Cleaning out the Litterbox of Proteomic Scientists' Favorite Pet: Optimized Data Analysis Avoiding Trypsin Artifacts

Veneer Is a Webtool for Rapid, Standardized, and Transparent Interpretation, Annotation, and Reporting of Mammalian Cell Surface N-Glycocapture Data

Currently, no consensus exists regarding criteria required to designate a protein within a proteomic data set as a cell surface protein. Most published proteomic studies rely on varied ontology annotations or computational predictions instead of experimental evidence when attributing protein localization. Consequently, standardized approaches for analyzing and reporting cell surface proteome data sets would increase confidence in localization claims and promote data use by other researchers. Recently, we developed Veneer, a web-based bioinformatic tool that analyzes results from cell surface N-glycocapture workflows─the most popular cell surface proteomics method used to date that generates experimental evidence of subcellular location. Veneer assigns protein localization based on defined experimental and bioinformatic evidence. In this study, we updated the criteria and process for assigning protein localization and added new functionality to Veneer. Results of Veneer analysis of 587 cell surface N-glycocapture data sets from 32 published studies demonstrate the importance of applying defined criteria when analyzing cell surface proteomics data sets and exemplify how Veneer can be used to assess experimental quality and facilitate data extraction for informing future biological studies and annotating public repositories.

Fecal-adherent mucus is a non-invasive source of primary human MUC2 for structural and functional characterization in health and disease

The O-glycoprotein Mucin-2 (MUC2) forms the protective colon mucus layer. While animal models have demonstrated the importance of Muc2, few studies have explored human MUC2 in similar depth. Recent studies have revealed that secreted MUC2 is bound to human feces. We hypothesized human fecal MUC2 (HF-MUC2) was accessible for purification and downstream structural and functional characterization. We tested this via histologic and quantitative imaging on human fecal sections; extraction from feces for proteomic and O-glycomic characterization; and functional studies via growth and metabolic assays in vitro. Quantitative imaging of solid fecal sections showed a continuous mucus layer of varying thickness along human fecal sections with barrier functions intact. Lectin profiling showed HF-MUC2 bound several lectins but was weak to absent for Ulex europaeus 1 (α1,2 fucose-binding) and Sambucus nigra agglutinin (α2,6 sialic acid-binding), and did not have obvious b1/b2 barrier layers. HF-MUC2 separated by electrophoresis showed high molecular weight glycoprotein bands (∼1-2 MDa). Proteomics and Western analysis confirmed the enrichment of MUC2 and potential MUC2-associated proteins in HF-MUC2 extracts. MUC2 O-glycomics revealed diverse fucosylation, moderate sialylation, and little sulfation versus porcine colonic MUC2 and murine fecal Muc2. O-glycans were functional and supported the growth of Bacteroides thetaiotaomicron (B. theta) and short-chain fatty acid (SCFA) production in vitro. MUC2 could be similarly analyzed from inflammatory bowel disease stools, which displayed an altered glycomic profile and differential growth and SCFA production by B. theta versus healthy samples. These studies describe a new non-invasive platform for human MUC2 characterization in health and disease.

Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023-10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.

Mapping the Arabidopsis thaliana proteome in PeptideAtlas and the nature of the unobserved (dark) proteome; strategies towards a complete proteome

This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected PTMs, and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for building the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome - the 'dark' proteome. This dark proteome is highly enriched for certain ( e.g. CLE, CEP, IDA, PSY) but not other ( e.g. THIONIN, CAP,) signaling peptides families, E3 ligases, TFs, and other proteins with unfavorable physicochemical properties. A machine learning model trained on RNA expression data and protein properties predicts the probability for proteins to be detected. The model aids in discovery of proteins with short-half life ( e.g. SIG1,3 and ERF-VII TFs) and completing the proteome. PeptideAtlas is linked to TAIR, JBrowse, PPDB, SUBA, UniProtKB and Plant PTM Viewer.

