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Haldane A , Flynn WF , He P , Vijayan RS , Levy RM
Structural propensities of kinase family proteins from a Potts model of residue co-variation
Protein Sci. 2016 Aug;25(8) :1378-84
PMID: 27241634 PMCID: PMC4972195
AbstractUnderstanding the conformational propensities of proteins is key to solving many problems in structural biology and biophysics. The co-variation of pairs of mutations contained in multiple sequence alignments of protein families can be used to build a Potts Hamiltonian model of the sequence patterns which accurately predicts structural contacts. This observation paves the way to develop deeper connections between evolutionary fitness landscapes of entire protein families and the corresponding free energy landscapes which determine the conformational propensities of individual proteins. Using statistical energies determined from the Potts model and an alignment of 2896 PDB structures, we predict the propensity for particular kinase family proteins to assume a "DFG-out" conformation implicated in the susceptibility of some kinases to type-II inhibitors, and validate the predictions by comparison with the observed structural propensities of the corresponding proteins and experimental binding affinity data. We decompose the statistical energies to investigate which interactions contribute the most to the conformational preference for particular sequences and the corresponding proteins. We find that interactions involving the activation loop and the C-helix and HRD motif are primarily responsible for stabilizing the DFG-in state. This work illustrates how structural free energy landscapes and fitness landscapes of proteins can be used in an integrated way, and in the context of kinase family proteins, can potentially impact therapeutic design strategies.
Notes1469-896x Haldane, Allan Flynn, William F He, Peng Vijayan, R S K Levy, Ronald M R01 GM030580/GM/NIGMS NIH HHS/United States Journal Article Research Support, N.I.H., Extramural United States Protein Sci. 2016 Aug;25(8):1378-84. doi: 10.1002/pro.2954. Epub 2016 Jun 26.