Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning

Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies

Efficacy of cancer immunotherapies relies on correct recognition of tumor antigens by lymphocytes, eliciting thus functional responses capable of eliminating tumor cells. Therefore, important efforts have been carried out in antigen identification, with the aim of understanding mechanisms of response to immunotherapy and to design safer and more efficient strategies. In addition to classical tumor-associated antigens identified during the last decades, implementation of next-generation sequencing methodologies is enabling the identification of neoantigens (neoAgs) arising from mutations, leading to the development of new neoAg-directed therapies. Moreover, there are numerous non-classical tumor antigens originated from other sources and identified by new methodologies. Here, we review the relevance of neoAgs in different immunotherapies and the results obtained by applying neoAg-based strategies. In addition, the different types of non-classical tumor antigens and the best approaches for their identification are described. This will help to increase the spectrum of targetable molecules useful in cancer immunotherapies.

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.

Detection of host cell microprotein impurities in antibody drug products

Chinese hamster ovary (CHO) cells are used to produce almost 90% of therapeutic monoclonal antibodies (mAbs) and antibody fusion proteins (Fc-fusion). The annotation of non-canonical translation events in these cellular factories remains incomplete, limiting our ability to study CHO cell biology and detect host cell protein (HCP) impurities in the final antibody drug product. We utilised ribosome footprint profiling (Ribo-seq) to identify novel open reading frames (ORFs) including N-terminal extensions and thousands of short ORFs (sORFs) predicted to encode microproteins. Mass spectrometry-based HCP analysis of eight commercial antibody drug products (7 mAbs and 1 Fc-fusion protein) using the extended protein sequence database revealed the presence of microprotein impurities. We present evidence that microprotein abundance varies with growth phase and can be affected by the cell culture environment. In addition, our work provides a vital resource to facilitate future studies of non-canonical translation and the regulation of protein synthesis in CHO cell lines.

Enhanced Sample Multiplexing-Based Targeted Proteomics with Intelligent Data Acquisition

Targeted proteomics has been playing an increasingly important role in hypothesis-driven protein research and clinical biomarker discovery. We previously created a workflow, Tomahto, to enable real-time targeted pathway proteomics assays using two-dimensional multiplexing technology. Coupled with the TMT 11-plex reagent, hundreds of proteins of interest from up to 11 samples can be targeted and accurately quantified in a single-shot experiment with remarkable sensitivity. However, room remains to further improve the sensitivity, accuracy, and throughput, especially for targeted studies demanding a high peptide-level success rate. Here, bearing in mind the goal to improve peptide-level targeting, we introduce several new functionalities in Tomahto, featuring the integration of gas-phase fractionation using the FAIMS device, an accompanying software program (TomahtoPrimer) to customize fragmentation for each peptide target, and support for higher multiplexing capacity with the latest TMTpro reagent. We demonstrate that adding these features to the Tomahto platform significantly improves overall success rate from 89% to 98% in a single 60 min targeted assay of 290 peptides across human cell lines, while boosting quantitative accuracy via reducing TMT reporter ion interference.

Mass Spectral Feature Analysis of Ubiquitylated Peptides Provides Insights into Probing the Dark Ubiquitylome

Ubiquitylation is a structurally and functionally diverse post-translational modification that involves the covalent attachment of the small protein ubiquitin to other protein substrates. Trypsin-based proteomics is the most common approach for globally identifying ubiquitylation sites. However, we estimate that such methods are unable to detect ∼40% of ubiquitylation sites in the human proteome, i.e., "the dark ubiquitylome", including many important for human health and disease. In this meta-analysis of three large ubiquitylomic data sets, we performed a series of bioinformatic analyses to assess experimental features that could aid in uniquely identifying site-specific ubiquitylation events. Spectral predictions from Prosit were compared to experimental spectra of tryptic ubiquitylated peptides, revealing previously uncharacterized fragmentation of the diGly scar. Analysis of the LysC-derived ubiquitylated peptides reveals systematic, multidimensional peptide fragmentation, including diagnostic b-ions from fragmentation of the LysC ubiquitin scar. Comprehensively, these findings provide diagnostic spectral signatures of modification events that could be applied to new analysis methods for nontryptic ubiquitylomics.

moPepGen: Rapid and Comprehensive Identification of Non-canonical Peptides

Gene expression is a multi-step transformation of biological information from its storage form (DNA) into functional forms (protein and some RNAs). Regulatory activities at each step of this transformation multiply a single gene into a myriad of proteoforms. Proteogenomics is the study of how genomic and transcriptomic variation creates this proteomic diversity, and is limited by the challenges of modeling the complexities of gene-expression. We therefore created moPepGen, a graph-based algorithm that comprehensively generates non-canonical peptides in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it enumerates previously unobservable noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization. By enabling efficient detection and quantitation of previously hidden proteins in both existing and new proteomic data, moPepGen facilitates all proteogenomics applications. It is available at: https://github.com/uclahs-cds/package-moPepGen.

