Topfind 2.0—Linking Protein Termini with Proteolytic Processing and Modifications Altering Protein Function Philipp F
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Gene Ontology Analysis with Cytoscape
Introduction to Systems Biology Exercise 6: Gene Ontology Analysis Overview: Gene Ontology (GO) is a useful resource in bioinformatics and systems biology. GO defines a controlled vocabulary of terms in biological process, molecular function, and cellular location, and relates the terms in a somewhat-organized fashion. This exercise outlines the resources available under Cytoscape to perform analyses for the enrichment of gene annotation (GO terms) in networks or sub-networks. In this exercise you will: • Learn how to navigate the Cytoscape Gene Ontology wizard to apply GO annotations to Cytoscape nodes • Learn how to look for enriched GO categories using the BiNGO plugin. What you will need: • The BiNGO plugin, developed by the Computational Biology Division, Dept. of Plant Systems Biology, Flanders Interuniversitary Institute for Biotechnology (VIB), described in Maere S, et al. Bioinformatics. 2005 Aug 15;21(16):3448-9. Epub 2005 Jun 21. • galFiltered.sif used in the earlier exercises and found under sampleData. Please download any missing files from http://www.cbs.dtu.dk/courses/27041/exercises/Ex3/ Download and install the BiNGO plugin, as follows: 1. Get BiNGO from the course page (see above) or go to the BiNGO page at http://www.psb.ugent.be/cbd/papers/BiNGO/. (This site also provides documentation on BiNGO). 2. Unzip the contents of BiNGO.zip to your Cytoscape plugins directory (make sure the BiNGO.jar file is in the plugins directory and not in a subdirectory). 3. If you are currently running Cytoscape, exit and restart. First, we will load the Gene Ontology data into Cytoscape. 1. Start Cytoscape, under Edit, Preferences, make sure your default species, “defaultSpeciesName”, is set to “Saccharomyces cerevisiae”. -
Mmnet: Metagenomic Analysis of Microbiome Metabolic Network
mmnet: Metagenomic analysis of microbiome metabolic network Yang Cao, Fei Li, Xiaochen Bo May 15, 2016 Contents 1 Introduction 1 1.1 Installation . .2 2 Analysis Pipeline: from raw Metagenomic Sequence Data to Metabolic Network Analysis 2 2.1 Prepare metagenomic sequence data . .3 2.2 Annotation of Metagenomic Sequence Reads . .3 2.3 Estimating the abundance of enzymatic genes . .5 2.4 Building reference metabolic dataset . .7 2.5 Constructing State Specific Network . .8 2.6 Topological network analysis . .9 2.7 Differential network analysis . 10 2.8 Network Visualization . 10 3 Analysis in Cytoscape 11 1 Introduction This manual is a brief introduction to structure, functions and usage of mmnet pack- age. The mmnet package provides a set of functions to support systems analysis of metagenomic data in R, including annotation of raw metagenomic sequence data, con- struction of metabolic network, and differential network analysis based on state specific metabolic network and enzymatic gene abundance. Meanwhile, the package supports an analysis pipeline for metagenomic systems bi- ology. We can simply start from raw metagenomic sequence data, optionally use MG- RAST to rapidly retrieve metagenomic annotation, fetch pathway data from KEGG using its API to construct metabolic network, and then utilize a metagenomic systems biology computational framework mentioned in [Greenblum et al., 2012] to establish further differential network analysis. The main features of mmnet: • Annotation of metagenomic sequence reads with MG-RAST • Estimating abundances of enzymatic gene based on functional annotation 1 • Constructing State Specific metabolic Network • Topological network analysis • Differential network analysis 1.1 Installation mmnet requires these packages: KEGGREST, igraph, Biobase, XML, RCurl, RJSO- NIO, stringr, ggplot2 and biom. -
PMAP: Databases for Analyzing Proteolytic Events and Pathways Yoshinobu Igarashi1, Emily Heureux1, Kutbuddin S
