Systematic Errors in Peptide and Protein Identification and Quantification by Modified Peptides

pyRBDome: a comprehensive computational platform for enhancing RNA-binding proteome data

High-throughput proteomics approaches have revolutionised the identification of RNA-binding proteins (RBPome) and RNA-binding sequences (RBDome) across organisms. Yet, the extent of noise, including false positives, associated with these methodologies, is difficult to quantify as experimental approaches for validating the results are generally low throughput. To address this, we introduce pyRBDome, a pipeline for enhancing RNA-binding proteome data in silico. It aligns the experimental results with RNA-binding site (RBS) predictions from distinct machine-learning tools and integrates high-resolution structural data when available. Its statistical evaluation of RBDome data enables quick identification of likely genuine RNA-binders in experimental datasets. Furthermore, by leveraging the pyRBDome results, we have enhanced the sensitivity and specificity of RBS detection through training new ensemble machine-learning models. pyRBDome analysis of a human RBDome dataset, compared with known structural data, revealed that although UV-cross-linked amino acids were more likely to contain predicted RBSs, they infrequently bind RNA in high-resolution structures. This discrepancy underscores the limitations of structural data as benchmarks, positioning pyRBDome as a valuable alternative for increasing confidence in RBDome datasets.

Understanding bacterial pathogen diversity: A proteogenomic analysis and use of an array of genome assemblies to identify novel virulence factors of the honey bee bacterial pathogen Paenibacillus larvae

Mass spectrometry proteomics data are typically evaluated against publicly available annotated sequences, but the proteogenomics approach is a useful alternative. A single genome is commonly utilized in custom proteomic and proteogenomic data analysis. We pose the question of whether utilizing numerous different genome assemblies in a search database would be beneficial. We reanalyzed raw data from the exoprotein fraction of four reference Enterobacterial Repetitive Intergenic Consensus (ERIC) I-IV genotypes of the honey bee bacterial pathogen Paenibacillus larvae and evaluated them against three reference databases (from NCBI-protein, RefSeq, and UniProt) together with an array of protein sequences generated by six-frame direct translation of 15 genome assemblies from GenBank. The wide search yielded 453 protein hits/groups, which UpSet analysis categorized into 50 groups based on the success of protein identification by the 18 database components. Nine hits that were not identified by a unique peptide were not considered for marker selection, which discarded the only protein that was not identified by the reference databases. We propose that the variability in successful identifications between genome assemblies is useful for marker mining. The results suggest that various strains of P. larvae can exhibit specific traits that set them apart from the established genotypes ERIC I-V.

Global, site-resolved analysis of ubiquitylation occupancy and turnover rate reveals systems properties

Ubiquitylation regulates most proteins and biological processes in a eukaryotic cell. However, the site-specific occupancy (stoichiometry) and turnover rate of ubiquitylation have not been quantified. Here we present an integrated picture of the global ubiquitylation site occupancy and half-life. Ubiquitylation site occupancy spans over four orders of magnitude, but the median ubiquitylation site occupancy is three orders of magnitude lower than that of phosphorylation. The occupancy, turnover rate, and regulation of sites by proteasome inhibitors are strongly interrelated, and these attributes distinguish sites involved in proteasomal degradation and cellular signaling. Sites in structured protein regions exhibit longer half-lives and stronger upregulation by proteasome inhibitors than sites in unstructured regions. Importantly, we discovered a surveillance mechanism that rapidly and site-indiscriminately deubiquitylates all ubiquitin-specific E1 and E2 enzymes, protecting them against accumulation of bystander ubiquitylation. The work provides a systems-scale, quantitative view of ubiquitylation properties and reveals general principles of ubiquitylation-dependent governance.

Demystifying PTM Identification Using MODplus: Best Practices and Pitfalls

Tandem mass spectrometry (MS/MS) facilitates the rapid identification of posttranslational modifications (PTMs), which play a pivotal role in regulating numerous biological processes. This chapter explores recent advancements that expand the types of detectable PTMs and enhance the speed of the PTM searches. We also delve into computational challenges associated with searching for a multitude of PTMs simultaneously. The latter section introduces an automated procedure to identify an extensive range of PTMs using MODplus, a free PTM analysis software tool. We guide the reader through the preparation of the modification search, the determination of optional search parameters, the execution of the search, and the analysis of results, exemplified by a case study using specific MS/MS dataset.

Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry

We present CFdb, a harmonized resource of interaction proteomics data from 411 co-fractionation mass spectrometry (CF-MS) datasets spanning 21,703 fractions. Meta-analysis of this resource charts protein abundance, phosphorylation, and interactions throughout the tree of life, including a reference map of the human interactome. We show how large-scale CF-MS data can enhance analyses of individual CF-MS datasets, and exemplify this strategy by mapping the honey bee interactome.

Fast alignment of mass spectra in large proteomics datasets, capturing dissimilarities arising from multiple complex modifications of peptides

BACKGROUND

In proteomics, the interpretation of mass spectra representing peptides carrying multiple complex modifications remains challenging, as it is difficult to strike a balance between reasonable execution time, a limited number of false positives, and a huge search space allowing any number of modifications without a priori. The scientific community needs new developments in this area to aid in the discovery of novel post-translational modifications that may play important roles in disease.

RESULTS

To make progress on this issue, we implemented SpecGlobX (SpecGlob eXTended to eXperimental spectra), a standalone Java application that quickly determines the best spectral alignments of a (possibly very large) list of Peptide-to-Spectrum Matches (PSMs) provided by any open modification search method, or generated by the user. As input, SpecGlobX reads a file containing spectra in MGF or mzML format and a semicolon-delimited spreadsheet describing the PSMs. SpecGlobX returns the best alignment for each PSM as output, splitting the mass difference between the spectrum and the peptide into one or more shifts while considering the possibility of non-aligned masses (a phenomenon resulting from many situations including neutral losses). SpecGlobX is fast, able to align one million PSMs in about 1.5 min on a standard desktop. Firstly, we remind the foundations of the algorithm and detail how we adapted SpecGlob (the method we previously developed following the same aim, but limited to the interpretation of perfect simulated spectra) to the interpretation of imperfect experimental spectra. Then, we highlight the interest of SpecGlobX as a complementary tool downstream to three open modification search methods on a large simulated spectra dataset. Finally, we ran SpecGlobX on a proteome-wide dataset downloaded from PRIDE to demonstrate that SpecGlobX functions just as well on simulated and experimental spectra. We then carefully analyzed a limited set of interpretations.

CONCLUSIONS

SpecGlobX is helpful as a decision support tool, providing keys to interpret peptides carrying complex modifications still poorly considered by current open modification search software. Better alignment of PSMs enhances confidence in the identification of spectra provided by open modification search methods and should improve the interpretation rate of spectra.

