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Biophys Chem. 2018 Apr;235:1-8. doi: 10.1016/j.bpc.2018.01.004. Epub 2018 Feb 1.

Development of CDK-targeted scoring functions for prediction of binding affinity.

Author information

1
Laboratory of Computational Systems Biology, School of Sciences, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681, Porto Alegre, RS 90619-900, Brazil; Graduate Program in Cellular and Molecular Biology, The Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681, Porto Alegre, RS 90619-900, Brazil.
2
Laboratory of Computational Systems Biology, School of Sciences, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681, Porto Alegre, RS 90619-900, Brazil.
3
Laboratory of Computational Systems Biology, School of Sciences, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681, Porto Alegre, RS 90619-900, Brazil; Graduate Program in Cellular and Molecular Biology, The Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681, Porto Alegre, RS 90619-900, Brazil. Electronic address: walter@azevedolab.net.

Abstract

Cyclin-dependent kinase (CDK) is an interesting biological macromolecule due to its role in cell cycle progression, transcription control, and neuronal development, to mention the most studied biological activities. Furthermore, the availability of hundreds of structural studies focused on the intermolecular interactions of CDK with competitive inhibitors makes possible to develop computational models to predict binding affinity, where the atomic coordinates of binary complexes involving CDK and ligands can be used to train a machine learning model. The present work is focused on the development of new machine learning models to predict binding affinity for CDK. The CDK-targeted machine learning models were compared with classical scoring functions such as MolDock, AutoDock 4, and Vina Scores. The overall performance of our CDK-targeted scoring function was higher than the previously mentioned scoring functions, which opens the possibility of increasing the reliability of virtual screening studies focused on CDK.

KEYWORDS:

Bioinformatics; CDK; Docking; Drug design; Machine learning; Protein

PMID:
29407904
DOI:
10.1016/j.bpc.2018.01.004
[Indexed for MEDLINE]

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