MS2Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates

Multi-omics insights into the response of Aspergillus parasiticus to long-chain alkanes in relation to polyethylene modification

Plastic pollution presents a global challenge, with polyethylene (PE) being among the most persistent plastics due to its durability and environmental resilience. Long-chain alkane (lcAlk) degrading microbes are a potential source of PE-degrading enzymes, as both lcAlk and PE are large hydrophobic compounds that consist exclusively of C-C and C-H bonds. In this work, we employed a multi-omics approach to study the ability of Aspergillus parasiticus MM36, an isolate derived from Tenebrio molitor intestines, to metabolize lcAlk and secrete enzymes that are potentially capable of modifying PE. The fungus was grown with hexadecane (C16) or a mixture of lcAlk (C24 to C36) as carbon sources and culture supernatants were tested daily for their ability to modify PE. Proteomic analysis identified induced oxidases hypothetically involved in lcAlk and PE functionalization. Key enzymes include multicopper oxidases, peroxidases, an unspecific peroxygenase and FAD-dependent monooxygenases. Surfactant proteins facilitating enzymatic and cellular interaction with hydrophobic substrates, such as one hydrophobin, three hydrophobic surface-binding proteins (HsbA) and one cerato platanin, were present in all secretomes. Transcriptomic analysis comparing lcAlk to C16 cultures highlighted the enrichment of oxidoreductase activities and carboxylic acid metabolism in both lcAlk incubation days, with transmembrane transporters and transferases predominating on day 2 and biosynthetic processes on day 3. In C16 cultures, hydrolytic enzymes, including esterases, were upregulated alongside Baeyer-Villiger monooxygenases, suggesting a shift toward sub-terminal hydroxylation. Integrating transcriptomic and secretomic data, we propose a mechanism for lcAlk assimilation by A. parasiticus MM36, involving extracellular oxyfunctionalization, hydrocarbon uptake via surface-modifying proteins and channeling through membrane transporters for energy consumption and biosynthetic processes. This study provides insights into fungal mechanisms for alkane metabolism and highlights their potential relevance to plastic biotransformation.

Elucidating the enzymatic response of the white rot basidiomycete Abortiporus biennis for the downgrade of polystyrene

Plastic pollution is a growing global environmental concern, with polyolefins such as polyethylene and polypropylene, as well as polystyrene (PS) constituting a significant amount of plastic waste. Both polyolefins and PS, when inappropriately disposed of in the environment, contribute to environmental contamination since they degrade slowly, with both abiotic and biotic factors contributing to their downgrade. In terms of the microbial effect on plastics, in recent decades, several studies have focused on the biodeterioration and assimilation of polyolefins, while more comprehensive degradation of PS by diverse organisms, including bacteria, fungi, and even insect larvae, has been documented. The present study investigates the biocatalytic potential of the white-rot basidiomycete Abortiporus biennis LGAM 436 for PS degradation. Building on prior research, we examined the ability of this fungal strain to modify the structure of different PS forms, including commercial expanded polystyrene (EPS) foam and amorphous PS film. In addition, we explored the impact of olive oil mill wastewater (OOMW) effluent as an enzymatic inducer to enhance the degradation process. Through gel permeation chromatography (GPC), surface morphology changes, and FTIR-ATR analysis, we assessed the extent of PS degradation and identified relevant enzymatic activities via proteomics. The findings offer insights into the discovery of novel fungal biocatalysts for addressing plastic pollution, particularly through the action of high-redox oxidative enzymes.

MultiStageSearch: An Iterative Workflow for Unbiased Taxonomic Analysis of Pathogens Using Proteogenomics

The global SARS-CoV-2 pandemic emphasized the need for accurate pathogen diagnostics. While genomics is the gold standard, integrating mass spectrometry-based proteomics offers additional benefits. However, current proteomic and genomic reference databases are often biased toward specific taxa, such as pathogenic strains or model organisms, and proteomic databases are less comprehensive. These biases and gaps can lead to inaccurate identifications. To address these issues, we introduce MultiStageSearch, a multistep database search method that combines proteome and genome databases for taxonomic analysis. Initially, a generalist proteome database is used to infer potential species. Then, MultiStageSearch generates a specialized proteogenomic database for precise identification. This database is preprocessed to filter duplicates and cluster identical open reading frames to reduce genomic database biases. The workflow operates independently of strain-level NCBI taxonomy, enabling the identification of strains not represented in existing taxonomies. We benchmarked the workflow on viral and bacterial samples, demonstrating its superior performance in strain-level taxonomic inference compared to existing methods. MultiStageSearch offers a flexible and accurate approach for pathogen research and diagnostics, overcoming incomplete search spaces and biases inherent in reference databases.

Open-Source and FAIR Research Software for Proteomics

Scientific discovery relies on innovative software as much as experimental methods, especially in proteomics, where computational tools are essential for mass spectrometer setup, data analysis, and interpretation. Since the introduction of SEQUEST, proteomics software has grown into a complex ecosystem of algorithms, predictive models, and workflows, but the field faces challenges, including the increasing complexity of mass spectrometry data, limited reproducibility due to proprietary software, and difficulties integrating with other omics disciplines. Closed-source, platform-specific tools exacerbate these issues by restricting innovation, creating inefficiencies, and imposing hidden costs on the community. Open-source software (OSS), aligned with the FAIR Principles (Findable, Accessible, Interoperable, Reusable), offers a solution by promoting transparency, reproducibility, and community-driven development, which fosters collaboration and continuous improvement. In this manuscript, we explore the role of OSS in computational proteomics, its alignment with FAIR principles, and its potential to address challenges related to licensing, distribution, and standardization. Drawing on lessons from other omics fields, we present a vision for a future where OSS and FAIR principles underpin a transparent, accessible, and innovative proteomics community.

PCI-DB: a novel primary tissue immunopeptidome database to guide next-generation peptide-based immunotherapy development

BACKGROUND

Various cancer immunotherapies rely on the T cell-mediated recognition of peptide antigens presented on human leukocyte antigens (HLA). However, the identification and selection of naturally presented peptide targets for the development of personalized as well as off-the-shelf immunotherapy approaches remain challenging.

