pFind Studio: a computational solution for mass spectrometry-based proteomics
2021
frontiers in Chemistry2021. Zhao, Y et al.
Natl Inst Metrol, Ctr Adv Measurement Sci, Beijing, Peoples R China.
ABSTRACT:Diabetes has become a major public health concern worldwide, most of which are type 2 diabetes (T2D). The diagnosis of T2D is commonly based on plasma glucose levels, and there are no reliable clinical biomarkers available for early detection. Recent advances in proteome technologies offer new opportunity for the understanding of T2D; however, the underlying proteomic characteristics of T2D have not been thoroughly investigated yet. Here, using proteomic and glycoproteomic profiling, we provided a comprehensive landscape of molecular alterations in the fasting plasma of the 24 Chinese participants, including eight T2D patients, eight prediabetic (PDB) subjects, and eight healthy control (HC) individuals. Our analyses identified a diverse set of potential biomarkers that might enhance the efficiency and accuracy based on current existing biological indicators of (pre)diabetes. Through integrative omics analysis, we showed the capability of glycoproteomics as a complement to proteomics or metabolomics, to provide additional insights into the pathogenesis of (pre)diabetes. We have newly identified systemic site-specific N-glycosylation alterations underlying T2D patients in the complement activation pathways, including decreased levels of N-glycopeptides from C1s, MASP1, and CFP proteins, and increased levels of N-glycopeptides from C2, C4, C4BPA, C4BPB, and CFH. These alterations were not observed at proteomic levels, suggesting new opportunities for the diagnosis and treatment of this disease. Our results demonstrate a great potential role of glycoproteomics in understanding (pre)diabetes and present a new direction for diabetes research which deserves more attention.
Use: pGlyco
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY2021. Wang, ZY et al.
Chinese Acad Sci, Dalian Inst Chem Phys, CAS Key Lab Separat Sci Analyt Chem, Dalian 116023, Peoples R China.
ABSTRACT:Protein N-glycosylation in human milk whey plays a substantial role in infant health during postnatal development. Changes in site-specific glycans in milk whey reflect the needs of infants under different circumstances. However, the conventional glycoproteomics analysis of milk whey cannot reveal the changes in site-specific glycans because the attached glycans are typically enzymatically removed from the glycoproteins prior to analysis. In this study, N-glycoproteomics analysis of milk whey was performed without removing the attached glycans, and 330 and 327 intact glycopeptides were identified in colostrum and mature milk whey, respectively. Label-free quantification of site-specific glycans was achieved by analyzing the identified intact glycopeptides, which revealed 9 significantly upregulated site-specific glycans on 6 glycosites and 11 significantly downregulated sitespecific glycans on 8 glycosites. Some interesting change trends in N-glycans attached to specific glycosites in human milk whey were observed. Bisecting GlcNAc was found attached to 11 glycosites on 8 glycoproteins in colostrum and mature milk. The dynamic changes in site-specific glycans revealed in this study provide insights into the role of protein N-glycosylation during infant development.
Use: pQuant; pGlyco
FRONTIERS IN ONCOLOGY2021. Cao, XY et al.
Fudan Univ, Inst Biomed Sci, Shanghai, Peoples R China.
ABSTRACT:The diagnosis of AFP (alpha-fetoprotein)-negative HCC (hepatocellular carcinoma) mostly relies on imaging and pathological examinations, and it lacks valuable and practical markers. Protein N-glycosylation is a crucial post-translation modifying process related to many biological functions in an organism. Alteration of N-glycosylation correlates with inflammatory diseases and infectious diseases including hepatocellular carcinoma. Here, serum N-linked intact glycopeptides with molecular weight (MW) of 40-55 kDa were analyzed in a discovery set (n = 40) including AFP-negative HCC and liver cirrhosis (LC) patients using label-free quantification methodology. Quantitative lens culinaris agglutin (LCA) ELISA was further used to confirm the difference of glycosylation on serum PON1 in liver diseases (n = 56). Then, the alteration of site-specific intact N-glycopeptides of PON1 was comprehensively assessed by using Immunoprecipitation (IP) and mass spectrometry based O-16/O-18 C-terminal labeling quantification method to distinguish AFP-negative HCC from LC patients in a validation set (n = 64). Totally 195 glycopeptides were identified using a dedicated search engine pGlyco. Among them, glycopeptides from APOH, HPT/HPTR, and PON1 were significantly changed in AFP-negative HCC as compared to LC. In addition, the reactivity of PON1 with LCA in HCC patients with negative AFP was significantly elevated than that in cirrhosis patients. The two glycopeptides HAN(253)WTLTPLK (H5N4S2) and (H5N4S1) corresponding to PON1 were significantly increased in AFP-negative HCC patients, as compared with LC patients. Variations in PON1 glycosylation may be associated with AFP-negative HCC and might be helpful to serve as potential glycomic-based biomarkers to distinguish AFP-negative HCC from cirrhosis.