Electrochemical biosensor for trypsin activity assay based on cleavage of immobilized tyrosine-containing peptide

In this work, we proposed a biosensor for trypsin proteolytic activity assay using immobilization of model peptides on screen-printed electrodes (SPE) modified with gold nanoparticles (AuNPs) prepared by electrosynthetic method. Sensing of proteolytic activity was based on electrochemical oxidation of tyrosine residues of peptides. We designed peptides containing N-terminal cysteine residue for immobilization on an SPE, modified with gold nanoparticles, trypsin-specific cleavage site and tyrosine residue as a redox label. The peptides were immobilized on SPE by formation of chemical bonds between mercapto groups of the N-terminal cysteine residues and AuNPs. After the incubation with trypsin, time-dependent cleavage of the immobilized peptides was observed by decline in tyrosine electrochemical oxidation signal. The kinetic parameters of trypsin, such as the catalytic constant (k_cat), the Michaelis constant (K_M) and the catalytic efficiency (k_cat/K_M), toward the CGGGRYR peptide were determined as 0.33 ± 0.01 min^-1, 198 ± 24 nM and 0.0016 min^-1 nM^-1, respectively. Using the developed biosensor, we demonstrated the possibility of analysis of trypsin specificity toward the peptides with amino acid residues disrupting proteolysis. Further, we designed the peptides with proline or glutamic acid residues after the cleavage site (CGGRPYR and CGGREYR), and trypsin had reduced activity toward both of them according to the existing knowledge of the enzyme specificity. The developed biosensor system allows one to perform a comparative analysis of the protease steady-state kinetic parameters and specificity toward model peptides with different amino acid sequences.

A Pipeline for Peptide Detection Using Multiple Decoys

Target-decoy competition has been commonly used for over a decade to control the false discovery rate when analyzing tandem mass spectrometry (MS/MS) data. We recently developed a framework that uses multiple decoys to increase the number of detected peptides in MS/MS data. Here, we present a pipeline of Apache licensed, open-source software that allows the user to readily take advantage of our framework.

MS Amanda 2.0: Advancements in the standalone implementation

RATIONALE

Database search engines are the preferred method to identify peptides in mass spectrometry data. However, valuable software is in this context not only defined by a powerful algorithm to separate correct from false identifications, but also by constant maintenance and continuous improvements.

METHODS

In 2014, we presented our peptide identification algorithm MS Amanda, showing its suitability for identifying peptides in high-resolution tandem mass spectrometry data and its ability to outperform widely used tools to identify peptides. Since then, we have continuously worked on improvements to enhance its usability and to support new trends and developments in this fast-growing field, while keeping the original scoring algorithm to assess the quality of a peptide spectrum match unchanged.

RESULTS

We present the outcome of these efforts, MS Amanda 2.0, a faster and more flexible standalone version with the original scoring algorithm. The new implementation has led to a 3-5× speedup, is able to handle new ion types and supports standard data formats. We also show that MS Amanda 2.0 works best when using only the most common ion types in a particular search instead of all possible ion types.

CONCLUSIONS

MS Amanda is available free of charge from https://ms.imp.ac.at/index.php?action=msamanda.

Taming the Huntington's Disease Proteome: What Have We Learned?

Mass spectrometry (MS) is a physical technique used to identify specific chemicals and molecules by precise analysis of their mass and charge; this technology has been adapted for biological sciences applications. Investigators have used MS to identify differential expressions of proteins in Huntington's disease (HD), to discover Huntingtin (HTT) interacting proteins and to analyze HTT proteoforms. Using systems biology and computational approaches, data from MS screens have been leveraged to find differentially expressed pathways. This review summarizes the data from most of the MS studies done in the HD field in the last 20 years and compares it to the protein data reported before the use of MS technology. The MS results validate early findings in the field such as differential expression of PDE10a and DARPP-32 and identify new changes. We offer a perspective on the MS approach in HD, particularly for identification of disease pathways, the challenges in interpreting data across different studies, and its application to protein studies moving forward.

