Refining Protein Subcellular Localization
The study of protein subcellular localization is important to elucidate protein function. Even in well-studied organisms such as yeast, experimental methods have not been able to provide a full coverage of localization. The development of bioinformatic predictors of localization can bridge this gap....
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| Format: | Online |
| Language: | English |
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Public Library of Science
2005
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1289393/ |
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pubmed-1289393 |
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pubmed-12893932005-12-01 Refining Protein Subcellular Localization Scott, Michelle S Calafell, Sara J Thomas, David Y Hallett, Michael T Research Article The study of protein subcellular localization is important to elucidate protein function. Even in well-studied organisms such as yeast, experimental methods have not been able to provide a full coverage of localization. The development of bioinformatic predictors of localization can bridge this gap. We have created a Bayesian network predictor called PSLT2 that considers diverse protein characteristics, including the combinatorial presence of InterPro motifs and protein interaction data. We compared the localization predictions of PSLT2 to high-throughput experimental localization datasets. Disagreements between these methods generally involve proteins that transit through or reside in the secretory pathway. We used our multi-compartmental predictions to refine the localization annotations of yeast proteins primarily by distinguishing between soluble lumenal proteins and soluble proteins peripherally associated with organelles. To our knowledge, this is the first tool to provide this functionality. We used these sub-compartmental predictions to characterize cellular processes on an organellar scale. The integration of diverse protein characteristics and protein interaction data in an appropriate setting can lead to high-quality detailed localization annotations for whole proteomes. This type of resource is instrumental in developing models of whole organelles that provide insight into the extent of interaction and communication between organelles and help define organellar functionality. Public Library of Science 2005-11 2005-11-25 /pmc/articles/PMC1289393/ /pubmed/16322766 http://dx.doi.org/10.1371/journal.pcbi.0010066 Text en Copyright: © 2005 Scott et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Scott, Michelle S Calafell, Sara J Thomas, David Y Hallett, Michael T |
| spellingShingle |
Scott, Michelle S Calafell, Sara J Thomas, David Y Hallett, Michael T Refining Protein Subcellular Localization |
| author_facet |
Scott, Michelle S Calafell, Sara J Thomas, David Y Hallett, Michael T |
| author_sort |
Scott, Michelle S |
| title |
Refining Protein Subcellular Localization |
| title_short |
Refining Protein Subcellular Localization |
| title_full |
Refining Protein Subcellular Localization |
| title_fullStr |
Refining Protein Subcellular Localization |
| title_full_unstemmed |
Refining Protein Subcellular Localization |
| title_sort |
refining protein subcellular localization |
| description |
The study of protein subcellular localization is important to elucidate protein function. Even in well-studied organisms such as yeast, experimental methods have not been able to provide a full coverage of localization. The development of bioinformatic predictors of localization can bridge this gap. We have created a Bayesian network predictor called PSLT2 that considers diverse protein characteristics, including the combinatorial presence of InterPro motifs and protein interaction data. We compared the localization predictions of PSLT2 to high-throughput experimental localization datasets. Disagreements between these methods generally involve proteins that transit through or reside in the secretory pathway. We used our multi-compartmental predictions to refine the localization annotations of yeast proteins primarily by distinguishing between soluble lumenal proteins and soluble proteins peripherally associated with organelles. To our knowledge, this is the first tool to provide this functionality. We used these sub-compartmental predictions to characterize cellular processes on an organellar scale. The integration of diverse protein characteristics and protein interaction data in an appropriate setting can lead to high-quality detailed localization annotations for whole proteomes. This type of resource is instrumental in developing models of whole organelles that provide insight into the extent of interaction and communication between organelles and help define organellar functionality. |
| publisher |
Public Library of Science |
| publishDate |
2005 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1289393/ |
| _version_ |
1611379442476646400 |