Abstract
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.
Highlights
Cyclin-dependent kinases (CDKs) are poised to play a central role in the orderly transition of the eukaryotic cells through different stages of the mitotic cell division cycle [1]
Though initially considered as tumour suppressors based on their ability to block cell proliferation, CDK inhibitors (CDKIs) play pertinent roles in the regulation of a myriad of cellular processes including transcription, apoptosis, cell migration and cytoskeletal dynamics, which may be oncogenic under certain circumstances [3,11]
It was found that 10 sequences did not find any significant hit, bringing forth that general methods of similarity-based searches do not provide a reliable solution to the identification of CDKIs and a method specific to these proteins should be developed
Summary
Cyclin-dependent kinases (CDKs) are poised to play a central role in the orderly transition of the eukaryotic cells through different stages of the mitotic cell division cycle [1]. Analysis of the structural aspects of cellular CDKIs leads to the identification of inhibitory lead peptides amenable to peptidomimetic development. Conversion of these peptides into pharmaceutically useful molecules provides a wealth of potential drug candidates capable of inhibiting CDKs, blocking cell-cycle progression, modulating transcription and inducing apoptosis selectively in cancer cells. Some of these, such as flavopiridol (L868275, HMR1275; Aventis), 7-hydroxystaurosporine (UCN01, KW-2401; Kyowa Hakko Kogyo) and roscovitine (Rroscovitine, CYC202; Cyclacel), have already reached the stage of clinical evaluation [14,15]. CDKIs constitute potential targets for therapeutic stem cell manipulations [20]
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