CPred: Charge State Prediction for Modified and Unmodified Peptides in Electrospray Ionization

The mass-to-charge ratio serves as a critical parameter in peptide identification via mass spectrometry, enabling the precise determination of peptide masses and facilitating their differentiation based on unique charge characteristics, especially when peptides are ionized by tools like electrospray ionization, which produces multiply charged ions. We developed a neural network called CPred, which can accurately predict the charge state distribution from +1 to +7 for the modified and unmodified peptides. CPred was trained on the large-scale synthetic training data, consisting of tryptic and non-tryptic peptides, and various fragmentation methods. The model was further evaluated on independent, external test data sets. Results were evaluated through the Pearson correlation coefficient and showed high correlations of up to 0.9997117 between the predicted and acquired charge state distributions. The effect of specifying modifications in the neural network and feature importance was further investigated, revealing the value of modifications and vital peptide properties in holding on to protons. CPreds' accurate predictions of the charge state distribution can play an essential role in boosting confidence in peptide identifications during rescoring as a novel feature.

Integrated, high-throughput metabolomics approach for metabolite analysis of four sprout types

In this study, we combined two distinct extraction and separation techniques with the aim of comprehensively collecting metabolite features in sprouts, particularly hydrophilic compounds. By synergistically analyzing the data using MS-DIAL and MetaboAnalystR, we obtained a greater number of annotated metabolites and explored differences in annotation across analytical tools. We found that this approach significantly increased the number of detected metabolite features and the final identification counts. Furthermore, we explored the functional component characteristics of four sprout types. This study provides data supporting the potential of sprouts as nutritious vegetables and functional food ingredients, emphasizing their value in the development of functional foods.

Deep Learning Powers Protein Identification from Precursor MS Information

Proteome analysis currently heavily relies on tandem mass spectrometry (MS/MS), which does not fully utilize MS1 features, as many precursors remain unselected for MS/MS fragmentation, especially in the cases of low abundance samples and wide abundance dynamic range samples. Therefore, leveraging MS1 features as a complement to MS/MS has become an attractive option to improve the coverage of feature identification. Herein, we propose MonoMS1, an approach combining deep learning-based retention time, ion mobility, detectability prediction, and logistic regression-based scoring for MS1 feature identification. The approach achieved a significant increase in MS1 feature identification based on an E. coli data set. Application of MonoMS1 to data sets with wide dynamic range, such as human serum proteome samples, and with low sample abundance, such as single-cell proteome samples, enabled substantial complementation of MS/MS-based peptide and protein identification. This method opens a new avenue for proteomic analysis and can boost proteomic research on complex samples.

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.

SeFilter-DIA: Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics

Mass spectrometry is crucial in proteomics analysis, particularly using Data Independent Acquisition (DIA) for reliable and reproducible mass spectrometry data acquisition, enabling broad mass-to-charge ratio coverage and high throughput. DIA-NN, a prominent deep learning software in DIA proteome analysis, generates peptide results but may include low-confidence peptides. Conventionally, biologists have to manually screen peptide fragment ion chromatogram peaks (XIC) for identifying high-confidence peptides, a time-consuming and subjective process prone to variability. In this study, we introduce SeFilter-DIA, a deep learning algorithm, aiming at automating the identification of high-confidence peptides. Leveraging compressed excitation neural network and residual network models, SeFilter-DIA extracts XIC features and effectively discerns between high and low-confidence peptides. Evaluation of the benchmark datasets demonstrates SeFilter-DIA achieving 99.6% AUC on the test set and 97% for other performance indicators. Furthermore, SeFilter-DIA is applicable for screening peptides with phosphorylation modifications. These results demonstrate the potential of SeFilter-DIA to replace manual screening, providing an efficient and objective approach for high-confidence peptide identification while mitigating associated limitations.

AI-powered omics-based drug pair discovery for pyroptosis therapy targeting triple-negative breast cancer

Due to low success rates and long cycles of traditional drug development, the clinical tendency is to apply omics techniques to reveal patient-level disease characteristics and individualized responses to treatment. However, the heterogeneous form of data and uneven distribution of targets make drug discovery and precision medicine a non-trivial task. This study takes pyroptosis therapy for triple-negative breast cancer (TNBC) as a paradigm and uses data mining of a large TNBC cohort and drug databases to establish a biofactor-regulated neural network for rapidly screening and optimizing compound pyroptosis drug pairs. Subsequently, biomimetic nanococrystals are prepared using the preferred combination of mitoxantrone and gambogic acid for rational drug delivery. The unique mechanism of obtained nanococrystals regulating pyroptosis genes through ribosomal stress and triggering pyroptosis cascade immune effects are revealed in TNBC models. In this work, a target omics-based intelligent compound drug discovery framework explores an innovative drug development paradigm, which repurposes existing drugs and enables precise treatment of refractory diseases.

Imputation of cancer proteomics data with a deep model that learns from many datasets

Missing values are a major challenge in the analysis of mass spectrometry proteomics data. Missing values hinder reproducibility, decrease statistical power for identifying differentially expressed (DE) proteins and make it challenging to analyze low-abundance proteins. We present Lupine, a deep learning-based method for imputing, or estimating, missing values in tandem mass tag (TMT) proteomics data. Lupine is, to our knowledge, the first imputation method that is designed to learn jointly from many datasets, and we provide evidence that this approach leads to more accurate predictions. We validated Lupine by applying it to TMT data from >1,000 cancer patient samples spanning ten cancer types from the Clinical Proteomics Tumor Atlas Consortium (CPTAC). Lupine outperforms the state of the art for TMT imputation, identifies more DE proteins than other methods, corrects for TMT batch effects, and learns a meaningful representation of proteins and patient samples. Lupine is implemented as an open source Python package.

