Receptor tyrosine kinase (RTK)–RAS signalling through the downstream mitogen-activated protein kinase (MAPK) cascade regulates cell proliferation and survival. The SHOC2–MRAS–PP1C holophosphatase complex functions as a key regulator of RTK–RAS signalling by removing an inhibitory phosphorylation event on the RAF family of proteins to potentiate MAPK signalling1. SHOC2 forms a ternary complex with MRAS and PP1C, and human germline gain-of-function mutations in this complex result in congenital RASopathy syndromes2,3,4,5. However, the structure and assembly of this complex are poorly understood. Here we use cryo-electron microscopy to resolve the structure of the SHOC2–MRAS–PP1C complex. We define the biophysical principles of holoenzyme interactions, elucidate the assembly order of the complex, and systematically interrogate the functional consequence of nearly all of the possible missense variants of SHOC2 through deep mutational scanning. We show that SHOC2 binds PP1C and MRAS through the concave surface of the leucine-rich repeat region and further engages PP1C through the N-terminal disordered region that contains a cryptic RVXF motif. Complex formation is initially mediated by interactions between SHOC2 and PP1C and is stabilized by the binding of GTP-loaded MRAS. These observations explain how mutant versions of SHOC2 in RASopathies and cancer stabilize the interactions of complex members to enhance holophosphatase activity. Together, this integrative structure–function model comprehensively defines key binding interactions within the SHOC2–MRAS–PP1C holophosphatase complex and will inform therapeutic development .
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 per month
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
The coordinates and structure factors for apo-SHOC2 and cryo-EM structures have been deposited in the PDB (apo-SHOC2 X-ray structure, PDB ID 7T7A; complex cryo-EM structure, PDB ID 7UPI). The variant information for disease-associated mutations for complex members is publicly available (ClinVar: https://www.ncbi.nlm.nih.gov/clinvar/ and COSMIC: https://cancer.sanger.ac.uk/cosmic).
Molecular Operating Environment (MOE) and Schrödinger software are publicly available for commercial and non-commercial use. Custom code for DMS analyses are available at: https://github.com/jkwonbio/Structure-function-analysis-of-the-SHOC2-MRAS-PP1C-holophosphatase-complex.git.
Rodriguez-Viciana, P., Oses-Prieto, J., Burlingame, A., Fried, M. & McCormick, F. A phosphatase holoenzyme comprised of Shoc2/Sur8 and the catalytic subunit of PP1 functions as an M-Ras effector to modulate Raf activity. Mol. Cell 22, 217–230 (2006).
Cordeddu, V. et al. Mutation of SHOC2 promotes aberrant protein N-myristoylation and causes Noonan-like syndrome with loose anagen hair. Nat. Genet. 41, 1022–1026 (2009).
Higgins, E. M. et al. Elucidation of MRAS-mediated Noonan syndrome with cardiac hypertrophy. JCI Insight 2, e91225 (2017).
Gripp, K. W. et al. A novel rasopathy caused by recurrent de novo missense mutations in PPP1CB closely resembles Noonan syndrome with loose anagen hair. Am. J. Med. Genet. A 170, 2237–2247 (2016).
Ma, L. et al. De novo missense variants in PPP1CB are associated with intellectual disability and congenital heart disease. Hum. Genet. 135, 1399–1409 (2016).
Simanshu, D. K., Nissley, D. V. & McCormick, F. RAS proteins and their regulators in human disease. Cell 170, 17–33 (2017).
Gripp, K. W. & Lin, A. E. Costello syndrome: a Ras/mitogen activated protein kinase pathway syndrome (rasopathy) resulting from HRAS germline mutations. Genet. Med. 14, 285–292 (2012).
Young, L. C. et al. SHOC2–MRAS–PP1 complex positively regulates RAF activity and contributes to Noonan syndrome pathogenesis. Proc. Natl Acad. Sci. USA 115, E10576–E10585 (2018).
Wang, T. et al. Gene essentiality profiling reveals gene networks and synthetic lethal interactions with oncogenic Ras. Cell 168, 890–903 (2017).
Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens. Nature 568, 511–516 (2019).
Sulahian, R. et al. Synthetic lethal Interaction of SHOC2 depletion with MEK inhibition in RAS-driven cancers. Cell Rep. 29, 118–134 (2019).
Boned Del Río, I. et al. SHOC2 complex-driven RAF dimerization selectively contributes to ERK pathway dynamics. Proc. Natl Acad. Sci. USA 116, 13330–13339 (2019).
Klein, S. A., Majumdar, A. & Barrick, D. A second backbone: the contribution of a buried asparagine ladder to the global and local stability of a leucine-rich repeat protein. Biochemistry 58, 3480–3493 (2019).
Motta, M. et al. SHOC2 subcellular shuttling requires the KEKE motif-rich region and N-terminal leucine-rich repeat domain and impacts on ERK signalling. Hum. Mol. Genet. 25, 3824–3835 (2016).
