Science SignalingBiochemistryPeer-reviewed journal article
Ionizable networks mediate pH-dependent allostery in SH2 signaling proteins
Papa Kobina Van DyckiD, Luke J. PiszkiniD, Elijah A. Gorski, Eduarda Tartarella Nascimento, Joshua A. AbebeiD, Logan M. Hoffmann, Jeffrey W. PengiD, Katharine A. WhiteiD
Abstract
Intracellular pH dynamics regulate normal cell biology, but most molecular drivers remain unknown. We developed a computational pipeline to identify pH-sensitive proteins and their mechanisms. We applied the pipeline to SHP2, a pH-sensitive signaling protein, with unknown mechanism. We found that SHP2 phosphatase activity is pH-sensitive in vitro and in cells, and mutation of identified His116 and Glu252 abolishes pH-sensitive function. We also discovered that Src is an unrecognized pH-dependent kinase and mutation of the identified ionizable network abolishes pH-sensitive activity. Importantly, we found that Src kinase activity was pH sensitive even in the presence of EGF and with a phosphomimetic (Src-Y527E) mutant required for auto-inhibitory SH2 domain binding. Thus, the identified pH-sensitive regulation of Src kinase activity functions in concert with established mechanisms of Src regulation by phosphorylation. Constant pH molecular dynamics simulations performed on both SHP2 and Src support allosteric regulation mediated by pH-dependent binding of inhibitory SH2 domains to the respective catalytic domains. Finally, we show that evolutionarily conserved putative pH-sensing networks were identified across SH2 domain-containing signaling proteins. Taken together, our computational, biophysical, and cellular analyses reveal a role for pHi dynamics in allosterically regulating activity of modular SH2 signaling proteins to control biology. One-sentence summaryThis paper investigates the role of pH in the allosteric regulation of signaling proteins with SH2 domains, specifically focusing on SHP2 and Src
- organism: human
- assay: proteomics
- research focus: cell signaling & trafficking
- approach: statistical model
- approach: simulation
- Citations
- 7
- FWCI
- 1.68
- Field percentile
- top 15%
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