Predicting peptide properties from mass spectrometry data using deep attention-based multitask network and uncertainty quantification

Database search algorithms reduce the number of potential candidate peptides against which scoring needs to be performed using a single (i.e. mass) property for filtering. While useful, filtering based on one property may lead to exclusion of non-abundant spectra and uncharacterized peptides - potentially exacerbating the streetlight effect. Here we present ProteoRift, a novel attention and multitask deep-network, which can predict multiple peptide properties (length, missed cleavages, and modification status) directly from spectra. We demonstrate that ProteoRift can predict these properties with up to 97% accuracy resulting in search-space reduction by more than 90%. As a result, our end-to-end pipeline is shown to exhibit 8x to 12x speedups with peptide deduction accuracy comparable to algorithmic techniques. We also formulate two uncertainty estimation metrics, which can distinguish between in-distribution and out-of-distribution data (ROC-AUC 0.99) and predict high-scoring mass spectra against correct peptide (ROC-AUC 0.94). These models and metrics are integrated in an end-to-end ML pipeline available at https://github.com/pcdslab/ProteoRift.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

Mdm2 requires Sprouty4 to regulate focal adhesion formation and metastasis independent of p53

Although the E3 ligase Mdm2 and its homologue and binding partner MdmX are the major regulators of the p53 tumor suppressor protein, it is now evident that Mdm2 and MdmX have multiple functions that do not involve p53. As one example, it is known that Mdm2 can regulate cell migration, although mechanistic insight into this function is still lacking. Here we show in cells lacking p53 expression that knockdown of Mdm2 or MdmX, as well as pharmacological inhibition of the Mdm2/MdmX complex, not only reduces cell migration and invasion, but also impairs cell spreading and focal adhesion formation. In addition, Mdm2 knockdown decreases metastasis in vivo. Interestingly, Mdm2 downregulates the expression of Sprouty4, which is required for the Mdm2 mediated effects on cell migration, focal adhesion formation and metastasis. Further, our findings indicate that Mdm2 dampening of Sprouty4 is a prerequisite for maintaining RhoA levels in the cancer cells that we have studied. Taken together we describe a molecular mechanism whereby the Mdm2/MdmX complex through Sprouty4 regulates cellular processes leading to increase metastatic capability independently of p53.

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.

Proteomic and Cytokine Profiling in Plasma from Patients with Normal-Tension Glaucoma and Ocular Hypertension

Primary open-angle glaucoma (POAG) is subdivided depending on eye pressure. Patients with normal-tension glaucoma (NTG) have never had high intraocular pressure (IOP) measured while patients with ocular hypertension (OHT) have high eye pressure but no signs of glaucoma. Although IOP is considered to be a risk factor for all glaucoma patients, it is reasonable to assume that other risk factors such as inflammation play a role. We aimed to characterize the proteome and cytokine profile during hypoxia in plasma from patients with NTG (n = 10), OHT (n = 10), and controls (n = 10). Participants were exposed to hypoxia for two hours, followed by 30 min of normoxia. Samples were taken before ("baseline"), during ("hypoxia"), and after hypoxia ("recovery"). Proteomics based on liquid chromatography coupled with mass spectrometry (LC-MS) was performed. Cytokines were measured by Luminex assays. Bioinformatic analyses indicated the involvement of complement and coagulation cascades in NTG and OHT. Regulation of high-density lipoprotein 3 (HDL3) apolipoproteins suggested that changes in cholesterol metabolism are related to OHT. Hypoxia decreased the level of tumor necrosis factor-α (TNF-α) in OHT patients compared to controls. Circulating levels of interleukin-1β (IL-1β) and C-reactive protein (CRP) were decreased in NTG patients compared to controls during hypoxia. After recovery, plasma interleukin-6 (IL-6) was upregulated in patients with NTG and OHT. Current results indicate an enhanced systemic immune response in patients with NTG and OHT, which correlates with pathogenic events in glaucoma. Apolipoproteins may have anti-inflammatory effects, enabling OHT patients to withstand inflammation and development of glaucoma despite high IOP.

Proceedings of the EuBIC-MS developers meeting 2023

The 2023 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers Meeting was held from January 15th to January 20th, 2023, in Congressi Stefano Franscin at Monte Verità in Ticino, Switzerland. The participants were scientists and developers working in computational mass spectrometry (MS), metabolomics, and proteomics. The 5-day program was split between introductory keynote lectures and parallel hackathon sessions focusing on "Artificial Intelligence in proteomics" to stimulate future directions in the MS-driven omics areas. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts and actively contribute to highly relevant research projects. We successfully produced several new tools applicable to the proteomics community by improving data analysis and facilitating future research.

A framework for quality control in quantitative proteomics

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and on ProteomeXchange under the identifier PXD051318.

Monitoring Epoxidized Soybean Oil Degradation Using Liquid Chromatography-Mass Spectrometry and In Silico Spectral Libraries

Epoxidized soybean oil (ESO) is routinely used as a bioderived plasticizer and stabilizer in polyvinyl chloride (PVC), as it prolongs material integrity during dehydrochlorination. During this process, the epoxide moieties of ESO are progressively converted to chlorohydrins, which amplify ESO's inherent structural complexity. Past characterization efforts utilized separation-mass spectrometry (MS) analysis of the hydrolyzed acyl chains to simplify the complexity. However, this approach significantly increases the complexity of sample preparation and cannot directly monitor the chlorination of individual ESO species during aging. Here, we present a comprehensive LC-MS/MS data acquisition and in silico spectral library identification workflow optimized for intact ESO byproduct analysis. Detailed MS/MS fragmentation rules derived from synthesized standards were coupled with improved fragment ion intensity modeling capabilities to generate a high-fidelity spectral library for rapid ESO byproduct identification. Identification confidence was further bolstered by using retention time modeling to filter spurious MS/MS matches. Finally, we paired this informatic approach with an optimized extraction procedure and reversed-phase separation to generate a detailed timeline of more than 400 ESO species and byproducts during PVC thermal aging. These developments significantly improve our ability to detect, characterize, and understand ESO degradation in complex PVC formulations with new levels of molecular resolution.