METHODS

Over 10,000 raw mass spectrometry (MS) files from over 3,000 tissue samples were analyzed, summing to approximately seven terabytes of data. The raw MS data were processed using the standardized and open-source nf-core pipelines MHCquant2 and epitopeprediction, providing a uniform procedure for data handling. A global false discovery rate was applied to minimize false-positive identifications.

RESULTS

Here, we introduce the open-access Peptides for Cancer Immunotherapy Database (PCI-DB, https://pci-db.org/), a comprehensive resource of immunopeptidome data originating from various malignant and benign primary tissues that provides the research community with a convenient tool to facilitate the identification of peptide targets for immunotherapy development. The PCI-DB includes >6.6 million HLA class I and >3.4 million HLA class II peptides from over 40 tissue types and cancer entities. First application of the database provided insights into the representation of cancer-testis antigens across malignant and benign tissues, enabling the identification and characterization of cross-tumor entity and entity-specific tumor-associated antigens (TAAs) as well as naturally presented neoepitopes from frequent cancer mutations. Further, we used the PCI-DB to design personalized peptide vaccines for two patients suffering from metastatic cancer. In a retrospective analysis, PCI-DB enabled the composition of both a multi-peptide vaccine comprising non-mutated, highly frequent TAAs matching the immunopeptidome of the individual patient's tumor and a neoepitope-based vaccine matching the mutational profile of a patient with cancer. Both vaccine approaches induced potent and long-lasting T-cell responses, accompanied by long-term survival of these patients with advanced cancer.

CONCLUSION

The PCI-DB provides a highly versatile tool to broaden the understanding of cancer-related antigen presentation and, ultimately, supports the development of novel immunotherapies.

ProSIMSIt: The Best of Both Worlds in Data-Driven Rescoring and Identification Transfer

Multibatch isobaric labeling experiments are frequently applied for clinical and pharmaceutical studies of large sample cohorts. To tackle the critical issue of missing values in such studies, we introduce the ProSIMSIt pipeline. It combines the advantages of tandem mass spectrum clustering via SIMSI-Transfer and data-driven rescoring via Prosit and Oktoberfest. We demonstrate that these two tools are complementary and mutually beneficial. On large-scale cancer cohort data, ProSIMSIt increased the number of peptide spectrum matches (PSMs) by 40% on both global and phosphoproteome data sets. Furthermore, on data from proteome-wide drug-response profiling of post-translational modifications (decryptM), our pipeline substantially increased drug-PTM relations and revealed previously unseen downstream effects of drug target inhibition. ProSIMSIt is available as an open-source Python package with a simple command line interface that allows easy application to MaxQuant result files.

Maximizing Immunopeptidomics-Based Bacterial Epitope Discovery by Multiple Search Engines and Rescoring

Mass spectrometry-based discovery of bacterial immunopeptides presented by infected cells allows untargeted discovery of bacterial antigens that can serve as vaccine candidates. However, reliable identification of bacterial epitopes is challenged by their extremely low abundance. Here, we describe an optimized bioinformatic framework to enhance the confident identification of bacterial immunopeptides. Immunopeptidomics data of cell cultures infected with Listeria monocytogenes were searched by four different search engines, PEAKS, Comet, Sage and MSFragger, followed by data-driven rescoring with MS2Rescore. Compared with individual search engine results, this integrated workflow boosted immunopeptide identification by an average of 27% and led to the high-confidence detection of 18 additional bacterial peptides (+27%) matching 15 different Listeria proteins (+36%). Despite the strong agreement between the search engines, a small number of spectra (<1%) had ambiguous matches to multiple peptides and were excluded to ensure high-confidence identifications. Finally, we demonstrate our workflow with sensitive timsTOF SCP data acquisition and find that rescoring, now with inclusion of ion mobility features, identifies 76% more peptides compared to Q Exactive HF acquisition. Together, our results demonstrate how integration of multiple search engine results along with data-driven rescoring maximizes immunopeptide identification, boosting the detection of high-confidence bacterial epitopes for vaccine development.

PEPSeek-Mediated Identification of Novel Epitopes From Viral and Bacterial Pathogens and the Impact on Host Cell Immunopeptidomes

Here, we develop PEPSeek, a web-server-based software to allow higher performance in the identification of pathogen-derived epitope candidates detected via mass spectrometry in MHC class I immunopeptidomes. We apply it to human and mouse cell lines infected with SARS-CoV-2, Listeria monocytogenes, or Chlamydia trachomatis, thereby identifying a large number of novel antigens and epitopes that we prove to be recognized by CD8+ T cells. In infected cells, we identified antigenic peptide features that suggested how the processing and presentation of pathogenic antigens differ between pathogens. The quantitative tools of PEPSeek also helped to define how C. trachomatis infection cycle could impact the antigenic landscape of the host human cell system, likely reflecting metabolic changes that occurred in the infected cells.

diaPASEF Analysis for HLA-I Peptides Enables Quantification of Common Cancer Neoantigens

Human leukocyte antigen class I (HLA-I) molecules present short peptide sequences from endogenous or foreign proteins to cytotoxic T cells. The low abundance of HLA-I peptides poses significant technical challenges for their identification and accurate quantification. While mass spectrometry is currently a method of choice for direct system-wide identification of cellular immunopeptidomes, there is still a need for enhanced sensitivity in detecting and quantifying tumor-specific epitopes. As gas phase separation in data-dependent MS data acquisition increased HLA-I peptide detection by up to 50%, here, we aimed to evaluate the performance of data-independent acquisition (DIA) in combination with parallel accumulation serial fragmentation ion mobility (diaPASEF) for high-sensitivity identification of HLA presented peptides. Our streamlined diaPASEF workflow enabled identification of 11,412 unique peptides from 12.5 million A375 cells and 3426 8-11mers from as low as 500,000 cells with high reproducibility. By taking advantage of HLA binder-specific in silico predicted spectral libraries, we were able to further increase the number of identified HLA-I peptides. We applied SILAC-DIA to a mixture of labeled HLA-I peptides, calculated heavy-to-light ratios for 7742 peptides across five conditions and demonstrated that diaPASEF achieves high quantitative accuracy up to 5-fold dilution. Finally, we identified and quantified shared neoantigens in a monoallelic C1R cell line model. By spiking in heavy synthetic peptides, we verified the identification of the peptide sequences and calculated relative abundances for 13 neoantigens. Taken together, diaPASEF analysis workflows for HLA-I peptides can increase the peptidome coverage for lower sample amounts. The sensitivity and quantitative precision provided by DIA can enable the detection and quantification of less abundant peptide species such as neoantigens across samples from the same background.

