bioRxivBiochemistry
Substrate recruitment via eIF2γ enhances catalytic efficiency of a holophosphatase that terminates the Integrated Stress Response
Y. Yan, M. Shetty, H. P. Harding, G. George, A. Zyryanova, K. Labbe, A. Mafi, Q. Hao, C. Sidrauski, D. Ron
Abstract
Dephosphorylation of pSer51 of the subunit of translation initiation factor 2 (eIF2P) terminates signalling in the integrated stress response (ISR). A trimeric mammalian holophosphatase comprised of a PP1 catalytic subunit, the conserved C-terminally located [~]70 amino acid core of a substrate-specific regulatory subunit (PPP1R15A/GADD34 or PPP1R15B/CReP) and G-actin (an essential co-factor) efficiently dephosphorylate eIF2P in vitro. Unlike their viral or invertebrate counterparts, with whom they share the conserved 70 residue core, the mammalian PPP1R15s are large proteins of more than 600 residues. Genetic and cellular observations point to a functional role for regions outside the conserved core of mammalian PPP1R15A in dephosphorylating its natural substrate, the eIF2 trimer. We have combined deep learning technology, all-atom molecular dynamics simulations, X-Ray crystallography and biochemistry to uncover binding of the {gamma} subunit of eIF2 to a short helical peptide repeated four times in the functionally-important N-terminus of human PPP1R15A that extends past its conserved core. Binding entails insertion of Phe and Trp residues that project from one face of an -helix formed by the conserved repeats of PPP1R15A into a hydrophobic groove exposed on the surface of eIF2{gamma} in the eIF2 trimer. Replacing these conserved Phe and Trp residues with Ala compromises PPP1R15A function in cells and in vitro. These findings suggest mechanisms by which contacts between a distant subunit of eIF2 and elements of PPP1R15A distant to the holophosphatase active site contribute to dephosphorylation of eIF2P by the core PPP1R15 holophosphatase and to efficient termination of the ISR in mammals.
- organism: human
- assay: proteomics
- research focus: cell signaling & trafficking
- approach: deep learning
- approach: mechanistic / dynamical model
- approach: experimental assay
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