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.

Use of Nonhuman Sera as a Highly Cost-Effective Internal Standard for Quantitation of Multiple Human Proteins Using Species-Specific Tryptic Peptides: Applicability in Clinical LC-MS Analyses

Quantitation of proteins using liquid chromatography-tandem mass spectrometry (LC-MS/MS) is complex, with a multiplicity of options ranging from label-free techniques to chemically and metabolically labeling proteins. Increasingly, for clinically relevant analyses, stable isotope-labeled (SIL) internal standards (ISs) represent the "gold standard" for quantitation due to their similar physiochemical properties to the analyte, wide availability, and ability to multiplex to several peptides. However, the purchase of SIL-ISs is a resource-intensive step in terms of cost and time, particularly for screening putative biomarker panels of hundreds of proteins. We demonstrate an alternative strategy utilizing nonhuman sera as the IS for quantitation of multiple human proteins. We demonstrate the effectiveness of this strategy using two high abundance clinically relevant analytes, vitamin D binding protein [Gc globulin] (DBP) and albumin (ALB). We extend this to three putative risk markers for cardiovascular disease: plasma protease C1 inhibitor (SERPING1), annexin A1 (ANXA1), and protein kinase, DNA-activated catalytic subunit (PRKDC). The results show highly specific, reproducible, and linear measurement of the proteins of interest with comparable precision and accuracy to the gold standard SIL-IS technique. This approach may not be applicable to every protein, but for many proteins it can offer a cost-effective solution to LC-MS/MS protein quantitation.

Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry-Based Proteomics

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given m/z range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.

Sequence-to-sequence translation from mass spectra to peptides with a transformer model

A fundamental challenge in mass spectrometry-based proteomics is the identification of the peptide that generated each acquired tandem mass spectrum. Approaches that leverage known peptide sequence databases cannot detect unexpected peptides and can be impractical or impossible to apply in some settings. Thus, the ability to assign peptide sequences to tandem mass spectra without prior information-de novo peptide sequencing-is valuable for tasks including antibody sequencing, immunopeptidomics, and metaproteomics. Although many methods have been developed to address this problem, it remains an outstanding challenge in part due to the difficulty of modeling the irregular data structure of tandem mass spectra. Here, we describe Casanovo, a machine learning model that uses a transformer neural network architecture to translate the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. We train a Casanovo model from 30 million labeled spectra and demonstrate that the model outperforms several state-of-the-art methods on a cross-species benchmark dataset. We also develop a version of Casanovo that is fine-tuned for non-enzymatic peptides. Finally, we demonstrate that Casanovo's superior performance improves the analysis of immunopeptidomics and metaproteomics experiments and allows us to delve deeper into the dark proteome.

An integrated metaproteomics workflow for studying host-microbe dynamics in bronchoalveolar lavage samples applied to cystic fibrosis disease

Airway microbiota are known to contribute to lung diseases, such as cystic fibrosis (CF), but their contributions to pathogenesis are still unclear. To improve our understanding of host-microbe interactions, we have developed an integrated analytical and bioinformatic mass spectrometry (MS)-based metaproteomics workflow to analyze clinical bronchoalveolar lavage (BAL) samples from people with airway disease. Proteins from BAL cellular pellets were processed and pooled together in groups categorized by disease status (CF vs. non-CF) and bacterial diversity, based on previously performed small subunit rRNA sequencing data. Proteins from each pooled sample group were digested and subjected to liquid chromatography tandem mass spectrometry (MS/MS). MS/MS spectra were matched to human and bacterial peptide sequences leveraging a bioinformatic workflow using a metagenomics-guided protein sequence database and rigorous evaluation. Label-free quantification revealed differentially abundant human peptides from proteins with known roles in CF, like neutrophil elastase and collagenase, and proteins with lesser-known roles in CF, including apolipoproteins. Differentially abundant bacterial peptides were identified from known CF pathogens (e.g., Pseudomonas), as well as other taxa with potentially novel roles in CF. We used this host-microbe peptide panel for targeted parallel-reaction monitoring validation, demonstrating for the first time an MS-based assay effective for quantifying host-microbe protein dynamics within BAL cells from individual CF patients. Our integrated bioinformatic and analytical workflow combining discovery, verification, and validation should prove useful for diverse studies to characterize microbial contributors in airway diseases. Furthermore, we describe a promising preliminary panel of differentially abundant microbe and host peptide sequences for further study as potential markers of host-microbe relationships in CF disease pathogenesis.IMPORTANCEIdentifying microbial pathogenic contributors and dysregulated human responses in airway disease, such as CF, is critical to understanding disease progression and developing more effective treatments. To this end, characterizing the proteins expressed from bacterial microbes and human host cells during disease progression can provide valuable new insights. We describe here a new method to confidently detect and monitor abundance changes of both microbe and host proteins from challenging BAL samples commonly collected from CF patients. Our method uses both state-of-the art mass spectrometry-based instrumentation to detect proteins present in these samples and customized bioinformatic software tools to analyze the data and characterize detected proteins and their association with CF. We demonstrate the use of this method to characterize microbe and host proteins from individual BAL samples, paving the way for a new approach to understand molecular contributors to CF and other diseases of the airway.

