GPS-ARM: Computational Analysis of the APC-C Recognition Motif by Predicting D-Boxes and KEN-BoxesReportar como inadecuado




GPS-ARM: Computational Analysis of the APC-C Recognition Motif by Predicting D-Boxes and KEN-Boxes - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

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.



Autor: Zexian Liu , Fang Yuan , Jian Ren, Jun Cao, Yanhong Zhou, Qing Yang , Yu Xue

Fuente: http://plos.srce.hr/



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