scholarly journals SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides

2020 ◽  
Author(s):  
Stefan Schulze ◽  
Anne Oltmanns ◽  
Christian Fufezan ◽  
Julia Krägenbring ◽  
Michael Mormann ◽  
...  
Keyword(s):  
Large Scale ◽  
Protein Glycosylation ◽  
Supplementary Information ◽  
Cyanidioschyzon Merolae ◽  
Human Breast Milk ◽  
The Novel ◽  
Post Translational Modification ◽  
Cellular Functions ◽  
Domains Of Life

AbstractMotivationProtein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes.ResultsTherefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae.AvailabilityThe source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating [email protected] and [email protected] informationSupplementary data are available online.

scholarly journals SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides

2020 ◽  
Author(s):  
Stefan Schulze ◽  
Anne Oltmanns ◽  
Christian Fufezan ◽  
Julia Krägenbring ◽  
Michael Mormann ◽  
...  
Keyword(s):  
Large Scale ◽  
Protein Glycosylation ◽  
Supplementary Information ◽  
Cyanidioschyzon Merolae ◽  
Human Breast Milk ◽  
The Novel ◽  
Post Translational Modification ◽  
Cellular Functions ◽  
Domains Of Life

Abstract Motivation Protein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes. Results Therefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae. Availability and implementation The source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems. Supplementary information Supplementary data are available at Bioinformatics online.

Integrated mass spectrometry-based multi-omics for elucidating mechanisms of bacterial virulence

2021 ◽  
Author(s):  
Lok Man ◽  
William P. Klare ◽  
Ashleigh L. Dale ◽  
Joel A. Cain ◽  
Stuart J. Cordwell
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Large Scale ◽  
Lipid A ◽  
Phenotypic Characterization ◽  
Post Translational Modification ◽  
Form Of Life ◽  
Antimicrobial Interventions

Despite being considered the simplest form of life, bacteria remain enigmatic, particularly in light of pathogenesis and evolving antimicrobial resistance. After three decades of genomics, we remain some way from understanding these organisms, and a substantial proportion of genes remain functionally unknown. Methodological advances, principally mass spectrometry (MS), are paving the way for parallel analysis of the proteome, metabolome and lipidome. Each provides a global, complementary assay, in addition to genomics, and the ability to better comprehend how pathogens respond to changes in their internal (e.g. mutation) and external environments consistent with infection-like conditions. Such responses include accessing necessary nutrients for survival in a hostile environment where co-colonizing bacteria and normal flora are acclimated to the prevailing conditions. Multi-omics can be harnessed across temporal and spatial (sub-cellular) dimensions to understand adaptation at the molecular level. Gene deletion libraries, in conjunction with large-scale approaches and evolving bioinformatics integration, will greatly facilitate next-generation vaccines and antimicrobial interventions by highlighting novel targets and pathogen-specific pathways. MS is also central in phenotypic characterization of surface biomolecules such as lipid A, as well as aiding in the determination of protein interactions and complexes. There is increasing evidence that bacteria are capable of widespread post-translational modification, including phosphorylation, glycosylation and acetylation; with each contributing to virulence. This review focuses on the bacterial genotype to phenotype transition and surveys the recent literature showing how the genome can be validated at the proteome, metabolome and lipidome levels to provide an integrated view of organism response to host conditions.

scholarly journals Network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i516-i524
Author(s):  
Midori Iida ◽  
Michio Iwata ◽  
Yoshihiro Yamanishi
Keyword(s):  
Large Scale ◽  
Expression Patterns ◽  
Therapeutic Targets ◽  
Molecular Networks ◽  
Supplementary Information ◽  
New Associations ◽  
Disease States ◽  
Molecular Features ◽  
Novel Approach

