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GPS-ARM: Computational Analysis of the APC/C Recognition Motif by Predicting D-Boxes and KEN-Boxes
Anaphase-promoting complex/cyclosome (APC/C), an E3 ubiquitin ligase incorporated with Cdh1 and/or Cdc20 recognizes and interacts with specific substrates, and faithfully orchestrates the proper cell cycle events by targeting proteins for proteasomal degradation. Experimental identification of APC/C...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3315528/ https://www.ncbi.nlm.nih.gov/pubmed/22479614 http://dx.doi.org/10.1371/journal.pone.0034370 |
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author | Liu, Zexian Yuan, Fang Ren, Jian Cao, Jun Zhou, Yanhong Yang, Qing Xue, Yu |
author_facet | Liu, Zexian Yuan, Fang Ren, Jian Cao, Jun Zhou, Yanhong Yang, Qing Xue, Yu |
author_sort | Liu, Zexian |
collection | PubMed |
description | Anaphase-promoting complex/cyclosome (APC/C), an E3 ubiquitin ligase incorporated with Cdh1 and/or Cdc20 recognizes and interacts with specific substrates, and faithfully orchestrates the proper cell cycle events by targeting proteins for proteasomal degradation. Experimental identification of APC/C substrates is largely dependent on the discovery of APC/C recognition motifs, e.g., the D-box and KEN-box. Although a number of either stringent or loosely defined motifs proposed, these motif patterns are only of limited use due to their insufficient powers of prediction. We report the development of a novel GPS-ARM software package which is useful for the prediction of D-boxes and KEN-boxes in proteins. Using experimentally identified D-boxes and KEN-boxes as the training data sets, a previously developed GPS (Group-based Prediction System) algorithm was adopted. By extensive evaluation and comparison, the GPS-ARM performance was found to be much better than the one using simple motifs. With this powerful tool, we predicted 4,841 potential D-boxes in 3,832 proteins and 1,632 potential KEN-boxes in 1,403 proteins from H. sapiens, while further statistical analysis suggested that both the D-box and KEN-box proteins are involved in a broad spectrum of biological processes beyond the cell cycle. In addition, with the co-localization information, we predicted hundreds of mitosis-specific APC/C substrates with high confidence. As the first computational tool for the prediction of APC/C-mediated degradation, GPS-ARM is a useful tool for information to be used in further experimental investigations. The GPS-ARM is freely accessible for academic researchers at: http://arm.biocuckoo.org. |
format | Online Article Text |
id | pubmed-3315528 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-33155282012-04-04 GPS-ARM: Computational Analysis of the APC/C Recognition Motif by Predicting D-Boxes and KEN-Boxes Liu, Zexian Yuan, Fang Ren, Jian Cao, Jun Zhou, Yanhong Yang, Qing Xue, Yu PLoS One Research Article Anaphase-promoting complex/cyclosome (APC/C), an E3 ubiquitin ligase incorporated with Cdh1 and/or Cdc20 recognizes and interacts with specific substrates, and faithfully orchestrates the proper cell cycle events by targeting proteins for proteasomal degradation. Experimental identification of APC/C substrates is largely dependent on the discovery of APC/C recognition motifs, e.g., the D-box and KEN-box. Although a number of either stringent or loosely defined motifs proposed, these motif patterns are only of limited use due to their insufficient powers of prediction. We report the development of a novel GPS-ARM software package which is useful for the prediction of D-boxes and KEN-boxes in proteins. Using experimentally identified D-boxes and KEN-boxes as the training data sets, a previously developed GPS (Group-based Prediction System) algorithm was adopted. By extensive evaluation and comparison, the GPS-ARM performance was found to be much better than the one using simple motifs. With this powerful tool, we predicted 4,841 potential D-boxes in 3,832 proteins and 1,632 potential KEN-boxes in 1,403 proteins from H. sapiens, while further statistical analysis suggested that both the D-box and KEN-box proteins are involved in a broad spectrum of biological processes beyond the cell cycle. In addition, with the co-localization information, we predicted hundreds of mitosis-specific APC/C substrates with high confidence. As the first computational tool for the prediction of APC/C-mediated degradation, GPS-ARM is a useful tool for information to be used in further experimental investigations. The GPS-ARM is freely accessible for academic researchers at: http://arm.biocuckoo.org. Public Library of Science 2012-03-29 /pmc/articles/PMC3315528/ /pubmed/22479614 http://dx.doi.org/10.1371/journal.pone.0034370 Text en Liu 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. |
spellingShingle | Research Article Liu, Zexian Yuan, Fang Ren, Jian Cao, Jun Zhou, Yanhong Yang, Qing Xue, Yu GPS-ARM: Computational Analysis of the APC/C Recognition Motif by Predicting D-Boxes and KEN-Boxes |
title | GPS-ARM: Computational Analysis of the APC/C Recognition Motif by Predicting D-Boxes and KEN-Boxes |
title_full | GPS-ARM: Computational Analysis of the APC/C Recognition Motif by Predicting D-Boxes and KEN-Boxes |
title_fullStr | GPS-ARM: Computational Analysis of the APC/C Recognition Motif by Predicting D-Boxes and KEN-Boxes |
title_full_unstemmed | GPS-ARM: Computational Analysis of the APC/C Recognition Motif by Predicting D-Boxes and KEN-Boxes |
title_short | GPS-ARM: Computational Analysis of the APC/C Recognition Motif by Predicting D-Boxes and KEN-Boxes |
title_sort | gps-arm: computational analysis of the apc/c recognition motif by predicting d-boxes and ken-boxes |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3315528/ https://www.ncbi.nlm.nih.gov/pubmed/22479614 http://dx.doi.org/10.1371/journal.pone.0034370 |
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