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Classifying kinase conformations using a machine learning approach.

Daniel Ian McSkimming1, Khaled Rasheed2, Natarajan Kannan3,4

  • 1Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA.

BMC Bioinformatics
|February 4, 2017
PubMed
Summary
This summary is machine-generated.

Machine learning accurately classifies protein kinase conformations using activation segment orientation. This unbiased approach reveals evolutionary differences and residues critical for kinase function and drug targeting.

Keywords:
Activation segmentClassifierKinase conformationMachine learning

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Area of Science:

  • Biochemistry
  • Structural Biology
  • Computational Biology

Background:

  • Protein kinases regulate cellular signaling through conformational changes between active and inactive states.
  • Identifying these conformational features is crucial for developing targeted therapies for diseases involving kinase dysregulation.
  • The vast number of kinase structures and their conformational diversity present challenges for traditional analysis methods.

Purpose of the Study:

  • To develop an unbiased machine learning approach for classifying all known eukaryotic protein kinase conformations.
  • To identify key conformational features, specifically activation segment orientation, that accurately distinguish kinase states.
  • To investigate evolutionary differences and functional specificities within the protein kinase superfamily.

Main Methods:

  • Utilized an unbiased, informatics-based machine learning approach to analyze all available eukaryotic protein kinase crystal structures in the Protein Data Bank (PDB).
  • Employed measurements of activation segment orientation, including φ, ψ, χ1, and pseudo-dihedral angles, for classification.
  • Analyzed the statistical dependence of K-E salt bridge formation on activation segment orientation and identified kinase group-specific variations.

Main Results:

  • The machine learning model accurately classifies kinase conformations based on activation segment orientation, outperforming existing methods.
  • Activation segment orientation is statistically linked to K-E salt bridge formation.
  • Evolutionary differences were identified between tyrosine and serine/threonine kinases regarding activation segment conformation.
  • The method successfully identified conformational changes associated with allosteric regulatory protein binding.
  • Inactive kinase structures exhibit the greatest variation due to kinase group and family-specific side chain orientations.

Conclusions:

  • Developed the first comprehensive machine learning-based classification of protein kinase active/inactive conformations, surpassing previous efforts in scope and measurement.
  • The unbiased classification of inactive structures revealed specific residues associated with kinase functional specificity.
  • A publicly accessible program for classifying new crystal structures has been developed and made available at https://github.com/esbg/kinconform.