論文

査読有り
2011年5月

Calpain Cleavage Prediction Using Multiple Kernel Learning

PLOS ONE
  • David A. duVerle
  • ,
  • Yasuko Ono
  • ,
  • Hiroyuki Sorimachi
  • ,
  • Hiroshi Mamitsuka

6
5
開始ページ
e19035
終了ページ
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1371/journal.pone.0019035
出版者・発行元
PUBLIC LIBRARY SCIENCE

Calpain, an intracellular C alpha(2+)-dependent cysteine protease, is known to play a role in a wide range of e metabolic path through limited proteolysis of its substrate However, only a limited number of he e substrates are currently known, e exact mechanism of substrate recognition and cleavage by calpain still largely unknown While previous research has successfully applied standard machine-learning algorithms to accurately predict substrate cleavage by other similar types of proteases, their approach does not extend well to calpain, possibly due to its particular mode of proteolytic action and limited amount of experimental data. Through the use of Multiple Kernel Learning, a recent extension to the classic Support Vector Machine framework, we were able to train complex models based on rich, heterogeneous feature sets, leading to significantly improved prediction quality (6% over highest AUC score produced by state-of-the-art methods). In addition producing a stronger machine-learning model for the prediction of calpain cleavage, we were able to highlight importance and role of each feature of substrate sequences in defining specificity: primary sequence, secondary structure and solvent accessibility. Most notably, we showed there existed significant specificity differences across calpain sub-types despite previous assumption to the contrary. Prediction accuracy further successfully validated using, as an unbiased mutated sequences of calpastatin endogenous inhibitor of calpain) modified to no longer block calpain's proteolytic action. An online implementation of our prediction tool is available at http.//calpain.org.

リンク情報
DOI
https://doi.org/10.1371/journal.pone.0019035
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/21559271
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000290223500006&DestApp=WOS_CPL
ID情報
  • DOI : 10.1371/journal.pone.0019035
  • ISSN : 1932-6203
  • PubMed ID : 21559271
  • Web of Science ID : WOS:000290223500006

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