Enhanced Global Post-translational Modification Discovery with MetaMorpheus

Fused in sarcoma regulates DNA replication timing and kinetics

Fused in sarcoma (FUS) encodes an RNA-binding protein with diverse roles in transcriptional activation and RNA splicing. While oncogenic fusions of FUS and transcription factor DNA-binding domains are associated with soft tissue sarcomas, dominant mutations in FUS can cause amyotrophic lateral sclerosis. FUS has also been implicated in genome maintenance. However, the underlying mechanisms of its actions in genome stability are unknown. Here, we applied gene editing, functional reconstitution, and integrated proteomics and transcriptomics to illuminate roles for FUS in DNA replication and repair. Consistent with a supportive role in DNA double-strand break repair, FUS-deficient cells exhibited subtle alterations in the recruitment and retention of double-strand break-associated factors, including 53BP1 and BRCA1. FUS-/- cells also exhibited reduced proliferative potential that correlated with reduced speed of replication fork progression, diminished loading of prereplication complexes, enhanced micronucleus formation, and attenuated expression and splicing of S-phase-associated genes. Finally, FUS-deficient cells exhibited genome-wide alterations in DNA replication timing that were reversed upon re-expression of FUS complementary DNA. We also showed that FUS-dependent replication domains were enriched in transcriptionally active chromatin and that FUS was required for the timely replication of transcriptionally active DNA. These findings suggest that alterations in DNA replication kinetics and programming contribute to genome instability and functional defects in FUS-deficient cells.

Accurate Retention Time Prediction Based on Monolinked Peptide Information to Confidently Identify Cross-Linked Peptides

Cross-linking mass spectrometry methods have not been successfully applied to protein-protein interaction discovery at a proteome-wide level mainly due to the computation complexity (O (n²)) issue. In a previous report, we proposed a decision tree searching strategy (DTSS), which can reduce complexity by orders of magnitude. In this study, we further found that the monolinked peptides carry out the information on the retention time of the corresponding cross-linked pairs; therefore, the retention time of cross-linked peptide pairs can be predicted accurately. By utilizing the retention time as an extra filter, the false positive rate can be reduced by around 86% with a sensitivity loss of 10%. The method combined with DTSS (T-DTSS) not only benefits improving identification confidence but also leads to lower cutoff scores and facilitates substantially increasing inter-cross-link identification. T-DTSS was successfully applied to the identification of inter-cross-links obtained from Escherichia coli cell lysate cross-linked by a newly synthesized enrichable cross-linker, pDSBE. The approach can be applicable to both cleavable and noncleavable methods.

Decoding post translational modification crosstalk with proteomics

Post-translational modification (PTM) of proteins allows cells to regulate protein functions, transduce signals and respond to perturbations. PTMs expand protein functionality and diversity, which leads to increased proteome complexity. PTM crosstalk describes the combinatorial action of multiple PTMs on the same or on different proteins for higher order regulation. Here we review how recent advances in proteomic technologies, mass spectrometry instrumentation, and bioinformatics spurred the proteome-wide identification of PTM crosstalk through measurements of PTM sites. We provide an overview of the basic modes of PTM crosstalk, the proteomic methods to elucidate PTM crosstalk, and approaches that can inform about the functional consequences of PTM crosstalk.

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Description of basic modules and different modes of PTM crosstalk.

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Overview of current proteomic methods to identify and infer PTM crosstalk.

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Discussion of large-scale approaches to characterize functional PTM crosstalk.

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Future directions and potential proteomic methods for elucidating PTM crosstalk.

Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric

Proteomic technology has improved at a staggering pace in recent years, with even practitioners challenged to keep up with new methods and hardware. The most common metric used for method performance is the number of peptides and proteins identified. While this metric may be helpful for proteomics researchers shopping for new hardware, this is often not the most biologically relevant metric. Biologists often utilize proteomics in the search for protein regulators that are of a lower relative copy number in the cell. In this review, I re-evaluate untargeted proteomics data using a simple graphical representation of the absolute copy number of proteins present in a single cancer cell as a metric. By comparing single-shot proteomics data to the coverage of the most in-depth proteomic analysis of that cell line acquired to date, we can obtain a rapid metric of method performance. Using a simple copy number metric allows visualization of how proteomics has developed in both sensitivity and overall dynamic range when using both relatively long and short acquisition times. To enable reanalysis beyond what is presented here, two available web applications have been developed for single- and multi-experiment comparisons with reference protein copy number data for multiple cell lines and organisms.

Automated Assignment of Proteoform Classification Levels

Proteoform identification is required to fully understand the biological diversity present in a sample. However, these identifications are often ambiguous because of the challenges in analyzing full length proteins by mass spectrometry. A five-level proteoform classification system was recently developed to delineate the ambiguity of proteoform identifications and to allow for comparisons across software platforms and acquisition methods. Widespread adoption of this system requires software tools to provide classification of the proteoform identifications. We describe here an implementation of the five-level classification system in the software program MetaMorpheus, which provides both bottom-up and top-down identifications. Additionally, we developed a stand-alone program called ProteoformClassifier that allows users to classify proteoform results from any search program, provided that the program writes output that includes the information necessary to evaluate proteoform ambiguity. This stand-alone program includes a small test file and database to evaluate if a given program provides sufficient information to evaluate ambiguity. If the program does not, then ProteoformClassifier provides meaningful feedback to assist developers with implementing the classification system. We tested currently available top-down software programs and found that none of them (other than MetaMorpheus) provided sufficient information regarding identification ambiguity to permit classification.

Evaluation of open search methods based on theoretical mass spectra comparison

Background

Mass spectrometry remains the privileged method to characterize proteins. Nevertheless, most of the spectra generated by an experiment remain unidentified after their analysis, mostly because of the modifications they carry. Open Modification Search (OMS) methods offer a promising answer to this problem. However, assessing the quality of OMS identifications remains a difficult task.

Methods

Aiming at better understanding the relationship between (1) similarity of pairs of spectra provided by OMS methods and (2) relevance of their corresponding peptide sequences, we used a dataset composed of theoretical spectra only, on which we applied two OMS strategies. We also introduced two appropriately defined measures for evaluating the above mentioned spectra/sequence relevance in this context: one is a color classification representing the level of difficulty to retrieve the proper sequence of the peptide that generated the identified spectrum ; the other, called LIPR, is the proportion of common masses, in a given Peptide Spectrum Match (PSM), that represent dissimilar sequences. These two measures were also considered in conjunction with the False Discovery Rate (FDR).