Comparative Analysis of Data-Driven Rescoring Platforms for Improved Peptide Identification in HeLa Digest Samples

Mass spectrometry is a critical tool to understand complex changes in biological processes. Despite significant advances in search engine technology, many spectra remain unassigned. This research evaluates the performance of three rescoring platforms, Oktoberfest, MS2Rescore, and inSPIRE, using MaxQuant output. The results indicated a substantial increase in identifications at the peptide level (40%-53%) and PSM level (64%-67%). However, some peptides were lost due to limitations in processing posttranslational modifications (PTMs)-with up to 75% of lost peptides exhibiting PTMs. Each platform displayed distinct strengths and weaknesses. For instance, inSPIRE performed best in terms of peptide identifications and unique peptides, while MS2Rescore performed better for PSMs at higher FDR values. Differences in platform performance stemmed from different sources: original search engine feature selection, type of ion series predicted, retention time predictor, and PTMs compatibility. Overall, inSPIRE showed a superior ability to harness original search engine results. Taken all together, rescoring platforms clearly outperformed original search results; however, they demanded additional computation time (up to 77%) and manual adjustments. The findings here underline the necessity of integrating rescoring platforms into current proteomics pipelines but also address some challenges in their implementation and optimization. Future integrated platforms may help enhance adoption.

TIMS2Rescore: A Data Dependent Acquisition-Parallel Accumulation and Serial Fragmentation-Optimized Data-Driven Rescoring Pipeline Based on MS2Rescore

The high throughput analysis of proteins with mass spectrometry (MS) is highly valuable for understanding human biology, discovering disease biomarkers, identifying therapeutic targets, and exploring pathogen interactions. To achieve these goals, specialized proteomics subfields, including plasma proteomics, immunopeptidomics, and metaproteomics, must tackle specific analytical challenges, such as an increased identification ambiguity compared to routine proteomics experiments. Technical advancements in MS instrumentation can mitigate these issues by acquiring more discerning information at higher sensitivity levels. This is exemplified by the incorporation of ion mobility and parallel accumulation and serial fragmentation (PASEF) technologies in timsTOF instruments. In addition, AI-based bioinformatics solutions can help overcome ambiguity issues by integrating more data into the identification workflow. Here, we introduce TIMS2Rescore, a data-driven rescoring workflow optimized for DDA-PASEF data from timsTOF instruments. This platform includes new timsTOF MS2PIP spectrum prediction models and IM2Deep, a new deep learning-based peptide ion mobility predictor. Furthermore, to fully streamline data throughput, TIMS2Rescore directly accepts Bruker raw mass spectrometry data and search results from ProteoScape and many other search engines, including Sage and PEAKS. We showcase TIMS2Rescore performance on plasma proteomics, immunopeptidomics (HLA class I and II), and metaproteomics data sets. TIMS2Rescore is open-source and freely available at https://github.com/compomics/tims2rescore.

A Scalable, Web-Based Platform for Proteomics Data Processing, Result Storage and Analysis

The exponential increase in proteomics data presents critical challenges for conventional processing workflows. These pipelines often consist of fragmented software packages, glued together using complex in-house scripts or error-prone manual workflows running on local hardware, which are costly to maintain and scale. The MSAID Platform offers a fully automated, managed proteomics data pipeline, consolidating formerly disjointed functions into unified, API-driven services that cover the entire process from raw data to biological insights. Backed by the cloud-native search algorithm CHIMERYS, as well as scalable cloud compute instances and data lakes, the platform facilitates efficient processing of large data sets, automation of processing via the command line, systematic result storage, analysis, and visualization. The data lake supports elastically growing storage and unified query capabilities, facilitating large-scale analyses and efficient reuse of previously processed data, such as aggregating longitudinally acquired studies. Users interact with the platform via a web interface, CLI client, or API, providing flexible, automated access. Readily available tools for accessing result data include browser-based interrogation and one-click visualizations for statistical analysis. The platform streamlines research processes, making advanced and automated proteomic workflows accessible to a broader range of scientists. The MSAID Platform is globally available via https://platform.msaid.io.

ProHap enables human proteomic database generation accounting for population diversity

Amid the advances in genomics, the availability of large reference panels of human haplotypes is key to account for human diversity within and across populations. However, mass spectrometry-based proteomics does not benefit from this information. To address this gap, we introduce ProHap, a Python-based tool that constructs protein sequence databases from phased genotypes of reference panels. ProHap enables researchers to account for haplotype diversity in proteomic searches.

Exploring protein conformations with limited proteolysis coupled to mass spectrometry

Limited proteolysis coupled to mass spectrometry (LiP-MS) has emerged as a powerful proteomic tool for studying protein conformations. Since its introduction in 2014, LiP-MS has expanded its scope to explore complex biological systems and shed light on disease mechanisms, and has been used for protein drug research. This review discusses the evolution of the technique, recent technical advances, including enhanced protocols and integration of machine learning, and diverse applications across various experimental models. Despite its achievements, challenges in protein extraction and conformotypic peptide identification remain. Ongoing methodological refinements will be crucial to overcome these challenges and enhance the capabilities of the technique. However, LiP-MS offers significant potential for future discoveries in structural proteomics and medical research.

Immunopeptidomics for autoimmunity: unlocking the chamber of immune secrets

T cells mediate pathogenesis of several autoimmune disorders by recognizing self-epitopes presented on Major Histocompatibility Complex (MHC) or Human Leukocyte Antigen (HLA) complex. The majority of autoantigens presented to T cells in various autoimmune disorders are not known, which has impeded autoantigen identification. Recent advances in immunopeptidomics have started to unravel the repertoire of antigenic epitopes presented on MHC. In several autoimmune diseases, immunopeptidomics has led to the identification of novel autoantigens and has enhanced our understanding of the mechanisms behind autoimmunity. Especially, immunopeptidomics has provided key evidence to explain the genetic risk posed by HLA alleles. In this review, we shed light on how immunopeptidomics can be leveraged to discover potential autoantigens. We highlight the application of immunopeptidomics in Type 1 Diabetes (T1D), Systemic Lupus Erythematosus (SLE), and Rheumatoid Arthritis (RA). Finally, we highlight the practical considerations of implementing immunopeptidomics successfully and the technical challenges that need to be addressed. Overall, this review will provide an important context for using immunopeptidomics for understanding autoimmunity.