Quantitative Proteomic Analysis of Cyanide and Mercury Detoxification by Pseudomonas pseudoalcaligenes CECT 5344

The cyanide-degrading bacterium Pseudomonas pseudoalcaligenes CECT 5344 uses cyanide and different metal-cyanide complexes as the sole nitrogen source. Under cyanotrophic conditions, this strain was able to grow with up to 100 μM mercury, which was accumulated intracellularly. A quantitative proteomic analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been applied to unravel the molecular basis of the detoxification of both cyanide and mercury by the strain CECT 5344, highlighting the relevance of the cyanide-insensitive alternative oxidase CioAB and the nitrilase NitC in the tolerance and assimilation of cyanide, independently of the presence or absence of mercury. Proteins overrepresented in the presence of cyanide and mercury included mercury transporters, mercuric reductase MerA, transcriptional regulator MerD, arsenate reductase and arsenical resistance proteins, thioredoxin reductase, glutathione S-transferase, proteins related to aliphatic sulfonates metabolism and sulfate transport, hemin import transporter, and phosphate starvation induced protein PhoH, among others. A transcriptional study revealed that from the six putative merR genes present in the genome of the strain CECT 5344 that could be involved in the regulation of mercury resistance/detoxification, only the merR2 gene was significantly induced by mercury under cyanotrophic conditions. A bioinformatic analysis allowed the identification of putative MerR2 binding sites in the promoter regions of the regulatory genes merR5, merR6, arsR, and phoR, and also upstream from the structural genes encoding glutathione S-transferase (fosA and yghU), dithiol oxidoreductase (dsbA), metal resistance chaperone (cpxP), and amino acid/peptide extruder involved in quorum sensing (virD), among others. IMPORTANCE Cyanide, mercury, and arsenic are considered very toxic chemicals that are present in nature as cocontaminants in the liquid residues generated by different industrial activities like mining. Considering the huge amounts of toxic cyanide- and mercury-containing wastes generated at a large scale and the high biotechnological potential of P. pseudoalcaligenes CECT 5344 in the detoxification of cyanide present in these industrial wastes, in this work, proteomic, transcriptional, and bioinformatic approaches were used to characterize the molecular response of this bacterium to cyanide and mercury, highlighting the mechanisms involved in the simultaneous detoxification of both compounds. The results generated could be applied for developing bioremediation strategies to detoxify wastes cocontaminated with cyanide, mercury, and arsenic, such as those generated at a large scale in the mining industry.

MSBooster: improving peptide identification rates using deep learning-based features

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

Proteomic Analysis of Arsenic Resistance during Cyanide Assimilation by Pseudomonas pseudoalcaligenes CECT 5344

Wastewater from mining and other industries usually contains arsenic and cyanide, two highly toxic pollutants, thereby creating the need to develop bioremediation strategies. Here, molecular mechanisms triggered by the simultaneous presence of cyanide and arsenite were analyzed by quantitative proteomics, complemented with qRT-PCR analysis and determination of analytes in the cyanide-assimilating bacterium Pseudomonas pseudoalcaligenes CECT 5344. Several proteins encoded by two ars gene clusters and other Ars-related proteins were up-regulated by arsenite, even during cyanide assimilation. Although some proteins encoded by the cio gene cluster responsible for cyanide-insensitive respiration decreased in the presence of arsenite, the nitrilase NitC required for cyanide assimilation was unaffected, thus allowing bacterial growth with cyanide and arsenic. Two complementary As-resistance mechanisms were developed in this bacterium, the extrusion of As(III) and its extracellular sequestration in biofilm, whose synthesis increased in the presence of arsenite, and the formation of organoarsenicals such as arseno-phosphoglycerate and methyl-As. Tetrahydrofolate metabolism was also stimulated by arsenite. In addition, the ArsH2 protein increased in the presence of arsenite or cyanide, suggesting its role in the protection from oxidative stress caused by both toxics. These results could be useful for the development of bioremediation strategies for industrial wastes co-contaminated with cyanide and arsenic.

Peptidomics

Peptides are biopolymers, typically consisting of 2-50 amino acids. They are biologically produced by the cellular ribosomal machinery or by non-ribosomal enzymes and, sometimes, other dedicated ligases. Peptides are arranged as linear chains or cycles, and include post-translational modifications, unusual amino acids and stabilizing motifs. Their structure and molecular size render them a unique chemical space, between small molecules and larger proteins. Peptides have important physiological functions as intrinsic signalling molecules, such as neuropeptides and peptide hormones, for cellular or interspecies communication, as toxins to catch prey or as defence molecules to fend off enemies and microorganisms. Clinically, they are gaining popularity as biomarkers or innovative therapeutics; to date there are more than 60 peptide drugs approved and more than 150 in clinical development. The emerging field of peptidomics comprises the comprehensive qualitative and quantitative analysis of the suite of peptides in a biological sample (endogenously produced, or exogenously administered as drugs). Peptidomics employs techniques of genomics, modern proteomics, state-of-the-art analytical chemistry and innovative computational biology, with a specialized set of tools. The complex biological matrices and often low abundance of analytes typically examined in peptidomics experiments require optimized sample preparation and isolation, including in silico analysis. This Primer covers the combination of techniques and workflows needed for peptide discovery and characterization and provides an overview of various biological and clinical applications of peptidomics.

Holistic view of biological nitrogen fixation and phosphorus mobilization in Azotobacter chroococcum NCIMB 8003

Nitrogen (N) and phosphorus (P) deficiencies are two of the most agronomic problems that cause significant decrease in crop yield and quality. N and P chemical fertilizers are widely used in current agriculture, causing environmental problems and increasing production costs. Therefore, the development of alternative strategies to reduce the use of chemical fertilizers while maintaining N and P inputs are being investigated. Although dinitrogen is an abundant gas in the atmosphere, it requires biological nitrogen fixation (BNF) to be transformed into ammonium, a nitrogen source assimilable by living organisms. This process is bioenergetically expensive and, therefore, highly regulated. Factors like availability of other essential elements, as phosphorus, strongly influence BNF. However, the molecular mechanisms of these interactions are unclear. In this work, a physiological characterization of BNF and phosphorus mobilization (PM) from an insoluble form (Ca₃(PO₄)₂) in Azotobacter chroococcum NCIMB 8003 was carried out. These processes were analyzed by quantitative proteomics in order to detect their molecular requirements and interactions. BNF led to a metabolic change beyond the proteins strictly necessary to carry out the process, including the metabolism related to other elements, like phosphorus. Also, changes in cell mobility, heme group synthesis and oxidative stress responses were observed. This study also revealed two phosphatases that seem to have the main role in PM, an exopolyphosphatase and a non-specific alkaline phosphatase PhoX. When both BNF and PM processes take place simultaneously, the synthesis of nitrogenous bases and L-methionine were also affected. Thus, although the interdependence is still unknown, possible biotechnological applications of these processes should take into account the indicated factors.