Results

According to our measures, the strategy that selects the best candidate by taking the mass difference between two spectra into account yields better quality results. Besides, although the FDR remains an interesting indicator in OMS methods (as shown by LIPR), it is questionable: indeed, our color classification shows that a non negligible proportion of relevant spectra/sequence interpretations corresponds to PSMs coming from the decoy database.

Conclusions

The three above mentioned measures allowed us to clearly determine which of the two studied OMS strategies outperformed the other, both in terms of number of identifications and of accuracy of these identifications. Even though quality evaluation of PSMs in OMS methods remains challenging, the study of theoretical spectra is a favorable framework for going further in this direction.

Posttranslational modifications in systems biology

The biological complexity cannot be captured by genes or proteins alone. The protein posttranslational modifications (PTMs) impart functional diversity to the proteome and regulate protein structure, activity, localization and interactions. Their dynamics drive cellular signaling, growth and development while their dysregulation causes many diseases. Mass spectrometry based quantitative profiling of PTMs and bioinformatics analysis tools allow systems level insights into their network architecture. High-resolution profiling of PTM networks will advance disease understanding and precision medicine. It can accelerate the discovery of biomarkers and drug targets. This requires better tools for unbiased, high-throughput and accurate PTM identification, site localization and automated annotation on a systems level.

Cloudy with a Chance of Peptides: Accessibility, Scalability, and Reproducibility with Cloud-Hosted Environments

Cloud-hosted environments offer known benefits when computational needs outstrip affordable local workstations, enabling high-performance computation without a physical cluster. What has been less apparent, especially to novice users, is the transformative potential for cloud-hosted environments to bridge the digital divide that exists between poorly funded and well-resourced laboratories, and to empower modern research groups with remote personnel and trainees. Using cloud-based proteomic bioinformatic pipelines is not predicated on analyzing thousands of files, but instead can be used to improve accessibility during remote work, extreme weather, or working with under-resourced remote trainees. The general benefits of cloud-hosted environments also allow for scalability and encourage reproducibility. Since one possible hurdle to adoption is awareness, this paper is written with the nonexpert in mind. The benefits and possibilities of using a cloud-hosted environment are emphasized by describing how to setup an example workflow to analyze a previously published label-free data-dependent acquisition mass spectrometry data set of mammalian urine. Cost and time of analysis are compared using different computational tiers, and important practical considerations are described. Overall, cloud-hosted environments offer the potential to solve large computational problems, but more importantly can enable and accelerate research in smaller research groups with inadequate infrastructure and suboptimal local computational resources.

Advances in quantitative high-throughput phosphoproteomics with sample multiplexing

Eukaryotic protein phosphorylation modulates nearly every major biological process. Phosphorylation regulates protein activity, mediates cellular signal transduction, and manipulates cellular structure. Consequently, the dysregulation of kinase and phosphatase pathways has been linked to a multitude of diseases. Mass spectrometry-based proteomic techniques are increasingly used for the global interrogation of perturbations in phosphorylation-based cellular signaling. Strategies for studying phosphoproteomes require high-specificity enrichment, sensitive detection, and accurate localization of phosphorylation sites with advanced LC-MS/MS techniques and downstream informatics. Sample multiplexing with isobaric tags has also been integral to recent advancements in throughput and sensitivity for phosphoproteomic studies. Each of these facets of phosphoproteomics analysis present distinct challenges and thus opportunities for improvement and innovation. Here, we review current methodologies, explore persistent challenges, and discuss the outlook for isobaric tag-based quantitative phosphoproteomic analysis.

Proteomic profile of bone "collagen" extracted for stable isotopes: Implications for bulk and single amino acid analyses

RATIONALE

Protein studies in archaeology and paleontology have been dominated by stable isotope studies to understand diet and trophic levels, but recent applications of proteomic techniques have resulted in a more complete understanding of protein diagenesis than stable isotopes alone. In stable isotope analyses, samples are retained or discarded based on their properties. Proteomics can directly determine what proteins are present within the sample and may be able to allow previously discarded samples to be analyzed.

METHODS

Protein samples that had been previously analyzed for stable isotopes, including those with marginal and poor sample quality, were characterized by liquid chromatography/mass spectrometry using an LTQ Orbitrap Velos mass spectrometer after separation on a Dionex Ultimate 3000 LC system. Data were analyzed using MetaMorpheus and custom R scripts.

RESULTS

We found a variety of proteins in addition to collagen, although collagen I was found in the majority of the samples (most samples >80%). We also found a positive correlation between total deamidation and wt% N, suggesting that deamidation may impact the overall nitrogen signal in bulk analyses. The amino acid profiles of samples, including those of marginal or poor stable isotope quality, reflect the expected collagen I percentages, allowing their use in single amino acid stable isotope analyses.

CONCLUSIONS

All the samples regardless of quality were found to have high concentrations of collagen I, making interpretations of dietary routing based on collagen I reasonably valid. The amino acid profiles on the marginal and poor samples reflect an expected collagen I profile and allow these samples to be recovered for single amino acid analyses.

LYTACs that engage the asialoglycoprotein receptor for targeted protein degradation

Selective protein degradation platforms have afforded new development opportunities for therapeutics and tools for biological inquiry. The first lysosome targeting chimeras (LYTACs) targeted extracellular and membrane proteins for degradation by bridging a target protein to the cation-independent mannose-6-phosphate receptor (CI-M6PR). Here, we developed LYTACs that engage the asialoglycoprotein receptor (ASGPR), a liver-specific lysosomal targeting receptor, to degrade extracellular proteins in a cell type-specific manner. We conjugated binders to a tri-GalNAc motif that engages ASGPR to drive downregulation of proteins. Degradation of EGFR by GalNAc-LYTAC attenuated EGFR signaling compared to inhibition with an antibody. Furthermore, we demonstrated that a LYTAC comprising a 3.4 kDa peptide binder linked to a tri-GalNAc ligand degrades integrins and reduces cancer cell proliferation. Degradation with a single tri-GalNAc ligand prompted site-specific conjugation on antibody scaffolds, which improved the pharmacokinetic profile of GalNAc-LYTACs in vivo. GalNAc-LYTACs thus represent an avenue for cell-type restricted protein degradation.

