Application of de Novo Sequencing to Large-Scale Complex Proteomics Data Sets

De Novo Sequencing of Peptides from Tandem Mass Spectra and Applications in Proteogenomics

The changes in protein expression are hallmarks of development and disease. Protein expression can be established qualitatively and quantitatively using mass spectrometry (MS). Samples are prepared, proteins extracted and then analyzed using MS and MS/MS. The resulting spectra need to be processed computationally to assign peptide spectrum match. Database searches employ sequence databases or spectral libraries for matching possible peptides with the measured spectra. This route is well established but fails when peptides are not found in sequence repositories. In this case, de novo sequencing of MS/MS spectra can be employed. Many computational algorithms that establish the peptide sequence from MS/MS spectrum alone are available. While de novo sequencing assigns a sequence to an MS/MS spectrum, this assignment can be used in further processes for genome annotation. For example, novel exons can be assigned, known exons can be extended, and splice sites can be validated at the protein level. We compiled an extensive list of such algorithms, grouped them, and discussed the selected approaches. We also provide a roadmap of how de novo sequencing can enter mainstream proteogenomic analysis. In the future, de novo predictions can be added to sample-specific protein databases, including RNA-seq translations. These enriched databases can then be used for proteogenomics studies with existing pipelines.

Crucial Parameters for Immunopeptidome Characterization: A Systematic Evaluation

Immunopeptidomics is the area of knowledge focused on the study of peptides assembled in the major histocompatibility complex (MHC), or human leukocyte antigen (HLA) in humans, which could activate the immune response via specific and selective T cell recognition. Advances in high-sensitivity mass spectrometry have enabled the detailed identification and quantification of the immunopeptidome, significantly impacting fields like oncology, infections, and autoimmune diseases. Current immunopeptidomics approaches primarily focus on workflows to identify immunopeptides from HLA molecules, requiring the isolation of the HLA from relevant cells or tissues. Common critical steps in these workflows, such as cell lysis, HLA immunoenrichment, and peptide isolation, significantly influence outcomes. A systematic evaluation of these steps led to the creation of an 'Immunopeptidome Score' to enhance the reproducibility and robustness of these workflows. This score, derived from LC-MS/MS datasets (ProteomeXchange identifier PXD038165), in combination with available information from public databases, aids in optimizing the immunopeptidome characterization process. The 'Immunopeptidome Score' has been applied in a systematic analysis of protein extraction, HLA immunoprecipitation, and peptide recovery yields across several tumor cell lines enabling the selection of peptides with optimal features and, therefore, the identification of potential biomarker and therapeutic targets.

NovoLign: metaproteomics by sequence alignment

Tremendous advances in mass spectrometric and bioinformatic approaches have expanded proteomics into the field of microbial ecology. The commonly used spectral annotation method for metaproteomics data relies on database searching, which requires sample-specific databases obtained from whole metagenome sequencing experiments. However, creating these databases is complex, time-consuming, and prone to errors, potentially biasing experimental outcomes and conclusions. This asks for alternative approaches that can provide rapid and orthogonal insights into metaproteomics data. Here, we present NovoLign, a de novo metaproteomics pipeline that performs sequence alignment of de novo sequences from complete metaproteomics experiments. The pipeline enables rapid taxonomic profiling of complex communities and evaluates the taxonomic coverage of metaproteomics outcomes obtained from database searches. Furthermore, the NovoLign pipeline supports the creation of reference sequence databases for database searching to ensure comprehensive coverage. We assessed the NovoLign pipeline for taxonomic coverage and false positive annotations using a wide range of in silico and experimental data, including pure reference strains, laboratory enrichment cultures, synthetic communities, and environmental microbial communities. In summary, we present NovoLign, a de novo metaproteomics pipeline that employs large-scale sequence alignment to enable rapid taxonomic profiling, evaluation of database searching outcomes, and the creation of reference sequence databases. The NovoLign pipeline is publicly available via: https://github.com/hbckleikamp/NovoLign.

