Literature DB >> 12036347

Structural classification of protein kinases using 3D molecular interaction field analysis of their ligand binding sites: target family landscapes.

Thorsten Naumann1, Hans Matter.   

Abstract

Protein kinases are critical components of signaling pathways and trigger various biological events. Several members of this superfamily are interesting targets for novel therapeutic approaches. All known eukaryotic protein kinases exhibit a conserved catalytic core domain with an adenosine 5'-triphosphate (ATP) binding site, which often is targeted in drug discovery programs. However, as ATP is common to kinases and other proteins, specific protein-ligand interactions are crucial prerequisites for valuable ATP site-directed ligands. In the present study, a set of 26 X-ray structures of eukaryotic protein kinases were classified into subfamilies with similar protein-ligand interactions in the ATP binding site using a chemometrical approach based on principal component analysis (PCA) and consensus PCA. This classification does not rely on protein sequence similarities, as descriptors are derived from three-dimensional (3D) binding site information only computed using GRID molecular interaction fields. The resulting classification, which we refer to as "target family landscape", lead to the identification of common binding pattern and specific interaction sites for particular kinase subfamilies. Moreover, those findings are in good agreement with experimental selectivity profiles for a series of 2,6,9-substituted purines as CDK inhibitors. Their interpretation in structural terms unveiled favorable substitutions toward selective CDK inhibitors and thus allowed for a rational design of specific ligands with minimized side effects. Additional 3D-quantitative structure-activity relationship (QSAR) analyses of a larger set of CDK-directed purines lead to the identification of essential structural requirements for affinity in this CDK ATP binding site. The combined interpretation of 3D-QSAR and the kinase target family landscape provides a consistent view to protein-ligand interactions, which are both favorable for affinity and selectivity in this important subfamily.

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Year:  2002        PMID: 12036347     DOI: 10.1021/jm011002c

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  12 in total

1.  Mapping protein pockets through their potential small-molecule binding volumes: QSCD applied to biological protein structures.

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Journal:  J Comput Aided Mol Des       Date:  2004-01       Impact factor: 3.686

2.  Peroxisome proliferator-activated receptors target family landscape: a chemometrical approach to ligand selectivity based on protein binding site analysis.

Authors:  Bernard Pirard
Journal:  J Comput Aided Mol Des       Date:  2003-11       Impact factor: 3.686

Review 3.  Chemogenomic approaches to rational drug design.

Authors:  D Rognan
Journal:  Br J Pharmacol       Date:  2007-05-29       Impact factor: 8.739

4.  Protein kinase-inhibitor database: structural variability of and inhibitor interactions with the protein kinase P-loop.

Authors:  Ronak Y Patel; Robert J Doerksen
Journal:  J Proteome Res       Date:  2010-09-03       Impact factor: 4.466

5.  Cross-reactivity virtual profiling of the human kinome by X-react(KIN): a chemical systems biology approach.

Authors:  Michal Brylinski; Jeffrey Skolnick
Journal:  Mol Pharm       Date:  2010-11-08       Impact factor: 4.939

6.  Catalysis of nucleophilic aromatic substitutions in the 2,6,8-trisubstituted purines and application in the synthesis of combinatorial libraries.

Authors:  Claudia Riva-Toniolo; Sascha Müller; Josette Schaub; Wolfgang K D Brill
Journal:  Mol Divers       Date:  2003       Impact factor: 2.943

7.  Mapping of ligand-binding cavities in proteins.

Authors:  C David Andersson; Brian Y Chen; Anna Linusson
Journal:  Proteins       Date:  2010-05-01

8.  Deciphering the Arginine-binding preferences at the substrate-binding groove of Ser/Thr kinases by computational surface mapping.

Authors:  Avraham Ben-Shimon; Masha Y Niv
Journal:  PLoS Comput Biol       Date:  2011-11-17       Impact factor: 4.475

9.  Classifying kinase conformations using a machine learning approach.

Authors:  Daniel Ian McSkimming; Khaled Rasheed; Natarajan Kannan
Journal:  BMC Bioinformatics       Date:  2017-02-02       Impact factor: 3.169

10.  The landscape of the prion protein's structural response to mutation revealed by principal component analysis of multiple NMR ensembles.

Authors:  Deena M A Gendoo; Paul M Harrison
Journal:  PLoS Comput Biol       Date:  2012-08-09       Impact factor: 4.475

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