Improved SILAC Quantification with Data-Independent Acquisition to Investigate Bortezomib-Induced Protein Degradation

Stable isotope labeling by amino acids in cell culture (SILAC) coupled to data-dependent acquisition (DDA) is a common approach to quantitative proteomics with the desirable benefit of reducing batch effects during sample processing and data acquisition. More recently, using data-independent acquisition (DIA/SWATH) to systematically measure peptides has gained popularity for its comprehensiveness, reproducibility, and accuracy of quantification. The complementary advantages of these two techniques logically suggests combining them. Here we develop a SILAC-DIA-MS workflow using free, open-source software. We empirically determine that using DIA achieves similar peptide detection numbers as DDA and that DIA improves the quantitative accuracy and precision of SILAC by an order of magnitude. Finally, we apply SILAC-DIA-MS to determine protein turnover rates of cells treated with bortezomib, an FDA-approved 26S proteasome inhibitor for multiple myeloma and mantle cell lymphoma. We observe that SILAC-DIA produces more sensitive protein turnover models. Of the proteins determined to be differentially degraded by both acquisition methods, we find known proteins that are degraded by the ubiquitin-proteasome pathway, such as HNRNPK, EIF3A, and IF4A1/EIF4A-1, and a slower turnover for CATD, a protein implicated in invasive breast cancer. With improved quantification from DIA, we anticipate that this workflow will make SILAC-based experiments like protein turnover more sensitive.

SMITER-A Python Library for the Simulation of LC-MS/MS Experiments

SMITER (Synthetic mzML writer) is a Python-based command-line tool designed to simulate liquid-chromatography-coupled tandem mass spectrometry LC-MS/MS runs. It enables the simulation of any biomolecule amenable to mass spectrometry (MS) since all calculations are based on chemical formulas. SMITER features a modular design, allowing for an easy implementation of different noise and fragmentation models. By default, SMITER uses an established noise model and offers several methods for peptide fragmentation, and two models for nucleoside fragmentation and one for lipid fragmentation. Due to the rich Python ecosystem, other modules, e.g., for retention time (RT) prediction, can easily be implemented for the tailored simulation of any molecule of choice. This facilitates the generation of defined gold-standard LC-MS/MS datasets for any type of experiment. Such gold standards, where the ground truth is known, are required in computational mass spectrometry to test new algorithms and to improve parameters of existing ones. Similarly, gold-standard datasets can be used to evaluate analytical challenges, e.g., by predicting co-elution and co-fragmentation of molecules. As these challenges hinder the detection or quantification of co-eluents, a comprehensive simulation can identify and thus, prevent such difficulties before performing actual MS experiments. SMITER allows the creation of such datasets easily, fast, and efficiently.

Binary Classifier for Computing Posterior Error Probabilities in MetaMorpheus

MetaMorpheus is a free, open-source software program for the identification of peptides and proteoforms from data-dependent acquisition tandem MS experiments. There is inherent uncertainty in these assignments for several reasons, including the limited overlap between experimental and theoretical peaks, the m/z uncertainty, and noise peaks or peaks from coisolated peptides that produce false matches. False discovery rates provide only a set-wise approximation for incorrect spectrum matches. Here we implemented a binary decision tree calculation within MetaMorpheus to compute a posterior error probability, which provides a measure of uncertainty for each peptide-spectrum match. We demonstrate its utility for increasing identifications and resolving ambiguities in bottom-up, top-down, proteogenomic, and nonspecific digestion searches.

The proteome of granulovacuolar degeneration and neurofibrillary tangles in Alzheimer's disease

Granulovacuolar degeneration (GVD) is a common feature in Alzheimer's disease (AD). The occurrence of GVD is closely associated with that of neurofibrillary tangles (NFTs) and GVD is even considered to be a pre-NFT stage in the disease process of AD. Currently, the composition of GVD bodies, the mechanisms associated with GVD and how GVD exactly relates to NFTs is not well understood. By combining immunohistochemistry (IHC) and laser microdissection (LMD) we isolated neurons with GVD and those bearing tangles separately from human post-mortem AD hippocampus (n = 12) using their typical markers casein kinase (CK)1δ and phosphorylated tau (AT8). Control neurons were isolated from cognitively healthy cases (n = 12). 3000 neurons per sample were used for proteome analysis by label free LC-MS/MS. In total 2596 proteins were quantified across samples and a significant change in abundance of 115 proteins in GVD and 197 in tangle bearing neurons was observed compared to control neurons. With IHC the presence of PPIA, TOMM34, HSP70, CHMP1A, TPPP and VXN was confirmed in GVD containing neurons. We found multiple proteins localizing specifically to the GVD bodies, with VXN and TOMM34 being the most prominent new protein markers for GVD bodies. In general, protein groups related to protein folding, proteasomal function, the endolysosomal pathway, microtubule and cytoskeletal related function, RNA processing and glycolysis were found to be changed in GVD neurons. In addition to these protein groups, tangle bearing neurons show a decrease in ribosomal proteins, as well as in various proteins related to protein folding. This study, for the first time, provides a comprehensive human based quantitative assessment of protein abundances in GVD and tangle bearing neurons. In line with previous functional data showing that tau pathology induces GVD, our data support the model that GVD is part of a pre-NFT stage representing a phase in which proteostasis and cellular homeostasis is disrupted. Elucidating the molecular mechanisms and cellular processes affected in GVD and its relation to the presence of tau pathology is highly relevant for the identification of new drug targets for therapy.

Putting Proteomics Into Immunotherapy for Glioblastoma

In glioblastoma, the most aggressive brain cancer, a complex microenvironment of heterogeneity and immunosuppression, are considerable hurdles to classify the subtypes and promote treatment progression. Treatments for glioblastoma are similar to standard therapies for many other cancers and do not effectively prolong the survival of patients, due to the unique location and heterogeneous characteristics of glioblastoma. Immunotherapy has shown a promising effect for many other tumors, but its application for glioma still has some challenges. The recent breakthrough of high-throughput liquid chromatography-mass spectrometry (LC-MS/MS) systems has allowed researchers to update their strategy for identifying and quantifying thousands of proteins in a much shorter time with lesser effort. The protein maps can contribute to generating a complete map of regulatory systems to elucidate tumor mechanisms. In particular, newly developed unicellular proteomics could be used to determine the microenvironment and heterogeneity. In addition, a large scale of differentiated proteins provides more ways to precisely classify tumor subtypes and construct a larger library for biomarkers and biotargets, especially for immunotherapy. A series of advanced proteomic studies have been devoted to the different aspects of immunotherapy for glioma, including monoclonal antibodies, oncolytic viruses, dendritic cell (DC) vaccines, and chimeric antigen receptor (CAR) T cells. Thus, the application of proteomics in immunotherapy may accelerate research on the treatment of glioblastoma. In this review, we evaluate the frontline applications of proteomics strategies for immunotherapy in glioblastoma research.