Published online 8 October 2008 Nucleic Acids Research, 2009, Vol. 37, Database issue D611–D618 doi:10.1093/nar/gkn683 PMAP: databases for analyzing proteolytic events and pathways Yoshinobu Igarashi1, Emily Heureux1, Kutbuddin S. Doctor1, Priti Talwar1, Svetlana Gramatikova1, Kosi Gramatikoff1, Ying Zhang1, Michael Blinov3, Salmaz S. Ibragimova2, Sarah Boyd4, Boris Ratnikov1, Piotr Cieplak1, Adam Godzik1, Jeffrey W. Smith1, Andrei L. Osterman1 and Alexey M. Eroshkin1,* 1The Center on Proteolytic Pathways, The Cancer Research Center and The Inflammatory and Infectious Disease Center at The Burnham Institute for Medical Research, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA, 2Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Lavrentieva 10, Novosibirsk 630090, Russia, 3Center of Cell Analysis and Modeling, University of Connecticut Health Center, Farmington, CT 06030, USA and 4Faculty of Information Technology, Monash University, Clayton, Victoria 3800, Australia Received August 15, 2008; Revised September 19, 2008; Accepted September 23, 2008 ABSTRACT Proteolysis is essential to almost all fundamental cellular processes including proliferation, death and migration The Proteolysis MAP (PMAP, http://www. (1–4). Equally as important, mis-regulated proteolysis proteolysis.org) is a user-friendly website intended can cause diseases ranging from emphysema (5) and to aid the scientific community in reasoning about thrombosis (6), to arthritis (7) and Alzheimer’s (8). There proteolytic networks and pathways. PMAP is com- are a number of online resources containing information prised of five databases, linked together in one on proteases including SwissProt (the oldest), HPRD environment. The foundation databases, Protease- (human protein reference database) (9) and UniProt (10). -
How to Use and Integrate.Pdf
Journal of Proteomics 171 (2018) 37–52 Contents lists available at ScienceDirect Journal of Proteomics journal homepage: www.elsevier.com/locate/jprot How to use and integrate bioinformatics tools to compare proteomic data from distinct conditions? A tutorial using the pathological similarities between Aortic Valve Stenosis and Coronary Artery Disease as a case-study Fábio Trindade a,b,⁎, Rita Ferreira c, Beatriz Magalhães a, Adelino Leite-Moreira b, Inês Falcão-Pires b,RuiVitorinoa,b a Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal b Unidade de Investigação Cardiovascular, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina, Universidade do Porto, Porto, Portugal c QOPNA, Mass Spectrometry Center, Department of Chemistry, University of Aveiro, Aveiro, Portugal article info abstract Article history: Nowadays we are surrounded by a plethora of bioinformatics tools, powerful enough to deal with the large Received 22 December 2016 amounts of data arising from proteomic studies, but whose application is sometimes hard to find. Therefore, Received in revised form 28 February 2017 we used a specific clinical problem – to discriminate pathophysiology and potential biomarkers between two Accepted 19 March 2017 similar cardiovascular diseases, aortic valve stenosis (AVS) and coronary artery disease (CAD) – to make a Available online 21 March 2017 step-by-step guide through four bioinformatics tools: STRING, DisGeNET, Cytoscape and ClueGO. Proteome data was collected from articles available on PubMed centered on proteomic studies enrolling subjects with Keywords: fi fi Proteomics AVS or CAD. Through the analysis of gene ontology provided by STRING and ClueGO we could nd speci cbio- Bioinformatics logical phenomena associated with AVS, such as down-regulation of elastic fiber assembly, and with CAD, such STRING as up-regulation of plasminogen activation. -
A Roadmap for Metagenomic Enzyme Discovery
Natural Product Reports View Article Online REVIEW View Journal A roadmap for metagenomic enzyme discovery Cite this: DOI: 10.1039/d1np00006c Serina L. Robinson, * Jorn¨ Piel and Shinichi Sunagawa Covering: up to 2021 Metagenomics has yielded massive amounts of sequencing data offering a glimpse into the biosynthetic potential of the uncultivated microbial majority. While genome-resolved information about microbial communities from nearly every environment on earth is now available, the ability to accurately predict biocatalytic functions directly from sequencing data remains challenging. Compared to primary metabolic pathways, enzymes involved in secondary metabolism often catalyze specialized reactions with diverse