Computational phosphoproteomics: from identification to localization

Computational approaches to identify sites of phosphorylation

Due to their oftentimes ambiguous nature, phosphopeptide positional isomers can present challenges in bottom-up mass spectrometry-based workflows as search engine scores alone are often not enough to confidently distinguish them. Additional scoring algorithms can remedy this by providing confidence metrics in addition to these search results, reducing ambiguity. Here we describe challenges to interpreting phosphoproteomics data and review several different approaches to determine sites of phosphorylation for both data-dependent and data-independent acquisition-based workflows. Finally, we discuss open questions regarding neutral losses, gas-phase rearrangement, and false localization rate estimation experienced by both types of acquisition workflows and best practices for managing ambiguity in phosphosite determination.

Phosphoproteomic Approaches for Identifying Phosphatase and Kinase Substrates

Protein phosphorylation is a ubiquitous post-translational modification controlled by the opposing activities of protein kinases and phosphatases, which regulate diverse biological processes in all kingdoms of life. One of the key challenges to a complete understanding of phosphoregulatory networks is the unambiguous identification of kinase and phosphatase substrates. Liquid chromatography-coupled mass spectrometry (LC-MS/MS) and associated phosphoproteomic tools enable global surveys of phosphoproteome changes in response to signaling events or perturbation of phosphoregulatory network components. Despite the power of LC-MS/MS, it is still challenging to directly link kinases and phosphatases to specific substrate phosphorylation sites in many experiments. Here, we survey common LC-MS/MS-based phosphoproteomic workflows for identifying protein kinase and phosphatase substrates, noting key advantages and limitations of each. We conclude by discussing the value of inducible degradation technologies coupled with phosphoproteomics as a new approach that overcomes some limitations of current methods for substrate identification of kinases, phosphatases, and other regulatory enzymes.

Shared and unique phosphoproteomics responses in skeletal muscle from exercise models and in hyperammonemic myotubes

Skeletal muscle generation of ammonia, an endogenous cytotoxin, is increased during exercise. Perturbations in ammonia metabolism consistently occur in chronic diseases, and may blunt beneficial skeletal muscle molecular responses and protein homeostasis with exercise. Phosphorylation of skeletal muscle proteins mediates cellular signaling responses to hyperammonemia and exercise. Comparative bioinformatics and machine learning-based analyses of published and experimentally derived phosphoproteomics data identified differentially expressed phosphoproteins that were unique and shared between hyperammonemic murine myotubes and skeletal muscle from exercise models. Enriched processes identified in both hyperammonemic myotubes and muscle from exercise models with selected experimental validation included protein kinase A (PKA), calcium signaling, mitogen-activated protein kinase (MAPK) signaling, and protein homeostasis. Our approach of feature extraction from comparative untargeted "omics" data allows for selection of preclinical models that recapitulate specific human exercise responses and potentially optimize functional capacity and skeletal muscle protein homeostasis with exercise in chronic diseases.

Integrated proteomics, phosphoproteomics and metabolomics analyses reveal similarities amongst giant cell granulomas of the jaws with different genetic mutations

BACKGROUND

Giant cell granuloma of the jaws are benign osteolytic lesions of the jaws. These lesions are genetically characterized by mutually exclusive somatic mutations at TRPV4, KRAS, and FGFR1, and a fourth molecular subgroup which is wild-type for the three mutations. Irrespective of the molecular background, giant cell granulomas show MAPK/ERK activation. However, it remains unclear if these mutations lead to differences in their molecular signaling in giant cell granulomas.

METHODS

Metabolomics, proteomics and phosphoproteomics analyses were carried out in formalin-fixed paraffin-embedded samples of giant cell granuloma of the jaws. The study cohort consisted of five lesions harboring mutations in FGFR1, six in KRAS, five in TRPV4 and five that were wild-type for these mutations.

RESULTS

Lesions harboring KRAS or FGFR1 mutations showed overall similar proteomics and metabolomics profiles. In all four groups, metabolic pathways showed similarity in apoptosis, cell signaling, gene expression, cell differentiation and erythrocyte activity. Lesions harboring TRPV4 mutations showed a greater number of enriched pathways related to tissue architecture. On the other hand, the wild-type group presented increased number of enriched pathways related to protein metabolism compared to the other groups.

CONCLUSION

Despite some minor differences, our results revealed an overall similar molecular profile among the groups with different mutational profile at the metabolic, proteic and phosphopeptidic levels. This article is protected by copyright. All rights reserved.