Open Science Resources for the Mass Spectrometry-Based Analysis of SARS-CoV-2

The SARS-CoV-2 virus is the causative agent of the 2020 pandemic leading to the COVID-19 respiratory disease. With many scientific and humanitarian efforts ongoing to develop diagnostic tests, vaccines, and treatments for COVID-19, and to prevent the spread of SARS-CoV-2, mass spectrometry research, including proteomics, is playing a role in determining the biology of this viral infection. Proteomics studies are starting to lead to an understanding of the roles of viral and host proteins during SARS-CoV-2 infection, their protein-protein interactions, and post-translational modifications. This is beginning to provide insights into potential therapeutic targets or diagnostic strategies that can be used to reduce the long-term burden of the pandemic. However, the extraordinary situation caused by the global pandemic is also highlighting the need to improve mass spectrometry data and workflow sharing. We therefore describe freely available data and computational resources that can facilitate and assist the mass spectrometry-based analysis of SARS-CoV-2. We exemplify this by reanalyzing a virus-host interactome data set to detect protein-protein interactions and identify host proteins that could potentially be used as targets for drug repurposing.

An Algorithm to Improve the Speed of Semi and Non-Specific Enzyme Searches in Proteomics

Background

The identification of non-specifically cleaved peptides in proteomics and peptidomics poses a significant computational challenge. Current strategies for the identification of such peptides are typically time consuming and hinder routine data analysis.

Objective

We aimed to design an algorithm that would improve the speed of semi- and non-specific enzyme searches and could be applicable to existing search programs.

Method

We developed a novel search algorithm that leverages fragment-ion redundancy to simultaneously search multiple non-specifically cleaved peptides at once. Briefly, a theoretical peptide tandem mass spectrum is generated using only the fragment-ion series from a single terminus. This spectrum serves as a proxy for several shorter theoretical peptides sharing the same terminus. After database searching, amino acids are removed from the opposing terminus until the observed and theoretical precursor masses match within a given mass tolerance.

Results

The algorithm was implemented in the search program MetaMorpheus and found to perform an order of magnitude faster than the traditional MetaMorpheus search and produce superior results.

Conclusion

We report a speedy non-specific enzyme search algorithm which is open-source and enables search programs to utilize fragment-ion redundancy to achieve a notable increase in search speed.

Enhancing Open Modification Searches via a Combined Approach Facilitated by Ursgal

The identification of peptide sequences and their post-translational modifications (PTMs) is a crucial step in the analysis of bottom-up proteomics data. The recent development of open modification search (OMS) engines allows virtually all PTMs to be searched for. This not only increases the number of spectra that can be matched to peptides but also greatly advances the understanding of the biological roles of PTMs through the identification, and the thereby facilitated quantification, of peptidoforms (peptide sequences and their potential PTMs). Whereas the benefits of combining results from multiple protein database search engines have been previously established, similar approaches for OMS results have been missing so far. Here we compare and combine results from three different OMS engines, demonstrating an increase in peptide spectrum matches of 8-18%. The unification of search results furthermore allows for the combined downstream processing of search results, including the mapping to potential PTMs. Finally, we test for the ability of OMS engines to identify glycosylated peptides. The implementation of these engines in the Python framework Ursgal facilitates the straightforward application of the OMS with unified parameters and results files, thereby enabling yet unmatched high-throughput, large-scale data analysis.

Misincorporation Proteomics Technologies: A Review

Proteinopathies are diseases caused by factors that affect proteoform conformation. As such, a prevalent hypothesis is that the misincorporation of noncanonical amino acids into a proteoform results in detrimental structures. However, this hypothesis is missing proteomic evidence, specifically the detection of a noncanonical amino acid in a peptide sequence. This review aims to outline the current state of technology that can be used to investigate mistranslations and misincorporations whilst framing the pursuit as Misincorporation Proteomics (MiP). The current availability of technologies explored herein is mass spectrometry, sample enrichment/preparation, data analysis techniques, and the hyphenation of approaches. While many of these technologies show potential, our review reveals a need for further development and refinement of approaches is still required.

Free Open Source Software for Protein and Peptide Mass Spectrometry- based Science

In the field of biology, and specifically in protein and peptide science, the power of mass spectrometry is that it is applicable to a vast spectrum of applications. Mass spectrometry can be applied to identify proteins and peptides in complex mixtures, to identify and locate post-translational modifications, to characterize the structure of proteins and peptides to the most detailed level or to detect protein-ligand non-covalent interactions. Thanks to the Free and Open Source Software (FOSS) movement, scientists have limitless opportunities to deepen their skills in software development to code software that solves mass spectrometric data analysis problems. After the conversion of raw data files into open standard format files, the entire spectrum of data analysis tasks can now be performed integrally on FOSS platforms, like GNU/Linux, and only with FOSS solutions. This review presents a brief history of mass spectrometry open file formats and goes on with the description of FOSS projects that are commonly used in protein and peptide mass spectrometry fields of endeavor: identification projects that involve mostly automated pipelines, like proteomics and peptidomics, and bio-structural characterization projects that most often involve manual scrutiny of the mass data. Projects of the last kind usually involve software that allows the user to delve into the mass data in an interactive graphics-oriented manner. Software projects are thus categorized on the basis of these criteria: software libraries for software developers vs desktop-based graphical user interface, software for the end-user and automated pipeline-based data processing vs interactive graphics-based mass data scrutiny.

Proteomic approaches to drive advances in helminth extracellular vesicle research

Helminths can interact with their hosts in many different ways, including through the secretion of soluble molecules (such as lipids, glycans and proteins) and extracellular vesicles (EVs). The field of helminth secreted EVs has significantly advanced in recent years, mainly due to the molecular characterisation of EV proteomes and research highlighting the potential of EVs and their constituent molecules in the diagnosis and control of parasitic infections. Despite these advancements, the lack of appropriate isolation and purification methods is impeding the discovery of suitable biomarkers for the differentiation of helminth EV populations. In the present review we offer our viewpoint on the different proteomic techniques and approaches that have been developed, as well as solutions to common pitfalls and challenges that could be applied to advance the study of helminth EVs.