De Novo Sequencing of Synthetic Bis-cysteine Peptide Macrocycles Enabled by "Chemical Linearization" of Compound Mixtures

A "chemical linearization" approach was applied to synthetic peptide macrocycles to enable their de novo sequencing from mixtures using nanoliquid chromatography-tandem mass spectrometry (nLC-MS/MS). This approach─previously applied to individual macrocycles but not to mixtures─involves cleavage of the peptide backbone at a defined position to give a product capable of generating sequence-determining fragment ions. Here, we first established the compatibility of "chemical linearization" by Edman degradation with a prominent macrocycle scaffold based on bis-Cys peptides cross-linked with the m-xylene linker, which are of major significance in therapeutics discovery. Then, using macrocycle libraries of known sequence composition, the ability to recover accurate de novo assignments to linearized products was critically tested using performance metrics unique to mixtures. Significantly, we show that linearized macrocycles can be sequenced with lower recall compared to linear peptides but with similar accuracy, which establishes the potential of using "chemical linearization" with synthetic libraries and selection procedures that yield compound mixtures. Sodiated precursor ions were identified as a significant source of high-scoring but inaccurate assignments, with potential implications for improving automated de novo sequencing more generally.

Direct Identification of Urinary Tract Pathogens by MALDI-TOF/TOF Analysis and De Novo Peptide Sequencing

For mass spectrometry-based diagnostics of microorganisms, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used to identify urinary tract pathogens. However, it requires a lengthy culture step for accurate pathogen identification, and is limited by a relatively small number of available species in peptide spectral libraries (≤3329). Here, we propose a method for pathogen identification that overcomes the above limitations, and utilizes the MALDI-TOF/TOF MS instrument. Tandem mass spectra of the analyzed peptides were obtained by chemically activated fragmentation, which allowed mass spectrometry analysis in negative and positive ion modes. Peptide sequences were elucidated de novo, and aligned with the non-redundant National Center for Biotechnology Information Reference Sequence Database (NCBInr). For data analysis, we developed a custom program package that predicted peptide sequences from the negative and positive MS/MS spectra. The main advantage of this method over a conventional MALDI-TOF MS peptide analysis is identification in less than 24 h without a cultivation step. Compared to the limited identification with peptide spectra libraries, the NCBI database derived from genome sequencing currently contains 20,917 bacterial species, and is constantly expanding. This paper presents an accurate method that is used to identify pathogens grown on agar plates, and those isolated directly from urine samples, with high accuracy.

Affinity Selection from Synthetic Peptide Libraries Enabled by De Novo MS/MS Sequencing

Recently, de novo MS/MS peptide sequencing has enabled the application of affinity selections to synthetic peptide mixtures that approach the diversity of phage libraries (> 108 random peptides). In conjunction with ‘split-mix’ solid phase synthesis to access equimolar peptide mixtures, this approach provides a straightforward means to examine synthetic peptide libraries of considerably higher diversity than has been feasible historically. Here, we offer a critical perspective on this work, report emerging data, and highlight opportunities for further methods refinement. With continued development, ‘affinity selection–mass spectrometry’ may become a complimentary approach to phage display, in vitro selection, and DNA-encoded libraries for the discovery of synthetic ligands that modulate protein function.

Proteogenomics analysis of CUG codon translation in the human pathogen Candida albicans

Background

Yeasts of the CTG-clade lineage, which includes the human-infecting Candida albicans, Candida parapsilosis and Candida tropicalis species, are characterized by an altered genetic code. Instead of translating CUG codons as leucine, as happens in most eukaryotes, these yeasts, whose ancestors are thought to have lost the relevant leucine-tRNA gene, translate CUG codons as serine using a serine-tRNA with a mutated anticodon, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\mathrm{tRNA}}_{\mathrm{CAG}}^{\mathrm{Ser}} $$\end{document}tRNACAGSer. Previously reported experiments have suggested that 3–5% of the CTG-clade CUG codons are mistranslated as leucine due to mischarging of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\mathrm{tRNA}}_{\mathrm{CAG}}^{\mathrm{Ser}} $$\end{document}tRNACAGSer. The mistranslation was suggested to result in variable surface proteins explaining fast host adaptation and pathogenicity.

Results

In this study, we reassess this potential mistranslation by high-resolution mass spectrometry-based proteogenomics of multiple CTG-clade yeasts, including various C. albicans strains, isolated from colonized and from infected human body sites, and C. albicans grown in yeast and hyphal forms. Our data do not support a bias towards CUG codon mistranslation as leucine. Instead, our data suggest that (i) CUG codons are mistranslated at a frequency corresponding to the normal extent of ribosomal mistranslation with no preference for specific amino acids, (ii) CUG codons are as unambiguous (or ambiguous) as the related CUU leucine and UCC serine codons, (iii) tRNA anticodon loop variation across the CTG-clade yeasts does not result in any difference of the mistranslation level, and (iv) CUG codon unambiguity is independent of C. albicans’ strain pathogenicity or growth form.

