Title:Understanding the Structural Basis for Inhibition of Cyclin-Dependent Kinases. New Pieces in the Molecular Puzzle
Volume: 18
Issue: 9
Author(s): Nayara M. Bernhardt Levin, Val Oliveira Pintro, Mauricio Boff de Avila, Bruna Boldrini de Mattos and Walter Filgueira De Azevedo Jr.*
Affiliation:
- Laboratory of Computational Systems Biology, Faculty of Biosciences - Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681, Porto Alegre-RS 90619- 900,Brazil
Keywords:
Cyclin-dependent kinase, binding affinity, drug design, machine learning, neurodegenerative disease, inhibitors.
Abstract: Background: Cyclin-dependent kinases (CDKs) comprise an important protein family for
development of drugs, mostly aimed for use in treatment of cancer but there is also potential for development
of drugs for neurodegenerative diseases and diabetes. Since the early 1990s, structural
studies have been carried out on CDKs, in order to determine the structural basis for inhibition of this
protein target.
Objective: Our goal here is to review recent structural studies focused on CDKs. We concentrate on
latest developments in the understanding of the structural basis for inhibition of CDKs, relating structures
and ligand-binding information.
Method: Protein crystallography has been successfully applied to elucidate over 400 CDK structures.
Most of these structures are complexed with inhibitors. We use this richness of structural information
to describe the major structural features determining the inhibition of this enzyme.
Results: Structures of CDK1, 2, 4-9, 12 13, and 16 have been elucidated. Analysis of these structures
in complex with a wide range of different competitive inhibitors, strongly indicate some common features
that can be used to guide the development of CDK inhibitors, such as a pattern of hydrogen
bonding and the presence of halogen atoms in the ligand structure.
Conclusion: Nowadays we have structural information for hundreds of CDKs. Combining the structural
and functional information we may say that a pattern of intermolecular hydrogen bonds is of
pivotal importance for inhibitor specificity. In addition, machine learning techniques have shown improvements
in predicting binding affinity for CDKs.