Combining Metabolic Pulse Labeling and Quantitative Proteomics to Monitor Protein Synthesis Upon Viral Infection

Viruses like influenza A virus (IAV) hijack host cells in order to replicate. To actively and abundantly synthesize viral proteins, they reprogram the cellular transcriptional and translational landscape. Here, we present a proteomic approach that allows us to quantify the differences in host and viral protein synthesis comparatively for different strains of IAV. The method is based on combining quantitative proteomics using stable isotope labelling by amino acids in cell culture (SILAC) and bioorthogonal labeling with methionine analogs. This methodology accurately quantifies synthesis of host and viral proteins with high temporal resolution and faithfully detects global changes in cellular translation capacity. It thus provides unique insights into the dynamics of protein synthesis as the infection progresses.

Investigating pathological epigenetic aberrations by epi-proteomics

Epigenetics includes a complex set of processes that alter gene activity without modifying the DNA sequence, which ultimately determines how the genetic information common to all the cells of an organism is used to generate different cell types. Dysregulation in the deposition and maintenance of epigenetic features, which include histone posttranslational modifications (PTMs) and histone variants, can result in the inappropriate expression or silencing of genes, often leading to diseased states, including cancer. The investigation of histone PTMs and variants in the context of clinical samples has highlighted their importance as biomarkers for patient stratification and as key players in aberrant epigenetic mechanisms potentially targetable for therapy. Mass spectrometry (MS) has emerged as the most powerful and versatile tool for the comprehensive, unbiased and quantitative analysis of histone proteoforms. In recent years, these approaches-which we refer to as "epi-proteomics"-have demonstrated their usefulness for the investigation of epigenetic mechanisms in pathological conditions, offering a number of advantages compared with the antibody-based methods traditionally used to profile clinical samples. In this review article, we will provide a critical overview of the MS-based approaches that can be employed to study histone PTMs and variants in clinical samples, with a strong focus on the latest advances in this area, such as the analysis of uncommon modifications and the integration of epi-proteomics data into multi-OMICs approaches, as well as the challenges to be addressed to fully exploit the potential of this novel field of research.

Group-walk: a rigorous approach to group-wise false discovery rate analysis by target-decoy competition

MOTIVATION

Target-decoy competition (TDC) is a commonly used method for false discovery rate (FDR) control in the analysis of tandem mass spectrometry data. This type of competition-based FDR control has recently gained significant popularity in other fields after Barber and Candès laid its theoretical foundation in a more general setting that included the feature selection problem. In both cases, the competition is based on a head-to-head comparison between an (observed) target score and a corresponding decoy (knockoff) score. However, the effectiveness of TDC depends on whether the data are homogeneous, which is often not the case: in many settings, the data consist of groups with different score profiles or different proportions of true nulls. In such cases, applying TDC while ignoring the group structure often yields imbalanced lists of discoveries, where some groups might include relatively many false discoveries and other groups include relatively very few. On the other hand, as we show, the alternative approach of applying TDC separately to each group does not rigorously control the FDR.

RESULTS

We developed Group-walk, a procedure that controls the FDR in the target-decoy/knockoff setting while taking into account a given group structure. Group-walk is derived from the recently developed AdaPT-a general framework for controlling the FDR with side-information. We show using simulated and real datasets that when the data naturally divide into groups with different characteristics Group-walk can deliver consistent power gains that in some cases are substantial. These groupings include the precursor charge state (4% more discovered peptides at 1% FDR threshold), the peptide length (3.6% increase) and the mass difference due to modifications (26% increase).

AVAILABILITY AND IMPLEMENTATION

Group-walk is available at https://cran.r-project.org/web/packages/groupwalk/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Multiaspect Examinations of Possible Alternative Mappings of Identified Variant Peptides: A Case Study on the HEK293 Cell Line

Adopting proteogenomics approach to validate single nucleotide variation events by identifying corresponding single amino acid variant peptides from mass spectrometry (MS)-based proteomics data facilitates translational and clinical research. Although variant peptides are usually identified from MS data with a stringent false discovery rate (FDR), FDR control could fail to eliminate dubious results caused by several issues; thus, postexamination to eliminate dubious results is required. However, comprehensive postexaminations of identification results are still lacking. Therefore, we propose a framework of three bottom-up levels, peptide-spectrum match, peptide, and variant event levels, that consists of rigorous 11-aspect examinations from the MS perspective to further confirm the reliability of variant events. As a proof of concept and showing feasibility, we demonstrate 11 examinations on the identified variant peptides from an HEK293 cell line data set, where various database search strategies were applied to maximize the number of identified variant PSMs with an FDR <1% for postexaminations. The results showed that only FDR criterion is insufficient to validate identified variant peptides and the 11 postexaminations can reveal low-confidence variant events detected by shotgun proteomics experiments. Therefore, we suggest that postexaminations of identified variant events based on the proposed framework are necessary for proteogenomics studies.

Mechanisms and minimization of false discovery of metabolic bio-orthogonal non-canonical amino acid proteomics

Metabolic proteomics has been widely used to characterize dynamic protein networks in many areas of biomedicine, including in the arena of tissue aging and rejuvenation. Bio-orthogonal non-canonical amino acid tagging (BONCAT) is based on mutant methionine-tRNA synthases (MetRS) that incorporates metabolic tags, e.g., azido-nor leucine, ANL, into newly synthesized proteins. BONCAT revolutionizes metabolic proteomics, because mutant MetRS transgene allows one to identify cell type specific proteomes in mixed biological environments. This is not possible with other methods, such as stable isotope labeling with amino acids in cell culture (SILAC), isobaric tags for relative and absolute quantitation (iTRAQ) and tandem mass tags (TMT). At the same time, an inherent weakness of BONCAT is that after click chemistry-based enrichment, all identified proteins are assumed to have been metabolically tagged, but there is no confirmation in Mass Spectrometry data that only tagged proteins are detected. As we show here, such assumption is incorrect and accurate negative controls uncover a surprisingly high degree of false positives in BONCAT proteomics. We show not only how to reveal the false discovery and thus improve the accuracy of the analyses and conclusions but also approaches for avoiding it through minimizing non-specific detection of biotin, biotin-independent direct detection of metabolic tags, and improvement of signal to noise ratio through machine learning algorithms.

Uncovering the impacts of alternative splicing on the proteome with current omics techniques

The high‐throughput sequencing of cellular RNAs has underscored a broad effect of isoform diversification through alternative splicing on the transcriptome. Moreover, the differential production of transcript isoforms from gene loci has been recognized as a critical mechanism in cell differentiation, organismal development, and disease. Yet, the extent of the impact of alternative splicing on protein production and cellular function remains a matter of debate. Multiple experimental and computational approaches have been developed in recent years to address this question. These studies have unveiled how molecular changes at different steps in the RNA processing pathway can lead to differences in protein production and have functional effects. New and emerging experimental technologies open exciting new opportunities to develop new methods to fully establish the connection between messenger RNA expression and protein production and to further investigate how RNA variation impacts the proteome and cell function.

