Abstract
The importance of protein kinases made them a target for many drug design studies. They play an essential role in cell cycle development and many other biological processes. Kinases are divided into different subfamilies according to the type and mode of their enzymatic activity. Computational studies targeting kinase inhibitors identification is widely considered for modelling kinase-inhibitor. This modelling is expected to help in solving the selectivity problem arising from the high similarity between kinases and their binding profiles. In this study, we explore the ability of two machine-learning techniques in classifying compounds as inhibitors or non-inhibitors for two members of the cyclin-dependent kinases as a subfamily of protein kinases. Random forest and genetic programming were used to classify CDK5 and CDK2 kinases inhibitors. This classification is based on calculated values of chemical descriptors. In addition, the response of the classifiers to adding prior information about compounds promiscuity was investigated. The results from each classifier for the datasets were analyzed by calculating different accuracy measures and metrics. Confusion matrices, accuracy, ROC curves, AUC values, F1 scores, and Matthews correlation, were obtained for the outputs. The analysis of these accuracy measures showed a better performance for the RF classifier in most of the cases. In addition, the results show that promiscuity information improves the classification accuracy, but its significant effect was notably clear with GP classifiers.
Highlights
Different important biological processes in the human body is related to the process of phosphorylation
We extracted the values for the first 1497 compounds against two protein kinases belonging to the cyclin-dependent kinases subfamily, CDK2, and CDK5
Www.ijacsa.thesai.org classifier more than its improvement for Random Forest (RF) classifier on the training set level. This improvement is clearly noticeable in Genetic Programming (GP) results for the test sets, GP accuracy is still low on test sets compared to RF
Summary
Different important biological processes in the human body is related to the process of phosphorylation. Computer-based approaches is being utilized in order to help profile the activity of different inhibitors against kinases and to explore and tackle the selectivity problem Among these techniques is machine learning, which is widely utilized in biological and medical related problems. Genetic Programming (GP) [14] is a machine learning technique that simulates biological evolution and is used for modelling by regression or classification It starts by a random population, it continues to produce generations and individuals by performing evolutionary operations such as mutations, crossover, and selection, aiming to improve a fitness function. We use genetic programming and random forest classification techniques for classifying inhibitors and non-inhibitors for two of the cyclin-dependent kinases, CDK5 and CDK2. Both techniques were used for modelling chemical descriptors information.
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More From: International Journal of Advanced Computer Science and Applications
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