Kwon, J. J. & Hahn, W. C. A leucine-rich repeat protein provides a SHOC2 the RAS circuit: a structure-function perspective. Mol. Cell. Biol. 41, e00627-20 (2021).
Terrak, M., Kerff, F., Langsetmo, K., Tao, T. & Dominguez, R. Structural basis of protein phosphatase 1 regulation. Nature 429, 780–784 (2004).
Choy, M. S. et al. SDS22 selectively recognizes and traps metal-deficient inactive PP1. Proc. Natl Acad. Sci. USA 116, 20472–20481 (2019).
Young, L. C. et al. An MRAS, SHOC2, and SCRIB complex coordinates ERK pathway activation with polarity and tumorigenic growth. Mol. Cell 52, 679–692 (2013).
Egloff, M. P. et al. Structural basis for the recognition of regulatory subunits by the catalytic subunit of protein phosphatase 1. EMBO J. 16, 1876–1887 (1997).
Zhao, S. & Lee, E. Y. A protein phosphatase-1-binding motif identified by the panning of a random peptide display library. J. Biol. Chem. 272, 28368–28372 (1997).
Hannig, V., Jeoung, M., Jang, E. R., Phillips, J. A. 3rd & Galperin, E. A novel SHOC2 variant in Rasopathy. Hum. Mutat. 35, 1290–1294 (2014).
Mysore, V. P. et al. A structural model of a Ras–Raf signalosome. Nat. Struct. Mol. Biol. 28, 847–857 (2021).
Sieburth, D. S., Sun, Q. & Han, M. SUR-8, a conserved Ras-binding protein with leucine-rich repeats, positively regulates Ras-mediated signaling in C. elegans. Cell 94, 119–130 (1998).
Matsunaga-Udagawa, R. et al. The scaffold protein Shoc2/SUR-8 accelerates the interaction of Ras and Raf. J. Biol. Chem. 285, 7818–7826 (2010).
Li, W., Han, M. & Guan, K. L. The leucine-rich repeat protein SUR-8 enhances MAP kinase activation and forms a complex with Ras and Raf. Genes Dev. 14, 895–900 (2000).
Wakula, P., Beullens, M., Ceulemans, H., Stalmans, W. & Bollen, M. Degeneracy and function of the ubiquitous RVXF motif that mediates binding to protein phosphatase-1. J. Biol. Chem. 278, 18817–18823 (2003).
Krzyzosiak, A. et al. Target-based discovery of an inhibitor of the regulatory phosphatase PPP1R15B. Cell 174, 1216–1228.e19 (2018).
Parton, R. G. & Hancock, J. F. Lipid rafts and plasma membrane microorganization: insights from Ras. Trends Cell Biol. 14, 141–147 (2004).
Park, E. et al. Architecture of autoinhibited and active BRAF–MEK1–14-3-3 complexes. Nature 575, 545–550 (2019).
Freed, E., Symons, M., Macdonald, S. G., McCormick, F. & Ruggieri, R. Binding of 14-3-3 proteins to the protein kinase Raf and effects on its activation. Science 265, 1713–1716 (1994).
Tran, T. H. et al. KRAS interaction with RAF1 RAS-binding domain and cysteine-rich domain provides insights into RAS-mediated RAF activation. Nat. Commun. 12, 1176 (2021).
Battye, T. G. G., Kontogiannis, L., Johnson, O., Powell, H. R. & Leslie, A. G. W. iMOSFLM: a new graphical interface for diffraction-image processing with MOSFLM. Acta Crystallogr. D 67, 271–281 (2011).
McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).
Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).
Afonine, P. V. et al. Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr. D 74, 531–544 (2018).
Kimanius, D., Dong, L., Sharov, G., Nakane, T. & Scheres, S. H. W. New tools for automated cryo-EM single-particle analysis in RELION-4.0. Biochem. J. 478, 4169–4185 (2021).
Rohou, A. & Grigorieff, N. CTFFIND4: Fast and accurate defocus estimation from electron micrographs. J. Struct. Biol. 192, 216–221 (2015).
Grant, T., Rohou, A. & Grigorieff, N. cisTEM, user-friendly software for single-particle image processing. eLife7, e35383 (2018).
Bepler, T. et al. Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs. Nat. Methods 16, 1153–1160 (2019).
Kelker, M. S., Page, R. & Peti, W. Crystal structures of protein phosphatase-1 bound to nodularin-R and tautomycin: a novel scaffold for structure-based drug design of serine/threonine phosphatase inhibitors. J. Mol. Biol. 385, 11–21 (2009).
Shima, F. et al. Structural basis for conformational dynamics of GTP-bound Ras protein. J. Biol. Chem. 285, 22696–22705 (2010).
Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).
Barad, B. A. et al. EMRinger: side chain-directed model and map validation for 3D cryo-electron microscopy. Nat. Methods 12, 943–946 (2015).