Fast Deisotoping Algorithm and Its Implementation in the MSFragger Search Engine

Deisotoping, or the process of removing peaks in a mass spectrum resulting from the incorporation of naturally occurring heavy isotopes, has long been used to reduce complexity and improve the effectiveness of spectral annotation methods in proteomics. We have previously described MSFragger, an ultrafast search engine for proteomics, that did not utilize deisotoping in processing input spectra. Here, we present a new, high-speed parallelized deisotoping algorithm, based on elements of several existing methods, that we have incorporated into the MSFragger search engine. Applying deisotoping with MSFragger reveals substantial improvements to database search speed and performance, particularly for complex methods like open or nonspecific searches. Finally, we evaluate our deisotoping method on data from several instrument types and vendors, revealing a wide range in performance and offering an updated perspective on deisotoping in the modern proteomics environment.

PTM-Shepherd: Analysis and Summarization of Post-Translational and Chemical Modifications From Open Search Results

Open searching has proven to be an effective strategy for identifying both known and unknown modifications in shotgun proteomics experiments. Rather than being limited to a small set of user-specified modifications, open searches identify peptides with any mass shift that may correspond to a single modification or a combination of several modifications. Here we present PTM-Shepherd, a bioinformatics tool that automates characterization of post-translational modification profiles detected in open searches based on attributes, such as amino acid localization, fragmentation spectra similarity, retention time shifts, and relative modification rates. PTM-Shepherd can also perform multiexperiment comparisons for studying changes in modification profiles, e.g., in data generated in different laboratories or under different conditions. We demonstrate how PTM-Shepherd improves the analysis of data from formalin-fixed and paraffin-embedded samples, detects extreme underalkylation of cysteine in some data sets, discovers an artifactual modification introduced during peptide synthesis, and uncovers site-specific biases in sample preparation artifacts in a multicenter proteomics profiling study.

Meta-heterogeneity: Evaluating and Describing the Diversity in Glycosylation Between Sites on the Same Glycoprotein

Mass spectrometry-based glycoproteomics has gone through some incredible developments over the last few years. Technological advances in glycopeptide enrichment, fragmentation methods, and data analysis workflows have enabled the transition of glycoproteomics from a niche application, mainly focused on the characterization of isolated glycoproteins, to a mature technology capable of profiling thousands of intact glycopeptides at once. In addition to numerous biological discoveries catalyzed by the technology, we are also observing an increase in studies focusing on global protein glycosylation and the relationship between multiple glycosylation sites on the same protein. It has become apparent that just describing protein glycosylation in terms of micro- and macro-heterogeneity, respectively, the variation and occupancy of glycans at a given site, is not sufficient to describe the observed interactions between sites. In this perspective we propose a new term, meta-heterogeneity, to describe a higher level of glycan regulation: the variation in glycosylation across multiple sites of a given protein. We provide literature examples of extensive meta-heterogeneity on relevant proteins such as antibodies, erythropoietin, myeloperoxidase, and a number of serum and plasma proteins. Furthermore, we postulate on the possible biological reasons and causes behind the intriguing meta-heterogeneity observed in glycoproteins.

FLEXIQuant-LF to quantify protein modification extent in label-free proteomics data

Improvements in LC-MS/MS methods and technology have enabled the identification of thousands of modified peptides in a single experiment. However, protein regulation by post-translational modifications (PTMs) is not binary, making methods to quantify the modification extent crucial to understanding the role of PTMs. Here, we introduce FLEXIQuant-LF, a software tool for large-scale identification of differentially modified peptides and quantification of their modification extent without knowledge of the types of modifications involved. We developed FLEXIQuant-LF using label-free quantification of unmodified peptides and robust linear regression to quantify the modification extent of peptides. As proof of concept, we applied FLEXIQuant-LF to data-independent-acquisition (DIA) data of the anaphase promoting complex/cyclosome (APC/C) during mitosis. The unbiased FLEXIQuant-LF approach to assess the modification extent in quantitative proteomics data provides a better understanding of the function and regulation of PTMs. The software is available at https://github.com/SteenOmicsLab/FLEXIQuantLF.

Novel Proteomic Profiling of Epididymal Extracellular Vesicles in the Domestic Cat Reveals Proteins Related to Sequential Sperm Maturation with Differences Observed between Normospermic and Teratospermic Individuals

Extracellular vesicles (EVs) secreted by the epididymal epithelium transfer to spermatozoa key proteins that are essential in promoting motility and subsequent fertilization success. Using the domestic cat model, the objectives were to (1) characterize and compare protein content of EVs between segments of the epididymis, and (2) compare EV protein compositions between normo- and teratospermic individuals (producing >60% of abnormal spermatozoa). Epididymal EVs from adult cats were isolated and assessed via liquid chromatography tandem MS. Both male types shared 3008 proteins in total, with 98 and 20 EV proteins unique to normospermic and teratospermic males, respectively. Expression levels of several proteins changed between epididymal segments in both male types. Several proteins in both groups were related to sperm motility (e.g. hexokinase 1, adenylate kinase isoenzyme) and zona pellucida or oolemma binding (e.g. disintegrin and metalloproteinase domain proteins, zona binding proteins 1 and 2). Interestingly, seven cauda-derived EV proteins trended downward in teratospermic compared with normospermic males, which may relate to poor sperm quality. Collective results revealed, for the first time, EV proteins related to sequential sperm maturation with differences observed between normospermic and teratospermic individuals.

Photocleavable Surfactant-Enabled Extracellular Matrix Proteomics

The extracellular matrix (ECM) provides an architectural meshwork that surrounds and supports cells. The dysregulation of heavily post-translationally modified ECM proteins directly contributes to various diseases. Mass spectrometry (MS)-based proteomics is an ideal tool to identify ECM proteins and characterize their post-translational modifications, but ECM proteomics remains challenging owing to the extremely low solubility of the ECM. Herein, enabled by effective solubilization of ECM proteins using our recently developed photocleavable surfactant, Azo, we have developed a streamlined ECM proteomic strategy that allows fast tissue decellularization, efficient extraction and enrichment of ECM proteins, and rapid digestion prior to reversed-phase liquid chromatography (RPLC)-MS analysis. A total of 173 and 225 unique ECM proteins from mouse mammary tumors have been identified using 1D and 2D RPLC-MS/MS, respectively. Moreover, 87 (from 1DLC-MS/MS) and 229 (from 2DLC-MS/MS) post-translational modifications of ECM proteins, including glycosylation, phosphorylation, and hydroxylation, were identified and localized. This Azo-enabled ECM proteomics strategy will streamline the analysis of ECM proteins and promote the study of ECM biology.

