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      Profiling the Heme‐Binding Proteomes of Bacteria Using Chemical Proteomics

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          Abstract

          Heme is a cofactor with myriad roles and essential to almost all living organisms. Beyond classical gas transport and catalytic functions, heme is increasingly appreciated as a tightly controlled signalling molecule regulating protein expression. However, heme acquisition, biosynthesis and regulation is poorly understood beyond a few model organisms, and the heme‐binding proteome has not been fully characterised in bacteria. Yet as heme homeostasis is critical for bacterial survival, heme‐binding proteins are promising drug targets. Herein we report a chemical proteomics method for global profiling of heme‐binding proteins in live cells for the first time. Employing a panel of heme‐based clickable and photoaffinity probes enabled the profiling of 32–54 % of the known heme‐binding proteomes in Gram‐positive and Gram‐negative bacteria. This simple‐to‐implement profiling strategy could be interchangeably applied to different cell types and systems and fuel future research into heme biology.

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          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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            The PRIDE database and related tools and resources in 2019: improving support for quantification data

            Abstract The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.
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              PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API

              Abstract PANTHER (Protein Analysis Through Evolutionary Relationships, http://www.pantherdb.org) is a resource for the evolutionary and functional classification of protein-coding genes from all domains of life. The evolutionary classification is based on a library of over 15,000 phylogenetic trees, and the functional classifications include Gene Ontology terms and pathways. Here, we analyze the current coverage of genes from genomes in different taxonomic groups, so that users can better understand what to expect when analyzing a gene list using PANTHER tools. We also describe extensive improvements to PANTHER made in the past two years. The PANTHER Protein Class ontology has been completely refactored, and 6101 PANTHER families have been manually assigned to a Protein Class, providing a high level classification of protein families and their genes. Users can access the TreeGrafter tool to add their own protein sequences to the reference phylogenetic trees in PANTHER, to infer evolutionary context as well as fine-grained annotations. We have added human enhancer-gene links that associate non-coding regions with the annotated human genes in PANTHER. We have also expanded the available services for programmatic access to PANTHER tools and data via application programming interfaces (APIs). Other improvements include additional plant genomes and an updated PANTHER GO-slim.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Angewandte Chemie International Edition
                Angew Chem Int Ed
                Wiley
                1433-7851
                1521-3773
                February 20 2023
                January 23 2023
                February 20 2023
                : 62
                : 9
                Affiliations
                [1 ] Centre for Functional Protein Assemblies Technical University of Munich Ernst-Otto-Fischer-Straße 8 85748 Garching Germany
                Article
                10.1002/anie.202212111
                36495310
                89c05b91-93b5-49b3-93a4-db190a3f69e0
                © 2023

                http://creativecommons.org/licenses/by-nc/4.0/

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