Classifying kinase conformations using a machine learning approach.
BACKGROUND
Signaling proteins such as protein kinases adopt a diverse array of conformations to respond to regulatory signals in signaling pathways. Perhaps the most fundamental conformational change of a kinase is the transition between active and inactive states, and defining the conformational features associated with kinase activation is critical for selectively targeting abnormally regulated kinases in diseases. While manual examination of crystal structures have led to the identification of key structural features associated with kinase activation, the large number of kinase crystal structures (~3,500) and extensive conformational diversity displayed by the protein kinase superfamily poses unique challenges in fully defining the conformational features associated with kinase activation. Although some computational approaches have been proposed, they are typically based on a small subset of crystal structures using measurements biased towards the active site geometry.


RESULTS
We utilize an unbiased informatics based machine learning approach to classify all eukaryotic protein kinase conformations deposited in the PDB. We show that the orientation of the activation segment, measured by φ, ψ, χ1, and pseudo-dihedral angles more accurately classify kinase crystal conformations than existing methods. We show that the formation of the K-E salt bridge is statistically dependent upon the activation segment orientation and identify evolutionary differences between the activation segment conformation of tyrosine and serine/threonine kinases. We provide evidence that our method can identify conformational changes associated with the binding of allosteric regulatory proteins, and show that the greatest variation in inactive structures comes from kinase group and family specific side chain orientations.


CONCLUSIONS
We have provided the first comprehensive machine learning based classification of protein kinase active/inactive conformations, taking into account more structures and measurements than any previous classification effort. Further, our unbiased classification of inactive structures reveals residues associated with kinase functional specificity. To enable classification of new crystal structures, we have made our classifier publicly accessible through a stand-alone program housed at https://github.com/esbg/kinconform [DOI: 10.5281/zenodo.249090 ].
Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA.
Daniel Ian
DI
McSkimming
Department of Computer Science, University of Georgia, Athens, GA, 30602, USA.
Khaled
K
Rasheed
Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA. nkannan@uga.edu.|Department of Biochemistry & Molecular Biology, University of Georgia, Athens, GA, 30602, USA. nkannan@uga.edu.
Natarajan
N
Kannan
D020134:Catalytic Domain; D000069550:Machine Learning; D008958:Models, Molecular; D011487:Protein Conformation; D011494:Protein Kinases / Q000737:chemistry*
D011494:Protein Kinases
Activation segment; Classifier; Kinase conformation; Machine learning