New computational studies to support cyclin-dependent kinase 9 inhibitor screening and design

Abstract

Aims: Cyclin-dependent kinase 9 (CDK9) plays a major role in the regulation of transcription. Its overexpression -which occurs in several types of cancer- increases the levels of certain antiapoptotic proteins that can lead to tumorigenesis, therefore the identification of new, more potent and more selective inhibitors is essential.
 Methods: In this study we present a computational approach, which can facilitate lead selection and optimization.
 Results: First, a pharmacophore hypothesis based on the active compounds has been developed to identify the key features for the ligand-target interaction. This was followed by the docking of the compounds into the active site of CDK9, the poses and interactions with the amino acids were compared with those of the co-crystallized ligand. The mode of their binding further explained the characteristics of these inhibitors while the docking scores can be a factor in the selection of active compounds in the future. Finally, a field-based QSAR model was also created, to predict the activity of inhibitor candidates.
 Conclusion: With our current work we deepened our knowledge about the interactions between CDK9 and its inhibitors, which can contribute to the discovery of novel CDK9 inhibitors.