Improving the Prediction of Kinase Binding Affinity Using Homology Models

J. Chyan, M. Moll, and L. E. Kavraki, “Improving the Prediction of Kinase Binding Affinity Using Homology Models,” in Proceedings of the Computational Structural Bioinformatics Workshop at the ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, Washington, DC, 2013.


Kinases are a class of proteins very important to drug design; they play a pivotal role in many of the cell signaling pathways in the human body. Thus, many drug design studies involve finding inhibitors for kinases in the human kinome. However, identifying inhibitors of high selectivity is a difficult task. As a result, computational prediction methods have been developed to aid in this drug design problem. The recently published CCORPS method [3] is a semi-supervised learning method that identifies structural features in protein kinases that correlate with kinase binding affinity to inhibitors. However, CCORPS is dependent on the amount of available structural data. The amount of known structural data for proteins is extremely small compared to the amount of known protein sequences. To paint a clearer picture of how kinase structure relates to binding affinity, we propose extending the CCORPS method by integrating homology models for predicting kinase binding affinity. Our results show that using homology models significantly improves the prediction performance for some drugs while maintaining comparable performance for other drugs.


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