Abstract Motivation Disease states are distinguished from each other in terms of differing clinical phenotypes, but characteristic molecular features are often common to various diseases. Similarities between diseases can be explained by characteristic gene expression patterns. However, most disease–disease relationships remain uncharacterized. Results In this study, we proposed a novel approach for network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets. We performed large-scale analyses of omics data and molecular interaction networks for 79 diseases, including adrenoleukodystrophy, leukaemia, Alzheimer's disease, asthma, atopic dermatitis, breast cancer, cystic fibrosis and inflammatory bowel disease. We quantified disease–disease similarities based on proximities of abnormally expressed genes in various molecular networks, and showed that similarities between diseases could be explained by characteristic molecular network topologies. Furthermore, we developed a kernel matrix regression algorithm to predict the commonalities of drugs and therapeutic targets among diseases. Our comprehensive prediction strategy indicated many new associations among phenotypically diverse diseases. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Functional Characterization of Bacterial Oligosaccharyltransferases Involved in O-Linked Protein Glycosylation

2007 ◽  
Vol 189 (22) ◽  
pp. 8088-8098 ◽  
Author(s):  
Amirreza Faridmoayer ◽  
Messele A. Fentabil ◽  
Dominic C. Mills ◽  
John S. Klassen ◽  
Mario F. Feldman
Keyword(s):  
Sequence Similarity ◽  
Functional Characterization ◽  
Protein Glycosylation ◽  
Structural Components ◽  
En Bloc ◽  
E Coli ◽  
New Family ◽  
Type Iv ◽  
Domains Of Life

ABSTRACT Protein glycosylation is an important posttranslational modification that occurs in all domains of life. Pilins, the structural components of type IV pili, are O glycosylated in Neisseria meningitidis, Neisseria gonorrhoeae, and some strains of Pseudomonas aeruginosa. In this work, we characterized the P. aeruginosa 1244 and N. meningitidis MC58 O glycosylation systems in Escherichia coli. In both cases, sugars are transferred en bloc by an oligosaccharyltransferase (OTase) named PglL in N. meningitidis and PilO in P. aeruginosa. We show that, like PilO, PglL has relaxed glycan specificity. Both OTases are sufficient for glycosylation, but they require translocation of the undecaprenol-pyrophosphate-linked oligosaccharide substrates into the periplasm for activity. Whereas PilO activity is restricted to short oligosaccharides, PglL is able to transfer diverse oligo- and polysaccharides. This functional characterization supports the concept that despite their low sequence similarity, PilO and PglL belong to a new family of “O-OTases” that transfer oligosaccharides from lipid carriers to hydroxylated amino acids in proteins. To date, such activity has not been identified for eukaryotes. To our knowledge, this is the first report describing recombinant O glycoproteins synthesized in E. coli.

scholarly journals DeepNGlyPred: A Deep Neural Network-Based Approach for Human N-Linked Glycosylation Site Prediction

Molecules ◽  
2021 ◽  
Vol 26 (23) ◽  
pp. 7314
Author(s):  
Subash C. Pakhrin ◽  
Kiyoko F. Aoki-Kinoshita ◽  
Doina Caragea ◽  
Dukka B. KC
Keyword(s):  
Computational Prediction ◽  
Structural Features ◽  
Glycosylation Site ◽  
Protein Glycosylation ◽  
Evolutionary Information ◽  
Computational Technique ◽  
Post Translational Modification ◽  
Glycosylation Sites ◽  
Better Than

Protein N-linked glycosylation is a post-translational modification that plays an important role in a myriad of biological processes. Computational prediction approaches serve as complementary methods for the characterization of glycosylation sites. Most of the existing predictors for N-linked glycosylation utilize the information that the glycosylation site occurs at the N-X-[S/T] sequon, where X is any amino acid except proline. Not all N-X-[S/T] sequons are glycosylated, thus the N-X-[S/T] sequon is a necessary but not sufficient determinant for protein glycosylation. In that regard, computational prediction of N-linked glycosylation sites confined to N-X-[S/T] sequons is an important problem. Here, we report DeepNGlyPred a deep learning-based approach that encodes the positive and negative sequences in the human proteome dataset (extracted from N-GlycositeAtlas) using sequence-based features (gapped-dipeptide), predicted structural features, and evolutionary information. DeepNGlyPred produces SN, SP, MCC, and ACC of 88.62%, 73.92%, 0.60, and 79.41%, respectively on N-GlyDE independent test set, which is better than the compared approaches. These results demonstrate that DeepNGlyPred is a robust computational technique to predict N-Linked glycosylation sites confined to N-X-[S/T] sequon. DeepNGlyPred will be a useful resource for the glycobiology community.