Protein changes in synaptosomes of Huntington's disease knock-in mice are dependent on age and brain region

Molecular changes at synapses are thought to underly the deficits in motor and cognitive dysfunction seen in Huntington's disease (HD). Previously we showed in synaptosome preparations age dependent changes in levels of selected proteins examined by western blot assay in the striatum of Q140/Q140 HD mice. To assess if CAG repeat length influenced protein changes at the synapse, we examined synaptosomes from 6-month old heterozygote HD mice with CAG repeat lengths ranging from 50 to 175. Analysis of 19 selected proteins showed that increasing CAG repeat length in huntingtin (HTT) increased the number of affected proteins in HD striatal synaptosomes. Moreover, SDS-soluble total HTT (WT plus mutant HTT) and pThr3 HTT were reduced with increasing CAG repeat length, and there was no pSer421 mutant HTT detected in any HD mice. A LC-MS/MS and bioinfomatics study of synaptosomes from 2 and 6-month old striatum and cortex of Q140/Q7 HD mice showed enrichment of synaptic proteins and an influence of age, gender and brain region on the number of protein changes. HD striatum at 6 months had the most protein changes that included many HTT protein interactors, followed by 2-month old HD striatum, 2-month old HD cortex and 6-month HD cortex. SDS-insoluble mutant HTT was detected in HD striatal synaptosomes consistent with the presence of aggregates. Proteins changed in cortex differed from those in striatum. Pathways affected in HD striatal synaptosomes that were not identified in whole striatal lysates of the same HD mouse model included axon guidance, focal adhesion, neurotrophin signaling, regulation of actin cytoskeleton, endocytosis, and synaptic vesicle cycle. Results suggest that synaptosomes prepared from HD mice are highly informative for monitoring protein changes at the synapse and may be preferred for assessing the effects of experimental therapies on synaptic function in HD.

Is There a Trojan Horse to Aggressive Pancreatic Cancer Biology? A Review of the Trypsin-PAR2 Axis to Proliferation, Early Invasion, and Metastasis

Purpose: Pancreatic cancer is one of the most lethal of solid tumors and is associated with aggressive cancer biology. The purpose is to review the role of trypsin and effect on molecular and cellular processes potentially explaining the aggressive biology in pancreatic cancer.

Methods: A narrative literature review of studies investigating trypsin and its effect on protease systems in cancer, with special reference to pancreatic cancer biology.

Results: Proteases, such as trypsin, provides a significant advantage to developing tumors through the ability to remodel the extracellular matrix, promote cell invasion and migration, and facilitate angiogenesis. Trypsin is a digestive enzyme produced by the exocrine pancreas that is also related to mechanisms of proliferation, invasion and metastasis. Several of these mechanisms may be co-regulated or influenced by activation of proteinase-activated receptor 2 (PAR-2). The current role in pancreatic cancer is not clear but emerging data suggest several potential mechanisms. Trypsin may act as a Trojan horse in the pancreatic gland, facilitating several molecular pathways from the onset, which leads to rapid progression of the disease. Pancreatic cancer cell lines containing PAR-2 proliferate upon exposure to trypsin, whereas cancer cell lines not containing PAR-2 fail to proliferate upon trypsin expression. Several mechanisms of action include a proinflammatory environment, signals inducing proliferation and migration, and direct and indirect evidence for mechanisms promoting invasion and metastasis. Novel techniques (such as organoid models) and increased understanding of mechanisms (such as the microbiome) may yield improved understanding into the role of trypsin in pancreatic carcinogenesis.

Conclusion: Trypsin is naturally present in the pancreatic gland and may experience pathological activation intracellularly and in the neoplastic environment, which speeds up molecular mechanisms of proliferation, invasion, and metastasis. Further investigation of these processes will provide important insights into how pancreatic cancer evolves, and suggest new ways for treatment.

Irreversible oxidative post-translational modifications in heart disease

Introduction: Development of specific biomarkers aiding early diagnosis of heart failure is an ongoing challenge. Biomarkers commonly used in clinical routine usually act as readouts of an already existing acute condition rather than disease initiation. Functional decline of cardiac muscle is greatly aggravated by increased oxidative stress and damage of proteins. Oxidative post-translational modifications occur already at early stages of tissue damage and are thus regarded as potential up-coming disease markers. Areas covered: Clinical practice regarding commonly used biomarkers for heart disease is briefly summarized. The types of oxidative post-translational modification in cardiac pathologies are discussed with a special focus on available quantitative techniques and characteristics of individual modifications with regard to their stability and analytical accessibility. As irreversible oxidative modifications trigger protein degradation pathways or cause protein aggregation, both influencing biomarker abundance, a chapter is dedicated to their regulation in the heart.