Conclusions

Our findings imply that C. albicans does not decode CUG ambiguously. This suggests that the proposed misleucylation of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ {\mathrm{tRNA}}_{\mathrm{CAG}}^{\mathrm{Ser}} $$\end{document}tRNACAGSer might be as prevalent as every other misacylation or mistranslation event and, if at all, be just one of many reasons causing phenotypic diversity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-021-01197-9.

Advances in Proteomics Allow Insights Into Neuronal Proteomes

Protein–protein interaction networks and signaling complexes are essential for normal brain function and are often dysregulated in neurological disorders. Nevertheless, unraveling neuron- and synapse-specific proteins interaction networks has remained a technical challenge. New techniques, however, have allowed for high-resolution and high-throughput analyses, enabling quantification and characterization of various neuronal protein populations. Over the last decade, mass spectrometry (MS) has surfaced as the primary method for analyzing multiple protein samples in tandem, allowing for the precise quantification of proteomic data. Moreover, the development of sophisticated protein-labeling techniques has given MS a high temporal and spatial resolution, facilitating the analysis of various neuronal substructures, cell types, and subcellular compartments. Recent studies have leveraged these novel techniques to reveal the proteomic underpinnings of well-characterized neuronal processes, such as axon guidance, long-term potentiation, and homeostatic plasticity. Translational MS studies have facilitated a better understanding of complex neurological disorders, such as Alzheimer’s disease (AD), Schizophrenia (SCZ), and Autism Spectrum Disorder (ASD). Proteomic investigation of these diseases has not only given researchers new insight into disease mechanisms but has also been used to validate disease models and identify new targets for research.

Automated Ligand Purification Platform Accelerates Immunopeptidome Analysis by Mass Spectrometry

Major histocompatibility complex (MHC)-presented peptides (pMHC) give insight into T cell immune responses, a critical step toward developing a new generation of targeted immunotherapies. Recent instrumentation advances have propelled mass spectrometry to being arguably the most robust technology for discovering and quantifying naturally presented pMHC from cells and tissues. However, sample preparation has remained a major limitation due to time-consuming and labor-intensive workflows. We developed a high-throughput and automated platform with enhanced speed, sensitivity, and reproducibility relative to prior studies. This pipeline is capable of processing up to 96 samples in 6 h or less yielding high-quality pMHC mixtures ready for mass spectrometry. Here, we describe our efforts to optimize purification and mass spectrometer parameters, ultimately allowing us to identify as many as almost 5000 pMHC I and 7400 pMHC II from as little as 2.5 × 107 Raji cells each. We believe that this platform will facilitate and accelerate immunopeptidome profiling and benefit clinical research for immunotherapies.

Flying blind, or just flying under the radar? The underappreciated power of de novo methods of mass spectrometric peptide identification

Mass spectrometry-based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics, and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo methods infer peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph-based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs.

Current Challenges and Implications of Proteogenomic Approaches in Prostate Cancer

In the recent past, next-generation sequencing (NGS) approaches have heralded the omics era. With NGS data burgeoning, there arose a need to disseminate the omic data better. Proteogenomics has been vividly used for characterising the functions of candidate genes and is applied in ascertaining various diseased phenotypes, including cancers. However, not much is known about the role and application of proteogenomics, especially Prostate Cancer (PCa). In this review, we outline the need for proteogenomic approaches, their applications and their role in PCa.

Comment on "A subset of HLA-I peptides are not genomically templated: Evidence for cis- and trans-spliced peptide ligands"

There is still no convincing evidence for the frequent occurrence of posttranslationally spliced HLA-I peptides.

Lys-Sequencer: An algorithm for de novo sequencing of peptides by paired single residue transposed Lys-C and Lys-N digestion coupled with high-resolution mass spectrometry

RATIONALE

Database-dependent identification of proteins by mass spectrometry is well established, but has limitations when there are novel proteins, mutations, splice variants, and post-translational modifications (PTMs) not available in the established reference database. De novo sequencing as a database-independent approach could address these limitations by deducing peptide sequences directly from experimental tandem mass spectrometry spectra, while concomitantly yielding residue-by-residue confidence metrics.

