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GPS-CCD: A Novel Computational Program for the Prediction of Calpain Cleavage Sites

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  • Zexian Liu
  • Jun Cao
  • Xinjiao Gao
  • Qian Ma
  • Jian Ren
  • Yu Xue

Abstract

As one of the most essential post-translational modifications (PTMs) of proteins, proteolysis, especially calpain-mediated cleavage, plays an important role in many biological processes, including cell death/apoptosis, cytoskeletal remodeling, and the cell cycle. Experimental identification of calpain targets with bona fide cleavage sites is fundamental for dissecting the molecular mechanisms and biological roles of calpain cleavage. In contrast to time-consuming and labor-intensive experimental approaches, computational prediction of calpain cleavage sites might more cheaply and readily provide useful information for further experimental investigation. In this work, we constructed a novel software package of GPS-CCD (Calpain Cleavage Detector) for the prediction of calpain cleavage sites, with an accuracy of 89.98%, sensitivity of 60.87% and specificity of 90.07%. With this software, we annotated potential calpain cleavage sites for hundreds of calpain substrates, for which the exact cleavage sites had not been previously determined. In this regard, GPS-CCD 1.0 is considered to be a useful tool for experimentalists. The online service and local packages of GPS-CCD 1.0 were implemented in JAVA and are freely available at: http://ccd.biocuckoo.org/.

Suggested Citation

  • Zexian Liu & Jun Cao & Xinjiao Gao & Qian Ma & Jian Ren & Yu Xue, 2011. "GPS-CCD: A Novel Computational Program for the Prediction of Calpain Cleavage Sites," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-7, April.
  • Handle: RePEc:plo:pone00:0019001
    DOI: 10.1371/journal.pone.0019001
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    Cited by:

    1. Ashley I Heinson & Rob M Ewing & John W Holloway & Christopher H Woelk & Mahesan Niranjan, 2019. "An evaluation of different classification algorithms for protein sequence-based reverse vaccinology prediction," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-13, December.
    2. Bi-Qing Li & Yu-Dong Cai & Kai-Yan Feng & Gui-Jun Zhao, 2012. "Prediction of Protein Cleavage Site with Feature Selection by Random Forest," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-9, September.

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