Machine learning-based peptide-spectrum match rescoring opens up the immunopeptidome.

Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and vaccine development. However, identifying immunopeptides remains challenging due to their non-tryptic nature, which results in distinct spectral characteristics. Moreover, the absence of strict digestion rules leads to extensive search spaces, further amplified by the incorporation of somatic mutations, pathogen genomes, unannotated open reading frames, and post-translational modifications. This inflation in search space leads to an increase in random high-scoring matches, resulting in fewer identifications at a given false discovery rate. Peptide-spectrum match rescoring has emerged as a machine learning-based solution to address challenges in mass spectrometry-based immunopeptidomics data analysis. It involves post-processing unfiltered spectrum annotations to better distinguish between correct and incorrect peptide-spectrum matches. Recently, features based on predicted peptidoform properties, including fragment ion intensities, retention time, and collisional cross section, have been used to improve the accuracy and sensitivity of immunopeptide identification. In this review, we describe the diverse bioinformatics pipelines that are currently available for peptide-spectrum match rescoring and discuss how they can be used for the analysis of immunopeptidomics data. Finally, we provide insights into current and future machine learning solutions to boost immunopeptide identification.

Open Modification Searching of SARS-CoV-2-Human Protein Interaction Data Reveals Novel Viral Modification Sites.

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus 2019 disease, has led to an ongoing global pandemic since 2019. Mass spectrometry can be used to understand the molecular mechanisms of viral infection by SARS-CoV-2, for example, by determining virus-host protein-protein interactions through which SARS-CoV-2 hijacks its human hosts during infection, and to study the role of post-translational modifications. We have reanalyzed public affinity purification-mass spectrometry data using open modification searching to investigate the presence of post-translational modifications in the context of the SARS-CoV-2 virus-host protein-protein interaction network. Based on an over twofold increase in identified spectra, our detected protein interactions show a high overlap with independent mass spectrometry-based SARS-CoV-2 studies and virus-host interactions for alternative viruses, as well as previously unknown protein interactions. In addition, we identified several novel modification sites on SARS-CoV-2 proteins that we investigated in relation to their interactions with host proteins. A detailed analysis of relevant modifications, including phosphorylation, ubiquitination, and S-nitrosylation, provides important hypotheses about the functional role of these modifications during viral infection by SARS-CoV-2.

The challenges of peptidomics in complementing proteomics in a clinical context.

Naturally occurring peptides, including growth factors, hormones, and neurotransmitters, represent an important class of biomolecules and have crucial roles in human physiology. The study of these peptides in clinical samples is therefore as relevant as ever. Compared to more routine proteomics applications in clinical research, peptidomics research questions are more challenging and have special requirements with regard to sample handling, experimental design, and bioinformatics. In this review, we describe the issues that confront peptidomics in a clinical context. After these hurdles are (partially) overcome, peptidomics will be ready for a successful translation into medical practice.

PRiSM: A prototype for exhaustive, restriction-free database searching for mass spectrometry-based identification.

RATIONALE: The current methods for identifying peptides in mass spectral product ion data still struggle to do so for the majority of spectra. Based on the experimental setup and other assumptions, such methods restrict the search space to speed up computations, but at the cost of creating blind spots. The proteomics community would greatly benefit from a method that is capable of covering the entire search space without using any restrictions, thus establishing a baseline for identification. METHODS: We conceived the "mass pattern paradigm" (MPP) that enables the creation of such an identification method, and we implemented it into a prototype database search engine "PRiSM" (PRotein-Spectrum Matching). We then assessed its operational characteristics by applying it to publicly available high-precision mass spectra of low and high identification difficulty. We used those characteristics to gain theoretical insights into trade-offs between sensitivity and speed when trying to establish a baseline for identification. RESULTS: Of 100 low difficulty spectra, PRiSM and SEQUEST agree on 84 identifications (of which 75 are statistically significant). Of 15 of 100 spectra not identified in a previous study (using SEQUEST), 13 are considered reliable after visual inspection and represent 3 proteins (out of 9 in total) not detected previously. CONCLUSIONS: Despite leaving noise intact, the simple PRiSM prototype can make statistically reliable identifications, while controlling the false discovery rate by fitting a null distribution. It also identifies some spectra previously unidentifiable in an "extremely open" SEQUEST search, paving the way to establishing a baseline for identification in proteomics.

QCQuan: A Web Tool for the Automated Assessment of Protein Expression and Data Quality of Labeled Mass Spectrometry Experiments.

