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Descriptor-Based Profile Analysis of Kinase Inhibitors to Predict Inhibitory Activity and to Grasp Kinase Selectivity

  • Park, Hyejin (Bioinformatics & Molecular Design Research Center) ;
  • Kim, Kyeung Kyu (Department of Molecular Cell Biology, Samsung Biomedical Research Institute, Sungkyunkwan University School of Medicine) ;
  • Kim, ChangHoon (Department of Biotechnology, Yonsei University) ;
  • Shin, Jae-Min (Bioinformatics & Molecular Design Research Center) ;
  • No, Kyoung Tai (MACROGEN)
  • Received : 2013.06.03
  • Accepted : 2013.06.12
  • Published : 2013.09.20

Abstract

Protein kinases (PKs) are an important source of drug targets, especially in oncology. With 500 or more kinases in the human genome and only few kinase inhibitors approved, kinase inhibitor discovery is becoming more and more valuable. Because the discovery of kinase inhibitors with an increased selectivity is an important therapeutic concept, many researchers have been trying to address this issue with various methodologies. Although many attempts to predict the activity and selectivity of kinase inhibitors have been made, the issue of selectivity has not yet been resolved. Here, we studied kinase selectivity by generating predictive models and analyzing their descriptors by using kinase-profiling data. The 5-fold cross-validation accuracies for the 51 models were between 72.4% and 93.7% and the ROC values for all the 51 models were over 0.7. The phylogenetic tree based on the descriptor distance is quite different from that generated on the basis of sequence alignment.

Keywords

Descriptor;Profile;Kinase;SVM

Acknowledgement

Supported by : Ministry for Health, Welfare & Family Affairs

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