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Protein Kinases and Phosphatases02:54

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Redefining the Protein Kinase Conformational Space with Machine Learning.

Peter Man-Un Ung1, Rayees Rahman1, Avner Schlessinger1

  • 1Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.

Cell Chemical Biology
|June 5, 2018
PubMed
Summary
This summary is machine-generated.

Protein kinases adopt diverse shapes critical for function. A new classifier, Kinformation, maps these protein kinase conformations, revealing new states and guiding drug design for better kinase inhibitors.

Keywords:
cheminformaticsclassificationconformationdrug discoveryinhibitorprotein kinaserandom forestselectivitystructure

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Area of Science:

  • Biochemistry
  • Structural Biology
  • Pharmacology

Background:

  • Protein kinases are crucial enzymes with dynamic structures.
  • Conformational states are vital for kinase catalytic activity and regulation.
  • Understanding the full conformational space is key for drug development.

Purpose of the Study:

  • To define the conformational landscape of protein kinase catalytic domains.
  • To develop a computational tool for classifying kinase conformations.
  • To identify chemical features associated with specific kinase conformations.

Main Methods:

  • Analysis of structural features from the αC helix and DFG motif.
  • Development of a random forest classifier (Kinformation).
  • Classification of 3,708 protein kinase structures from the Protein Data Bank (PDB).
  • Network analysis of small molecules bound to different kinase conformations.

Main Results:

  • A refined classification scheme for kinase conformational states, including known active/inactive and a novel state.
  • Identification of specific chemical substructures linked to each conformation.
  • Demonstration that Kinformation accurately annotates kinase structures.

Conclusions:

  • The study expands the understanding of protein kinase conformational diversity.
  • Kinformation provides a valuable tool for structural analysis and annotation.
  • Findings will aid in developing more precise protein kinase inhibitors and improve structural modeling.