This article is categorized under:

RNA Processing > Splicing Regulation/Alternative Splicing

Translation > Regulation

RNA Evolution and Genomics > Computational Analyses of RNA

Biochemical Mapping of Pyrodinium bahamense Unveils Molecular Underpinnings behind Organismal Processes

Proteins, lipids, and carbohydrates from the harmful algal bloom (HAB)-causing organism Pyrodinium bahamense were characterized to obtain insights into the biochemical processes in this environmentally relevant dinoflagellate. Shotgun proteomics using label-free quantitation followed by proteome mapping using the P. bahamense transcriptome and translated protein databases of Marinovum algicola, Alexandrium sp., Cylindrospermopsis raciborskii, and Symbiodinium kawagutii for annotation enabled the characterization of the proteins in P. bahamense. The highest number of annotated hits were obtained from M. algicola and highlighted the contribution of microorganisms associated with P. bahamense. Proteins involved in dimethylsulfoniopropionate (DMSP) degradation such as propionyl CoA synthethase and acryloyl-CoA reductase were identified, suggesting the DMSP cleavage pathway as the preferred route in this dinoflagellate. Most of the annotated proteins were involved in amino acid biosynthesis and carbohydrate degradation and metabolism, indicating the active roles of these molecules in the vegetative stage of P. bahamense. This characterization provides baseline information on the cellular machinery and the molecular basis of the ecophysiology of P. bahamense.

Clipper: p-value-free FDR control on high-throughput data from two conditions

High-throughput biological data analysis commonly involves identifying features such as genes, genomic regions, and proteins, whose values differ between two conditions, from numerous features measured simultaneously. The most widely used criterion to ensure the analysis reliability is the false discovery rate (FDR), which is primarily controlled based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions. Clipper is a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper outperforms existing methods for a broad range of applications in high-throughput data analysis.

Proteomes Are of Proteoforms: Embracing the Complexity

Proteomes are complex-much more so than genomes or transcriptomes. Thus, simplifying their analysis does not simplify the issue. Proteomes are of proteoforms, not canonical proteins. While having a catalogue of amino acid sequences provides invaluable information, this is the Proteome-lite. To dissect biological mechanisms and identify critical biomarkers/drug targets, we must assess the myriad of proteoforms that arise at any point before, after, and between translation and transcription (e.g., isoforms, splice variants, and post-translational modifications [PTM]), as well as newly defined species. There are numerous analytical methods currently used to address proteome depth and here we critically evaluate these in terms of the current 'state-of-the-field'. We thus discuss both pros and cons of available approaches and where improvements or refinements are needed to quantitatively characterize proteomes. To enable a next-generation approach, we suggest that advances lie in transdisciplinarity via integration of current proteomic methods to yield a unified discipline that capitalizes on the strongest qualities of each. Such a necessary (if not revolutionary) shift cannot be accomplished by a continued primary focus on proteo-genomics/-transcriptomics. We must embrace the complexity. Yes, these are the hard questions, and this will not be easy…but where is the fun in easy?

Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments

Co-fractionation mass spectrometry (CF-MS) has emerged as a powerful technique for interactome mapping. However, there is little consensus on optimal strategies for the design of CF-MS experiments or their computational analysis. Here, we reanalyzed a total of 206 CF-MS experiments to generate a uniformly processed resource containing over 11 million measurements of protein abundance. We used this resource to benchmark experimental designs for CF-MS studies and systematically optimize computational approaches to network inference. We then applied this optimized methodology to reconstruct a draft-quality human interactome by CF-MS and predict over 700,000 protein-protein interactions across 27 eukaryotic species or clades. Our work defines new resources to illuminate proteome organization over evolutionary timescales and establishes best practices for the design and analysis of CF-MS studies.

A predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry

Proteogenomics is an increasingly common method for species identification as it allows for rapid and inexpensive interrogation of an unknown organism’s proteome—even when the proteome is partially degraded. The proteomic method typically uses tandem mass spectrometry to survey all peptides detectable in a sample that frequently contains hundreds or thousands of proteins. Species identification is based on detection of a small numbers of species-specific peptides. Genetic analysis of proteins by mass spectrometry, however, is a developing field, and the bone proteome, typically consisting of only two proteins, pushes the limits of this technology. Nearly 20% of highly confident spectra from modern human bone samples identify non-human species when searched against a vertebrate database—as would be necessary with a fragment of unknown bone. These non-human peptides are often the result of current limitations in mass spectrometry or algorithm interpretation errors. Consequently, it is difficult to know if a “species-specific” peptide used to identify a sample is actually present in that sample. Here we evaluate the causes of peptide sequence errors and propose an unbiased, probabilistic approach to determine the likelihood that a species is correctly identified from bone without relying on species-specific peptides.

Influence of Post-Translational Modifications on Protein Identification in Database Searches

Comprehensive analysis of post-translation modifications (PTMs) is an important mission of proteomics. However, the consideration of PTMs increases the search space and may therefore impair the efficiency of protein identification. Using thousands of proteomic searches, we investigated the practical aspects of considering multiple PTMs in Byonic searches for the maximization of protein and peptide hits. The inclusion of all PTMs, which occur with at least 2% frequency in the sample, has an advantageous effect on protein and peptide identification. A linear relationship was established between the number of considered PTMs and the number of reliably identified peptides and proteins. Even though they handle multiple modifications less efficiently, the results of MASCOT (using the Percolator function) and Andromeda (the search engine included in MaxQuant) became comparable to those of Byonic, in the case of a few PTMs.

Identification of spliced peptides in pancreatic islets uncovers errors leading to false assignments

Proteasomal spliced peptides (PSPs) have been identified in the class I major histocompatibility complex (MHC) peptidomes of several tumors and have emerged as novel neoantigens that can stimulate highly specific T cells. Much debate has surrounded the percentage of PSPs in the immunopeptidome; reported numbers have ranged from <1-5% to 12-45%. Recently, our laboratory demonstrated in nonobese diabetic (NOD) mice that hybrid insulin peptides (HIPs), a special class of spliced peptides, are formed during insulin granule degradation in crinosomes of the pancreatic β cells and that modified peptides comprised a significant source of false positive HIP assignments. Herein, this study is extended to crinosomes isolated from other mouse strains and to two recent MHC class I studies, to see if modified peptides explained discrepancies in reported percentages of PSPs. This analysis revealed that both MHC-I peptidomes contained many spectra erroneously assigned as PSPs. While many false positive PSPs did arise from modified peptides, others arose from probable data processing errors. Thus, the reported numbers of PSPs in the literature are likely elevated due to errors associated with data processing and analysis.

A semi-tryptic peptide centric metaproteomic mining approach and its potential utility in capturing signatures of gut microbial proteolysis

BACKGROUND

Proteolysis regulation allows gut microbes to respond rapidly to dynamic intestinal environments by fast degradation of misfolded proteins and activation of regulatory proteins. However, alterations of gut microbial proteolytic signatures under complex disease status such as inflammatory bowel disease (IBD, including Crohn's disease (CD) and ulcerative colitis (UC)), have not been investigated. Metaproteomics holds the potential to investigate gut microbial proteolysis because semi-tryptic peptides mainly derive from endogenous proteolysis.