METHODS

Equal amounts of bovine serum albumin (BSA) sample aliquots were digested separately with Lys-C and Lys-N complementary peptidases, separated by reversed-phase ultra-high-performance liquid chromatography (UPLC), and analyzed by collision-induced dissociation (CID)-based mass spectrometry on an Orbitrap mass spectrometer. In the Lys-Sequencer algorithm, matched tandem mass spectra with equal precursor ion mass from complementary digestions were paired, and fragment ion types were identified based on the unique mass relationship between fragment ions extracted from a spectrum pair followed by de novo sequencing of peptides with identification confidence assigned at the residue level.

RESULTS

In all the matched spectrum pairs, 34 top-ranked BSA peptides were identified, from which 391 amino acid residues were identified correctly, covering ~67% of the full sequence of BSA (583 residues) with only ~6% (35 residues) exhibiting ambiguity in the sequence order (although amino acid compositions were still correctly assigned). Of note, this approach identified peptide sequences up to 17 amino acids in length without ambiguity, with the exception of the N-terminal or C-terminal peptides containing lysine (18-mer).

CONCLUSIONS

The algorithm ("Lys-Sequencer") developed in this work achieves high precision for de novo sequencing of peptides. This method facilitates the identification of point mutation and new PTMs in the protein characterization and discovery of new peptides and proteins with varying levels of confidence.

The Human Immunopeptidome Project: A Roadmap to Predict and Treat Immune Diseases

The science that investigates the ensembles of all peptides associated to human leukocyte antigen (HLA) molecules is termed "immunopeptidomics" and is typically driven by mass spectrometry (MS) technologies. Recent advances in MS technologies, neoantigen discovery and cancer immunotherapy have catalyzed the launch of the Human Immunopeptidome Project (HIPP) with the goal of providing a complete map of the human immunopeptidome and making the technology so robust that it will be available in every clinic. Here, we provide a long-term perspective of the field and we use this framework to explore how we think the completion of the HIPP will truly impact the society in the future. In this context, we introduce the concept of immunopeptidome-wide association studies (IWAS). We highlight the importance of large cohort studies for the future and how applying quantitative immunopeptidomics at population scale may provide a new look at individual predisposition to common immune diseases as well as responsiveness to vaccines and immunotherapies. Through this vision, we aim to provide a fresh view of the field to stimulate new discussions within the community, and present what we see as the key challenges for the future for unlocking the full potential of immunopeptidomics in this era of precision medicine.

A subset of HLA-I peptides are not genomically templated: Evidence for cis- and trans-spliced peptide ligands

The diversity of peptides displayed by class I human leukocyte antigen (HLA) plays an essential role in T cell immunity. The peptide repertoire is extended by various posttranslational modifications, including proteasomal splicing of peptide fragments from distinct regions of an antigen to form nongenomically templated cis-spliced sequences. Previously, it has been suggested that a fraction of the immunopeptidome constitutes such cis-spliced peptides; however, because of computational limitations, it has not been possible to assess whether trans-spliced peptides (i.e., the fusion of peptide segments from distinct antigens) are also bound and presented by HLA molecules, and if so, in what proportion. Here, we have developed and applied a bioinformatic workflow and demonstrated that trans-spliced peptides are presented by HLA-I, and their abundance challenges current models of proteasomal splicing that predict cis-splicing as the most probable outcome. These trans-spliced peptides display canonical HLA-binding sequence features and are as frequently identified as cis-spliced peptides found bound to a number of different HLA-A and HLA-B allotypes. Structural analysis reveals that the junction between spliced peptides is highly solvent exposed and likely to participate in T cell receptor interactions. These results highlight the unanticipated diversity of the immunopeptidome and have important implications for autoimmunity, vaccine design, and immunotherapy.

Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification?

