Machine learning methods for prediction of CDK-inhibitors.
Progression through the cell cycle involves the coordinated activities of a suite of cyclin/cyclin-dependent kinase (CDK) complexes. The activities of the complexes are regulated by CDK inhibitors (CDKIs). Apart from its role as cell cycle regulators, CDKIs are involved in apoptosis, transcriptional regulation, cell fate determination, cell migration and cytoskeletal dynamics. As the complexes perform crucial and diverse functions, these are important drug targets for tumour and stem cell therapeutic interventions. However, CDKIs are represented by proteins with considerable sequence heterogeneity and may fail to be identified by simple similarity search methods. In this work we have evaluated and developed machine learning methods for identification of CDKIs. We used different compositional features and evolutionary information in the form of PSSMs, from CDKIs and non-CDKIs for generating SVM and ANN classifiers. In the first stage, both the ANN and SVM models were evaluated using Leave-One-Out Cross-Validation and in the second stage these were tested on independent data sets. The PSSM-based SVM model emerged as the best classifier in both the stages and is publicly available through a user-friendly web interface at http://bioinfo.icgeb.res.in/cdkipred.
Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India.
Jayashree
J
Ramana
Dinesh
D
Gupta
D001185:Artificial Intelligence; D018844:Cyclin-Dependent Kinases / Q000037:antagonists & inhibitors*; D020407:Internet; D047428:Protein Kinase Inhibitors / Q000494:pharmacology*; D012372:ROC Curve
D047428:Protein Kinase Inhibitors; D018844:Cyclin-Dependent Kinases