RESULTS

We have developed a semi-tryptic peptide centric metaproteomic mining approach to obtain a snapshot of human gut microbial proteolysis signatures. This approach employed a comprehensive meta-database, two-step multiengine database search, and datasets with high-resolution fragmentation spectra to increase the confidence of semi-tryptic peptide identification. The approach was validated by discovering altered proteolysis signatures of Escherichia coli heat shock response. Utilizing two published large-scale metaproteomics datasets containing 623 metaproteomes from 447 fecal and 176 mucosal luminal interface (MLI) samples from IBD patients and healthy individuals, we obtain potential signatures of altered gut microbial proteolysis at taxonomic, functional, and cleavage site motif levels. The functional alterations mainly involved microbial carbohydrate transport and metabolism, oxidative stress, cell motility, protein synthesis, and maturation. Altered microbial proteolysis signatures of CD and UC mainly occurred in terminal ileum and descending colon, respectively. Microbial proteolysis patterns exhibited low correlations with β-diversity and moderate correlations with microbial protease and chaperones levels, respectively. Human protease inhibitors and immunoglobulins were mainly negatively associated with microbial proteolysis patterns, probably because of the inhibitory effects of these host factors on gut microbial proteolysis events.

CONCLUSIONS

This semi-tryptic peptide centric mining strategy offers a label-free approach to discover signatures of in vivo gut microbial proteolysis events if experimental conditions are well controlled. It can also capture in vitro proteolysis signatures to facilitate the evaluation and optimization of experimental conditions. Our findings highlight the complex and diverse proteolytic events of gut microbiome, providing a unique layer of information beyond taxonomic and proteomic abundance. Video abstract.

Beyond Target-Decoy Competition: Stable Validation of Peptide and Protein Identifications in Mass Spectrometry-Based Discovery Proteomics

In bottom-up discovery proteomics, target-decoy competition (TDC) is the most popular method for false discovery rate (FDR) control. Despite unquestionable statistical foundations, this method has drawbacks, including its hitherto unknown intrinsic lack of stability vis-à-vis practical conditions of application. Although some consequences of this instability have already been empirically described, they may have been misinterpreted. This article provides evidence that TDC has become less reliable as the accuracy of modern mass spectrometers improved. We therefore propose to replace TDC by a totally different method to control the FDR at the spectrum, peptide, and protein levels, while benefiting from the theoretical guarantees of the Benjamini-Hochberg framework. As this method is simpler to use, faster to compute, and more stable than TDC, we argue that it is better adapted to the standardization and throughput constraints of current proteomic platforms.

Visualisation tools for dependent peptide searches to support the exploration of in vitro protein modifications

Dependent peptide searching is a method for discovering covalently-modified peptides-and therefore proteins-in mass-spectrometry-based proteomics experiments. Being more permissive than standard search methods, it has the potential to discover novel modifications (e.g., post-translational modifications occurring in vivo, or modifications introduced in vitro). However, few studies have explored dependent peptide search results in an untargeted way. In the present study, we sought to evaluate dependent peptide searching as a means of characterising proteins that have been modified in vitro. We generated a model data set by analysing N-ethylmaleimide-treated bovine serum albumin, and performed dependent peptide searches using the popular MaxQuant software. To facilitate interpretation of the search results (hundreds of dependent peptides), we developed a series of visualisation tools (R scripts). We used the tools to assess the diversity of putative modifications in the albumin, and to pinpoint hypothesised modifications. We went on to explore the tools' generality via analyses of public data from studies of rat and human proteomes. Of 19 expected sites of modification (one in rat cofilin-1 and 18 across six different human plasma proteins), eight were found and correctly localised. Apparently, some sites went undetected because chemical enrichment had depleted necessary analytes (potential 'base' peptides). Our results demonstrate (i) the ability of the tools to provide accurate and informative visualisations, and (ii) the usefulness of dependent peptide searching for characterising in vitro protein modifications. Our model data are available via PRIDE/ProteomeXchange (accession number PXD013040).

Chemical RNA digestion enables robust RNA-binding site mapping at single amino acid resolution

RNA-binding sites (RBSs) can be identified by liquid chromatography and tandem mass spectrometry analyses of the protein-RNA conjugates created by crosslinking, but RBS mapping remains highly challenging due to the complexity of the formed RNA adducts. Here, we introduce RBS-ID, a method that uses hydrofluoride to fully cleave RNA into mono-nucleosides, thereby minimizing the search space to drastically enhance coverage and to reach single amino acid resolution. Moreover, the simple mono-nucleoside adducts offer a confident and quantitative measure of direct RNA-protein interaction. Using RBS-ID, we profiled ~2,000 human RBSs and probed Streptococcus pyogenes Cas9 to discover residues important for genome editing.

Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.

Reduced expression of enolase-1 correlates with high intracellular glucose levels and increased senescence in cisplatin-resistant ovarian cancer cells

Despite good responses to first-line treatment with platinum-based combination chemotherapy, most ovarian cancer patients will relapse and eventually develop a platinum-resistant disease with a poor overall prognosis. The molecular events leading to the cisplatin resistance of ovarian cancer cells are not fully understood. Here, we performed a proteomic analysis to identify protein candidates deregulated in a cisplatin-resistant ovarian cancer cell line (A2780CP20) in comparison to their sensitive counterpart (A2780). Forty-eight proteins were differentially abundant in A2780CP20, as compared with A2780, cells. Enolase-1 (ENO1) was significantly decreased in cisplatin-resistant ovarian cancer cells. Western blots and RT-PCR confirmed our findings. Ectopic ENO1 expression increased the sensitivity of ovarian cancer cells to cisplatin treatment. In contrast, small-interfering (siRNA)-based ENO1 silencing in A2780 cells reduced the sensitivity of these cells to cisplatin treatment. Whereas glucose consumption was lower, intracellular levels were higher in cisplatin-resistant ovarian cancer cells as compared with their cisplatin-sensitive counterparts. Senescence-associated β-galactosidase (β-Gal) levels were higher in cisplatin-resistant ovarian cancer cells as compared with cisplatin-sensitive ovarian cancer cells. β-Gal levels were decreased in ENO1 overexpressed clones. Protein levels of the cell cycle regulators and senescence markers p21 and p53 showed opposite expression patterns in cisplatin-resistant compared with cisplatin sensitive cells. Our studies suggest that decreased expression of ENO1 promotes glucose accumulation, induces senescence, and leads to cisplatin resistance of ovarian cancer cells.

