Predicting the protein half-life in tissue from its cellular properties.
Protein half-life is an important feature of protein homeostasis (proteostasis). The increasing number of in vivo and in vitro studies using high throughput proteomics provide estimates of the protein half-lives in tissues and cells. However, protein half-lives in cells and tissues are different. Du...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5515413?pdf=render |
| _version_ | 1818948167450755072 |
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| author | Mahbubur Rahman Rovshan G Sadygov |
| author_facet | Mahbubur Rahman Rovshan G Sadygov |
| author_sort | Mahbubur Rahman |
| collection | DOAJ |
| description | Protein half-life is an important feature of protein homeostasis (proteostasis). The increasing number of in vivo and in vitro studies using high throughput proteomics provide estimates of the protein half-lives in tissues and cells. However, protein half-lives in cells and tissues are different. Due to the resource requirements for researching tissues, more data is available from cellular studies than tissues. We have designed a multivariate linear model for predicting protein half-life in tissue from its cellular properties. Inputs to the model are cellular half-life, abundance, intrinsically disordered sequences, and transcriptional and translational rates. Before the modeling, we determined substructures in the data using the relative distance from the regression line of the protein half-lives in tissues and cells, identifying three separate clusters. The model was trained on and applied to predict protein half-lives from murine liver, brain and heart tissues. In each tissue type we observed similar prediction patterns of protein half-lives. We found that the model provides the best results when there is a strong correlation between tissue and cell culture protein half-lives. Additionally, we clustered the protein half-lives to determine variations in correlation coefficients between the protein half-lives in the tissue versus in cell culture. The clusters identify strongly and weakly correlated protein half-lives, further improves the overall prediction and identifies sub groupings which exhibit specific characteristics. The model described herein, is generalizable to other data sets and has been implemented in a freely available R code. |
| first_indexed | 2024-12-20T08:42:30Z |
| format | Article |
| id | doaj.art-cb912d71235f46b98c4e31c6beb13796 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T08:42:30Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-cb912d71235f46b98c4e31c6beb137962022-12-21T19:46:21ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01127e018042810.1371/journal.pone.0180428Predicting the protein half-life in tissue from its cellular properties.Mahbubur RahmanRovshan G SadygovProtein half-life is an important feature of protein homeostasis (proteostasis). The increasing number of in vivo and in vitro studies using high throughput proteomics provide estimates of the protein half-lives in tissues and cells. However, protein half-lives in cells and tissues are different. Due to the resource requirements for researching tissues, more data is available from cellular studies than tissues. We have designed a multivariate linear model for predicting protein half-life in tissue from its cellular properties. Inputs to the model are cellular half-life, abundance, intrinsically disordered sequences, and transcriptional and translational rates. Before the modeling, we determined substructures in the data using the relative distance from the regression line of the protein half-lives in tissues and cells, identifying three separate clusters. The model was trained on and applied to predict protein half-lives from murine liver, brain and heart tissues. In each tissue type we observed similar prediction patterns of protein half-lives. We found that the model provides the best results when there is a strong correlation between tissue and cell culture protein half-lives. Additionally, we clustered the protein half-lives to determine variations in correlation coefficients between the protein half-lives in the tissue versus in cell culture. The clusters identify strongly and weakly correlated protein half-lives, further improves the overall prediction and identifies sub groupings which exhibit specific characteristics. The model described herein, is generalizable to other data sets and has been implemented in a freely available R code.http://europepmc.org/articles/PMC5515413?pdf=render |
| spellingShingle | Mahbubur Rahman Rovshan G Sadygov Predicting the protein half-life in tissue from its cellular properties. PLoS ONE |
| title | Predicting the protein half-life in tissue from its cellular properties. |
| title_full | Predicting the protein half-life in tissue from its cellular properties. |
| title_fullStr | Predicting the protein half-life in tissue from its cellular properties. |
| title_full_unstemmed | Predicting the protein half-life in tissue from its cellular properties. |
| title_short | Predicting the protein half-life in tissue from its cellular properties. |
| title_sort | predicting the protein half life in tissue from its cellular properties |
| url | http://europepmc.org/articles/PMC5515413?pdf=render |
| work_keys_str_mv | AT mahbuburrahman predictingtheproteinhalflifeintissuefromitscellularproperties AT rovshangsadygov predictingtheproteinhalflifeintissuefromitscellularproperties |