substrates, making these pathways rich resources for the discovery of new enzymology. To date, functional insights gained from studies on environmental DNA (eDNA) have largely relied on PCR- or activity-based screening of eDNA fragments cloned in fosmid or cosmid libraries. As an alternative, Creative Commons Attribution-NonCommercial 3.0 Unported Licence. shotgun metagenomics holds underexplored potential for the discovery of new enzymes directly from eDNA by avoiding common biases introduced through PCR- or activity-guided functional metagenomics workflows. However, inferring new enzyme functions directly from eDNA is similar to searching for a ‘needle in a haystack’ without direct links between genotype and phenotype. The goal of this review is to provide a roadmap to navigate shotgun metagenomic sequencing data and identify new candidate biosynthetic enzymes. We cover both computational and experimental strategies to mine metagenomes and explore protein sequence space with a spotlight on natural product biosynthesis. Specifically, we compare in silico methods for enzyme discovery including phylogenetics, sequence similarity networks, This article is licensed under a genomic context, 3D structure-based approaches, and machine learning techniques. -
Some Considerations for Analyzing Biodiversity Using Integrative
Some considerations for analyzing biodiversity using integrative metagenomics and gene networks Lucie Bittner, Sébastien Halary, Claude Payri, Corinne Cruaud, Bruno de Reviers, Philippe Lopez, Eric Bapteste To cite this version: Lucie Bittner, Sébastien Halary, Claude Payri, Corinne Cruaud, Bruno de Reviers, et al.. Some considerations for analyzing biodiversity using integrative metagenomics and gene networks. Biology Direct, BioMed Central, 2010, 5 (5), pp.47. 10.1186/1745-6150-5-47. hal-02922363 HAL Id: hal-02922363 https://hal.archives-ouvertes.fr/hal-02922363 Submitted on 26 Aug 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Bittner et al. Biology Direct 2010, 5:47 http://www.biology-direct.com/content/5/1/47 HYPOTHESIS Open Access Some considerations for analyzing biodiversity using integrative metagenomics and gene networks Lucie Bittner1†, Sébastien Halary2†, Claude Payri3, Corinne Cruaud4, Bruno de Reviers1, Philippe Lopez2, Eric Bapteste2* Abstract Background: Improving knowledge of biodiversity will benefit conservation biology, enhance bioremediation studies, and could lead to new medical treatments. However there is no standard approach to estimate and to compare the diversity of different environments, or to study its past, and possibly, future evolution. -
Node and Edge Attributes
Cytoscape User Manual Table of Contents Cytoscape User Manual ........................................................................................................ 3 Introduction ...................................................................................................................... 48 Development .............................................................................................................. 4 License ...................................................................................................................... 4 What’s New in 2.7 ....................................................................................................... 4 Please Cite Cytoscape! ................................................................................................. 7 Launching Cytoscape ........................................................................................................... 8 System requirements .................................................................................................... 8 Getting Started .......................................................................................................... 56 Quick Tour of Cytoscape ..................................................................................................... 12 The Menus ............................................................................................................... 15 Network Management ................................................................................................. 18 The -