Expanding N-glycopeptide identifications by modeling fragmentation, elution, and glycome connectivity

Accurate glycopeptide identification in mass spectrometry-based glycoproteomics is a challenging problem at scale. Recent innovation has been made in increasing the scope and accuracy of glycopeptide identifications, with more precise uncertainty estimates for each part of the structure. We present a dynamically adapting relative retention time model for detecting and correcting ambiguous glycan assignments that are difficult to detect from fragmentation alone, a layered approach to glycopeptide fragmentation modeling that improves N-glycopeptide identification in samples without compromising identification quality, and a site-specific method to increase the depth of the glycoproteome confidently identifiable even further. We demonstrate our techniques on a set of previously published datasets, showing the performance gains at each stage of optimization. These techniques are provided in the open-source glycomics and glycoproteomics platform GlycReSoft available at https://github.com/mobiusklein/glycresoft .

Magnetic-field-driven targeting of exosomes modulates immune and metabolic changes in dystrophic muscle

Exosomes are promising therapeutics for tissue repair and regeneration to induce and guide appropriate immune responses in dystrophic pathologies. However, manipulating exosomes to control their biodistribution and targeting them in vivo to achieve adequate therapeutic benefits still poses a major challenge. Here we overcome this limitation by developing an externally controlled delivery system for primed annexin A1 myo-exosomes (Exomyo). Effective nanocarriers are realized by immobilizing the Exomyo onto ferromagnetic nanotubes to achieve controlled delivery and localization of Exomyo to skeletal muscles by systemic injection using an external magnetic field. Quantitative muscle-level analyses revealed that macrophages dominate the uptake of Exomyo from these ferromagnetic nanotubes in vivo to synergistically promote beneficial muscle responses in a murine animal model of Duchenne muscular dystrophy. Our findings provide insights into the development of exosome-based therapies for muscle diseases and, in general, highlight the formulation of effective functional nanocarriers aimed at optimizing exosome biodistribution.

Rapid assay development for low input targeted proteomics using a versatile linear ion trap

Advances in proteomics and mass spectrometry enable the study of limited cell populations, where high-mass accuracy instruments are typically required. While triple quadrupoles offer fast and sensitive low-mass accuracy measurements, these instruments are effectively restricted to targeted proteomics. Linear ion traps (LITs) offer a versatile, cost-effective alternative capable of both targeted and global proteomics. Here, we describe a workflow using a new hybrid quadrupole-LIT instrument that rapidly develops targeted proteomics assays from global data-independent acquisition (DIA) measurements without needing high-mass accuracy. Using an automated software approach for scheduling parallel reaction monitoring assays (PRM), we show consistent quantification across three orders of magnitude in a matched-matrix background. We demonstrate measuring low-level proteins such as transcription factors and cytokines with quantitative linearity below two orders of magnitude in a 1 ng background proteome without requiring stable isotope-labeled standards. From a 1 ng sample, we found clear consistency between proteins in subsets of CD4+ and CD8+ T cells measured using high dimensional flow cytometry and LIT-based proteomics. Based on these results, we believe hybrid quadrupole-LIT instruments represent an economical solution to democratizing mass spectrometry in a wide variety of laboratory settings.

IS-PRM-Based Peptide Targeting Informed by Long-Read Sequencing for Alternative Proteome Detection

Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of predefined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (lrRNA-seq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNaseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This lrRNA-seq-informed Tomahto targeted approach is a new modality for generating protein-level evidence of alternative isoforms─a critical first step in designing functional studies and eventually clinical assays.

Plasma prolactin axis shift from placental to pituitary origin in late prepartum mice

The placenta secretes a prolactin (PRL)-like hormone PRL3B1 (placental lactogen II), a luteotropic hormone essential for maintaining pregnancy until labor in mice. A report from 1984 examined the secretion pattern of PRL3B1 in prepartum mice. In the current study, we found contradictory findings in the secretion pattern that invalidate the previous report. By measuring maternal plasma PRL3B1 and PRL every 4 hrs from gestational day 17 (G17), we newly discovered that maternal plasma PRL3B1 levels decrease rapidly in prepartum C57BL/6 mice. Interestingly, the onset of this decline coincided with the PRL surge at G18, demonstrating a plasma prolactin axis shift from placental to pituitary origin. We also found that maternal plasma progesterone regression precedes the onset of the PRL shift. The level of Prl3b1 mRNA was determined by RT-qPCR in the placenta and remained stable until parturition, implying that PRL3B1 peptide production or secretion was suppressed. We hypothesized that production of the PRL family, the 25 paralogous PRL proteins exclusively expressed in mice placenta, would decrease alongside PRL3B1 during this period. To investigate this hypothesis and to seek proteomic changes, we performed a shotgun proteome analysis of the placental tissue using data-independent acquisition mass spectrometry (DIA-MS). Up to 5,891 proteins were identified, including 17 PRL family members. Relative quantitative analysis between embryonic day 17 (E17) and E18 placentas showed no significant difference in the expression of PRL3B1 and most PRL family members except PRL7C1. These results suggest that PRL3B1 secretion from the placenta is suppressed at G18 (E18).