While peptide identifications in mass spectrometry (MS)-based shotgun proteomics are mostly obtained using database search methods, high-resolution spectrum data from modern MS instruments nowadays offer the prospect of improving the performance of computational de novo peptide sequencing. The major benefit of de novo sequencing is that it does not require a reference database to deduce full-length or partial tag-based peptide sequences directly from experimental tandem mass spectrometry spectra. Although various algorithms have been developed for automated de novo sequencing, the prediction accuracy of proposed solutions has been rarely evaluated in independent benchmarking studies. The main objective of this work is to provide a detailed evaluation on the performance of de novo sequencing algorithms on high-resolution data. For this purpose, we processed four experimental data sets acquired from different instrument types from collision-induced dissociation and higher energy collisional dissociation (HCD) fragmentation mode using the software packages Novor, PEAKS and PepNovo. Moreover, the accuracy of these algorithms is also tested on ground truth data based on simulated spectra generated from peak intensity prediction software. We found that Novor shows the overall best performance compared with PEAKS and PepNovo with respect to the accuracy of correct full peptide, tag-based and single-residue predictions. In addition, the same tool outpaced the commercial competitor PEAKS in terms of running time speedup by factors of around 12-17. Despite around 35% prediction accuracy for complete peptide sequences on HCD data sets, taken as a whole, the evaluated algorithms perform moderately on experimental data but show a significantly better performance on simulated data (up to 84% accuracy). Further, we describe the most frequently occurring de novo sequencing errors and evaluate the influence of missing fragment ion peaks and spectral noise on the accuracy. Finally, we discuss the potential of de novo sequencing for now becoming more widely used in the field.

TagGraph reveals vast protein modification landscapes from large tandem mass spectrometry datasets

Although mass spectrometry is well suited to identifying thousands of potential protein post-translational modifications (PTMs), it has historically been biased towards just a few. To measure the entire set of PTMs across diverse proteomes, software must overcome the dual challenges of covering enormous search spaces and distinguishing correct from incorrect spectrum interpretations. Here, we describe TagGraph, a computational tool that overcomes both challenges with an unrestricted string-based search method that is as much as 350-fold faster than existing approaches, and a probabilistic validation model that we optimized for PTM assignments. We applied TagGraph to a published human proteomic dataset of 25 million mass spectra and tripled confident spectrum identifications compared to its original analysis. We identified thousands of modification types on almost 1 million sites in the proteome. We show alternative contexts for highly abundant yet understudied PTMs such as proline hydroxylation, and its unexpected association with cancer mutations. By enabling broad characterization of PTMs, TagGraph informs as to how their functions and regulation intersect.

Increased Sensitivity of Mass Spectrometry by Alkaline Two-Dimensional Liquid Chromatography: Deep Cover of the Human Proteome in Gene-Centric Mode

Currently, great interest is paid to the identification of "missing" proteins that have not been detected in any biological material at the protein level (PE1). In this paper, using the Universal Proteomic Standard sets 1 and 2 (UPS1 and UPS2, respectively) as an example, we characterized mass spectrometric approaches from the point of view of sensitivity (Sn), specificity (Sp), and accuracy (Ac). The aim of the paper was to show the utility of a mass spectra approach for protein detection. This sets consists of 48 high-purity human proteins without single aminoacid polymorphism (SAP) or post translational modification (PTM). The UPS1 set consists of the same 48 proteins at 5 pmols each, and in UPS2, proteins were grouped into 5 groups in accordance with their molar concentration, ranging from 10^-11 to 10^-6 M. Single peptides from the 92% and 96% of all sets of proteins could be detected in a pure solution of UPS2 and UPS1, respectively, by selected reaction monitoring with stable isotope-labeled standards (SRM-SIS). We also found that, in the presence of a biological matrix such as Escherichia coli extract or human blood plasma (HBP), SRM-SIS makes it possible to detect from 63% to 79% of proteins in the UPS2 set (sensitivity) with the highest specificity (∼100%) and an accuracy of 80% by increasing the sensitivity of shotgun and selected reaction monitoring combined with a stable-isotope-labeled peptide standard (SRM-SIS technology) by fractionating samples using reverse-phase liquid chromatography under alkaline conditions (2D-LC_alk). It is shown that this technique of sample fractionation allows the SRM-SIS to detect 98% of the single peptides from the proteins present in the pure solution of UPS2 (47 out of 48 proteins). When the extracts of E. coli or Pichia pastoris are added as biological matrixes to the UPS2, 46, and 45 out of 48 proteins (∼95%) can be detected, respectively, using the SRM-SIS combined with 2D-LC_alk. The combination of the 2D-LC_alk SRM-SIS and shotgun technologies allows us to increase the sensitivity up to 100% in the case of the proteins of the UPS2 set. The usage of that technology can be a solution for identifying the so-called "missing" proteins and, eventually, creating the deep proteome of a particular chromosome of tissue or organs. Experimental data have been deposited in the PeptideAtlas SRM Experiment Library with the dataset identifier PASS01192 and the PRIDE repository with the dataset identifier PXD007643.