N-Degradomic Analysis Reveals a Proteolytic Network Processing the Podocyte Cytoskeleton
BRIEF COMMUNICATION www.jasn.org N-Degradomic Analysis Reveals a Proteolytic Network Processing the Podocyte Cytoskeleton †‡ † | † Markus M. Rinschen,* § Ann-Kathrin Hoppe,* Florian Grahammer, ¶ Martin Kann,* † † † †‡ Linus A. Völker,* Eva-Maria Schurek,* Julie Binz,* Martin Höhne,* § Fatih Demir,** | †† † ‡‡ Milena Malisic,** Tobias B. Huber, ¶ Christine Kurschat,* Jayachandran N. Kizhakkedathu, †‡ †‡ Bernhard Schermer,* § Pitter F. Huesgen,** and Thomas Benzing* § *Department II of Internal Medicine, †Center for Molecular Medicine Cologne (CMMC), ‡Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), and §Systems Biology of Ageing Cologne (Sybacol), BRIEF COMMUNICATION University of Cologne, Cologne, Germany; |Department of Medicine III, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; ¶Department of Medicine IV, Medical Center and Faculty of Medicine – University of Freiburg, Freiburg, Germany; **Central Institute for Engineering, Electronics and Analytics, ZEA-3, Forschungszentrum Jülich, Jülich, Germany; ††BIOSS Centre for Biological Signalling Studies and Center for Biological Systems Analysis (ZBSA), Albert-Ludwigs-University, Freiburg, Germany; and ‡‡Centre for Blood Research, Department of Pathology and Laboratory Medicine, Department of Chemistry, University of British Columbia, Vancouver, Canada ABSTRACT Regulated intracellular proteostasis, controlled in part by proteolysis, is essential in of these genes leads to altered signaling maintaining the integrity of podocytes -
Caspase Substrates and Inhibitors
Downloaded from http://cshperspectives.cshlp.org/ on September 27, 2021 - Published by Cold Spring Harbor Laboratory Press Caspase Substrates and Inhibitors Marcin Pore˛ba1, Aleksandra Stro´z˙yk1, Guy S. Salvesen2, and Marcin Dra˛g1 1Department of Bioorganic Chemistry, Faculty of Chemistry, Wroclaw University of Technology, 50-370 Wrocław, Poland 2Program in Cell Death Research, Sanford-Burnham Medical Research Institute, La Jolla, California 92024 Correspondence: [email protected] Caspases are proteases at the heart of networks that govern apoptosis and inflammation. The past decade has seen huge leaps in understanding the biology and chemistry of the caspases, largely through the development of synthetic substrates and inhibitors. Such agents are used to define the role of caspases in transmitting life and death signals, in imaging caspases in situ and in vivo, and in deconvoluting the networks that govern cell behavior. Additionally, focused proteomics methods have begun to reveal the natural substrates of caspases in the thousands. Together, these chemical and proteomics technologies are setting the scene for designing and implementing control of caspase activity as appropriate targets for disease therapy. WHAT IT TAKES TO BE A CASPASE group, known as cysteine protease clan CD that he need to recycle scarce amino acids in early also contains the plant metacaspases (Vercam- Tbiotic environments required that proteases men et al. 2004), and mammalian and plant were among the first proteins to appear in the proteases of the legumain family (involved in most primitive replicating organisms. Since that specific processing events), the eukaryotic pro- distant origin, proteases have diversified into tease separase (required for sisterchromatid sep- every biological niche, occupied every cellular aration during mitosis), and the bacterial prote- and extracellular compartment, and are encod- ases clostripain and gingipain (Chen et al. -
Full Text (PDF)
BACK TO BASICS | PROTEASES IN LUNG DISEASE Protean proteases: at the cutting edge of lung diseases Clifford Taggart1, Marcus A. Mall2,3,4, Gilles Lalmanach5, Didier Cataldo6, Andreas Ludwig7, Sabina Janciauskiene8, Nicole Heath3,4,9, Silke Meiners10, Christopher M. Overall11, Carsten Schultz4,9, Boris Turk12 and Keren S. Borensztajn13,14 Affiliations: 1Airway Innate Immunity Research group (AiiR), Centre for Experimental Medicine, Queen’s University Belfast, UK. 2Dept of Translational Pulmonology, University of Heidelberg, Heidelberg, Germany. 3Division of Pediatric Pulmonology & Allergy and Cystic Fibrosis Center, Dept of Pediatrics, University of Heidelberg, Heidelberg, Germany. 4Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany. 5INSERM UMR1100 Centre d’Etude des Pathologies Respiratoires (CEPR), Equipe: Mécanismes Protéolytiques dans l’Inflammation, Université François Rabelais, Tours, France. 6Laboratory of Tumors and Development and Dept of Respiratory Diseases, University of Liege, Liege, Belgium. 7Inflammation Pharmacology Research Group, Institute of Pharmacology and Toxicology, RWTH Aachen University, Aachen, Germany. 8Dept of Respiratory Medicine, a member of The German Center for Lung Research (DZL), Hannover Medical School, Hannover, Germany. 9European Molecular Biology Laboratory (EMBL), Heidelberg, Germany. 10Comprehensive Pneumology Center (CPC), University Hospital, Ludwig- Maximilians University, Helmholtz Zentrum München, Member of the German Center for -
Proteolytic Cleavage—Mechanisms, Function
Review Cite This: Chem. Rev. 2018, 118, 1137−1168 pubs.acs.org/CR Proteolytic CleavageMechanisms, Function, and “Omic” Approaches for a Near-Ubiquitous Posttranslational Modification Theo Klein,†,⊥ Ulrich Eckhard,†,§ Antoine Dufour,†,¶ Nestor Solis,† and Christopher M. Overall*,†,‡ † ‡ Life Sciences Institute, Department of Oral Biological and Medical Sciences, and Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada ABSTRACT: Proteases enzymatically hydrolyze peptide bonds in substrate proteins, resulting in a widespread, irreversible posttranslational modification of the protein’s structure and biological function. Often regarded as a mere degradative mechanism in destruction of proteins or turnover in maintaining physiological homeostasis, recent research in the field of degradomics has led to the recognition of two main yet unexpected concepts. First, that targeted, limited proteolytic cleavage events by a wide repertoire of proteases are pivotal regulators of most, if not all, physiological and pathological processes. Second, an unexpected in vivo abundance of stable cleaved proteins revealed pervasive, functionally relevant protein processing in normal and diseased tissuefrom 40 to 70% of proteins also occur in vivo as distinct stable proteoforms with undocumented N- or C- termini, meaning these proteoforms are stable functional cleavage products, most with unknown functional implications. In this Review, we discuss the structural biology aspects and mechanisms -
Protein TAILS: When Termini Tell Tales of Proteolysis and Function
Available online at www.sciencedirect.com Protein TAILS: when termini tell tales of proteolysis and function 1,2,3 1,2,3 Philipp F Lange and Christopher M Overall Among the hundreds of posttranslational modifications, limited functions of bioactive proteins will be opaque and hence proteolysis, also known as processing, is special: It is hypotheses based on traditional shotgun analyses, may be irreversible, near ubiquitous, and by trimming peptide chains misleading or even worse, totally wrong. from their ends or cutting proteins into two, proteolysis forms shorter chains displaying new termini. The unique chemistry PTM of proteins constitutes a highly diverse and dynamic and location of a-amino-termini and carboxyl-termini in a regulatory layer affecting all aspects of a protein from protein engender special chemical and physical properties to a protein folding, localization, interaction and bioactivity protein. Hence, modification of protein termini is often to its stability and ultimately degradation. Therefore, each associated with new biological activities of a protein. We distinctly modified version of a protein, also called a protein highlight recent proteomic developments enabling high species, and not just the initial translated version, needs to throughput identification of protein termini. This has be considered as the functional units comprising the pro- revolutionized degradomics and protein characterization by teome [3]. The diversity of reversible and irreversible mapping the specificity of terminal modifications and of modifications as well as the extensive modification machin- proteases, and has been used to directly identify new protease ery [4] and the possibility of combinatorial effects substrates and molecular pathways altered by proteolysis.