Proteomic analysis of lung responses to SARS-CoV-2 infection in aged non-human primates: clinical and research relevance

With devastating health and socioeconomic impact worldwide, much work is left to understand the Coronavirus Disease 2019 (COVID-19), with emphasis in the severely affected elderly population. Here, we present a proteomics study of lung tissue obtained from aged vs. young rhesus macaques (Macaca mulatta) and olive baboons (Papio Anubis) infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using age as a variable, we identified common proteomic profiles in the lungs of aged infected non-human primates (NHPs), including key regulators of immune function, as well as cell and tissue remodeling, and discuss the potential clinical relevance of such parameters. Further, we identified key differences in proteomic profiles between both NHP species, and compared those to what is known about SARS-CoV-2 in humans. Finally, we explored the translatability of these animal models in the context of aging and the human presentation of the COVID-19.

Deep-time phylogenetic inference by paleoproteomic analysis of dental enamel

In temperate and subtropical regions, ancient proteins are reported to survive up to about 2 million years, far beyond the known limits of ancient DNA preservation in the same areas. Accordingly, their amino acid sequences currently represent the only source of genetic information available to pursue phylogenetic inference involving species that went extinct too long ago to be amenable for ancient DNA analysis. Here we present a complete workflow, including sample preparation, mass spectrometric data acquisition and computational analysis, to recover and interpret million-year-old dental enamel protein sequences. During sample preparation, the proteolytic digestion step, usually an integral part of conventional bottom-up proteomics, is omitted to increase the recovery of the randomly degraded peptides spontaneously generated by extensive diagenetic hydrolysis of ancient proteins over geological time. Similarly, we describe other solutions we have adopted to (1) authenticate the endogenous origin of the protein traces we identify, (2) detect and validate amino acid variation in the ancient protein sequences and (3) attempt phylogenetic inference. Sample preparation and data acquisition can be completed in 3-4 working days, while subsequent data analysis usually takes 2-5 days. The workflow described requires basic expertise in ancient biomolecules analysis, mass spectrometry-based proteomics and molecular phylogeny. Finally, we describe the limits of this approach and its potential for the reconstruction of evolutionary relationships in paleontology and paleoanthropology.

Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders

In this review, we discuss the cutting-edge developments in mass spectrometry proteomics and metabolomics that have brought improvements for the identification of new disease-based biomarkers. A special focus is placed on psychiatric disorders, for example, schizophrenia, because they are considered to be not a single disease entity but rather a spectrum of disorders with many overlapping symptoms. This review includes descriptions of various types of commonly used mass spectrometry platforms for biomarker research, as well as complementary techniques to maximize data coverage, reduce sample heterogeneity, and work around potentially confounding factors. Finally, we summarize the different statistical methods that can be used for improving data quality to aid in reliability and interpretation of proteomics findings, as well as to enhance their translatability into clinical use and generalizability to new data sets.

Data-independent acquisition in metaproteomics

INTRODUCTION

Metaproteomics offers insights into the function of complex microbial communities, while it is also capable of revealing microbe-microbe and host-microbe interactions. Data-independent acquisition (DIA) mass spectrometry is an emerging technology, which holds great potential to achieve deep and accurate metaproteomics with higher reproducibility yet still facing a series of challenges due to the inherent complexity of metaproteomics and DIA data.

AREAS COVERED

This review offers an overview of the DIA metaproteomics approaches, covering aspects such as database construction, search strategy, and data analysis tools. Several cases of current DIA metaproteomics studies are presented to illustrate the procedures. Important ongoing challenges are also highlighted. Future perspectives of DIA methods for metaproteomics analysis are further discussed. Cited references are searched through and collected from Google Scholar and PubMed.

EXPERT OPINION

Considering the inherent complexity of DIA metaproteomics data, data analysis strategies specifically designed for interpretation are imperative. From this point of view, we anticipate that deep learning methods and de novo sequencing methods will become more prevalent in the future, potentially improving protein coverage in metaproteomics. Moreover, the advancement of metaproteomics also depends on the development of sample preparation methods, data analysis strategies, etc. These factors are key to unlocking the full potential of metaproteomics.

Calib-RT: an open source python package for peptide retention time calibration in DIA mass spectrometry data

MOTIVATION

The data independent acquisition (DIA) mass spectrometry (MS) method is increasingly popular in the field of proteomics. But the loss of the correspondence between peptide ions and their spectra in DIA makes the identification challenging. One effective approach to reduce false positive identification is to calculate the deviation between the peptide's estimated retention time (RT) and measured RT. During this process, scaling the spectral library RT into the estimated RT, known as the RT calibration, is a prerequisite for calculating the deviation. Currently, within the DIA algorithm ecosystem, there is a lack of engine-independent and readily usable RT calibration toolkits.

RESULTS

In this work, we introduce Calib-RT, a RT calibration method tailored to the characteristics of RT data. This method can achieve the nonlinear calibration across various data scales and tolerate a certain level of noise interference. Calib-RT is expected to enrich the open source DIA algorithm toolchain and assist in the development of DIA identification algorithms.

AVAILABILITY AND IMPLEMENTATION

Calib-RT is released as an open source software under the MIT license and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/chenghui03/Calib_RT.

