Jordan B Burton, Nicholas J Carruthers, Zhanjun Hou, Larry H Matherly, Paul M Stemmer
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The mapping in organellar space facilitates discrimination between the test and control groups and aids in identification of proteins of interest. The same representation of proteins in organellar space is used in the analysis of extracellular vesicle proteomes for which protein abundance and fold change data are evaluated. Vesicular protein organellar localization patterns provide information about the subcellular origin of the proteins in the samples which are isolates from the extracellular milieu. The organellar localization patterns are indicative of the provenance of the vesicular proteome origin and allow discrimination between proteomes prepared using different enrichment methods. The patterns in LOPIT displays are easy to understand and compare which aids in the biological interpretation of proteome data.</p>","PeriodicalId":20877,"journal":{"name":"Proteomes","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2022-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149908/pdf/","citationCount":"0","resultStr":"{\"title\":\"Pattern Analysis of Organellar Maps for Interpretation of Proteomic Data.\",\"authors\":\"Jordan B Burton, Nicholas J Carruthers, Zhanjun Hou, Larry H Matherly, Paul M Stemmer\",\"doi\":\"10.3390/proteomes10020018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Localization of organelle proteins by isotope tagging (LOPIT) maps are a coordinate-directed representation of proteome data that can aid in biological interpretation. 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Pattern Analysis of Organellar Maps for Interpretation of Proteomic Data.
Localization of organelle proteins by isotope tagging (LOPIT) maps are a coordinate-directed representation of proteome data that can aid in biological interpretation. Analysis of organellar association for proteins as displayed using LOPIT is evaluated and interpreted for two types of proteomic data sets. First, test and control group protein abundances and fold change data obtained in a proximity labeling experiment are plotted on a LOPIT map to evaluate the likelihood of true protein interactions. Selection of true positives based on co-localization of proteins in the organellar space is shown to be consistent with carboxylase enrichment which serves as a positive control for biotinylation in streptavidin affinity selected proteome data sets. The mapping in organellar space facilitates discrimination between the test and control groups and aids in identification of proteins of interest. The same representation of proteins in organellar space is used in the analysis of extracellular vesicle proteomes for which protein abundance and fold change data are evaluated. Vesicular protein organellar localization patterns provide information about the subcellular origin of the proteins in the samples which are isolates from the extracellular milieu. The organellar localization patterns are indicative of the provenance of the vesicular proteome origin and allow discrimination between proteomes prepared using different enrichment methods. The patterns in LOPIT displays are easy to understand and compare which aids in the biological interpretation of proteome data.
ProteomesBiochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
6.50
自引率
3.00%
发文量
37
审稿时长
11 weeks
期刊介绍:
Proteomes (ISSN 2227-7382) is an open access, peer reviewed journal on all aspects of proteome science. Proteomes covers the multi-disciplinary topics of structural and functional biology, protein chemistry, cell biology, methodology used for protein analysis, including mass spectrometry, protein arrays, bioinformatics, HTS assays, etc. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. Therefore, there is no restriction on the length of papers. Scope: -whole proteome analysis of any organism -disease/pharmaceutical studies -comparative proteomics -protein-ligand/protein interactions -structure/functional proteomics -gene expression -methodology -bioinformatics -applications of proteomics