Proteomics Can Rise to the Challenge of Pseudogenes' Coding Nature

Throughout the past decade, technological advances in genomics and transcriptomics have revealed pervasive translation throughout mammalian genomes. These putative proteins are usually excluded from proteomics analyses, as they are absent from common protein repositories. A sizable portion of these noncanonical proteins is translated from pseudogenes. Pseudogenes are commonly termed defective copies of coding genes unable to produce proteins. Here, we suggest that proteomics can help in their annotation. First, we define important terms and review specific examples underlining the caveats in pseudogene annotation and their coding potential. Then, we will discuss the challenges inherent to pseudogenes that have thus far rendered complex their confidence in omics data. Finally, we identify recent developments in experimental procedures, instrumentation, and computational methods in proteomics that put the field in a unique position to solve the pseudogene annotation conundrum.

Enhancing Mass spectrometry-based tumor immunopeptide identification: machine learning filter leveraging HLA binding affinity, aliphatic index and retention time deviation

Accurately identifying neoantigens is crucial for developing effective cancer vaccines and improving tumor immunotherapy. Mass spectrometry-based immunopeptidomics has emerged as a promising approach to identifying human leukocyte antigen (HLA) peptides presented on the surface of cancer cells, but false-positive identifications remain a significant challenge. In this study, liquid chromatography-tandem mass spectrometry-based proteomics and next-generation sequencing were utilized to identify HLA-presenting neoantigenic peptides resulting from non-synonymous single nucleotide variations in tumor tissues from 18 patients with renal cell carcinoma or pancreatic cancer. Machine learning was utilized to evaluate Mascot identifications through the prediction of MS/MS spectral consistency, and four descriptors for each candidate sequence: the max Mascot ion score, predicted HLA binding affinity, aliphatic index and retention time deviation, were selected as important features in filtering out identifications with inadequate fragmentation consistency. This suggests that incorporating rescoring filters based on peptide physicochemical characteristics could enhance the identification rate of MS-based immunopeptidomics compared to the traditional Mascot approach predominantly used for proteomics, indicating the potential for optimizing neoantigen identification pipelines as well as clinical applications.

Machine learning-enhanced immunopeptidomics applied to T-cell epitope discovery for COVID-19 vaccines

Next-generation T-cell-directed vaccines for COVID-19 focus on establishing lasting T-cell immunity against current and emerging SARS-CoV-2 variants. Precise identification of conserved T-cell epitopes is critical for designing effective vaccines. Here we introduce a comprehensive computational framework incorporating a machine learning algorithm-MHCvalidator-to enhance mass spectrometry-based immunopeptidomics sensitivity. MHCvalidator identifies unique T-cell epitopes presented by the B7 supertype, including an epitope from a + 1-frameshift in a truncated Spike antigen, supported by ribosome profiling. Analysis of 100,512 COVID-19 patient proteomes shows Spike antigen truncation in 0.85% of cases, revealing frameshifted viral antigens at the population level. Our EpiTrack pipeline tracks global mutations of MHCvalidator-identified CD8 + T-cell epitopes from the BNT162b4 vaccine. While most vaccine epitopes remain globally conserved, an immunodominant A*01-associated epitope mutates in Delta and Omicron variants. This work highlights SARS-CoV-2 antigenic features and emphasizes the importance of continuous adaptation in T-cell vaccine development.

Theoretical Assessment of Indistinguishable Peptides in Mass Spectrometry-Based Proteomics

Mass-spectrometry-based proteomics has advanced with the integration of experimental and predicted spectral libraries, which have significantly improved peptide identification in complex search spaces. However, challenges persist in distinguishing some peptides with close retention times and nearly identical fragmentation patterns. In this study, we conducted a theoretical assessment to quantify the prevalence of indistinguishable peptides within the human canonical proteome and immunopeptidome using state-of-the-art retention time and spectrum prediction models. By quantifying the proportion of peptides posing challenges to unequivocal identification, we set the theoretical nonaccessible portion within a given proteome, and underscore the effectiveness of contemporary analytical methodologies in resolving the complexity of the human proteome and immunopeptidome via mass spectrometry.

Machine Learning Strategies to Tackle Data Challenges in Mass Spectrometry-Based Proteomics

In computational proteomics, machine learning (ML) has emerged as a vital tool for enhancing data analysis. Despite significant advancements, the diversity of ML model architectures and the complexity of proteomics data present substantial challenges in the effective development and evaluation of these tools. Here, we highlight the necessity for high-quality, comprehensive data sets to train ML models and advocate for the standardization of data to support robust model development. We emphasize the instrumental role of key data sets like ProteomeTools and MassIVE-KB in advancing ML applications in proteomics and discuss the implications of data set size on model performance, highlighting that larger data sets typically yield more accurate models. To address data scarcity, we explore algorithmic strategies such as self-supervised pretraining and multitask learning. Ultimately, we hope that this discussion can serve as a call to action for the proteomics community to collaborate on data standardization and collection efforts, which are crucial for the sustainable advancement and refinement of ML methodologies in the field.

Exploring the dynamic landscape of immunopeptidomics: Unravelling posttranslational modifications and navigating bioinformatics terrain

Immunopeptidomics is becoming an increasingly important field of study. The capability to identify immunopeptides with pivotal roles in the human immune system is essential to shift the current curative medicine towards personalized medicine. Throughout the years, the field has matured, giving insight into the current pitfalls. Nowadays, it is commonly accepted that generalizing shotgun proteomics workflows is malpractice because immunopeptidomics faces numerous challenges. While many of these difficulties have been addressed, the road towards the ideal workflow remains complicated. Although the presence of Posttranslational modifications (PTMs) in the immunopeptidome has been demonstrated, their identification remains highly challenging despite their significance for immunotherapies. The large number of unpredictable modifications in the immunopeptidome plays a pivotal role in the functionality and these challenges. This review provides a comprehensive overview of the current advancements in immunopeptidomics. We delve into the challenges associated with identifying PTMs within the immunopeptidome, aiming to address the current state of the field.