Enhanced trypsin on a budget: Stabilization, purification and high-temperature application of inexpensive commercial trypsin for proteomics applications

Trypsin is by far the most commonly used protease in proteomics. Even though the amount of protease used in each experiment is very small, digestion of large amounts of protein prior to enrichment can be rather costly. The price of commercial trypsin is highly dependent on the quality of the enzyme, which is determined by its purity, activity, and chemical modifications. In this study we evaluated several strategies for improving the quality of crude trypsin by reductive methylation and affinity purification. We present a protocol applicable to most proteomics laboratories for obtaining a highly stable and pure trypsin preparation using reductive methylation and purification by benzamidine-sepharose. The entire workflow can be performed within a day and yields ~4 mg per batch but is completely scalable. The methylated product was benchmarked against sequencing grade trypsin from Promega and they were found to be comparable for one hour digestions at elevated temperatures, where residual chymotryptic activity was found to be negligible.

Morph-specific protein patterns in the femoral gland secretions of a colour polymorphic lizard

Colour polymorphism occurs when two or more genetically-based colour morphs permanently coexist within an interbreeding population. Colouration is usually associated to other life-history traits (ecological, physiological, behavioural, reproductive …) of the bearer, thus being the phenotypic marker of such set of genetic features. This visual badge may be used to inform conspecifics and to drive those decision making processes which may contribute maintaining colour polymorphism under sexual selection context. The importance of such information suggests that other communication modalities should be recruited to ensure its transfer in case visual cues were insufficient. Here, for the first time, we investigated the potential role of proteins from femoral gland secretions in signalling colour morph in a polymorphic lizard. As proteins are thought to convey identity-related information, they represent the ideal cues to build up the chemical modality used to badge colour morphs. We found strong evidence for the occurrence of morph-specific protein profiles in the three main colour-morphs of the common wall lizard, which showed both qualitative and quantitative differences in protein expression. As lizards are able to detect proteins by tongue-flicking and vomeronasal organ, this result support the hypothesis that colour polymorphic lizards may use a multimodal signal to inform about colour-morph.

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

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

Quantification of Cellular Folate Species by LC-MS after Stabilization by Derivatization

Folate cofactors play a key role in one-carbon metabolism. Analysis of individual folate species is hampered by the low chemical stability and high interconvertibility of folates, which can lead to severe experimental bias. Here, we present a complete workflow that employs simultaneous extraction and stabilization of folates by derivatization. We perform reductive methylation employing stable isotope labeled reagents to retain information on the position and redox state of one-carbon units as well as the redox state of the pteridine ring. The derivatives are analyzed by a targeted LC(HILIC)-MS/MS method without the need for deconjugation, thereby also preserving the glutamation state of folates. The presented method does not only improve analyte coverage and sensitivity as compared to other published methods, it also greatly simplifies sample handling and storage. Finally, we report differences in the response of bacterial and mammalian systems to pharmacological inhibition of dihydrofolate reductase.

Resolution Ladder for High-Resolution Mass Spectrometry

High-resolution mass spectrometry has become a key technology in life sciences. Since it is often unfeasible to find pairs of analytes with an appropriate mass difference to actually quantify the resolution experimentally, resolution is usually calculated from the shape of a single mass peak. In this study we show that the commonly employed strategy yields a poor measure of true resolution since it does not account for interactions that take place between ions of very similar mass and might be further distorted by signal processing effects. We present a straightforward and easily adaptable method to create a ladder of mass pairs to experimentally quantify actual mass resolution over a wide m/z range, compare the experimental resolution to the single peak based calculated resolution, and demonstrate the applicability of mass resolution ladders to study interactions of similar ions in various types of widely used mass spectrometers.

Param-Medic: A Tool for Improving MS/MS Database Search Yield by Optimizing Parameter Settings

In shotgun proteomics analysis, user-specified parameters are critical to database search performance and therefore to the yield of confident peptide-spectrum matches (PSMs). Two of the most important parameters are related to the accuracy of the mass spectrometer. Precursor mass tolerance defines the peptide candidates considered for each spectrum. Fragment mass tolerance or bin size determines how close observed and theoretical fragments must be to be considered a match. For either of these two parameters, too wide a setting yields randomly high-scoring false PSMs, whereas too narrow a setting erroneously excludes true PSMs, in both cases, lowering the yield of peptides detected at a given false discovery rate. We describe a strategy for inferring optimal search parameters by assembling and analyzing pairs of spectra that are likely to have been generated by the same peptide ion to infer precursor and fragment mass error. This strategy does not rely on a database search, making it usable in a wide variety of settings. In our experiments on data from a variety of instruments including Orbitrap and Q-TOF acquisitions, this strategy yields more high-confidence PSMs than using settings based on instrument defaults or determined by experts. Param-Medic is open-source and cross-platform. It is available as a standalone tool ( http://noble.gs.washington.edu/proj/param-medic/ ) and has been integrated into the Crux proteomics toolkit ( http://crux.ms ), providing automatic parameter selection for the Comet and Tide search engines.

