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Redefining the Protein Kinase Conformational Space with Machine Learning.

ABSTRACT: Protein kinases are dynamic, adopting different conformational states that are critical for their catalytic activity. We assess a range of structural features derived from the conserved ?C helix and DFG motif to define the conformational space of the catalytic domain of protein kinases. We then construct Kinformation, a random forest classifier, to annotate the conformation of 3,708 kinase structures in the PDB. Our classification scheme captures known active and inactive kinase conformations and defines an additional conformational state, thereby refining the current understanding of the kinase conformational space. Furthermore, network analysis of the small molecules recognized by each conformation captures chemical substructures that are associated with each conformation type. Our description of the kinase conformational space is expected to improve modeling of protein kinase structures, as well as guide the development of conformation-specific kinase inhibitors with optimal pharmacological profiles.

SUBMITTER: Ung PM

PROVIDER: S-EPMC6054563 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Redefining the Protein Kinase Conformational Space with Machine Learning.

Ung Peter Man-Un PM Rahman Rayees R Schlessinger Avner A

Cell chemical biology 20180531 7

Protein kinases are dynamic, adopting different conformational states that are critical for their catalytic activity. We assess a range of structural features derived from the conserved αC helix and DFG motif to define the conformational space of the catalytic domain of protein kinases. We then construct Kinformation, a random forest classifier, to annotate the conformation of 3,708 kinase structures in the PDB. Our classification scheme captures known active and inactive kinase conformations an ...[more]

PMID: 29861272

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