scholarly journals Large-scale analysis of post-translational modifications in E. coli under glucose-limiting conditions

2016 ◽  
Author(s):  
Colin W Brown ◽  
Viswanadham Sridhara ◽  
Daniel R Boutz ◽  
Maria D Person ◽  
Edward M Marcotte ◽  
...  
Keyword(s):  
Large Scale ◽  
Biological Function ◽  
Growth Conditions ◽  
Growth Cycle ◽  
Post Translational Modification ◽  
Proteomics Data ◽  
Proteomic Data ◽  
Large Scale Analysis ◽  
Wide Range ◽  
Domains Of Life

AbstractBackgroundPost-translational modification (PTM) of proteins is central to many cellular processes across all domains of life, but despite decades of study and a wealth of genomic and proteomic data the biological function of many PTMs remains unknown. This is especially true for prokaryotic PTM systems, many of which have only recently been recognized and studied in depth. It is increasingly apparent that a deep sampling of abundance across a wide range of environmental stresses, growth conditions, and PTM types, rather than simply cataloging targets for a handful of modifications, is critical to understanding the complex pathways that govern PTM deposition and downstream effects.ResultsWe utilized a deeply-sampled dataset of MS/MS proteomic analysis covering 9 timepoints spanning theEscherichia coligrowth cycle and an unbiased PTM search strategy to construct a temporal map of abundance for all PTMs within a 400 Da window of mass shifts. Using this map, we are able to identify novel targets and temporal patterns for N-terminal Nα acetylation, C-terminal glutamylation, and asparagine deamidation. Furthermore, we identify a possible relationship between N-terminal Na acetylation and regulation of protein degradation in stationary phase, pointing to a previously unrecognized biological function for this poorly-understood PTM.ConclusionsUnbiased detection of PTM in MS/MS proteomics data facilitates the discovery of novel modification types and previously unobserved dynamic changes in modification across growth timepoints.

scholarly journals Glycans as Biomarkers: Status and Perspectives

2011 ◽  
Vol 30 (3) ◽  
pp. 213-223 ◽  
Author(s):  
Miroslava Janković
Keyword(s):  
Large Scale ◽  
Biomarker Discovery ◽  
High Sensitivity ◽  
Disease Diagnosis ◽  
Protein Glycosylation ◽  
Post Translational Modification ◽  
Different Types ◽  
Progression Of Disease ◽  
New Biomarkers ◽  
Selection Of

Glycans as Biomarkers: Status and PerspectivesProtein glycosylation is a ubiquitous and complex co- and post-translational modification leading to glycan formation, i.e. oligosaccharide chains covalently attached to peptide backbones. The significance of changes in glycosylation for the beginning, progress and outcome of different human diseases is widely recognized. Thus, glycans are considered as unique structures to diagnose, predict susceptibility to and monitor the progression of disease. In the »omics« era, the glycome, a glycan analogue of the proteome and genome, holds considerable promise as a source of new biomarkers. In the design of a strategy for biomarker discovery, new principles and platforms for the analysis of relatively small amounts of numerous glycoproteins are needed. Emerging glycomics technologies comprising different types of mass spectrometry and affinity-based arrays are next in line to deliver new analytical procedures in the field of biomarkers. Screening different types of glycomolecules, selection of differentially expressed components, their enrichment and purification or identification are the most challenging parts of experimental and clinical glycoproteomics. This requires large-scale technologies enabling high sensitivity, proper standardization and validation of the methods to be used. Further progress in the field of applied glycoscience requires an integrated systematic approach in order to explore properly all opportunities for disease diagnosis.