Infrared spectroscopic characterization of monocytic microvesicles (microparticles) released upon lipopolysaccharide stimulation

Microvesicles (MVs) are involved in cell-cell interactions, including disease pathogenesis. Nondestructive Fourier-transform infrared (FTIR) spectra from MVs were assessed as a technique to provide new biochemical insights into a LPS-induced monocyte model of septic shock. FTIR spectroscopy provided a quick method to investigate relative differences in biomolecular content of different MV populations that was complementary to traditional semiquantitative omics approaches, with which it is difficult to provide information on relative changes between classes (proteins, lipids, nucleic acids, carbohydrates) or protein conformations. Time-dependent changes were detected in biomolecular contents of MVs and in the monocytes from which they were released. Differences in phosphatidylcholine and phosphatidylserine contents were observed in MVs released under stimulation, and higher relative concentrations of RNA and α-helical structured proteins were present in stimulated MVs compared with MVs from resting cells. FTIR spectra of stimulated monocytes displayed changes that were consistent with those observed in the corresponding MVs they released. LPS-stimulated monocytes had reduced concentrations of nucleic acids, α-helical structured proteins, and phosphatidylcholine compared with resting monocytes but had an increase in total lipids. FTIR spectra of MV biomolecular content will be important in shedding new light on the mechanisms of MVs and the different roles they play in physiology and disease pathogenesis.-Lee, J., Wen, B., Carter, E. A., Combes, V., Grau, G. E. R., Lay, P. A. Infrared spectroscopic characterization of monocytic microvesicles (microparticles) released upon lipopolysaccharide stimulation.

PpEst is a novel PBAT degrading polyesterase identified by proteomic screening of Pseudomonas pseudoalcaligenes

A novel esterase, PpEst, that hydrolyses the co-aromatic-aliphatic polyester poly(1,4-butylene adipate-co-terephthalate) (PBAT) was identified by proteomic screening of the Pseudomonas pseudoalcaligenes secretome. PpEst was induced by the presence of PBAT in the growth media and had predicted arylesterase (EC 3.1.1.2) activity. PpEst showed polyesterase activity on both whole and milled PBAT film releasing terephthalic acid and 4-(4-hydroxybutoxycarbonyl)benzoic acid while end product inhibition by 4-(4-hydroxybutoxycarbonyl)benzoic acid was observed. Modelling of an aromatic polyester mimicking oligomer into the PpEst active site indicated that the binding pocket could be big enough to accommodate large polymers. This is the first report of a PBAT degrading enzyme being identified by proteomic screening and shows that this approach can contribute to the discovery of new polymer hydrolysing enzymes. Moreover, these results indicate that arylesterases could be an interesting enzyme class for identifications of polyesterases.

Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets

Mass spectrometry (MS) is the main technology used in proteomics approaches. However, on average 75% of spectra analysed in an MS experiment remain unidentified. We propose to use spectrum clustering at a large-scale to shed a light on these unidentified spectra. PRoteomics IDEntifications database (PRIDE) Archive is one of the largest MS proteomics public data repositories worldwide. By clustering all tandem MS spectra publicly available in PRIDE Archive, coming from hundreds of datasets, we were able to consistently characterize three distinct groups of spectra: 1) incorrectly identified spectra, 2) spectra correctly identified but below the set scoring threshold, and 3) truly unidentified spectra. Using a multitude of complementary analysis approaches, we were able to identify less than 20% of the consistently unidentified spectra. The complete spectrum clustering results are available through the new version of the PRIDE Cluster resource (http://www.ebi.ac.uk/pride/cluster). This resource is intended, among other aims, to encourage and simplify further investigation into these unidentified spectra.