O-Pair Search with MetaMorpheus for O-glycopeptide characterization

We report O-Pair Search, an approach to identify O-glycopeptides and localize O-glycosites. Using paired collision- and electron-based dissociation spectra, O-Pair Search identifies O-glycopeptides via an ion-indexed open modification search and localizes O-glycosites using graph theory and probability-based localization. O-Pair Search reduces search times more than 2,000-fold compared to current O-glycopeptide processing software, while defining O-glycosite localization confidence levels and generating more O-glycopeptide identifications. Beyond the mucin-type O-glycopeptides discussed here, O-Pair Search also accepts user-defined glycan databases, making it compatible with many types of O-glycosylation. O-Pair Search is freely available within the open-source MetaMorpheus platform at https://github.com/smith-chem-wisc/MetaMorpheus .

Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco

Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale glycoproteomics datasets, but interpreting these data remains challenging. We present MSFragger-Glyco, a glycoproteomics mode of the MSFragger search engine, for fast and sensitive identification of N- and O-linked glycopeptides and open glycan searches. Reanalysis of recent N-glycoproteomics data resulted in annotation of 80% more glycopeptide spectrum matches (glycoPSMs) than previously reported. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same glycans as the original search, and yielded 4- to 6-fold increases when expanding searches to include additional glycan compositions and other modifications. Expanded searches also revealed many sulfated and complex glycans that remained hidden to the original search. With greatly improved spectral annotation, coupled with the speed of index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.

Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco

Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale glycoproteomics datasets, but interpreting these data remains challenging. We present MSFragger-Glyco, a glycoproteomics mode of the MSFragger search engine, for fast and sensitive identification of N- and O-linked glycopeptides and open glycan searches. Reanalysis of recent N-glycoproteomics data resulted in annotation of 80% more glycopeptide spectrum matches (glycoPSMs) than previously reported. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same glycans as the original search, and yielded 4- to 6-fold increases when expanding searches to include additional glycan compositions and other modifications. Expanded searches also revealed many sulfated and complex glycans that remained hidden to the original search. With greatly improved spectral annotation, coupled with the speed of index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.

Emerging mass spectrometry-based proteomics methodologies for novel biomedical applications

Research into the basic biology of human health and disease, as well as translational human research and clinical applications, all benefit from the growing accessibility and versatility of mass spectrometry (MS)-based proteomics. Although once limited in throughput and sensitivity, proteomic studies have quickly grown in scope and scale over the last decade due to significant advances in instrumentation, computational approaches, and bio-sample preparation. Here, we review these latest developments in MS and highlight how these techniques are used to study the mechanisms, diagnosis, and treatment of human diseases. We first describe recent groundbreaking technological advancements for MS-based proteomics, including novel data acquisition techniques and protein quantification approaches. Next, we describe innovations that enable the unprecedented depth of coverage in protein signaling and spatiotemporal protein distributions, including studies of post-translational modifications, protein turnover, and single-cell proteomics. Finally, we explore new workflows to investigate protein complexes and structures, and we present new approaches for protein-protein interaction studies and intact protein or top-down MS. While these approaches are only recently incipient, we anticipate that their use in biomedical MS proteomics research will offer actionable discoveries for the improvement of human health.

What Are We Missing by Using Hydrophilic Enrichment? Improving Bacterial Glycoproteome Coverage Using Total Proteome and FAIMS Analyses

Hydrophilic interaction liquid chromatography (HILIC) glycopeptide enrichment is an indispensable tool for the high-throughput characterization of glycoproteomes. Despite its utility, HILIC enrichment is associated with a number of shortcomings, including requiring large amounts of starting materials, potentially introducing chemical artifacts such as formylation when high concentrations of formic acid are used, and biasing/undersampling specific classes of glycopeptides. Here, we investigate HILIC enrichment-independent approaches for the study of bacterial glycoproteomes. Using three Burkholderia species (Burkholderia cenocepacia, Burkholderia Dolosa, and Burkholderia ubonensis), we demonstrate that short aliphatic O-linked glycopeptides are typically absent from HILIC enrichments, yet are readily identified in whole proteome samples. Using high-field asymmetric waveform ion mobility spectrometry (FAIMS) fractionation, we show that at high compensation voltages (CVs), short aliphatic glycopeptides can be enriched from complex samples, providing an alternative means to identify glycopeptide recalcitrant to hydrophilic-based enrichment. Combining whole proteome and FAIMS analyses, we show that the observable glycoproteome of these Burkholderia species is at least 25% larger than what was initially thought. Excitingly, the ability to enrich glycopeptides using FAIMS appears generally applicable, with the N-linked glycopeptides of Campylobacter fetus subsp. fetus also being enrichable at high FAIMS CVs. Taken together, these results demonstrate that FAIMS provides an alternative means to access glycopeptides and is a valuable tool for glycoproteomic analysis.

Assembly Mechanism of Mucin and von Willebrand Factor Polymers

The respiratory and intestinal tracts are exposed to physical and biological hazards accompanying the intake of air and food. Likewise, the vasculature is threatened by inflammation and trauma. Mucin glycoproteins and the related von Willebrand factor guard the vulnerable cell layers in these diverse systems. Colon mucins additionally house and feed the gut microbiome. Here, we present an integrated structural analysis of the intestinal mucin MUC2. Our findings reveal the shared mechanism by which complex macromolecules responsible for blood clotting, mucociliary clearance, and the intestinal mucosal barrier form protective polymers and hydrogels. Specifically, cryo-electron microscopy and crystal structures show how disulfide-rich bridges and pH-tunable interfaces control successive assembly steps in the endoplasmic reticulum and Golgi apparatus. Remarkably, a densely O-glycosylated mucin domain performs an organizational role in MUC2. The mucin assembly mechanism and its adaptation for hemostasis provide the foundation for rational manipulation of barrier function and coagulation.

