Journal of Molecular BiologyMolecular BiologyPeer-reviewed journal article
ERK Allosteric Activation: The Importance of Two Ordered Phosphorylation Events
Abstract
ERK, a coveted proliferation drug target, is a pivotal kinase in the Ras/ERK signaling cascade. Despite this, crucial questions about its activation have not been fully explored on the foundational, conformational level. Such questions include (i) Why ERKs activation demands dual phosphorylation; (ii) What is the role of each phosphorylation site in the activation loop; and (iii) Exactly how the (ordered) phosphorylation steps affect the conformational ensembles of the activation loop, their propensities and restriction to a narrower range favoring ERKs catalytic action. Here we used explicit molecular dynamics simulations to study ERKs stability and the conformational changes in different stages along the activation process. The initial monophosphorylation event elongates the activation loop to enable the successive phosphorylations, which reintroduce stability/compactness through newly formed salt bridges. The interactions formed by the monophosphorylation are site-dependent, with threonines phosphorylation presenting stronger electrostatic interactions compared to tyrosines. Dual phosphorylated ERKs revealed a compact kinase structure which allows the HRD catalytic motif to stabilize the ATP. We further observe that the hinge and the homodimerization binding site responded to a tri-state signaling code based solely on the phosphorylation degree (unphosphorylated, monophosphorylated, dual phosphorylated) of the activation loop, confirming that the activation loop can allosterically influence distant regions. Last, our findings indicate that threonine phosphorylation as the second step is necessary for ERK to become effectively activated and that activation depends on the phosphorylation order. Collectively, we offer ERKs dual allosteric phosphorylation code in activation and explain why the phosphorylation site order is crucial.
- assay: protein structure
- research focus: protein structure prediction
- approach: statistical model
- approach: simulation
- Citations
- 11
- FWCI
- 3.29
- Field percentile
- top 7%
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