MS2Rescore 3.0 Is a Modular, Flexible, and User-Friendly Platform to Boost Peptide Identifications, as Showcased with MS Amanda 3.0

Rescoring of peptide-spectrum matches (PSMs) has emerged as a standard procedure for the analysis of tandem mass spectrometry data. This emphasizes the need for software maintenance and continuous improvement for such algorithms. We introduce MS2Rescore 3.0, a versatile, modular, and user-friendly platform designed to increase peptide identifications. Researchers can install MS2Rescore across various platforms with minimal effort and benefit from a graphical user interface, a modular Python API, and extensive documentation. To showcase this new version, we connected MS2Rescore 3.0 with MS Amanda 3.0, a new release of the well-established search engine, addressing previous limitations on automatic rescoring. Among new features, MS Amanda now contains additional output columns that can be used for rescoring. The full potential of rescoring is best revealed when applied on challenging data sets. We therefore evaluated the performance of these two tools on publicly available single-cell data sets, where the number of PSMs was substantially increased, thereby demonstrating that MS2Rescore offers a powerful solution to boost peptide identifications. MS2Rescore's modular design and user-friendly interface make data-driven rescoring easily accessible, even for inexperienced users. We therefore expect the MS2Rescore to be a valuable tool for the wider proteomics community. MS2Rescore is available at https://github.com/compomics/ms2rescore.

Tumor Neoepitope-Based Vaccines: A Scoping Review on Current Predictive Computational Strategies

Therapeutic cancer vaccines have been considered in recent decades as important immunotherapeutic strategies capable of leading to tumor regression. In the development of these vaccines, the identification of neoepitopes plays a critical role, and different computational methods have been proposed and employed to direct and accelerate this process. In this context, this review identified and systematically analyzed the most recent studies published in the literature on the computational prediction of epitopes for the development of therapeutic vaccines, outlining critical steps, along with the associated program's strengths and limitations. A scoping review was conducted following the PRISMA extension (PRISMA-ScR). Searches were performed in databases (Scopus, PubMed, Web of Science, Science Direct) using the keywords: neoepitope, epitope, vaccine, prediction, algorithm, cancer, and tumor. Forty-nine articles published from 2012 to 2024 were synthesized and analyzed. Most of the identified studies focus on the prediction of epitopes with an affinity for MHC I molecules in solid tumors, such as lung carcinoma. Predicting epitopes with class II MHC affinity has been relatively underexplored. Besides neoepitope prediction from high-throughput sequencing data, additional steps were identified, such as the prioritization of neoepitopes and validation. Mutect2 is the most used tool for variant calling, while NetMHCpan is favored for neoepitope prediction. Artificial/convolutional neural networks are the preferred methods for neoepitope prediction. For prioritizing immunogenic epitopes, the random forest algorithm is the most used for classification. The performance values related to the computational models for the prediction and prioritization of neoepitopes are high; however, a large part of the studies still use microbiome databases for training. The in vitro/in vivo validations of the predicted neoepitopes were verified in 55% of the analyzed studies. Clinical trials that led to successful tumor remission were identified, highlighting that this immunotherapeutic approach can benefit these patients. Integrating high-throughput sequencing, sophisticated bioinformatics tools, and rigorous validation methods through in vitro/in vivo assays as well as clinical trials, the tumor neoepitope-based vaccine approach holds promise for developing personalized therapeutic vaccines that target specific tumor cancers.

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.

Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification

Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.

Koina: Democratizing machine learning for proteomics research

Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.

Neoantigen-targeted dendritic cell vaccination in lung cancer patients induces long-lived T cells exhibiting the full differentiation spectrum

Non-small cell lung cancer (NSCLC) is known for high relapse rates despite resection in early stages. Here, we present the results of a phase I clinical trial in which a dendritic cell (DC) vaccine targeting patient-individual neoantigens is evaluated in patients with resected NSCLC. Vaccine manufacturing is feasible in six of 10 enrolled patients. Toxicity is limited to grade 1-2 adverse events. Systemic T cell responses are observed in five out of six vaccinated patients, with T cell responses remaining detectable up to 19 months post vaccination. Single-cell analysis indicates that the responsive T cell population is polyclonal and exhibits the near-entire spectrum of T cell differentiation states, including a naive-like state, but excluding exhausted cell states. Three of six vaccinated patients experience disease recurrence during the follow-up period of 2 years. Collectively, these data support the feasibility, safety, and immunogenicity of this treatment in resected NSCLC.

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.

Intensity and retention time prediction improves the rescoring of protein-nucleic acid cross-links

In protein-RNA cross-linking mass spectrometry, UV or chemical cross-linking introduces stable bonds between amino acids and nucleic acids in protein-RNA complexes that are then analyzed and detected in mass spectra. This analytical tool delivers valuable information about RNA-protein interactions and RNA docking sites in proteins, both in vitro and in vivo. The identification of cross-linked peptides with oligonucleotides of different length leads to a combinatorial increase in search space. We demonstrate that the peptide retention time prediction tasks can be transferred to the task of cross-linked peptide retention time prediction using a simple amino acid composition encoding, yielding improved identification rates when the prediction error is included in rescoring. For the more challenging task of including fragment intensity prediction of cross-linked peptides in the rescoring, we obtain, on average, a similar improvement. Further improvement in the encoding and fine-tuning of retention time and intensity prediction models might lead to further gains, and merit further research.

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.

Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model

Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIps is challenging due to their high diversity, low abundance, and patient individuality. Here, we develop a highly sensitive method for identifying HLAIps using liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS). In addition, we train a timsTOF-specific peak intensity MS2PIP model for tryptic and non-tryptic peptides and implement it in MS2Rescore (v3) together with the CCS predictor from ionmob. The optimized method, Thunder-DDA-PASEF, semi-selectively fragments singly and multiply charged HLAIps based on their IMS and m/z. Moreover, the method employs the high sensitivity mode and extended IMS resolution with fewer MS/MS frames (300 ms TIMS ramp, 3 MS/MS frames), doubling the coverage of immunopeptidomics analyses, compared to the proteomics-tailored DDA-PASEF (100 ms TIMS ramp, 10 MS/MS frames). Additionally, rescoring boosts the HLAIps identification by 41.7% to 33%, resulting in 5738 HLAIps from as little as one million JY cell equivalents, and 14,516 HLAIps from 20 million. This enables in-depth profiling of HLAIps from diverse human cell lines and human plasma. Finally, profiling JY and Raji cells transfected to express the SARS-CoV-2 spike protein results in 16 spike HLAIps, thirteen of which have been reported to elicit immune responses in human patients.