Use: pGlyco; pQuant
Analytical Chemistry2021. Liu, LY et al.
Chinese Acad Sci, Dalian Inst Chem Phys, CAS Key Lab Separat Sci Analyt Chem, Dalian 116023, Peoples R China.
ABSTRACT:Bottom-up proteomics has been increasingly applied in clinical research to study the disease pathophysiology and to discover disease biomarkers. However, glycoproteomic analysis always requires tedious experimental steps for intact glycopeptide enrichment, which has been the technique bottleneck for large-scale analysis of clinical samples. Herein, we developed an automated glycopeptide enrichment method for the analysis of serum site-specific N-glycoproteome. This automated method allowed for processing one sample within 20 min. It showed higher enrichment specificity, more intact glycopeptide identifications, and better quantitative reproducibility than the traditional manual method using microtip enrichment devices. We further applied this method to investigate the serum site-specific N-glycosylation changes between four patients with pancreatic cancer and seven healthy controls. The principal component analysis of intact N-glycopeptides showed good clustering across cancer and normal groups. Furthermore, we found that the site-specific glycoforms, monofucosylated and nonsialylated oligosaccharides, on IgG1 site 180 expressed a significant decrease in pancreatic cancer patients compared to healthy controls. Together, the automated method is a powerful tool for site-specific N-glycoproteomic analysis of complex biological samples, and it has great potential for clinical utilities.
Use: pGlyco
Analytical Chemistry2021. Kong, SY et al.
Fudan Univ, Peoples Hosp 5, Shanghai 200032, Peoples R China.
ABSTRACT:The heterogeneity and low abundance of protein glycosylation present challenging barriers to the analysis of intact glycopeptides, which is key to comprehensively understanding the role of glycosylation in an organism. Efficient and specific enrichment of intact glycopeptides could help greatly with this problem. Here, we propose a new enrichment strategy using a boronic acid (BA)-functionalized mesoporous graphene-silica composite (denoted as GO@mSiO(2)-GLYMO-APB) for isolating intact glycopeptides from complex biological samples. The merits of this composite, including high surface area and synergistic effect from size exclusion functionality of mesoporous material, hydrophilic interaction of silica, and the reversible covalent binding with BA, enable the effective and specific enrichment of both intact N- and O-glycopeptides. The results from the enrichment performance of the strategy evaluated by standard glycoproteins and the application to global N- and O-glycosylation analyses in human serum indicate the robustness and potential of the strategy for intact glycopeptide analysis.
Use: pGlyco
Journal of Proteome Research2021. Schulze, S et al.
Univ Penn, Dept Biol, Philadelphia, PA 19104 USA.
ABSTRACT:The identification of peptide sequences and their posttranslational 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.
Use: pGlyco
Science Advances2021. Li, SS et al.
ShanghaiTech Univ, iHuman Inst, Shanghai 201210, Peoples R China.
ABSTRACT:Transmembrane proteins play vital roles in mediating synaptic transmission, plasticity, and homeostasis in the brain. However, these proteins, especially the G protein-coupled receptors (GPCRs), are underrepresented in most large-scale proteomic surveys. Here, we present a new proteomic approach aided by deep learning models for comprehensive profiling of transmembrane protein families in multiple mouse brain regions. Our multiregional proteome profiling highlights the considerable discrepancy between messenger RNA and protein distribution, especially for region-enriched GPCRs, and predicts an endogenous GPCR interaction network in the brain. Furthermore, our new approach reveals the transmembrane proteome remodeling landscape in the brain of a mouse depression model, which led to the identification of two previously unknown GPCR regulators of depressive-like behaviors. Our study provides an enabling technology and rich data resource to expand the understanding of transmembrane proteome organization and dynamics in the brain and accelerate the discovery of potential therapeutic targets for depression treatment.
Use: pDeep
Nature Communications2021. Lou, RH et al.
ShanghaiTech Univ, iHuman Inst, Shanghai 201210, Peoples R China; ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 201210, Peoples R China; ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China; Shanghai Engn Res Ctr Intelligent Vis & Imaging, Shanghai 201210, Peoples R China
ABSTRACT:The coverage and throughput of data-independent acquisition (DIA)-based phosphoproteomics is limited by its dependence on experimental spectral libraries. Here the authors develop a DIA workflow based on in silico spectral libraries generated by a novel deep neural network to expand phosphoproteome coverage.Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we establish a DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expands the phosphoproteome coverage while maintaining high quantification performance, which leads to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server as well as an offline app to facilitate user access to model training, predictions and library generation.
Use: pDeep