Whole Blood Storage in CPDA1 Blood Bags Alters Erythrocyte Membrane Proteome

Autologous blood transfusion (ABT) has been frequently abused in endurance sport and is prohibited since the mid-1980s by the International Olympic Committee. Apart from any significant performance-enhancing effects, the ABT may pose a serious health issue due to aging erythrocyte-derived "red cell storage lesions." The current study investigated the effect of blood storage in citrate phosphate dextrose adenine (CPDA1) on the red blood cell (RBC) membrane proteome. One unit of blood was collected in CPDA1 blood bags from 6 healthy female volunteers. RBC membrane protein samples were prepared on days 0, 14, and 35 of storage. Proteins were digested in gel and peptides separated by nanoliquid chromatography coupled to tandem mass spectrometry resulting in the confident identification of 33 proteins that quantitatively change during storage. Comparative proteomics suggested storage-induced translocation of cytoplasmic proteins to the membrane while redox proteomics analysis identified 14 proteins prone to storage-induced oxidation. The affected proteins are implicated in the RBC energy metabolism and membrane vesiculation and could contribute to the adverse posttransfusion outcomes. Spectrin alpha chain, band 3 protein, glyceraldehyde-3-phosphate dehydrogenase, and ankyrin-1 were the main proteins affected by storage. Although potential biomarkers of stored RBCs were identified, the stability and lifetime of these markers posttransfusion remain unknown. In summary, the study demonstrated the importance of studying storage-induced alterations in the erythrocyte membrane proteome and the need to understand the clearance kinetics of transfused erythrocytes and identified protein markers.

Global Identification of Post-Translationally Spliced Peptides with Neo-Fusion

Post-translationally spliced peptides have recently garnered significant interest as potential targets for cancer immunotherapy and as contributors to autoimmune diseases such as type 1 diabetes, yet feasible identification methods for spliced peptides have yet to be developed. Here we present Neo-Fusion, a search program for discovering spliced peptides in tandem mass spectrometry data. Neo-Fusion utilizes two separated ion database searches to identify the two halves of each spliced peptide, and then it infers the full spliced sequence. This strategy allows for the identification of spliced peptides without peptide length constraints, providing a broadly applicable tool suitable for identification of spliced peptides in a variety of systems, such as the HLA-I and HLA-II immunopeptidomes and in vitro digested protein samples obtained from organelles, cells, or tissues of interest. Using simulated spliced peptides to benchmark Neo-Fusion, 25% of all simulated spliced peptides were identified at a measured false-discovery rate of 5% for HLA-I. Neo-Fusion provides the research community with a powerful new tool to aid in the study of the prevalence and biological significance of post-translationally spliced peptides.

Postnovo: Postprocessing Enables Accurate and FDR-Controlled de Novo Peptide Sequencing

De novo sequencing offers an alternative to database search methods for peptide identification from mass spectra. Since it does not rely on a predetermined database of expected or potential sequences in the sample, de novo sequencing is particularly appropriate for samples lacking a well-defined or comprehensive reference database. However, the low accuracy of many de novo sequence predictions has prevented the widespread use of the variety of sequencing tools currently available. Here, we present a new open-source tool, Postnovo, that postprocesses de novo sequence predictions to find high-accuracy results. Postnovo uses a predictive model to rescore and rerank candidate sequences in a manner akin to database search postprocessing tools such as Percolator. Postnovo leverages the output from multiple de novo sequencing tools in its own analyses, producing many times the length of amino acid sequence information (including both full- and partial-length peptide sequences) at an equivalent false discovery rate (FDR) compared to any individual tool. We present a methodology to reliably screen the sequence predictions to a desired FDR given the Postnovo sequence score. We validate Postnovo with multiple data sets and demonstrate its ability to identify proteins that are missed by database search even in samples with paired reference databases.

A Potential Golden Age to Come-Current Tools, Recent Use Cases, and Future Avenues for De Novo Sequencing in Proteomics

In shotgun proteomics, peptide and protein identification is most commonly conducted using database search engines, the method of choice when reference protein sequences are available. Despite its widespread use the database-driven approach is limited, mainly because of its static search space. In contrast, de novo sequencing derives peptide sequence information in an unbiased manner, using only the fragment ion information from the tandem mass spectra. In recent years, with the improvements in MS instrumentation, various new methods have been proposed for de novo sequencing. This review article provides an overview of existing de novo sequencing algorithms and software tools ranging from peptide sequencing to sequence-to-protein mapping. Various use cases are described for which de novo sequencing was successfully applied. Finally, limitations of current methods are highlighted and new directions are discussed for a wider acceptance of de novo sequencing in the community.