Proteomics and phosphoproteomics datasets of a muscle-specific STIM1 loss-of-function mouse model

STIM1 is an ER/SR transmembrane protein that interacts with ORAI1 to activate store operated Ca2+ entry (SOCE) upon ER/SR depletion of calcium. Normally highly expressed in skeletal muscle, STIM1 deficiency causes significant changes to mitochondrial ultrastructure that do not occur with loss of ORAI1 or other components of SOCE. The datasets in this article are from large-scale proteomics and phosphoproteomics experiments in an inducible mouse model of skeletal muscle-specific STIM1 knock out (KO). These data reveal statistically significant changes in the relative abundance of specific proteins and sites of protein phosphorylation in STIM1 KO gastrocnemius. Protein samples from five biological replicates of each condition (+/- STIM1) were enzymatically digested, the resulting peptides labeled with tandem mass tag (TMT) reagents, mixed, and fractionated. Phosphopeptides were enriched and a small amount of each input retained for protein abundance analysis. All phosphopeptide and input fractions were analyzed by nano LC-MS/MS on a Q Exactive Plus Orbitrap mass spectrometer, searched with Proteome Discoverer software, and processed with in-house R-scripts for data normalization and statistical analysis. Article published in Molecular Metabolism [1].

Profiling the Human Phosphoproteome to Estimate the True Extent of Protein Phosphorylation

Public phosphorylation databases such as PhosphoSitePlus (PSP) and PeptideAtlas (PA) compile results from published papers or openly available mass spectrometry (MS) data. However, there is no database-level control for false discovery of sites, likely leading to the overestimation of true phosphosites. By profiling the human phosphoproteome, we estimate the false discovery rate (FDR) of phosphosites and predict a more realistic count of true identifications. We rank sites into phosphorylation likelihood sets and analyze them in terms of conservation across 100 species, sequence properties, and functional annotations. We demonstrate significant differences between the sets and develop a method for independent phosphosite FDR estimation. Remarkably, we report estimated FDRs of 84, 98, and 82% within sets of phosphoserine (pSer), phosphothreonine (pThr), and phosphotyrosine (pTyr) sites, respectively, that are supported by only a single piece of identification evidence─the majority of sites in PSP. We estimate that around 62 000 Ser, 8000 Thr, and 12 000 Tyr phosphosites in the human proteome are likely to be true, which is lower than most published estimates. Furthermore, our analysis estimates that 86 000 Ser, 50 000 Thr, and 26 000 Tyr phosphosites are likely false-positive identifications, highlighting the significant potential of false-positive data to be present in phosphorylation databases.

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep-learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT, and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson’s correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep-learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

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Computational method selection substantially affects phosphopeptide identification.

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Deep-learning-derived metrics effectively discriminate correct and incorrect PSMs.

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Novel metrics enable computational method comparison on real application data.

Phosphoproteomics Identifies Significant Biomarkers Associated with the Proliferation and Metastasis of Prostate Cancer

The spider peptide toxins HNTX-III and JZTX-I are a specific inhibitor and activator of TTX-S VGSCs, respectively. They play important roles in regulating MAT-LyLu cell metastasis in prostate cancer. In order to identify key biomarkers involved in the regulation of MAT-LyLu cell metastasis, iTRAQ-based quantitative phosphoproteomics analysis was performed on cells treated with HNTX-III, JZTX-I and blank. A total of 554 unique phosphorylated proteins and 1779 distinct phosphorylated proteins were identified, while 55 and 36 phosphorylated proteins were identified as differentially expressed proteins in HNTX-III and JZTX-I treated groups compared with control groups. Multiple bioinformatics analysis based on quantitative phosphoproteomics data suggested that the differentially expressed phosphorylated proteins and peptides were significantly associated with the migration and invasion of prostate tumors. Specifically, the toxins HNTX-III and JZTX-I have opposite effects on tumor formation and metastasis by regulating the expression and phosphorylation level of causal proteins. Herein, we highlighted three key proteins EEF2, U2AF2 and FLNC which were down-regulated in HNTX-III treated cells and up-regulated in JZTX-I treated cells. They played significant roles in cancer related physiological and pathological processes. The differentially expressed phosphorylated proteins identified in this study may serve as potential biomarkers for precision medicine for prostate cancer in the near future.