Exploiting Charge State Distribution To Probe Intramolecular Interactions in Gas-Phase Phosphopeptides and Enhance Proteomics Analyses

Charging of analytes is a prerequisite for performing mass spectrometry analysis. In proteomics, electrospray ionization is the dominant technique for this process. Although the observation of differences in the peptide charge state distribution (CSD) is well-known among experimentalists, its analytical value remains underexplored. To investigate the utility of this dimension, we analyzed several public data sets, comprising over 250,000 peptide CSD profiles from the human proteome. We found that the dimensions of the CSD demonstrate high reproducibility across multiple laboratories, mass analyzers, and extensive time intervals. The general observation was that the CSD enabled effective partitioning of the peptide property space, resulting in enhanced discrimination between sequence and constitutional peptide isomers. Next, by evaluating the CSD values of phosphorylated peptides, we were able to differentiate between phosphopeptides that indicate the formation of intramolecular structures in the gas phase and those that do not. The reproducibility of the CSD values (mean cosine similarity above 0.97 for most of the experiments) qualified CSD data suitable to train a deep-learning model capable of accurately predicting CSD values (mean cosine similarity - 0.98). When we applied the CSD dimension to MS1- and MS2-based proteomics experiments, we consistently observed around a 5% increase in protein and peptide identification rate. Even though the CSD dimension is not as effective a discriminator as the widely used retention time dimension, it still holds the potential for application in direct infusion proteomics.

Splicing neoantigen discovery with SNAF reveals shared targets for cancer immunotherapy

Immunotherapy has emerged as a crucial strategy to combat cancer by "reprogramming" a patient's own immune system. Although immunotherapy is typically reserved for patients with a high mutational burden, neoantigens produced from posttranscriptional regulation may provide an untapped reservoir of common immunogenic targets for new targeted therapies. To comprehensively define tumor-specific and likely immunogenic neoantigens from patient RNA-Seq, we developed Splicing Neo Antigen Finder (SNAF), an easy-to-use and open-source computational workflow to predict splicing-derived immunogenic MHC-bound peptides (T cell antigen) and unannotated transmembrane proteins with altered extracellular epitopes (B cell antigen). This workflow uses a highly accurate deep learning strategy for immunogenicity prediction (DeepImmuno) in conjunction with new algorithms to rank the tumor specificity of neoantigens (BayesTS) and to predict regulators of mis-splicing (RNA-SPRINT). T cell antigens from SNAF were frequently evidenced as HLA-presented peptides from mass spectrometry (MS) and predict response to immunotherapy in melanoma. Splicing neoantigen burden was attributed to coordinated splicing factor dysregulation. Shared splicing neoantigens were found in up to 90% of patients with melanoma, correlated to overall survival in multiple cancer cohorts, induced T cell reactivity, and were characterized by distinct cells of origin and amino acid preferences. In addition to T cell neoantigens, our B cell focused pipeline (SNAF-B) identified a new class of tumor-specific extracellular neoepitopes, which we termed ExNeoEpitopes. ExNeoEpitope full-length mRNA predictions were tumor specific and were validated using long-read isoform sequencing and in vitro transmembrane localization assays. Therefore, our systematic identification of splicing neoantigens revealed potential shared targets for therapy in heterogeneous cancers.

OpenProt 2.0 builds a path to the functional characterization of alternative proteins

The OpenProt proteogenomic resource (https://www.openprot.org/) provides users with a complete and freely accessible set of non-canonical or alternative open reading frames (AltORFs) within the transcriptome of various species, as well as functional annotations of the corresponding protein sequences not found in standard databases. Enhancements in this update are largely the result of user feedback and include the prediction of structure, subcellular localization, and intrinsic disorder, using cutting-edge algorithms based on machine learning techniques. The mass spectrometry pipeline now integrates a machine learning-based peptide rescoring method to improve peptide identification. We continue to help users explore this cryptic proteome by providing OpenCustomDB, a tool that enables users to build their own customized protein databases, and OpenVar, a genomic annotator including genetic variants within AltORFs and protein sequences. A new interface improves the visualization of all functional annotations, including a spectral viewer and the prediction of multicoding genes. All data on OpenProt are freely available and downloadable. Overall, OpenProt continues to establish itself as an important resource for the exploration and study of new proteins.

Bioinformatics Pipeline for Processing Single-Cell Data

Single-cell proteomics can offer valuable insights into dynamic cellular interactions, but identifying proteins at this level is challenging due to their low abundance. In this chapter, we present a state-of-the-art bioinformatics pipeline for single-cell proteomics that combines the search engine Sage (via SearchGUI), identification rescoring with MS2Rescore, quantification through FlashLFQ, and differential expression analysis using MSqRob2. MS2Rescore leverages LC-MS/MS behavior predictors, such as MS2PIP and DeepLC, to recalibrate scores with Percolator or mokapot. Combining these tools into a unified pipeline, this approach improves the detection of low-abundance peptides, resulting in increased identifications while maintaining stringent FDR thresholds.

Exploring the Alternative Proteome with OpenProt and Mass Spectrometry

Proteogenomics has revealed the translation of unannotated open reading frames (ORFs) present in mRNAs and in noncoding RNAs (ncRNAs). OpenProt annotates all ORFs with a minimum of 30 codons in the transcriptome of several species and displays many functional features associated with the corresponding proteins. Two types of proteins are annotated: reference or canonical proteins which are proteins already annotated in UniProt, RefSeq, or Ensembl and noncanonical proteins. Noncanonical proteins form two groups: predicted novel isoforms that display a significant level of homology with a reference protein and alternative proteins that are new proteins with no significant homology to known proteins. This chapter describes how to check whether a gene and/or transcript contains multiple open reading frames and how to use OpenProt databases for the detection of alternative proteins and novel isoforms by mass spectrometry-based proteomics.

