Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pcbi.1003504
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dc.titleQuantitative Protein Localization Signatures Reveal an Association between Spatial and Functional Divergences of Proteins
dc.contributor.authorLoo L.-H.
dc.contributor.authorLaksameethanasan D.
dc.contributor.authorTung Y.-L.
dc.date.accessioned2019-11-06T09:24:45Z
dc.date.available2019-11-06T09:24:45Z
dc.date.issued2014
dc.identifier.citationLoo L.-H., Laksameethanasan D., Tung Y.-L. (2014). Quantitative Protein Localization Signatures Reveal an Association between Spatial and Functional Divergences of Proteins. PLoS Computational Biology 10 (3) : e1003504. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pcbi.1003504
dc.identifier.issn1553734X
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/161612
dc.description.abstractProtein subcellular localization is a major determinant of protein function. However, this important protein feature is often described in terms of discrete and qualitative categories of subcellular compartments, and therefore it has limited applications in quantitative protein function analyses. Here, we present Protein Localization Analysis and Search Tools (PLAST), an automated analysis framework for constructing and comparing quantitative signatures of protein subcellular localization patterns based on microscopy images. PLAST produces human-interpretable protein localization maps that quantitatively describe the similarities in the localization patterns of proteins and major subcellular compartments, without requiring manual assignment or supervised learning of these compartments. Using the budding yeast Saccharomyces cerevisiae as a model system, we show that PLAST is more accurate than existing, qualitative protein localization annotations in identifying known co-localized proteins. Furthermore, we demonstrate that PLAST can reveal protein localization-function relationships that are not obvious from these annotations. First, we identified proteins that have similar localization patterns and participate in closely-related biological processes, but do not necessarily form stable complexes with each other or localize at the same organelles. Second, we found an association between spatial and functional divergences of proteins during evolution. Surprisingly, as proteins with common ancestors evolve, they tend to develop more diverged subcellular localization patterns, but still occupy similar numbers of compartments. This suggests that divergence of protein localization might be more frequently due to the development of more specific localization patterns over ancestral compartments than the occupation of new compartments. PLAST enables systematic and quantitative analyses of protein localization-function relationships, and will be useful to elucidate protein functions and how these functions were acquired in cells from different organisms or species. A public web interface of PLAST is available at http://plast.bii.a-star.edu.sg. © 2014 Loo et al.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20191101
dc.subjectarticle
dc.subjectbudding
dc.subjectcell compartmentalization
dc.subjectcell organelle
dc.subjectcellular distribution
dc.subjectcomparative study
dc.subjectcomputer analysis
dc.subjectevolution
dc.subjectimage analysis
dc.subjectintracellular space
dc.subjectmicroscopy
dc.subjectnonhuman
dc.subjectprotein analysis
dc.subjectprotein function
dc.subjectprotein localization
dc.subjectProtein Localization Analysis and Search Tool
dc.subjectprotein structure
dc.subjectqualitative analysis
dc.subjectquantitative analysis
dc.subjectSaccharomyces cerevisiae
dc.subjectAlgorithms
dc.subjectAutomation
dc.subjectComputational Biology
dc.subjectDatabases, Protein
dc.subjectGreen Fluorescent Proteins
dc.subjectImage Processing, Computer-Assisted
dc.subjectInternet
dc.subjectMicroscopy, Fluorescence
dc.subjectModels, Statistical
dc.subjectOpen Reading Frames
dc.subjectProteins
dc.subjectSaccharomyces cerevisiae
dc.subjectSoftware
dc.typeArticle
dc.contributor.departmentPHARMACOLOGY
dc.description.doi10.1371/journal.pcbi.1003504
dc.description.sourcetitlePLoS Computational Biology
dc.description.volume10
dc.description.issue3
dc.description.pagee1003504
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