 cells. Cotreatment with Yashtimadhu choorna extract rescued rotenone-induced apoptosis and hyperphosphorylation of ERK-1/2. Quantitative proteomic analysis of six peptide fractions from independent biological replicates acquired 1,561,169 mass spectra, which when searched resulted in 565,008 peptide-spectrum matches mapping to 30,554 unique peptides that belonged to 4864 human proteins. Proteins commonly identified in biological replicates and >4 PSMs were considered for further analysis, leading to a refined set of 3720 proteins. Rotenone treatment differentially altered 144 proteins (fold ≥1.25 or ≤0.8), involved in mitochondrial, endoplasmic reticulum, and autophagy functions. Cotreatment with Yashtimadhu choorna extract rescued 84 proteins from the effect of rotenone and an additional regulation of 4 proteins. Network analysis highlighted the interaction of proteins and pathways regulated by them, which can be targeted for neuroprotection. Validation of proteomics data highlighted that Yashtimadhu confers neuroprotection by preventing mitochondrial oxidative stress and apoptosis. This discovery will pave the way for understanding the molecular action of Ayurveda drugs and developing novel therapeutics for PD.

DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics.

The identification of major histocompatibility complex (MHC)-binding peptides in mass spectrometry (MS)-based immunopeptideomics relies largely on database search engines developed for proteomics data analysis. However, because immunopeptidomics experiments do not involve enzymatic digestion at specific residues, an inflated search space leads to a high false positive rate and low sensitivity in peptide identification. In order to improve the sensitivity and reliability of peptide identification, a post-processing tool named DeepRescore is developed. DeepRescore combines peptide features derived from deep learning predictions, namely accurate retention timeand MS/MS spectra predictions, with previously used features to rescore peptide-spectrum matches. Using two public immunopeptidomics datasets, it is shown that rescoring by DeepRescore increases both the sensitivity and reliability of MHC-binding peptide and neoantigen identifications compared to existing methods. It is also shown that the performance improvement is, to a large extent, driven by the deep learning-derived features. DeepRescore is developed using NextFlow and Docker and is available at https://github.com/bzhanglab/DeepRescore.

ProtyQuant: Comparing label-free shotgun proteomics datasets using accumulated peptide probabilities

Comparing multiple label-free shotgun proteomics datasets requires various data processing and formatting steps, including peptide-spectrum matching, protein inference, and quantification. Finally, the compilation of results files into a format that allows for downstream analyses. ProtyQuant performs protein inference and quantification calculations, and combines the results of individual datasets into plain text tables. These are lightweight, human-readable, and easy to import into databases or statistical software. ProtyQuant reads validated pepXML from proteomic workflows such as the Trans-Proteomic Pipeline (TPP), which makes it compatible with many commercial and free search engines. For protein inference and quantification, a modified version of the PIPQ program (He et al. 2016) was integrated. In contrast to simple spectral-counting, PIPQ sums up peptide probabilities. For assigning peptides to proteins, three algorithms are available: Multiple Counting, Equal Division, and Linear Programming. The accumulated peptide probabilities (app) are used for both tasks, protein probability estimation, and quantification. ProtyQuant was tested using a reference dataset for label-free shotgun proteomics, obtained from different concentrations of 48 human UPS proteins spiked into yeast lysate. Compared to ProteinProphet, ProtyQuant detected up to 126 (15%) more proteins in the mixture, applying an equal false positive rate (FPR). Using the app values for label-free quantification showed suitable sensitivity and linearity. Strikingly, the app values represent a realistic measure of ‘Protein Presence,’ an integral concept of protein probability and quantity. ProtyQuant provides a graphical user interface (GUI) and scripts for console-based processing. It is available (GNU GLP v3) for Windows, Linux, and Docker from https://bitbucket.org/lababi/protyquant/. SignificanceIntegrating data from multiple shot-gun proteomics experiments overwhelms non-expert researchers. ProtyQuant complements well-established workflows by aiding the comparison of proteins across samples. Importantly, the probability and abundance of proteins are seen from a holistic point of view. The accumulated peptide probability (app) as an integral measure of ‘Protein Presence’ demonstrated reliable performance for both protein identification and quantification. Using the app as a single measure facilitates the compilation of reports in comparative proteomics.

Peptide-Spectrum Match Validation with Internal Standards (P-VIS): Internally-Controlled Validation of Mass Spectrometry-Based Peptide Identifications.

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.