Identification and Targeted Quantification of Endogenous Neuropeptides in the Nematode Caenorhabditis elegans Using Mass Spectrometry

The nematode Caenorhabditis elegans lends itself as an excellent model organism for peptidomics studies. Its ease of cultivation and quick generation time make it suitable for high-throughput studies. The nervous system, with its 302 neurons, is probably the best-known and studied endocrine tissue. Moreover, its neuropeptidergic signaling pathways display numerous similarities with those observed in other metazoans. Here, we describe two label-free approaches for neuropeptidomics in C. elegans: one for discovery purposes, and another for targeted quantification and comparisons of neuropeptide levels between different samples. Starting from a detailed peptide extraction procedure, we here outline the liquid chromatography tandem mass spectrometry (LC-MS/MS) setup and describe subsequent data analysis approaches.

Biological Factors and Statistical Limitations Prevent Detection of Most Noncanonical Proteins by Mass Spectrometry

Ribosome profiling experiments indicate pervasive translation of short open reading frames (ORFs) outside of annotated protein-coding genes. However, shotgun mass spectrometry experiments typically detect only a small fraction of the predicted protein products of this noncanonical translation. The rarity of detection could indicate that most predicted noncanonical proteins are rapidly degraded and not present in the cell; alternatively, it could reflect technical limitations. Here we leveraged recent advances in ribosome profiling and mass spectrometry to investigate the factors limiting detection of noncanonical proteins in yeast. We show that the low detection rate of noncanonical ORF products can largely be explained by small size and low translation levels and does not indicate that they are unstable or biologically insignificant. In particular, proteins encoded by evolutionarily young genes, including those with well-characterized biological roles, are too short and too lowly-expressed to be detected by shotgun mass spectrometry at current detection sensitivities. Additionally, we find that decoy biases can give misleading estimates of noncanonical protein false discovery rates, potentially leading to false detections. After accounting for these issues, we found strong evidence for four noncanonical proteins in mass spectrometry data, which were also supported by evolution and translation data. These results illustrate the power of mass spectrometry to validate unannotated genes predicted by ribosome profiling, but also its substantial limitations in finding many biologically relevant lowly-expressed proteins.

Retention Time and Fragmentation Predictors Increase Confidence in Identification of Common Variant Peptides

Precision medicine focuses on adapting care to the individual profile of patients, for example, accounting for their unique genetic makeup. Being able to account for the effect of genetic variation on the proteome holds great promise toward this goal. However, identifying the protein products of genetic variation using mass spectrometry has proven very challenging. Here we show that the identification of variant peptides can be improved by the integration of retention time and fragmentation predictors into a unified proteogenomic pipeline. By combining these intrinsic peptide characteristics using the search-engine post-processor Percolator, we demonstrate improved discrimination power between correct and incorrect peptide-spectrum matches. Our results demonstrate that the drop in performance that is induced when expanding a protein sequence database can be compensated, hence enabling efficient identification of genetic variation products in proteomics data. We anticipate that this enhancement of proteogenomic pipelines can provide a more refined picture of the unique proteome of patients and thereby contribute to improving patient care.

Ionmob: a Python package for prediction of peptide collisional cross-section values

MOTIVATION

Including ion mobility separation (IMS) into mass spectrometry proteomics experiments is useful to improve coverage and throughput. Many IMS devices enable linking experimentally derived mobility of an ion to its collisional cross-section (CCS), a highly reproducible physicochemical property dependent on the ion's mass, charge and conformation in the gas phase. Thus, known peptide ion mobilities can be used to tailor acquisition methods or to refine database search results. The large space of potential peptide sequences, driven also by posttranslational modifications of amino acids, motivates an in silico predictor for peptide CCS. Recent studies explored the general performance of varying machine-learning techniques, however, the workflow engineering part was of secondary importance. For the sake of applicability, such a tool should be generic, data driven, and offer the possibility to be easily adapted to individual workflows for experimental design and data processing.

RESULTS

We created ionmob, a Python-based framework for data preparation, training, and prediction of collisional cross-section values of peptides. It is easily customizable and includes a set of pretrained, ready-to-use models and preprocessing routines for training and inference. Using a set of ≈21 000 unique phosphorylated peptides and ≈17 000 MHC ligand sequences and charge state pairs, we expand upon the space of peptides that can be integrated into CCS prediction. Lastly, we investigate the applicability of in silico predicted CCS to increase confidence in identified peptides by applying methods of re-scoring and demonstrate that predicted CCS values complement existing predictors for that task.

AVAILABILITY AND IMPLEMENTATION

The Python package is available at github: https://github.com/theGreatHerrLebert/ionmob.

Real-Time Spectral Library Matching for Sample Multiplexed Quantitative Proteomics

Sample multiplexed quantitative proteomics assays have proved to be a highly versatile means to assay molecular phenotypes. Yet, stochastic precursor selection and precursor coisolation can dramatically reduce the efficiency of data acquisition and quantitative accuracy. To address this, intelligent data acquisition (IDA) strategies have recently been developed to improve instrument efficiency and quantitative accuracy for both discovery and targeted methods. Toward this end, we sought to develop and implement a new real-time spectral library searching (RTLS) workflow that could enable intelligent scan triggering and peak selection within milliseconds of scan acquisition. To ensure ease of use and general applicability, we built an application to read in diverse spectral libraries and file types from both empirical and predicted spectral libraries. We demonstrate that RTLS methods enable improved quantitation of multiplexed samples, particularly with consideration for quantitation from chimeric fragment spectra. We used RTLS to profile proteome responses to small molecule perturbations and were able to quantify up to 15% more significantly regulated proteins in half the gradient time compared to traditional methods. Taken together, the development of RTLS expands the IDA toolbox to improve instrument efficiency and quantitative accuracy for sample multiplexed analyses.

Unlocking the potential of microfluidics in mass spectrometry-based immunopeptidomics for tumor antigen discovery

The identification of tumor-specific antigens (TSAs) is critical for developing effective cancer immunotherapies. Mass spectrometry (MS)-based immunopeptidomics has emerged as a powerful tool for identifying TSAs as physical molecules. However, current immunopeptidomics platforms face challenges in measuring low-abundance TSAs in a precise, sensitive, and reproducible manner from small needle-tissue biopsies (<1 mg). Inspired by recent advances in single-cell proteomics, microfluidics technology offers a promising solution to these limitations by providing improved isolation of human leukocyte antigen (HLA)-associated peptides with higher sensitivity. In this context, we highlight the challenges in sample preparation and the rationale for developing microfluidics technology in immunopeptidomics. Additionally, we provide an overview of promising microfluidic methods, including microchip pillar arrays, valved-based systems, droplet microfluidics, and digital microfluidics, and discuss the latest research on their application in MS-based immunopeptidomics and single-cell proteomics.