Identification of Cytosolic Protein Targets of Catechol Estrogens in Breast Cancer Cells Using a Click Chemistry-Based Workflow

Catechol estrogens (CEs) are known to be toxic metabolites and the initiators of the oncogenesis of breast cancers via forming covalent adducts with DNAs. CEs shall also react with proteins, but their cellular protein targets remain unexplored. Here, we reported the identification of protein targets of CEs in the soluble cytosol of estrogen-sensitive breast cancer cells by multiple comparative proteomics using liquid chromatography-tandem mass spectrometry (LC-MS/MS) coupled with an improved click chemistry-based workflow. Multiple comparative proteomics composed of an experimental pair (probe versus solvent) and two control pairs (solvent versus solvent and probe versus solvent without enrichment) were studied using stable isotope dimethyl labeling. The use of 4-hydroxyethynylestradiol (4OHEE2) probe with an amide-free linker coupled with on-bead digestion and redigestion of the proteins cleaved from the beads was shown to greatly improve the recovery and identification of CE-adducted peptides. A total of 310 protein targets and 40 adduction sites were repeatedly (n ≥ 2) identified with D/H (probe/solvent) ratio >4 versus only one identified with D/H >4 from the two control pairs, suggesting that our workflow imposes only a very low background. Meanwhile, multiple comparative D/H ratios revealed that CEs may downregulate many target proteins involved in the metabolism or detoxification, suggesting a negative correlation between CE-induced adduction and expression of proteins acting on the alleviation of stress-induced cellular damages. The reported method and data will provide opportunities to study the progression of estrogen metabolism-derived diseases and biomarkers.

Comparing 22 Popular Phosphoproteomics Pipelines for Peptide Identification and Site Localization

Phosphorylation-driven cell signaling governs most biological functions and is widely studied using mass-spectrometry-based phosphoproteomics. Identifying the peptides and localizing the phosphorylation sites within them from the raw data is challenging and can be performed by several algorithms that return scores that are not directly comparable. This increases the heterogeneity among published phosphoproteomics data sets and prevents their direct integration. Here we compare 22 pipelines implemented in the main software tools used for bottom-up phosphoproteomics analysis (MaxQuant, Proteome Discoverer, PeptideShaker). We test six search engines (Andromeda, Comet, Mascot, MS Amanda, SequestHT, and X!Tandem) in combination with several localization scoring algorithms (delta score, D-score, PTM-score, phosphoRS, and Ascore). We show that these follow very different score distributions, which can lead to different false localization rates for the same threshold. We provide a strategy to discriminate correctly from incorrectly localized phosphorylation sites in a consistent manner across the tested pipelines. The results presented here can help users choose the most appropriate pipeline and cutoffs for their phosphoproteomics analysis.

Detection and Characterization of Catechol Quinone-Derived Protein Adducts Using Biomolecular Mass Spectrometry

The catechol quinone (CQ) motif is present in many biologically relevant molecules throughout endogenous metabolic products, foods, drugs, and environmental pollutants. The CQ derivatives may undergo Michael addition, and has been shown to yield covalent bonds with nucleophilic sites of cysteine, lysine, or histidine residue of proteins. The CQ-adducted proteins may exhibit cytotoxicity or biological functions different from their un-adducted forms. Identification, characterization, and quantification of relevant protein targets are essential but challenging goals. Mass spectrometry (MS) is well-suited for the analysis of proteins and protein modifications. Technical development of bottom-up proteomics has greatly advanced the field of biomolecular MS, including protein adductomics. This mini-review focuses on the use of biomolecular MS in (1) structural and functional characterization of CQ adduction on standards of proteins, (2) identification of endogenous adduction targets, and (3) quantification of adducted blood proteins as exposure index. The reactivity and outcome of CQ adduction are discussed with emphases on endogenous species, such as dopamine and catechol estrogens. Limitations and advancements in sample preparation, MS instrumentation, and software to facilitate protein adductomics are also discussed.

Estimating the total number of phosphoproteins and phosphorylation sites in eukaryotic proteomes

Background

Phosphorylation is the most frequent post-translational modification made to proteins and may regulate protein activity as either a molecular digital switch or a rheostat. Despite the cornucopia of high-throughput (HTP) phosphoproteomic data in the last decade, it remains unclear how many proteins are phosphorylated and how many phosphorylation sites (p-sites) can exist in total within a eukaryotic proteome. We present the first reliable estimates of the total number of phosphoproteins and p-sites for four eukaryotes (human, mouse, Arabidopsis, and yeast).

Results

In all, 187 HTP phosphoproteomic datasets were filtered, compiled, and studied along with two low-throughput (LTP) compendia. Estimates of the number of phosphoproteins and p-sites were inferred by two methods: Capture-Recapture, and fitting the saturation curve of cumulative redundant vs. cumulative non-redundant phosphoproteins/p-sites. Estimates were also adjusted for different levels of noise within the individual datasets and other confounding factors. We estimate that in total, 13 000, 11 000, and 3000 phosphoproteins and 230 000, 156 000, and 40 000 p-sites exist in human, mouse, and yeast, respectively, whereas estimates for Arabidopsis were not as reliable.

