Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques

Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques

S. Prasanthi (University of Hyderabad, India), S.Durga Bhavani (University of Hyderabad, India), T. Sobha Rani (University of Hyderabad, India) and Raju S. Bapi (University of Hyderabad, India)
Copyright: © 2013 |Pages: 11
DOI: 10.4018/978-1-4666-3604-0.ch050
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Abstract

Vast majority of successful drugs or inhibitors achieve their activity by binding to, and modifying the activity of a protein leading to the concept of druggability. A target protein is druggable if it has the potential to bind the drug-like molecules. Hence kinase inhibitors need to be studied to understand the specificity of a kinase inhibitor in choosing a particular kinase target. In this paper we focus on human kinase drug target sequences since kinases are known to be potential drug targets. Also we do a preliminary analysis of kinase inhibitors in order to study the problem in the protein-ligand space in future. The identification of druggable kinases is treated as a classification problem in which druggable kinases are taken as positive data set and non-druggable kinases are chosen as negative data set. The classification problem is addressed using machine learning techniques like support vector machine (SVM) and decision tree (DT) and using sequence-specific features. One of the challenges of this classification problem is due to the unbalanced data with only 48 druggable kinases available against 509 non-drugggable kinases present at Uniprot. The accuracy of the decision tree classifier obtained is 57.65 which is not satisfactory. A two-tier architecture of decision trees is carefully designed such that recognition on the non-druggable dataset also gets improved. Thus the overall model is shown to achieve a final performance accuracy of 88.37. To the best of our knowledge, kinase druggability prediction using machine learning approaches has not been reported in literature.
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Background

Vieth et al., (2004) conduct a study of kinase targets and inhibitors in order to identify medicinally relevant kinase space. Using both sequence based information and the small molecule selectivity information, they presented the first dendogram of kinases based on small molecule data. This study concludes that the structural basis of kinase inhibitor selectivity will require knowledge of complexes of one ligand with multiple targets. Classification of kinase inhibitors with a bayesian model was studied by Xia et al., (2004). Using Bayesian statistics, a model for general and specific kinase inhibitors was proposed. They have considered serine/ threonine and tyrosine kinase inhibitors (Amgen compounds) from CORP data set. Kinase model was generated using properties like number of hydrogen bond donors, halogens, aromatic residues, value of AlogP and molecular weight. The general kinase model described was trained on tyrosine kinase inhibitors achieving prediction accuracy of 80%.

In order to initiate the study of kinase inhibitors we need both kinase target and inhibitor features that are available in various databases.

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