O-Pair Search with MetaMorpheus for O-glycopeptide characterization

We report O-Pair Search, a new approach to identify O-glycopeptides and localize O-glycosites. Using paired collision- and electron-based dissociation spectra, O-Pair Search identifies O-glycopeptides using an ion-indexed open modification search and localizes O-glycosites using graph theory and probability-based localization. O-Pair Search reduces search times more than 2,000-fold compared to current O-glycopeptide processing software, while defining O-glycosite localization confidence levels and generating more O-glycopeptide identifications. Beyond the mucin-type O-glycopeptides discussed here, O-Pair Search also accepts user-defined glycan databases, making it compatible with many types of O-glycosylation. O-Pair Search is freely available within the open-source MetaMorpheus platform at https://github.com/smith-chem-wisc/MetaMorpheus.

Construction of Human Proteoform Families from 21 Tesla Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Top-Down Proteomic Data

Identification of proteoforms, the different forms of a protein, is important to understand biological processes. A proteoform family is the set of different proteoforms from the same gene. We previously developed the software program Proteoform Suite, which constructs proteoform families and identifies proteoforms by intact-mass analysis. Here, we have applied this approach to top-down proteomic data acquired at the National High Magnetic Field Laboratory 21 tesla Fourier transform ion cyclotron resonance mass spectrometer (data available on the MassIVE platform with identifier MSV000085978). We explored the ability to construct proteoform families and identify proteoforms from the high mass accuracy data that this instrument provides for a complex cell lysate sample from the MCF-7 human breast cancer cell line. There were 2830 observed experimental proteforms, of which 932 were identified, 44 were ambiguous, and 1854 were unidentified. Of the 932 unique identified proteoforms, 766 were identified by top-down MS2 analysis at 1% false discovery rate (FDR) using TDPortal, and 166 were additional intact-mass identifications (∼4.7% calculated global FDR) made using Proteoform Suite. We recently published a proteoform level schema to represent ambiguity in proteoform identifications. We implemented this proteoform level classification in Proteoform Suite for intact-mass identifications, which enables users to determine the ambiguity levels and sources of ambiguity for each intact-mass proteoform identification.

Spritz: A Proteogenomic Database Engine

Proteoforms are the workhorses of the cell, and subtle differences between their amino acid sequences or post-translational modifications (PTMs) can change their biological function. To most effectively identify and quantify proteoforms in genetically diverse samples by mass spectrometry (MS), it is advantageous to search the MS data against a sample-specific protein database that is tailored to the sample being analyzed, in that it contains the correct amino acid sequences and relevant PTMs for that sample. To this end, we have developed Spritz (https://smith-chem-wisc.github.io/Spritz/), an open-source software tool for generating protein databases annotated with sequence variations and PTMs. We provide a simple graphical user interface for Windows and scripts that can be run on any operating system. Spritz automatically sets up and executes approximately 20 tools, which enable the construction of a proteogenomic database from only raw RNA sequencing data. Sequence variations that are discovered in RNA sequencing data upon comparison to the Ensembl reference genome are annotated on proteins in these databases, and PTM annotations are transferred from UniProt. Modifications can also be discovered and added to the database using bottom-up mass spectrometry data and global PTM discovery in MetaMorpheus. We demonstrate that such sample-specific databases allow the identification of variant peptides, modified variant peptides, and variant proteoforms by searching bottom-up and top-down proteomic data from the Jurkat human T lymphocyte cell line and demonstrate the identification of phosphorylated variant sites with phosphoproteomic data from the U2OS human osteosarcoma cell line.

Diagenetiforms: A new term to explain protein changes as a result of diagenesis in paleoproteomics

The term proteoform describes all combinations of change in a protein, as elucidated through intact mass proteomics. Paleoproteomic studies have begun using digestion-free and top-down techniques to access information from ancient and historical remains. However, to discuss protein changes that uniquely occur to archaeological and paleontological proteomes as the result of diagenesis (i.e., physical and chemical change imparted by burial), a novel term is needed that both addresses issues of combinatorics and distinguishes diagenetic-specific alteration. SIGNIFICANCE: The term diagenetiform provides the opportunity to communicate clearly the sets of diagenetic changes found on preserved proteins. The diagenetiform nomenclature will allow for top-down paleoproteomic studies to accurately describe the total changes detected on ancient proteins.

Mass Spectrometry-Based Structural Analysis of Cysteine-Rich Metal-Binding Sites in Proteins with MetaOdysseus R Software

Identification of metal-binding sites in proteins and understanding metal-coupled protein folding mechanisms are aspects of high importance for the structure-to-function relationship. Mass spectrometry (MS) has brought a powerful adjunct perspective to structural biology, obtaining from metal-to-protein stoichiometry to quaternary structure information. Currently, the different experimental and/or instrumental setups usually require the use of multiple data analysis software, and in some cases, they lack some of the main data analysis steps (MS processing, scoring, identification). Here, we present a comprehensive data analysis pipeline that addresses charge-state deconvolution, statistical scoring, and mass assignment for native MS, bottom-up, and native top-down with emphasis on metal–protein complexes. We have evaluated all of the approaches using assemblies of increasing complexity, including free and chemically labeled proteins, from low- to high-resolution MS. In all cases, the results have been compared with common software and proved how MetaOdysseus outperformed them.

Chemical effects of diceCT staining protocols on fluid-preserved avian specimens

Diffusible iodine-based contrast-enhanced computed tomography (diceCT) techniques allow visualization of soft tissues of fluid-preserved specimens in three dimensions without dissection or histology. Two popular diceCT stains, iodine-potassium iodide (I2KI) dissolved in water and elemental iodine (I2) dissolved in 100% ethanol (EtOH), yield striking results. Despite the widespread use of these stains in clinical and biological fields, the molecular mechanisms that result in color change and radiopacity attributed to iodine staining are poorly understood. Requests to apply these stains to anatomical specimens preserved in natural history museums are increasing, yet curators have little information about the potential for degradation of treated specimens. To assess the molecular effects of iodine staining on typical museum specimens, we compared the two popular stains and two relatively unexplored stains (I2KI in 70% EtOH, I2 in 70% EtOH). House sparrows (Passer domesticus) were collected and preserved under uniform conditions following standard museum protocols, and each was then subjected to one of the stains. Results show that the three ethanol-based stains worked equally well (producing fully stained, life-like, publication quality scans) but in different timeframes (five, six, or eight weeks). The specimen in I2KI in water became degraded in physical condition, including developing flexible, demineralized bones. The ethanol-based methods also resulted in some demineralization but less than the water-based stain. The pH of the water-based stain was notably acidic compared to the water used as solvent in the stain. Our molecular analyses indicate that whereas none of the stains resulted in unacceptable levels of protein degradation, the bones of a specimen stained with I2KI in water demineralized throughout the staining process. We conclude that staining with I2KI or elemental I2 in 70% EtOH can yield high-quality soft-tissue visualization in a timeframe that is similar to that of better-known iodine-based stains, with lower risk of negative impacts on specimen condition.