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.

ProPept-MT: A Multi-Task Learning Model for Peptide Feature Prediction

In the realm of quantitative proteomics, data-independent acquisition (DIA) has emerged as a promising approach, offering enhanced reproducibility and quantitative accuracy compared to traditional data-dependent acquisition (DDA) methods. However, the analysis of DIA data is currently hindered by its reliance on project-specific spectral libraries derived from DDA analyses, which not only limits proteome coverage but also proves to be a time-intensive process. To overcome these challenges, we propose ProPept-MT, a novel deep learning-based multi-task prediction model designed to accurately forecast key features such as retention time (RT), ion intensity, and ion mobility (IM). Leveraging advanced techniques such as multi-head attention and BiLSTM for feature extraction, coupled with Nash-MTL for gradient coordination, ProPept-MT demonstrates superior prediction performance. Integrating ion mobility alongside RT, mass-to-charge ratio (m/z), and ion intensity forms 4D proteomics. Then, we outline a comprehensive workflow tailored for 4D DIA proteomics research, integrating the use of 4D in silico libraries predicted by ProPept-MT. Evaluation on a benchmark dataset showcases ProPept-MT's exceptional predictive capabilities, with impressive results including a 99.9% Pearson correlation coefficient (PCC) for RT prediction, a median dot product (DP) of 96.0% for fragment ion intensity prediction, and a 99.3% PCC for IM prediction on the test set. Notably, ProPept-MT manifests efficacy in predicting both unmodified and phosphorylated peptides, underscoring its potential as a valuable tool for constructing high-quality 4D DIA in silico libraries.

Systematic Assessment of Deep Learning-Based Predictors of Fragmentation Intensity Profiles

In recent years, several deep learning-based methods have been proposed for predicting peptide fragment intensities. This study aims to provide a comprehensive assessment of six such methods, namely Prosit, DeepMass:Prism, pDeep3, AlphaPeptDeep, Prosit Transformer, and the method proposed by Guan et al. To this end, we evaluated the accuracy of the predicted intensity profiles for close to 1.7 million precursors (including both tryptic and HLA peptides) corresponding to more than 18 million experimental spectra procured from 40 independent submissions to the PRIDE repository that were acquired for different species using a variety of instruments and different dissociation types/energies. Specifically, for each method, distributions of similarity (measured by Pearson's correlation and normalized angle) between the predicted and the corresponding experimental b and y fragment intensities were generated. These distributions were used to ascertain the prediction accuracy and rank the prediction methods for particular types of experimental conditions. The effect of variables like precursor charge, length, and collision energy on the prediction accuracy was also investigated. In addition to prediction accuracy, the methods were evaluated in terms of prediction speed. The systematic assessment of these six methods may help in choosing the right method for MS/MS spectra prediction for particular needs.

Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment

A pressing statistical challenge in the field of mass spectrometry proteomics is how to assess whether a given software tool provides accurate error control. Each software tool for searching such data uses its own internally implemented methodology for reporting and controlling the error. Many of these software tools are closed source, with incompletely documented methodology, and the strategies for validating the error are inconsistent across tools. In this work, we identify three different methods for validating false discovery rate (FDR) control in use in the field, one of which is invalid, one of which can only provide a lower bound rather than an upper bound, and one of which is valid but under-powered. The result is that the field has a very poor understanding of how well we are doing with respect to FDR control, particularly for the analysis of data-independent acquisition (DIA) data. We therefore propose a new, more powerful method for evaluating FDR control in this setting, and we then employ that method, along with an existing lower bounding technique, to characterize a variety of popular search tools. We find that the search tools for analysis of data-dependent acquisition (DDA) data generally seem to control the FDR at the peptide level, whereas none of the DIA search tools consistently controls the FDR at the peptide level across all the datasets we investigated. Furthermore, this problem becomes much worse when the latter tools are evaluated at the protein level. These results may have significant implications for various downstream analyses, since proper FDR control has the potential to reduce noise in discovery lists and thereby boost statistical power.

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.

Blood to Biomarker Quantitation in Under One Hour with Rapid Proteomics using a Hyperthermoacidic Protease

Multi-step multi-hour tryptic proteolysis has limited the utility of bottom-up proteomics for cases that require immediate quantitative information. The recently available hyperthermoacidic (HTA) protease "Krakatoa" digests samples in a single 5 to 30-minute step at pH 3 and >80 °C; conditions that disrupt most cells and tissues, denature proteins, and block disulfide reformation. The combination of quick single-step sample preparation with high throughput dual trapping column single analytical column (DTSC) liquid chromatography-mass spectrometry (LC-MS) achieves "Rapid Proteomics" in which the time from sample collection to actionable data is less than 1 hour. The presented development and systematic evaluation of this methodology found reproducible quantitation of over 160 proteins from just 1 microliter of whole blood. Furthermore, the preference of the HTA-protease for intact proteins over peptides allows for sensitive targeted quantitation of the Angiotensin I and II bioactive peptides in under half an hour. With these methods we analyzed serum and plasma from 53 individuals and quantified Angiotensin and proteins that were not detected with trypsin. This assessment of Rapid Proteomics suggests that concentration of circulating protein and peptide biomarkers could be measured in almost real-time by LC-MS.

TOC Figure

Rapid proteomics enables near real-time monitoring of circulating blood biomarkers. One microliter of blood is collected every 8 minutes, digested for 20 minutes, and then analyzed by targeted mass spectrometry for 8 minutes. This results in a 30-minute delay with datapoints every 8 minutes.