DIA-SIFT: A Precursor and Product Ion Filter for Accurate Stable Isotope Data-Independent Acquisition Proteomics

Quantitative mass spectrometry-based protein profiling is widely used to measure protein levels across different treatments or disease states, yet current mass spectrometry acquisition methods present distinct limitations. While data-independent acquisition (DIA) bypasses the stochastic nature of data-dependent acquisition (DDA), fragment spectra derived from DIA are often complex and challenging to deconvolve. In-line ion mobility separation (IMS) adds an additional dimension to increase peak capacity for more efficient product ion assignment. As a similar strategy to sequential window acquisition methods (SWATH), IMS-enabled DIA methods rival DDA methods for protein annotation. Here we evaluate IMS-DIA quantitative accuracy using stable isotope labeling by amino acids in cell culture (SILAC). Since SILAC analysis doubles the sample complexity, we find that IMS-DIA analysis is not sufficiently accurate for sensitive quantitation. However, SILAC precursor pairs share common retention and drift times, and both species cofragment to yield multiple quantifiable isotopic y-ion peak pairs. Since y-ion SILAC ratios are intrinsic for each quantified precursor, combined MS1 and y-ion ratio analysis significantly increases the total number of measurements. With increased sampling, we present DIA-SIFT ( SILAC Intrinsic Filtering Tool), a simple statistical algorithm to identify and eliminate poorly quantified MS1 and/or MS2 events. DIA-SIFT combines both MS1 and y-ion ratios, removes outliers, and provides more accurate and precise quantitation (<15% CV) without removing any proteins from the final analysis. Overall, pooled MS1 and MS2 quantitation increases sampling in IMS-DIA SILAC analyses for accurate and precise quantitation.

Enhancing bioactive peptide release and identification using targeted enzymatic hydrolysis of milk proteins

Milk proteins have been extensively studied for their ability to yield a range of bioactive peptides following enzymatic hydrolysis/digestion. However, many hurdles still exist regarding the widespread utilization of milk protein-derived bioactive peptides as health enhancing agents for humans. These mostly arise from the fact that most milk protein-derived bioactive peptides are not highly potent. In addition, they may be degraded during gastrointestinal digestion and/or have a low intestinal permeability. The targeted release of bioactive peptides during the enzymatic hydrolysis of milk proteins may allow the generation of particularly potent bioactive hydrolysates and peptides. Therefore, the development of milk protein hydrolysates capable of improving human health requires, in the first instance, optimized targeted release of specific bioactive peptides. The targeted hydrolysis of milk proteins has been aided by a range of in silico tools. These include peptide cutters and predictive modeling linking bioactivity to peptide structure [i.e., molecular docking, quantitative structure activity relationship (QSAR)], or hydrolysis parameters [design of experiments (DOE)]. Different targeted enzymatic release strategies employed during the generation of milk protein hydrolysates are reviewed herein and their limitations are outlined. In addition, specific examples are provided to demonstrate how in silico tools may help in the identification and discovery of potent milk protein-derived peptides. It is anticipated that the development of novel strategies employing a range of in silico tools may help in the generation of milk protein hydrolysates containing potent and bioavailable peptides, which in turn may be used to validate their health promoting effects in humans. Graphical abstract The targeted enzymatic hydrolysis of milk proteins may allow the generation of highly potent and bioavailable bioactive peptides.

Combinatorial Labeling Method for Improving Peptide Fragmentation in Mass Spectrometry

Annotation of peptide sequence from tandem mass spectra constitutes the central step of mass spectrometry-based proteomics. Peptide mass spectra are obtained upon gas-phase fragmentation. Identification of the protein from a set of experimental peptide spectral matches is usually referred as protein inference. Occurrence and intensity of these fragment ions in the MS/MS spectra are dependent on many factors such as amino acid composition, peptide basicity, activation mode, protease, etc. Particularly, chemical derivatizations of peptides were known to alter their fragmentation. In this study, the influence of acetylation, guanidinylation, and their combination on peptide fragmentation was assessed initially on a lipase (LipA) from Bacillus subtilis followed by a bovine six protein mix digest. The dual modification resulted in improved fragment ion occurrence and intensity changes, and this resulted in the equivalent representation of b- and y-type fragment ions in an ion trap MS/MS spectrum. The improved representation has allowed us to accurately annotate the peptide sequences de novo. Dual labeling has significantly reduced the false positive protein identifications in standard bovine six peptide digest. Our study suggests that the combinatorial labeling of peptides is a useful method to validate protein identifications for high confidence protein inference. Graphical Abstract ᅟ.