In the context of omics disciplines and especially proteomics and biomarker discovery, the analysis of a clinical sample using label-based tandem mass spectrometry (MS) can be affected by sample preparation effects or by the measurement process itself, resulting in an incorrect outcome. Detection and correction of these mistakes using state-of-the-art methods based on mixed models can use large amounts of (computing) time. MS-based proteomics laboratories are high-throughput and need to avoid a bottleneck in their quantitative pipeline by quickly discriminating between high- and low-quality data. To this end we developed an easy-to-use web-tool called QCQuan (available at qcquan.net ) which is built around the CONSTANd normalization algorithm. It automatically provides the user with exploratory and quality control information as well as a differential expression analysis based on conservative, simple statistics. In this document we describe in detail the scientifically relevant steps that constitute the workflow and assess its qualitative and quantitative performance on three reference data sets. We find that QCQuan provides clear and accurate indications about the scientific value of both a high- and a low-quality data set. Moreover, it performed quantitatively better on a third data set than a comparable workflow assembled using established, reliable software.

Mass spectrometric evidence for neuropeptide-amidating enzymes in Caenorhabditis elegans.

Neuropeptides constitute a vast and functionally diverse family of neurochemical signaling molecules and are widely involved in the regulation of various physiological processes. The nematode Caenorhabditis elegans is well-suited for the study of neuropeptide biochemistry and function, as neuropeptide biosynthesis enzymes are not essential for C. elegans viability. This permits the study of neuropeptide biosynthesis in mutants lacking certain neuropeptide-processing enzymes. Mass spectrometry has been used to study the effects of proprotein convertase and carboxypeptidase mutations on proteolytic processing of neuropeptide precursors and on the peptidome in C. elegans However, the enzymes required for the last step in the production of many bioactive peptides, the carboxyl-terminal amidation reaction, have not been characterized in this manner. Here, we describe three genes that encode homologs of neuropeptide amidation enzymes in C. elegans and used tandem LC-MS to compare neuropeptides in WT animals with those in newly generated mutants for these putative amidation enzymes. We report that mutants lacking both a functional peptidylglycine α-hydroxylating monooxygenase and a peptidylglycine α-amidating monooxygenase had a severely altered neuropeptide profile and also a decreased number of offspring. Interestingly, single mutants of the amidation enzymes still expressed some fully processed amidated neuropeptides, indicating the existence of a redundant amidation mechanism in C. elegans All MS data are available via ProteomeXchange with the identifier PXD008942. In summary, the key steps in neuropeptide processing in C. elegans seem to be executed by redundant enzymes, and loss of these enzymes severely affects brood size, supporting the need of amidated peptides for C. elegans reproduction.

Comprehensive Peptide Analysis of Mouse Brain Striatum Identifies Novel sORF-Encoded Polypeptides.

Bio-active peptides are involved in the regulation of most physiological processes in the body. Classical bio-active peptides (CBAPs) are cleaved from a larger precursor protein and stored in secretion vesicles from which they are released in the extracellular space. Recently, another non-classical type of bio-active peptides (NCBAPs) has gained interest. These typically are not secreted but instead appear to be translated from short open reading frames (sORF) and released directly into the cytoplasm. In contrast to CBAPs, these peptides are involved in the regulation of intra-cellular processes such as transcriptional control, calcium handling and DNA repair. However, bio-chemical evidence for the translation of sORFs remains elusive. Comprehensive analysis of sORF-encoded polypeptides (SEPs) is hampered by a number of methodological and biological challenges: the low molecular mass (many 4-10 kDa), the low abundance, transient expression and complications in data analysis. We developed a strategy to address a number of these issues. Our strategy is to exclude false positive identifications. In total sample, we identified 926 peptides originated from 37 known (neuro)peptide precursors in mouse striatum. In addition, four SEPs were identified including NoBody, a SEP that was previously discovered in humans and three novel SEPS from 5' untranslated transcript regions (UTRs).

MS imaging and mass spectrometric synaptosome profiling identify PEP-19/pcp4 as a synaptic molecule involved in spatial learning in mice.

The Morris water maze (MWM) spatial learning task has been demonstrated to involve a cognitive switch of action control to serve the transition from an early towards a late learning phase. However, the molecular mechanisms governing this switch are largely unknown. We employed MALDI MS imaging (MSI) to screen for changes in expression of small proteins in brain structures implicated in the different learning phases. We compared mice trained for 3days and 30days in the MWM, reflecting an early and a late learning phase in relation to the acquisition of a spatial learning task. An ion with m/z of 6724, identified as PEP-19/pcp4 by top-down tandem MS, was detected at higher intensity in the dorsal striatum of the late learning phase group compared with the early learning phase group. In addition, mass spectrometric analysis of synaptosomes confirmed the presence of PEP-19/pcp4 at the synapse. PEP-19/pcp4 has previously been identified as a critical determinant of synaptic plasticity in locomotor learning. Our findings extend PEP-19/pcp4 function to spatial learning in the forebrain and put MSI forward as a valid and unbiased research strategy for the discovery and identification of the molecular machinery involved in learning, memory and synaptic plasticity. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Fast and Reliable Quantitative Peptidomics with labelpepmatch.