Phosphosite Analysis of the Cytomegaloviral mRNA Export Factor pUL69 Reveals Serines with Critical Importance for Recruitment of Cellular Proteins Pin1 and UAP56/URH49

Human cytomegalovirus (HCMV) encodes the viral mRNA export factor pUL69, which facilitates the cytoplasmic accumulation of mRNA via interaction with the cellular RNA helicase UAP56 or URH49. We reported previously that pUL69 is phosphorylated by cellular CDKs and the viral CDK-like kinase pUL97. Here, we set out to identify phosphorylation sites within pUL69 and to characterize their importance. Mass spectrometry-based phosphosite mapping of pUL69 identified 10 serine/threonine residues as phosphoacceptors. Surprisingly, only a few of these sites localized to the N terminus of pUL69, which could be due to the presence of additional posttranslational modifications, like arginine methylation. As an alternative approach, pUL69 mutants with substitutions of putative phosphosites were analyzed by Phos-tag SDS-PAGE. This demonstrated that serines S46 and S49 serve as targets for phosphorylation by pUL97. Furthermore, we provide evidence that phosphorylation of these serines mediates cis/trans isomerization by the prolyl isomerase Pin1, thus forming a functional Pin1 binding motif. Surprisingly, while abrogation of the Pin1 motif did not affect the replication of recombinant cytomegaloviruses, mutation of serines next to the interaction site for UAP56/URH49 strongly decreased viral replication. This was correlated with a loss of UAP56/URH49 recruitment. Intriguingly, the critical serines S13 and S15 were located within a sequence resembling the UAP56 binding motif (UBM) of cellular mRNA adaptor proteins like REF and UIF. We propose that betaherpesviral mRNA export factors have evolved an extended UAP56/URH49 recognition sequence harboring phosphorylation sites to increase their binding affinities. This may serve as a strategy to successfully compete with cellular mRNA adaptor proteins for binding to UAP56/URH49.IMPORTANCE The multifunctional regulatory protein pUL69 of human cytomegalovirus acts as a viral RNA export factor with a critical role in efficient replication. Here, we identify serine/threonine phosphorylation sites for cellular and viral kinases within pUL69. We demonstrate that the pUL97/CDK phosphosites within alpha-helix 2 of pUL69 are crucial for its cis/trans isomerization by the cellular protein Pin1. Thus, we identified pUL69 as the first HCMV-encoded protein that is phosphorylated by cellular and viral serine/threonine kinases in order to serve as a substrate for Pin1. Furthermore, our study revealed that betaherpesviral mRNA export proteins contain extended binding motifs for the cellular mRNA adaptor proteins UAP56/URH49 harboring phosphorylated serines that are critical for efficient viral replication. Knowledge of the phosphorylation sites of pUL69 and the processes regulated by these posttranslational modifications is important in order to develop antiviral strategies based on a specific interference with pUL69 phosphorylation.

Mass spectrometry of in-gel digests reveals differences in amino acid sequences of high-molecular-weight glutenin subunits in spelt and emmer compared to common wheat

High-molecular-weight glutenin subunits (HMW-GS) play an important role for the baking quality of wheat. The ancient wheats emmer and spelt differ in their HMW-GS pattern compared to modern common wheat and this might be one reason for their comparatively poor baking quality. The aim of this study was to elucidate similarities and differences in the amino acid sequences of two 1Bx HMW-GS of common wheat, spelt and emmer. First, the sodium dodecyl polyacrylamide gel electrophoresis (SDS-PAGE) system was optimized to separate common wheat, spelt and emmer Bx6 and Bx7 from other HMW-GS (e.g., 1Ax and 1By) in high concentrations. The in-gel digests of the Bx6 and Bx7 bands were analyzed by untargeted LC-MS/MS experiments revealing different UniProtKB accessions in spelt and emmer compared to common wheat. The HMW-GS Bx6 and Bx7, respectively, of emmer and spelt showed differences in the amino acid sequences compared to those of common wheat. The identities of the peptide variations were confirmed by targeted LC-MS/MS. These peptides can be used to differentiate between Bx6 and Bx7 of spelt and emmer and Bx6 and Bx7 of common wheat. The findings should help to increase the reliability and curation status of wheat protein databases and to understand the effects of protein structure on the functional properties. Graphical abstract.

The extraradical proteins of Rhizophagus irregularis: A shotgun proteomics approach

Arbuscular Mycorrhizal fungi (AMF, Glomeromycota) form obligate symbiotic associations with the roots of most terrestrial plants. Our understanding of the molecular mechanisms enabling AMF propagation and AMF-host interaction is currently incomplete. Analysis of AMF proteomes could yield important insights and generate hypotheses on the nature and mechanism of AMF-plant symbiosis. Here, we examined the extraradical mycelium proteomic profile of the arbuscular mycorrhizal fungus Rhizophagus irregularis grown on Ri T-DNA transformed Chicory roots in a root organ culture setting. Our analysis detected 529 different peptides that mapped to 474 translated proteins in the R. irregularis genome. R. irregularis proteome was characterized by a high proportion of proteins (9.9 % of total, 21.4 % of proteins with functional prediction) mediating a wide range of signal transduction processes, e.g. Rho1 and Bmh2, Ca-signaling (calmodulin, and Ca channel protein), mTOR signaling (MAP3K7, and MAPKAP1), and phosphatidate signaling (phospholipase D1/2) proteins, as well as members of the Ras signaling pathway. In addition, the proteome contained an unusually large proportion (53.6 %) of hypothetical proteins, the majority of which (85.8 %) were Glomeromycota-specific. Forty-eight proteins were predicted to be surface/membrane associated, including multiple hypothetical proteins of yet-unrecognized functions. However, no evidence for the overproduction of specific proteins, previously implicated in promoting soil health and aggregation was obtained. Finally, the comparison of R. irregularis proteome to previously published AMF proteomes identified a core set of pathways and processes involved in AMF growth. We conclude that R. irregularis growth on chicory roots requires the activation of a wide range of signal transduction pathways, the secretion of multiple novel hitherto unrecognized Glomeromycota-specific proteins, and the expression of a wide array of surface-membrane associated proteins for cross kingdom cell-to-cell communications.

MODplus: Robust and Unrestrictive Identification of Post-Translational Modifications Using Mass Spectrometry

Post-translational modifications regulate various cellular processes and are of great biological interest. Unrestrictive searches of mass spectrometry data enable the detection of any type of modification. Here we propose MODplus, which makes practical unrestrictive searches possible by allowing (1) hundreds of modifications, (2) multiple modifications per peptide, (3) the whole proteome database, and (4) any tolerant values in search parameters. The utility of MODplus was demonstrated in large human data sets of HEK293 cells and TMT-labeled phosphorylation enrichment. Notably, MODplus supports identifying different modification types at multiple sites and reports real chemical and biological modifications, as it has been very labor intensive to link unrestrictive search results to real modifications. We also confirmed the presence of Missing Precursor (MP) spectra that were not identifiable using targeted precursor masses. The MP spectra mostly resulted in identifications of wrong modifications and negatively affected the overall performance, often by as much as 10%. MODplus can rapidly recognize MP spectra and correct their identifications, resulting in increased identification rate up to 70% in the HEK293 data set as well as improved reliability.

Protein Post-Translational Modification Crosstalk in Acute Myeloid Leukemia Calls for Action

BACKGROUND

Post-translational modification (PTM) crosstalk is a young research field. However, there is now evidence of the extraordinary characterization of the different proteoforms and their interactions in a biological environment that PTM crosstalk studies can describe. Besides gene expression and phosphorylation profiling of acute myeloid leukemia (AML) samples, the functional combination of several PTMs that might contribute to a better understanding of the complexity of the AML proteome remains to be discovered.

