Redefining the Protein Kinase Conformational Space with Machine Learning

Peter Man Un Ung, Rayees Rahman, Avner Schlessinger

Research output: Contribution to journalArticlepeer-review

51 Scopus citations


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 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. Ung and Rahman et al. constructed a machine-learning-based algorithm to refine the current classification of protein kinase structures into active and inactive conformations. Analysis of the small molecules recognized by each conformation captures conformation-specific chemical substructures.

Original languageEnglish
Pages (from-to)916-924.e2
JournalCell Chemical Biology
Issue number7
StatePublished - 19 Jul 2018


  • cheminformatics
  • classification
  • conformation
  • drug discovery
  • inhibitor
  • protein kinase
  • random forest
  • selectivity
  • structure


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