A workflow for targeted proteomics assay development using a versatile linear ion trap

Advances in proteomics and mass spectrometry have enabled the study of limited cell populations, such as single-cell proteomics, where high-mass accuracy instruments are typically required. While triple quadrupoles offer fast and sensitive nominal resolution measurements, these instruments are effectively limited to targeted proteomics. Linear ion traps (LITs) offer a versatile, cost-effective alternative capable of both targeted and global proteomics. We demonstrate a workflow using a newly released, hybrid quadrupole-LIT instrument for developing targeted proteomics assays from global data-independent acquisition (DIA) measurements without needing high-mass accuracy. Gas-phase fraction-based DIA enables rapid target library generation in the same background chemical matrix as each quantitative injection. Using a new software tool embedded within EncyclopeDIA for scheduling parallel reaction monitoring assays, we show consistent quantification across three orders of magnitude of input material. Using this approach, we demonstrate measuring peptide quantitative linearity down to 25x dilution in a background of only a 1 ng proteome without requiring stable isotope labeled standards. At 1 ng total protein on column, we found clear consistency between immune cell populations measured using flow cytometry and immune markers measured using LIT-based proteomics. We believe hybrid quadrupole-LIT instruments represent an economic solution to democratizing mass spectrometry in a wide variety of laboratory settings.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from cerebrospinal fluid (CSF) and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass offset searches.

Evidence for an RNAi-independent role of Arabidopsis DICER-LIKE2 in growth inhibition and basal antiviral resistance

Flowering plant genomes encode four or five DICER-LIKE (DCL) enzymes that produce small interfering RNAs (siRNAs) and microRNAs, which function in RNA interference (RNAi). Different RNAi pathways in plants effect transposon silencing, antiviral defense, and endogenous gene regulation. DCL2 acts genetically redundantly with DCL4 to confer basal antiviral defense. However, DCL2 may also counteract DCL4 since knockout of DCL4 causes growth defects that are suppressed by DCL2 inactivation. Current models maintain that RNAi via DCL2-dependent siRNAs is the biochemical basis of both effects. Here, we report that DCL2-mediated antiviral resistance and growth defects cannot be explained by the silencing effects of DCL2-dependent siRNAs. Both functions are defective in genetic backgrounds that maintain high levels of DCL2-dependent siRNAs, either with specific point mutations in DCL2 or with reduced DCL2 dosage because of heterozygosity for dcl2 knockout alleles. Intriguingly, all DCL2 functions require its catalytic activity, and the penetrance of DCL2-dependent growth phenotypes in dcl4 mutants correlates with DCL2 protein levels but not with levels of major DCL2-dependent siRNAs. We discuss this requirement and correlation with catalytic activity but not with resulting siRNAs, in light of other findings that reveal a DCL2 function in innate immunity activation triggered by cytoplasmic double-stranded RNA.

Mapping adipocyte interactome networks by HaloTag-enrichment-mass spectrometry

Mapping protein interaction complexes in their natural state in vivo is arguably the Holy Grail of protein network analysis. Detection of protein interaction stoichiometry has been an important technical challenge, as few studies have focused on this. This may, however, be solved by artificial intelligence (AI) and proteomics. Here, we describe the development of HaloTag-based affinity purification mass spectrometry (HaloMS), a high-throughput HaloMS assay for protein interaction discovery. The approach enables the rapid capture of newly expressed proteins, eliminating tedious conventional one-by-one assays. As a proof-of-principle, we used HaloMS to evaluate the protein complex interactions of 17 regulatory proteins in human adipocytes. The adipocyte interactome network was validated using an in vitro pull-down assay and AI-based prediction tools. Applying HaloMS to probe adipocyte differentiation facilitated the identification of previously unknown transcription factor (TF)-protein complexes, revealing proteome-wide human adipocyte TF networks and shedding light on how different pathways are integrated.

Environment modulates protein heterogeneity through transcriptional and translational stop codon readthrough

Stop codon readthrough events give rise to longer proteins, which may alter the protein's function, thereby generating short-lasting phenotypic variability from a single gene. In order to systematically assess the frequency and origin of stop codon readthrough events, we designed a library of reporters. We introduced premature stop codons into mScarlet, which enabled high-throughput quantification of protein synthesis termination errors in E. coli using fluorescent microscopy. We found that under stress conditions, stop codon readthrough may occur at rates as high as 80%, depending on the nucleotide context, suggesting that evolution frequently samples stop codon readthrough events. The analysis of selected reporters by mass spectrometry and RNA-seq showed that not only translation but also transcription errors contribute to stop codon readthrough. The RNA polymerase was more likely to misincorporate a nucleotide at premature stop codons. Proteome-wide detection of stop codon readthrough by mass spectrometry revealed that temperature regulated the expression of cryptic sequences generated by stop codon readthrough in E. coli. Overall, our findings suggest that the environment affects the accuracy of protein production, which increases protein heterogeneity when the organisms need to adapt to new conditions.

Deep Learning-Assisted Spectrum-Structure Correlation: State-of-the-Art and Perspectives

Spectrum-structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum-structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure-spectrum correlation) and further enabling library matching and de novo molecular generation (i.e., inverse spectrum-structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum-structure correlation soon, which would trigger substantial development of various disciplines.