The use of stable isotope tags in quantitative peptidomics offers many advantages, but the laborious identification of matching sets of labeled peptide peaks is still a major bottleneck. Here we present labelpepmatch, an R-package for fast and straightforward analysis of LC-MS spectra of labeled peptides. This open-source tool offers fast and accurate identification of peak pairs alongside an appropriate framework for statistical inference on quantitative peptidomics data, based on techniques from other -omics disciplines. A relevant case study on the desert locust Schistocerca gregaria proves our pipeline to be a reliable tool for quick but thorough explorative analyses.

Functional neuropeptidomics in invertebrates.

Neuropeptides are key messengers in almost all physiological processes. They originate from larger precursors and are extensively processed to become bioactive. Neuropeptidomics aims to comprehensively identify the collection of neuropeptides in an organism, organ, tissue or cell. The neuropeptidome of several invertebrates is thoroughly explored since they are important model organisms (and models for human diseases), disease vectors and pest species. The charting of the neuropeptidome is the first step towards understanding peptidergic signaling. This review will first discuss the latest developments in exploring the neuropeptidome. The physiological roles and modes of action of neuropeptides can be explored in two ways, which are largely orthogonal and therefore complementary. The first way consists of inferring the functions of neuropeptides by a forward approach where neuropeptide profiles are compared under different physiological conditions. Second is the reverse approach were neuropeptide collections are used to screen for receptor-binding. This is followed by localization studies and functional tests. This review will focus on how these different functional screening methods contributed to the field of invertebrate neuropeptidomics and expanded our knowledge of peptidergic signaling. This article is part of a Special Issue entitled: Neuroproteomics: Applications in Neuroscience and Neurology.

A quantitative peptidomics approach to unravel immunological functions of angiotensin converting enzyme in Locusta migratoria.

Locusta migratoria angiotensin converting enzyme (LmACE) is encoded by multiple exons displaying variable number of genomic duplications. Treatments of lipopolysaccharide (LPS) as well as peptidoglycan but not ß-1-3 glucan resulted in enhanced expression of angiotensin converting enzyme in hemocytes of Locusta migratoria. No such effect was observed in fat body cells. Differential peptidomics using locust plasma samples post infection with LPS in combination with both an LmACE transcript knockdown by RNAi and a functional knockdown using captopril allowed the identification of 5 circulating LPS induced peptides which only appear in the hemolymph of locust having full LmACE functionality. As these peptides originate from larger precursor proteins such as locust hemocyanin-like protein, having known antimicrobial properties, the obtained results suggest a possible direct or indirect role of LmACE in the release of these peptides from their precursors. Additionally, this experimental setup confirmed the role of LmACE in the clearance of multiple peptides from the hemolymph.

Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF.

Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. We analyze 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project on a timsTOF-Pro to generate a ground-truth dataset containing 93,227 MS/MS spectra of 74,847 unique peptides, that is used to fine-tune the deep learning-based fragment ion intensity prediction model Prosit. We demonstrate up to 3-fold improvement in the identification of immunopeptides, as well as increased detection of immunopeptides from low input samples.

Computational analysis of interactomes: current and future perspectives for bioinformatics approaches to model the host-pathogen interaction space.

Bacterial and viral pathogens affect their eukaryotic host partly by interacting with proteins of the host cell. Hence, to investigate infection from a systems' perspective we need to construct complete and accurate host-pathogen protein-protein interaction networks. Because of the paucity of available data and the cost associated with experimental approaches, any construction and analysis of such a network in the near future has to rely on computational predictions. Specifically, this challenge consists of a number of sub-problems: First, prediction of possible pathogen interactors (e.g. effector proteins) is necessary for bacteria and protozoa. Second, the prospective host binding partners have to be determined and finally, the impact on the host cell analyzed. This review gives an overview of current bioinformatics approaches to obtain and understand host-pathogen interactions. As an application example of the methods covered, we predict host-pathogen interactions of Salmonella and discuss the value of these predictions as a prospective for further research.

A short dasatinib and quercetin treatment is sufficient to reinstate potent adult neuroregenesis in the aged killifish.