OBJECTIVE

By reviewing current workflows for the simultaneous enrichment of several PTMs and bioinformatics tools to analyze mass spectrometry (MS)-based data, our major objective is to introduce the PTM crosstalk field to the AML research community.

RESULTS

After an introduction to PTMs and PTM crosstalk, this review introduces several protocols for the simultaneous enrichment of PTMs. Two of them allow a simultaneous enrichment of at least three PTMs when using 0.5-2 mg of cell lysate. We have reviewed many of the bioinformatics tools used for PTM crosstalk discovery as its complex data analysis, mainly generated from MS, becomes challenging for most AML researchers. We have presented several non-AML PTM crosstalk studies throughout the review in order to show how important the characterization of PTM crosstalk becomes for the selection of disease biomarkers and therapeutic targets.

CONCLUSION

Herein, we have reviewed the advances and pitfalls of the emerging PTM crosstalk field and its potential contribution to unravel the heterogeneity of AML. The complexity of sample preparation and bioinformatics workflows demands a good interaction between experts of several areas.

Proteomic Sample Preparation through Extraction by Unspecific Adsorption on Silica Beads for ArgC-like Digestion

Sample preparation for mass-spectrometry-based proteomic analyses usually requires intricate, multistep workflows that are often limited in capacity or suffer from sample loss. Here, we introduce a lean adsorption-based protocol (ABP) for the extraction of proteins from fresh cell lysates that enables us to modify and tag protein samples under harsh conditions, such as organic solvents, high salt concentrations, or low pH values. This offers high versatility while also reducing the required steps in the preparation process significantly. Protein identifications are slightly increased compared to traditional acetone precipitation followed by an in-solution digestion (AP/IS) or filter aided sample preparation (FASP) and proved complementary to both methods regarding proteome coverage. When combined with ArgC-like digestion, this approach delivered 5386 uniquely identified proteins, a substantial increase of 18.27% over tryptic digestion (4554), while decreasing spectra complexity due to a lower number of peptide to spectra matches per protein and the number of missed cleaved peptides. In addition, an increased number of identified membrane proteins and histones as well as improved fragmentation and intensity coverage were observed through comprehensive data analysis.

A Dual-Purpose Bromocoumarin Tag Enables Deep Profiling of the Cellular Cysteinome

Chemical proteomics enables comprehensive profiling of small molecules in complex proteomes. A critical component to understand the interactome of a small molecule is the precise location on a protein where the interaction takes place. Several approaches have been developed that take advantage of bio-orthogonal chemistry and subsequent enrichment steps to isolate peptides modified by small molecules. These methods rely on target identification at the level of mass spectrometry making it difficult to interpret an experiment when modified peptides are not identified. Herein, an approach in which fluorescence-triggered two-dimensional chromatography enables the isolation of small molecule-conjugated peptides prior to mass spectrometry analysis is described. In this study, a bromocoumarin moiety has been utilized that fluoresces and generates a distinct isotopic signature to locate and identify modified peptides. Profiling of a cellular cysteinome with the use of a bromocoumarin tag demonstrates that two-dimensional fluorescence-based chromatography separation can enable the identification of proteins containing reactive cysteine residues. Moreover, the method facilitates the interrogation of low abundance proteins with greater depth and sensitivity than a previously reported isotope-targeted approach. Lastly, this workflow enables the identification of small-molecule modified peptides from a protein-of-interest.

Comprehensive identification of peptides in tandem mass spectra using an efficient open search engine

We present a sequence-tag-based search engine, Open-pFind, to identify peptides in an ultra-large search space that includes coeluting peptides, unexpected modifications and digestions. Our method detects peptides with higher precision and speed than seven other search engines. Open-pFind identified 70-85% of the tandem mass spectra in four large-scale datasets and 14,064 proteins, each supported by at least two protein-unique peptides, in a human proteome dataset.

Fast Open Modification Spectral Library Searching through Approximate Nearest Neighbor Indexing

Open modification searching (OMS) is a powerful search strategy that identifies peptides carrying any type of modification by allowing a modified spectrum to match against its unmodified variant by using a very wide precursor mass window. A drawback of this strategy, however, is that it leads to a large increase in search time. Although performing an open search can be done using existing spectral library search engines by simply setting a wide precursor mass window, none of these tools have been optimized for OMS, leading to excessive runtimes and suboptimal identification results. We present the ANN-SoLo tool for fast and accurate open spectral library searching. ANN-SoLo uses approximate nearest neighbor indexing to speed up OMS by selecting only a limited number of the most relevant library spectra to compare to an unknown query spectrum. This approach is combined with a cascade search strategy to maximize the number of identified unmodified and modified spectra while strictly controlling the false discovery rate as well as a shifted dot product score to sensitively match modified spectra to their unmodified counterparts. ANN-SoLo achieves state-of-the-art performance in terms of speed and the number of identifications. On a previously published human cell line data set, ANN-SoLo confidently identifies more spectra than SpectraST or MSFragger and achieves a speedup of an order of magnitude compared with SpectraST. ANN-SoLo is implemented in Python and C++. It is freely available under the Apache 2.0 license at https://github.com/bittremieux/ANN-SoLo .

Plasma-based protein biomarkers can predict the risk of acute graft-versus-host disease and non-relapse mortality in patients undergoing allogeneic hematopoietic stem cell transplantation

Predictive biomarkers for acute graft-versus-host disease (aGVHD) is currently lacking. In this study, we employed an unbiased proteome profiling method to prospectively collected plasma samples from allogeneic hematopoietic stem cell transplantation (alloHSCT) recipients to identify protein biomarkers that predict the risk of aGVHD and non-relapse mortality (NRM). In the discovery set, including five aGVHD patients and five controls, we identified seven candidate proteins. Patients with high levels of these proteins tended to exhibit a higher risk of aGVHD and NRM compared to patients with low levels in post-engraftment plasma samples from an independent validation set (n = 89). Tissue inhibitor of metalloproteinase 1, plastin-2, and regenerating islet-derived protein 3-α were selected as the most-predictive biomarkers via an exhaustive variable screening algorithm and were collectively used to develop a biomarker panel score ranging from 0 to 3. The biomarker panel score correlated significantly with aGVHD and NRM risk in univariable and multivariable Cox models. Furthermore, using the biomarker panel score in conjunction with clinical predictors significantly improved the discriminatory performance of the Cox model in predicting aGVHD and NRM risk. Our findings suggest that plasma-derived protein biomarkers can be used to predict aGVHD and NRM before the onset of clinical manifestations.