scholarly journals Capturing site-specific heterogeneity with large-scale N-glycoproteome analysis

2019 ◽  
Author(s):  
Nicholas M. Riley ◽  
Alexander S. Hebert ◽  
Michael S. Westphall ◽  
Joshua J. Coon
Keyword(s):  
Large Scale ◽  
Electron Transfer Dissociation ◽  
Protein Glycosylation ◽  
Analytical Challenge ◽  
Post Translational Modification ◽  
Structural Domains ◽  
Site Specific ◽  
Future Studies ◽  
Site Heterogeneity ◽  
Degree Of Heterogeneity

ABSTRACTProtein glycosylation is a highly important, yet a poorly understood protein post-translational modification. Thousands of possible glycan structures and compositions create potential for tremendous site heterogeneity and analytical challenge. A lack of suitable analytical methods for large-scale analyses of intact glycopeptides has ultimately limited our abilities to both address the degree of heterogeneity across the glycoproteome and to understand how it contributes biologically to complex systems. Here we show that N-glycoproteome site-specific microheterogeneity can be captured via large-scale glycopeptide profiling with methods enabled by activated ion electron transfer dissociation (AI-ETD), ultimately characterizing 1,545 N-glycosites (>5,600 unique N-glycopeptides) from mouse brain tissue. Moreover, we have used this large-scale glycoproteomic data to develop several new visualizations that will prove useful for analyzing intact glycopeptides in future studies. Our data reveal that N-glycosylation profiles can differ between subcellular regions and structural domains and that N-glycosite heterogeneity manifests in several different forms, including dramatic differences in glycosites on the same protein.

scholarly journals ProteoCombiner: integrating bottom-up with top-down proteomics data for improved proteoform assessment

2020 ◽  
Author(s):  
Diogo B Lima ◽  
Mathieu Dupré ◽  
Magalie Duchateau ◽  
Quentin Giai Gianetto ◽  
Martial Rey ◽  
...  
Keyword(s):  
Search Engines ◽  
High Performance ◽  
Large Scale ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Post Translational Modification ◽  
Proteomics Data ◽  
Top Down ◽  
Proteomic Data ◽  
Demonstration Video

Abstract Motivation We present a high-performance software integrating shotgun with top-down proteomic data. The tool can deal with multiple experiments and search engines. Enable rapid and easy visualization, manual validation and comparison of the identified proteoform sequences including the post-translational modification characterization. Results We demonstrate the effectiveness of our approach on a large-scale Escherichia coli dataset; ProteoCombiner unambiguously shortlisted proteoforms among those identified by the multiple search engines. Availability and implementation ProteoCombiner, a demonstration video and user tutorial are freely available at https://proteocombiner.pasteur.fr, for academic use; all data are thus available from the ProteomeXchange consortium (identifier PXD017618). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS

2021 ◽  
Author(s):  
Daniel Prieto-Alhambra ◽  
Kristin Kostka ◽  
Talita Duarte-Salles ◽  
Albert Prats-Uribe ◽  
Anthony Sena ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
The United States ◽  
The Novel ◽  
Real World Data ◽  
Great Utility ◽  
Scale Characterization ◽  
Novel Coronavirus ◽  
The Uk

Abstract Background: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response [1,2]. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) [3] Characterizing Health Associated Risks, and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Methods: We conducted a descriptive cohort study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub [4]. Findings: We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts, and are available in an interactive website: https://data.ohdsi.org/Covid19CharacterizationCharybdis/. Interpretation: CHARYBDIS findings provide benchmarks that contribute to our understanding of COVID-19 progression, management and evolution over time. This can enable timely assessment of real-world outcomes of preventative and therapeutic options as they are introduced in clinical practice.

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