The young African turquoise killifish has a high regenerative capacity, but loses it with advancing age, adopting several aspects of the limited form of mammalian regeneration. We deployed a proteomic strategy to identify pathways that underpin the loss of regenerative power caused by aging. Cellular senescence stood out as a potential brake on successful neurorepair. We applied the senolytic cocktail Dasatinib and Quercetin (D + Q) to test clearance of chronic senescent cells from the aged killifish central nervous system (CNS) as well as rebooting the neurogenic output. Our results show that the entire aged killifish telencephalon holds a very high senescent cell burden, including the parenchyma and the neurogenic niches, which could be diminished by a short-term, late-onset D + Q treatment. Reactive proliferation of non-glial progenitors increased substantially and lead to restorative neurogenesis after traumatic brain injury. Our results provide a cellular mechanism for age-related regeneration resilience and a proof-of-concept of a potential therapy to revive the neurogenic potential in an already aged or diseased CNS.

Proteomic comparison between non-purified cerebrospinal fluid and cerebrospinal fluid-derived extracellular vesicles from patients with Alzheimer's, Parkinson's and Lewy body dementia.

Dementia is a leading cause of death worldwide, with increasing prevalence as global life expectancy increases. The most common neurodegenerative disorders are Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD). With this study, we took an in-depth look at the proteome of the (non-purified) cerebrospinal fluid (CSF) and the CSF-derived extracellular vesicles (EVs) of AD, PD, PD-MCI (Parkinson's disease with mild cognitive impairment), PDD and DLB patients analysed by label-free mass spectrometry. This has led to the discovery of differentially expressed proteins that may be helpful for differential diagnosis. We observed a greater number of differentially expressed proteins in CSF-derived EV samples (N = 276) compared to non-purified CSF (N = 169), with minimal overlap between both datasets. This finding suggests that CSF-derived EV samples may be more suitable for the discovery phase of a biomarker study, due to the removal of more abundant proteins, resulting in a narrower dynamic range. As disease-specific markers, we selected a total of 39 biomarker candidates identified in non-purified CSF, and 37 biomarker candidates across the different diseases under investigation in the CSF-derived EV data. After further exploration and validation of these proteins, they can be used to further differentiate between the included dementias and may offer new avenues for research into more disease-specific pharmacological therapeutics.

Characterising extracellular vesicles from individual low volume cerebrospinal fluid samples, isolated by SmartSEC.

Extracellular vesicles (EVs) are suggested to have a role in the progression of neurodegeneration, and are able to transmit pathological proteins from one cell to another. One of the biofluids from which EVs can be isolated is cerebrospinal fluid (CSF). However, so far, few studies have been performed on small volumes of CSF. Since pooling of patient samples possibly leads to the loss of essential individual patient information, and CSF samples are precious, it is important to have efficient techniques for the isolation of EVs from smaller volumes. In this study, the SmartSEC HT isolation kit from System Biosciences has been evaluated for this purpose. The SmartSEC HT isolation kit was used for isolation of EVs from 500 µL starting volumes of CSF, resulting in two possible EV fractions of 500 µL. Both fractions were characterised and compared to one another using a whole range of characterisation techniques. Results indicated the presence of EVs in both fractions, albeit fraction 1 showed more reproducible results over the different characterisation methods. For example, CMG (CellMask Green membrane stain) fluorescence nanotracking analysis (NTA), ExoView, and the particles/µg ratio demonstrated a clear difference between fraction 1 and 2, where fraction 1 came out as the one where most EVs were eluted with the least contamination. In the other methods, this difference was less noticeable. We successfully performed complementary characterisation tests using only 500 µL of CSF starting volume, and, conclude that fraction 1 consisted of sufficiently pure EVs for further biomarker studies. This means that future EV extractions may be based upon smaller CSF quantities, such as from individual patients. In that way, patient samples do not have to be pooled and individual patient information can be included in forthcoming studies, potentially linking EV content, size and distribution to individualised neurological diagnoses.

A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics.

In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction of dozens of novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, in turn inspiring specialized data analysis pipelines. Simultaneously, a growing number of proteomics datasets have been made publicly available through data repositories such as ProteomeXchange, Zenodo and Skyline Panorama. However, developing algorithms to mine this data and assessing the performance on different platforms is currently hampered by the lack of a single benchmark experimental design. Therefore, we acquired a hybrid proteome mixture on different instrument platforms and in all currently available families of data acquisition. Here, we present a comprehensive Data-Dependent and Data-Independent Acquisition (DDA/DIA) dataset acquired using several of the most commonly used current day instrumental platforms. The dataset consists of over 700 LC-MS runs, including adequate replicates allowing robust statistics and covering over nearly 10 different data formats, including scanning quadrupole and ion mobility enabled acquisitions. Datasets are available via ProteomeXchange (PXD028735).