Integrated Dissection of Cysteine Oxidative Post-translational Modification Proteome During Cardiac Hypertrophy

Cysteine oxidative modification of cellular proteins is crucial for many aspects of cardiac hypertrophy development. However, integrated dissection of multiple types of cysteine oxidative post-translational modifications (O-PTM) of proteomes in cardiac hypertrophy is currently missing. Here we developed a novel discovery platform that encompasses a customized biotin switch-based quantitative proteomics pipeline and an advanced analytic workflow to comprehensively profile the landscape of cysteine O-PTM in an ISO-induced cardiac hypertrophy mouse model. Specifically, we identified a total of 1655 proteins containing 3324 oxidized cysteine sites by at least one of the following three modifications: reversible cysteine O-PTM, cysteine sulfinylation (CysSO₂H), and cysteine sulfonylation (CysSO₃H). Analyzing the hypertrophy signatures that are reproducibly discovered from this computational workflow unveiled four biological processes with increased cysteine O-PTM. Among them, protein phosphorylation, creatine metabolism, and response to elevated Ca²⁺ pathways exhibited an elevation of cysteine O-PTM in early stages, whereas glucose metabolism enzymes were increasingly modified in later stages, illustrating a temporal regulatory map in cardiac hypertrophy. Our cysteine O-PTM platform depicts a dynamic and integrated landscape of the cysteine oxidative proteome, through the extracted molecular signatures, and provides critical mechanistic insights in cardiac hypertrophy. Data are available via ProteomeXchange with identifier PXD010336.

Quality control of single amino acid variations detected by tandem mass spectrometry

Study of single amino acid variations (SAVs) of proteins, resulting from single nucleotide polymorphisms, is of great importance for understanding the relationships between genotype and phenotype. In mass spectrometry based shotgun proteomics, identification of peptides with SAVs often suffers from high error rates on the variant sites detected. These site errors are due to multiple reasons and can be confirmed by manual inspection or genomic sequencing. Here, we present a software tool, named SAVControl, for site-level quality control of variant peptide identifications. It mainly includes strict false discovery rate control of variant peptide identifications and variant site verification by unrestrictive mass shift relocalization. SAVControl was validated on three colorectal adenocarcinoma cell line datasets with genomic sequencing evidences and tested on a colorectal cancer dataset from The Cancer Genome Atlas. The results show that SAVControl can effectively remove false detections of SAVs.

SIGNIFICANCE

Protein sequence variations caused by single nucleotide polymorphisms (SNPs) are single amino acid variations (SAVs). The investigation of SAVs may provide a chance for understanding the relationships between genotype and phenotype. Mass spectrometry (MS) based proteomics provides a large-scale way to detect SAVs. However, using the current analysis strategy to detect SAVs may lead to high rate of false positives. The SAVControl we present here is a computational workflow and software tool for site-level quality control of SAVs detected by MS. It accesses the confidence of detected variant sites by relocating the mass shift responsible for an SAV to search for alternative interpretations. In addition, it uses a strict false discovery rate control method for variant peptide identifications. The advantages of SAVControl were demonstrated on three colorectal adenocarcinoma cell line datasets and a colorectal cancer dataset. We believe that SAVControl will be a powerful tool for computational proteomics and proteogenomics.

Unrestricted Mass Spectrometric Data Analysis for Identification, Localization, and Quantification of Oxidative Protein Modifications

Oxidation generates multiple diverse post-translational modifications resulting in changes in protein structure and function associated with a wide range of diseases. Of these modifications, carbonylations have often been used as hallmarks of oxidative damage. However, accumulating evidence supports the hypothesis that other oxidation products may be quantitatively more important under physiological conditions. To address this issue, we have developed a holistic mass spectrometry-based approach for the simultaneous identification, localization, and quantification of a broad range of oxidative modifications based on so-called "dependent peptides". The strategy involves unrestricted database searches with rigorous filtering focusing on oxidative modifications. The approach was applied to bovine serum albumin and human serum proteins subjected to metal ion-catalyzed oxidation, resulting in the identification of a wide range of different oxidative modifications. The most common modification in the oxidized samples is hydroxylation, but carbonylation, decarboxylation, and dihydroxylation are also abundant, while carbonylation showed the largest increase in abundance relative to nonoxidized samples. Site-specific localization of modified residues reveals several "oxidation hotspots" showing high levels of modification occupancy, including specific histidine, tryptophan, methionine, glutamate, and aspartate residues. The majority of the modifications, however, occur at low occupancy levels on a diversity of side chains.

Beyond Read-Counts: Ribo-seq Data Analysis to Understand the Functions of the Transcriptome

By mapping the positions of millions of translating ribosomes in the cell, ribosome profiling (Ribo-seq) has established its role as a powerful tool to study gene expression. Several laboratories have introduced modifications to the experimental protocol and expanded the repertoire of biochemical methods to study translation transcriptome-wide. However, the diversity of protocols highlights a need for standardization. At the same time, different computational analysis strategies have used Ribo-seq data to identify the set of translated sequences with high confidence. In this review we present an overview of such methodologies, outlining their assumptions, data requirements, and availability. At the interface between RNA and proteins, Ribo-seq can complement data from multiple omics approaches, zooming in on the central role of translation in the molecular cell.

Reviewing evidence for systematic transcriptional deletions, nucleotide exchanges, and expanded codons, and peptide clusters in human mitochondria

Polymerization sometimes transforms sequences by (a) systematic deletions of mono-, dinucleotides after trinucleotides, or (b) 23 systematic nucleotide exchanges (9 symmetric, X<>Y, e.g. G<>T, 14 asymmetric, X > Y > Z > X, e.g. A > G > T > A), producing del- and swinger RNAs. Some peptides correspond to del- and swinger RNA translations, also according to tetracodons, codons expanded by a silent nucleotide. Here new analyzes assume different proteolytic patterns, partially alleviating false negative peptide detection biases, expanding noncanonical mitoproteome profiles. Mito-genomic, -transcriptomic and -proteomic evidence for noncanonical transcriptions and translations are reviewed and clusters of del- and swinger peptides (also along tetracodons) are described. Noncanonical peptide clusters indicate regulated expression of cryptically encoded mitochondrial protein coding genes. These candidate noncanonical proteins don't resemble known proteins.

SpecOMS: A Full Open Modification Search Method Performing All-to-All Spectra Comparisons within Minutes

The analysis of discovery proteomics experiments relies on algorithms that identify peptides from their tandem mass spectra. The almost exhaustive interpretation of these spectra remains an unresolved issue. At present, an important number of missing interpretations is probably due to peptides displaying post-translational modifications and variants that yield spectra that are particularly difficult to interpret. However, the emergence of a new generation of mass spectrometers that provide high fragment ion accuracy has paved the way for more efficient algorithms. We present a new software, SpecOMS, that can handle the computational complexity of pairwise comparisons of spectra in the context of large volumes. SpecOMS can compare a whole set of experimental spectra generated by a discovery proteomics experiment to a whole set of theoretical spectra deduced from a protein database in a few minutes on a standard workstation. SpecOMS can ingeniously exploit those capabilities to improve the peptide identification process, allowing strong competition between all possible peptides for spectrum interpretation. Remarkably, this software resolves the drawbacks (i.e., efficiency problems and decreased sensitivity) that usually accompany open modification searches. We highlight this promising approach using results obtained from the analysis of a public human data set downloaded from the PRIDE (PRoteomics IDEntification) database.