Open Database Searching Enables the Identification and Comparison of Bacterial Glycoproteomes without Defining Glycan Compositions Prior to Searching

Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach using N-linked glycopeptides of Campylobacter fetus subsp. fetus as well as O-linked glycopeptides of Acinetobacter baumannii and Burkholderia cenocepacia revealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.

Open Database Searching Enables the Identification and Comparison of Bacterial Glycoproteomes without Defining Glycan Compositions Prior to Searching

Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach using N-linked glycopeptides of Campylobacter fetus subsp. fetus as well as O-linked glycopeptides of Acinetobacter baumannii and Burkholderia cenocepacia revealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.

PeptideWitch-A Software Package to Produce High-Stringency Proteomics Data Visualizations from Label-Free Shotgun Proteomics Data

PeptideWitch is a python-based web module that introduces several key graphical and technical improvements to the Scrappy software platform, which is designed for label-free quantitative shotgun proteomics analysis using normalised spectral abundance factors. The program inputs are low stringency protein identification lists output from peptide-to-spectrum matching search engines for ‘control’ and ‘treated’ samples. Through a combination of spectral count summation and inner joins, PeptideWitch processes low stringency data, and outputs high stringency data that are suitable for downstream quantitation. Data quality metrics are generated, and a series of statistical analyses and graphical representations are presented, aimed at defining and presenting the difference between the two sample proteomes.

Identification of modified peptides using localization-aware open search

Identification of post-translationally or chemically modified peptides in mass spectrometry-based proteomics experiments is a crucial yet challenging task. We have recently introduced a fragment ion indexing method and the MSFragger search engine to empower an open search strategy for comprehensive analysis of modified peptides. However, this strategy does not consider fragment ions shifted by unknown modifications, preventing modification localization and limiting the sensitivity of the search. Here we present a localization-aware open search method, in which both modification-containing (shifted) and regular fragment ions are indexed and used in scoring. We also implement a fast mass calibration and optimization method, allowing optimization of the mass tolerances and other key search parameters. We demonstrate that MSFragger with mass calibration and localization-aware open search identifies modified peptides with significantly higher sensitivity and accuracy. Comparing MSFragger to other modification-focused tools (pFind3, MetaMorpheus, and TagGraph) shows that MSFragger remains an excellent option for fast, comprehensive, and sensitive searches for modified peptides in shotgun proteomics data.

Improving Proteoform Identifications in Complex Systems Through Integration of Bottom-Up and Top-Down Data

Cellular functions are performed by a vast and diverse set of proteoforms. Proteoforms are the specific forms of proteins produced as a result of genetic variations, RNA splicing, and post-translational modifications (PTMs). Top-down mass spectrometric analysis of intact proteins enables proteoform identification, including proteoforms derived from sequence cleavage events or harboring multiple PTMs. In contrast, bottom-up proteomics identifies peptides, which necessitates protein inference and does not yield proteoform identifications. We seek here to exploit the synergies between these two data types to improve the quality and depth of the overall proteomic analysis. To this end, we automated the large-scale integration of results from multiprotease bottom-up and top-down analyses in the software program Proteoform Suite and applied it to the analysis of proteoforms from the human Jurkat T lymphocyte cell line. We implemented the recently developed proteoform-level classification scheme for top-down tandem mass spectrometry (MS/MS) identifications in Proteoform Suite, which enables users to observe the level and type of ambiguity for each proteoform identification, including which of the ambiguous proteoform identifications are supported by bottom-up-level evidence. We used Proteoform Suite to find instances where top-down identifications aid in protein inference from bottom-up analysis and conversely where bottom-up peptide identifications aid in proteoform PTM localization. We also show the use of bottom-up data to infer proteoform candidates potentially present in the sample, allowing confirmation of such proteoform candidates by intact-mass analysis of MS1 spectra. The implementation of these capabilities in the freely available software program Proteoform Suite enables users to integrate large-scale top-down and bottom-up data sets and to utilize the synergies between them to improve and extend the proteomic analysis.

Identification and in silico bioinformatics analysis of PR10 proteins in cashew nut

Proteins from cashew nut can elicit mild to severe allergic reactions. Three allergenic proteins have already been identified, and it is expected that additional allergens are present in cashew nut. pathogenesis-related protein 10 (PR10) allergens from pollen have been found to elicit similar allergic reactions as those from nuts and seeds. Therefore, we investigated the presence of PR10 genes in cashew nut. Using RNA-seq analysis, we were able to identify several PR10-like transcripts in cashew nut and clone six putative PR10 genes. In addition, PR10 protein expression in raw cashew nuts was confirmed by immunoblotting and liquid chromatography-mass spectrometry (LC-MS/MS) analyses. An in silico allergenicity assessment suggested that all identified cashew PR10 proteins are potentially allergenic and may represent three different isoallergens.

Computational methods in mass spectrometry-based structural proteomics for studying protein structure, dynamics, and interactions

Mass spectrometry (MS) has made enormous contributions to comprehensive protein identification and quantification in proteomics. MS is also gaining momentum for structural biology in a variety of ways, complementing conventional structural biology techniques. Here, we will review how MS-based techniques, such as hydrogen/deuterium exchange, covalent labeling, and chemical cross-linking, enable the characterization of protein structure, dynamics, and interactions, especially from a perspective of their data analyses. Structural information encoded by chemical probes in intact proteins is decoded by interpreting MS data at a peptide level, i.e., revealing conformational and dynamic changes in local regions of proteins. The structural MS data are not amenable to data analyses in traditional proteomics workflow, requiring dedicated software for each type of data. We first provide basic principles of data interpretation, including isotopic distribution and peptide sequencing. We then focus particularly on computational methods for structural MS data analyses and discuss outstanding challenges in a proteome-wide large scale analysis.