Bioactive peptides, networks and systems biology.

Bioactive peptides are a group of diverse intercellular signalling molecules. Almost half a century of research on this topic has resulted in an enormous amount of data. In this essay, a general perspective to interpret all these data will be given. In classical endocrinology, neuropeptides were thought of as simple signalling molecules that each elicit one response. However, the fact that the total bioactive peptide signal is far from simple puts this view under pressure. Cells and tissues express many different bioactive peptides and they are also able to respond to many different bioactive peptides, indicating that multiple receptors and signal transduction pathways are present in a single cell. Therefore, the authors suggest that the bioactive peptide signalling system should be regarded in the context of network and systems biology. Bioactive peptides can best be viewed as an extension of the protein interaction network that allows regulating and fine-tuning the metabolism of the different cells and tissues in the body. The cell thus responds to the 'peptidome' instead of to a single peptide. The intracellular part of this signalling network consists of the various signalling transduction cascades. Recently, new systems biology approaches have emerged for the modelling of cell signalling. The network and systems biology approach is also able to shed new light on the evolution of intercellular signalling.

Ion Mobility Coupled to a Time-of-Flight Mass Analyzer Combined With Fragment Intensity Predictions Improves Identification of Classical Bioactive Peptides and Small Open Reading Frame-Encoded Peptides.

Bioactive peptides exhibit key roles in a wide variety of complex processes, such as regulation of body weight, learning, aging, and innate immune response. Next to the classical bioactive peptides, emerging from larger precursor proteins by specific proteolytic processing, a new class of peptides originating from small open reading frames (sORFs) have been recognized as important biological regulators. But their intrinsic properties, specific expression pattern and location on presumed non-coding regions have hindered the full characterization of the repertoire of bioactive peptides, despite their predominant role in various pathways. Although the development of peptidomics has offered the opportunity to study these peptides in vivo, it remains challenging to identify the full peptidome as the lack of cleavage enzyme specification and large search space complicates conventional database search approaches. In this study, we introduce a proteogenomics methodology using a new type of mass spectrometry instrument and the implementation of machine learning tools toward improved identification of potential bioactive peptides in the mouse brain. The application of trapped ion mobility spectrometry (tims) coupled to a time-of-flight mass analyzer (TOF) offers improved sensitivity, an enhanced peptide coverage, reduction in chemical noise and the reduced occurrence of chimeric spectra. Subsequent machine learning tools MS2PIP, predicting fragment ion intensities and DeepLC, predicting retention times, improve the database searching based on a large and comprehensive custom database containing both sORFs and alternative ORFs. Finally, the identification of peptides is further enhanced by applying the post-processing semi-supervised learning tool Percolator. Applying this workflow, the first peptidomics workflow combined with spectral intensity and retention time predictions, we identified a total of 167 predicted sORF-encoded peptides, of which 48 originating from presumed non-coding locations, next to 401 peptides from known neuropeptide precursors, linked to 66 annotated bioactive neuropeptides from within 22 different families. Additional PEAKS analysis expanded the pool of SEPs on presumed non-coding locations to 84, while an additional 204 peptides completed the list of peptides from neuropeptide precursors. Altogether, this study provides insights into a new robust pipeline that fuses technological advancements from different fields ensuring an improved coverage of the neuropeptidome in the mouse brain.

Identification of Non-Canonical Translation Products in C. elegans Using Tandem Mass Spectrometry.

Transcriptome and ribosome sequencing have revealed the existence of many non-canonical transcripts, mainly containing splice variants, ncRNA, sORFs and altORFs. However, identification and characterization of products that may be translated out of these remains a challenge. Addressing this, we here report on 552 non-canonical proteins and splice variants in the model organism C. elegans using tandem mass spectrometry. Aided by sequencing-based prediction, we generated a custom proteome database tailored to search for non-canonical translation products of C. elegans. Using this database, we mined available mass spectrometric resources of C. elegans, from which 51 novel, non-canonical proteins could be identified. Furthermore, we utilized diverse proteomic and peptidomic strategies to detect 40 novel non-canonical proteins in C. elegans by LC-TIMS-MS/MS, of which 6 were common with our meta-analysis of existing resources. Together, this permits us to provide a resource with detailed annotation of 467 splice variants and 85 novel proteins mapped onto UTRs, non-coding regions and alternative open reading frames of the C. elegans genome.