A microfluidics-enabled automated workflow of sample preparation for MS-based immunopeptidomics

Mass spectrometry (MS)-based immunopeptidomics is an attractive antigen discovery method with growing clinical implications. However, the current experimental approach to extract HLA-restricted peptides requires a bulky sample source, which remains a challenge for obtaining clinical specimens. We present an innovative workflow that requires a low sample volume, which streamlines the immunoaffinity purification (IP) and C18 peptide cleanup on a single microfluidics platform with automated liquid handling and minimal sample transfers, resulting in higher assay sensitivity. We also demonstrate how the state-of-the-art data-independent acquisition (DIA) method further enhances the depth of tandem MS spectra-based peptide sequencing. Consequently, over 4,000 and 5,000 HLA-I-restricted peptides were identified from as few as 0.2 million RA957 cells and a melanoma tissue of merely 5 mg, respectively. We also identified multiple immunogenic tumor-associated antigens and hundreds of peptides derived from non-canonical protein sources. This workflow represents a powerful tool for identifying the immunopeptidome of sparse samples.

Updated MS²PIP web server supports cutting-edge proteomics applications

Interest in the use of machine learning for peptide fragmentation spectrum prediction has been strongly on the rise over the past years, especially for applications in challenging proteomics identification workflows such as immunopeptidomics and the full-proteome identification of data independent acquisition spectra. Since its inception, the MS²PIP peptide spectrum predictor has been widely used for various downstream applications, mostly thanks to its accuracy, ease-of-use, and broad applicability. We here present a thoroughly updated version of the MS²PIP web server, which includes new and more performant prediction models for both tryptic- and non-tryptic peptides, for immunopeptides, and for CID-fragmented TMT-labeled peptides. Additionally, we have also added new functionality to greatly facilitate the generation of proteome-wide predicted spectral libraries, requiring only a FASTA protein file as input. These libraries also include retention time predictions from DeepLC. Moreover, we now provide pre-built and ready-to-download spectral libraries for various model organisms in multiple DIA-compatible spectral library formats. Besides upgrading the back-end models, the user experience on the MS²PIP web server is thus also greatly enhanced, extending its applicability to new domains, including immunopeptidomics and MS3-based TMT quantification experiments. MS²PIP is freely available at https://iomics.ugent.be/ms2pip/.

The biogenesis of the immunopeptidome

The immunopeptidome is the repertoire of peptides bound and presented by the MHC class I, class II, and non-classical molecules. The peptides are produced by the degradation of most cellular proteins, and in some cases, peptides are produced from extracellular proteins taken up by the cells. This review attempts to first describe some of its known and well-accepted concepts, and next, raise some questions about a few of the established dogmas in this field: The production of novel peptides by splicing is questioned, suggesting here that spliced peptides are extremely rare, if existent at all. The degree of the contribution to the immunopeptidome by degradation of cellular protein by the proteasome is doubted, therefore this review attempts to explain why it is likely that this contribution to the immunopeptidome is possibly overstated. The contribution of defective ribosome products (DRiPs) and non-canonical peptides to the immunopeptidome is noted and methods are suggested to quantify them. In addition, the common misconception that the MHC class II peptidome is mostly derived from extracellular proteins is noted, and corrected. It is stressed that the confirmation of sequence assignments of non-canonical and spliced peptides should rely on targeted mass spectrometry using spiking-in of heavy isotope-labeled peptides. Finally, the new methodologies and modern instrumentation currently available for high throughput kinetics and quantitative immunopeptidomics are described. These advanced methods open up new possibilities for utilizing the big data generated and taking a fresh look at the established dogmas and reevaluating them critically.

The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens

Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.

Challenges and Opportunities for Single-cell Computational Proteomics

Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it is necessary to realize that current algorithms being employed on single-cell data were designed for bulk data and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data.

Using mass spectrometry to identify neoantigens in autoimmune diseases: The type 1 diabetes example

In autoimmune diseases, recognition of self-antigens presented by major histocompatibility complex (MHC) molecules elicits unexpected attack of tissue by autoantibodies and/or autoreactive T cells. Post-translational modification (PTM) may alter the MHC-binding motif or TCR contact residues in a peptide antigen, transforming the tolerance to self to autoreactivity. Mass spectrometry-based immunopeptidomics provides a valuable mechanism for identifying MHC ligands that contain PTMs and can thus provide valuable insights into pathogenesis and therapeutics of autoimmune diseases. A plethora of PTMs have been implicated in this process, and this review highlights their formation and identification.

Scribe: Next Generation Library Searching for DDA Experiments

Spectrum library searching is a powerful alternative to database searching for data dependent acquisition experiments, but has been historically limited to identifying previously observed peptides in libraries. Here we present Scribe, a new library search engine designed to leverage deep learning fragmentation prediction software such as Prosit. Rather than relying on highly curated DDA libraries, this approach predicts fragmentation and retention times for every peptide in a FASTA database. Scribe embeds Percolator for false discovery rate correction and an interference tolerant, label-free quantification integrator for an end-to-end proteomics workflow. By leveraging expected relative fragmentation and retention time values, we find that library searching with Scribe can outperform traditional database searching tools both in terms of sensitivity and quantitative precision. Scribe and its graphical interface are easy to use, freely accessible, and fully open source.

Unified and Standardized Mass Spectrometry Data Processing in Python Using spectrum_utils

spectrum_utils is a Python package for mass spectrometry data processing and visualization. Since its introduction, spectrum_utils has grown into a fundamental software solution that powers various applications in proteomics and metabolomics, ranging from spectrum preprocessing prior to spectrum identification and machine learning applications to spectrum plotting from online data repositories and assisting data analysis tasks for dozens of other projects. Here, we present updates to spectrum_utils, which include new functionality to integrate mass spectrometry community data standards, enhanced mass spectral data processing, and unified mass spectral data visualization in Python. spectrum_utils is freely available as open source at https://github.com/bittremieux/spectrum_utils.