The potential impact of recent insights into proteomic changes associated with glaucoma

INTRODUCTION

Glaucoma, a major ocular neuropathy, is still far from being understood on a molecular scale. Proteomic workflows revealed glaucoma associated alterations in different eye components. By using state-of-the-art mass spectrometric (MS) based discovery approaches large proteome datasets providing important information about glaucoma related proteins and pathways could be generated. Corresponding proteomic information could be retrieved from various ocular sample species derived from glaucoma experimental models or from original human material (e.g. optic nerve head or aqueous humor). However, particular eye tissues with the potential for understanding the disease's molecular pathomechanism remains underrepresented. Areas covered: The present review provides an overview of the analysis depth achieved for the glaucomatous eye proteome. With respect to different eye regions and biofluids, proteomics related literature was found using PubMed, Scholar and UniProtKB. Thereby, the review explores the potential of clinical proteomics for glaucoma research. Expert commentary: Proteomics will provide important contributions to understanding the molecular processes associated with glaucoma. Sensitive discovery and targeted MS approaches will assist understanding of the molecular interplay of different eye components and biofluids in glaucoma. Proteomic results will drive the comprehension of glaucoma, allowing a more stringent disease hypothesis within the coming years.

De Novo Peptide Sequencing: Deep Mining of High-Resolution Mass Spectrometry Data

High resolution mass spectrometry has revolutionized proteomics over the past decade, resulting in tremendous amounts of data in the form of mass spectra, being generated in a relatively short span of time. The mining of this spectral data for analysis and interpretation though has lagged behind such that potentially valuable data is being overlooked because it does not fit into the mold of traditional database searching methodologies. Although the analysis of spectra by de novo sequences removes such biases and has been available for a long period of time, its uptake has been slow or almost nonexistent within the scientific community. In this chapter, we propose a methodology to integrate de novo peptide sequencing using three commonly available software solutions in tandem, complemented by homology searching, and manual validation of spectra. This simplified method would allow greater use of de novo sequencing approaches and potentially greatly increase proteome coverage leading to the unearthing of valuable insights into protein biology, especially of organisms whose genomes have been recently sequenced or are poorly annotated.

UVnovo: A de Novo Sequencing Algorithm Using Single Series of Fragment Ions via Chromophore Tagging and 351 nm Ultraviolet Photodissociation Mass Spectrometry

De novo peptide sequencing by mass spectrometry represents an important strategy for characterizing novel peptides and proteins, in which a peptide's amino acid sequence is inferred directly from the precursor peptide mass and tandem mass spectrum (MS/MS or MS(3)) fragment ions, without comparison to a reference proteome. This method is ideal for organisms or samples lacking a complete or well-annotated reference sequence set. One of the major barriers to de novo spectral interpretation arises from confusion of N- and C-terminal ion series due to the symmetry between b and y ion pairs created by collisional activation methods (or c, z ions for electron-based activation methods). This is known as the "antisymmetric path problem" and leads to inverted amino acid subsequences within a de novo reconstruction. Here, we combine several key strategies for de novo peptide sequencing into a single high-throughput pipeline: high-efficiency carbamylation blocks lysine side chains, and subsequent tryptic digestion and N-terminal peptide derivatization with the ultraviolet chromophore AMCA yield peptides susceptible to 351 nm ultraviolet photodissociation (UVPD). UVPD-MS/MS of the AMCA-modified peptides then predominantly produces y ions in the MS/MS spectra, specifically addressing the antisymmetric path problem. Finally, the program UVnovo applies a random forest algorithm to automatically learn from and then interpret UVPD mass spectra, passing results to a hidden Markov model for de novo sequence prediction and scoring. We show this combined strategy provides high-performance de novo peptide sequencing, enabling the de novo sequencing of thousands of peptides from an Escherichia coli lysate at high confidence.