An integrative proteogenomics approach reveals peptides encoded by annotated lincRNA in the mouse kidney inner medulla.

Long noncoding RNAs (lncRNAs) are intracellular transcripts longer than 200 nucleotides and lack protein-coding information. A subclass of lncRNA known as long intergenic noncoding RNAs (lincRNAs) are transcribed from genomic regions that share no overlap with annotated protein-coding genes. Increasing evidence has shown that some annotated lincRNA transcripts do in fact contain open reading frames (ORFs) encoding functional short peptides in the cell. Few robust methods for lincRNA-encoded peptide identification have been reported, and the tissue-specific expression of these peptides has been largely unexplored. Here we propose an integrative workflow for lincRNA-encoded peptide discovery and test it on the mouse kidney inner medulla (IM). In brief, low molecular weight protein fractions were enriched from homogenate of IMs and trypsinized into shorter peptides, which were sequenced by high resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS). To curate a hypothetical lincRNA-encoded peptide database for peptide-spectrum matching following LC-MS/MS, we performed RNA-Seq on IMs, computationally removed reads overlapping with annotated protein-coding genes, and remapped the remaining reads to a database of mouse noncoding transcripts to infer lincRNA expression. Expressed lincRNAs were searched for ORFs by an existing rule-based algorithm, and translated ORFs were used for peptide-spectrum matching. Peptides identified by LC-MS/MS were further evaluated by using several quality control criteria and bioinformatics methods. We discovered three novel lincRNA-encoded peptides, which are conserved in mouse, rat, and human. The workflow can be adapted for discovery of small protein-coding genes in any species or tissue where noncoding transcriptome information is available.

A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane.

Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.

MMS2plot: An R Package for Visualizing Multiple MS/MS Spectra for Groups of Modified and Non-Modified Peptides.

A large number of post-translational modifications (PTMs) in proteins are buried in the unassigned mass spectrometric (MS) spectra in shot-gun proteomics datasets. Because the modified peptide fragments are low in abundance relative to the corresponding non-modified versions, it is critical to develop tools that allow facile evaluation of assignment of PTMs based on the MS/MS spectra. Such tools will preferably have the ability to allow comparison of fragment ion spectra and retention time between the modified and unmodified peptide pairs or group. Herein, MMS2plot, an R package for visualizing peptide-spectrum matches (PSMs) for multiple peptides, is described. MMS2plot features a batch mode and generates the output images in vector graphics file format that facilitate evaluation and publication of the PSM assignment. MMS2plot is expected to play an important role in PTM discovery from large-scale proteomics datasets generated by liquid chromatography-MS/MS. The MMS2plot package is freely available at https://github.com/lileir/MMS2plot under the GPL-3 license.

A Cross-linking Mass Spectrometry Approach Defines Protein Interactions in Yeast Mitochondria

Protein cross-linking and the analysis of cross-linked peptides by mass spectrometry is currently receiving much attention. Not only is this approach applied to isolated complexes to provide information about spatial arrangements of proteins, but it is also increasingly applied to entire cells and their organelles. As in quantitative proteomics, the application of isotopic labeling further makes it possible to monitor quantitative changes in the protein-protein interactions between different states of a system. Here, we cross-linked mitochondria from Saccharomyces cerevisiae grown on either glycerol- or glucose-containing medium to monitor protein-protein interactions under non-fermentative and fermentative conditions. We investigated qualitatively the protein-protein interactions of the 400 most abundant proteins applying stringent data-filtering criteria, i.e. a minimum of two cross-linked peptide spectrum matches and a cut-off in the spectrum scoring of the used search engine. The cross-linker BS3 proved to be equally suited for connecting proteins in all compartments of mitochondria when compared with its water-insoluble but membrane-permeable derivative DSS. We also applied quantitative cross-linking to mitochondria of both the growth conditions using stable-isotope labeled BS3. Significant differences of cross-linked proteins under glycerol and glucose conditions were detected, however, mainly because of the different copy numbers of these proteins in mitochondria under both the conditions. Results obtained from the glycerol condition indicate that the internal NADH:ubiquinone oxidoreductase Ndi1 is part of an electron transport chain supercomplex. We have also detected several hitherto uncharacterized proteins and identified their interaction partners. Among those, Min8 was found to be associated with cytochrome c oxidase. BN-PAGE analyses of min8Δ mitochondria suggest that Min8 promotes the incorporation of Cox12 into cytochrome c oxidase.