Conclusions

Most of the phosphoproteins have been discovered for human, mouse, and yeast, while the dataset for Arabidopsis is still far from complete. The datasets for p-sites are not as close to saturation as those for phosphoproteins. Integration of the LTP data suggests that current HTP phosphoproteomics appears to be capable of capturing 70 % to 95 % of total phosphoproteins, but only 40 % to 60 % of total p-sites.

The Pivotal Role of Protein Phosphorylation in the Control of Yeast Central Metabolism

Protein phosphorylation is the most frequent eukaryotic post-translational modification and can act as either a molecular switch or rheostat for protein functions. The deliberate manipulation of protein phosphorylation has great potential for regulating specific protein functions with surgical precision, rather than the gross effects gained by the over/underexpression or complete deletion of a protein-encoding gene. In order to assess the impact of phosphorylation on central metabolism, and thus its potential for biotechnological and medical exploitation, a compendium of highly confident protein phosphorylation sites (p-sites) for the model organism Saccharomyces cerevisiae has been analyzed together with two more datasets from the fungal pathogen Candida albicans. Our analysis highlights the global properties of the regulation of yeast central metabolism by protein phosphorylation, where almost half of the enzymes involved are subject to this sort of post-translational modification. These phosphorylated enzymes, compared to the nonphosphorylated ones, are more abundant, regulate more reactions, have more protein–protein interactions, and a higher fraction of them are ubiquitinated. The p-sites of metabolic enzymes are also more conserved than the background p-sites, and hundreds of them have the potential for regulating metabolite production. All this integrated information has allowed us to prioritize thousands of p-sites in terms of their potential phenotypic impact. This multi-source compendium should enable the design of future high-throughput (HTP) mutation studies to identify key molecular switches/rheostats for the manipulation of not only the metabolism of yeast, but also that of many other biotechnologically and medically important fungi and eukaryotes.

The diverse and expanding role of mass spectrometry in structural and molecular biology

The emergence of proteomics has led to major technological advances in mass spectrometry (MS). These advancements not only benefitted MS-based high-throughput proteomics but also increased the impact of mass spectrometry on the field of structural and molecular biology. Here, we review how state-of-the-art MS methods, including native MS, top-down protein sequencing, cross-linking-MS, and hydrogen-deuterium exchange-MS, nowadays enable the characterization of biomolecular structures, functions, and interactions. In particular, we focus on the role of mass spectrometry in integrated structural and molecular biology investigations of biological macromolecular complexes and cellular machineries, highlighting work on CRISPR-Cas systems and eukaryotic transcription complexes.

Phosphoproteome Discovery in Human Biological Fluids

Phosphorylation plays a critical role in regulating protein function and thus influences a vast spectrum of cellular processes. With the advent of modern bioanalytical technologies, examination of protein phosphorylation on a global scale has become one of the major research areas. Phosphoproteins are found in biological fluids and interrogation of the phosphoproteome in biological fluids presents an exciting opportunity for discoveries that hold great potential for novel mechanistic insights into protein function in health and disease, and for translation to improved diagnostic and therapeutic approaches for the clinical setting. This review focuses on phosphoproteome discovery in selected human biological fluids: serum/plasma, urine, cerebrospinal fluid, saliva, and bronchoalveolar lavage fluid. Bioanalytical workflows pertinent to phosphoproteomics of biological fluids are discussed with emphasis on mass spectrometry-based approaches, and summaries of studies on phosphoproteome discovery in major fluids are presented.

A year (2014-2015) of plants in Proteomics journal. Progress in wet and dry methodologies, moving from protein catalogs, and the view of classic plant biochemists

The present review is an update of the previous one published in Proteomics 2015 Reviews special issue [Jorrin-Novo, J. V. et al., Proteomics 2015, 15, 1089-1112] covering the July 2014-2015 period. It has been written on the bases of the publications that appeared in Proteomics journal during that period and the most relevant ones that have been published in other high-impact journals. Methodological advances and the contribution of the field to the knowledge of plant biology processes and its translation to agroforestry and environmental sectors will be discussed. This review has been organized in four blocks, with a starting general introduction (literature survey) followed by sections focusing on the methodology (in vitro, in vivo, wet, and dry), proteomics integration with other approaches (systems biology and proteogenomics), biological information, and knowledge (cell communication, receptors, and signaling), ending with a brief mention of some other biological and translational topics to which proteomics has made some contribution.