Zhu, K. et al. Antibody structure determination using a combination of homology modeling, energy-based refinement, and loop prediction. Proteins 82, 1646–1655 (2014).
Salam, N. K., Adzhigirey, M., Sherman, W. & Pearlman, D. A. Structure-based approach to the prediction of disulfide bonds in proteins. Protein Eng. Des. Sel. 27, 365–374 (2014).
Beard, H., Cholleti, A., Pearlman, D., Sherman, W. & Loving, K. A. Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein–protein complexes. PLoS One 8, e82849 (2013).
Bowers, K. J. et al. Scalable algorithms for molecular dynamics simulations on commodity clusters. In SC ’06: Proc. 2006 ACM/IEEE Conference on Supercomputing 43 (IEEE, 2006).
Lu, C. et al. OPLS4: improving force field accuracy on challenging regimes of chemical space. J. Chem. Theory Comput. 17, 4291–4300 (2021).
Yang, X. et al. Define protein variant functions with high-complexity mutagenesis libraries and enhanced mutation detection software. Preprint at bioRxiv https://doi.org/10.1101/2021.06.16.448102 (2021).
Schymkowitz, J. et al. The FoldX web server: an online force field. Nucleic Acids Res. 33, W382–W388 (2005).
Tiberti, M. et al. MutateX: an automated pipeline for in-silico saturation mutagenesis of protein structures and structural ensembles. Brief. Bioinform.23, bbac074 (2022).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
This work was funded in part by NIH–NCI (F32CA243290, J.J.K.); the Lustgarten Foundation; the Doris Duke Charitable Foundation; the Pancreatic Cancer Action Network; NIH–NCI K08 CA218420-02 and P50CA127003 (A.J.A.); and the Dana-Farber Cancer Institute Hale Center for Pancreatic Cancer Research (A.J.A. and W.C.H.), U01 CA176058 (W.C.H.), U01 CA224146 (A.J.A. and W.C.H.), and U01 CA250549 (A.J.A. and W.C.H.). This work was also funded in part by the Deerfield–Broad Discovery Research Collaboration. Deerfield Management Company is a healthcare-focused investment management firm.
D.E.R. receives research funding from members of the Functional Genomics Consortium (Abbvie, BMS, Jannsen, Merck and Vir), and is a director of Addgene. D.L.M. consults for the legal consulting firm Shook, Hardy & Bacon. F.M. is a consultant for the following companies: Amgen, Daiichi, Frontiers Med, Exuma Biotech, Ideaya Biosciences, Kura Oncology, Leidos Biomedical Research, PellePharm, Pfizer, PMV Pharma and Quanta Therapeutics. F.M. is a consultant for and cofounder of the following companies (with ownership interest including stock options): BridgeBio; DNAtrix, Olema Pharmaceuticals and Quartz. F.M. is the scientific director of the National Cancer Institute RAS Initiative at the Frederick National Laboratory for Cancer Research–Leidos Biomedical Research. F.M. has been a recipient of research grants from Daiichi Sankyo and Gilead Sciences and has a current grant from Boehringer Ingelheim. W.C.H. is a consultant for Thermo Fisher Scientific, Solasta Ventures, MPM Capital, KSQ Therapeutics, Tyra Biosciences, Jubilant Therapeutics, Function Oncology, RAPPTA Therapeutics, Frontier Medicine and Calyx. A.J.A. has consulted for Oncorus, Arrakis Therapeutics, Syros Pharmaceuticals, Boehringer Ingelheim, T-knife Therapeutics, AstraZeneca, Mirati Therapeutics and Merck, and has research funding from Mirati Therapeutics, Syros Pharmaceuticals, Bristol Myers Squibb, Revolution Medicines, Novartis and Novo Ventures that is unrelated to this work. W.C.H. and A.J.A. have funding from Deerfield that is related to the work described here.
Peer review information
Nature thanks the anonymous reviewers for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Cryo-EM map of the SHOC2 holophosphatase complex, surface model of contact surfaces, and secondary structure annotations.
a, The cryo-EM map used for modelling the complex, coloured according to local resolution using the colour map shown. The map was sharpened by an automatically determined B-factor of −90.7922 Å2 and filtered to local resolution, both determined by the methods implemented in Relion. b, A 3D histogram of the angular distribution in the final particle set as determined during the final 3D map refinement. Both the size and colour of the bins correspond to particle counts. The views are the same as shown in (a), with the map itself rendered in the centre of the histograms in grey. c, Reference-free 2D class averages generated from the final particle set. d, The Fourier shell correlation (FSC) for the independently refined half-maps from the final 3D map refinement (blue), and the full map and atomic model (orange). The “gold standard” half-maps FSC was calculated and corrected for masking effects using Relion; the map-model FSC was calculated by PHENIX using a mask around the model based on the 2.9 Å global resolution. FSC=0.5 and 0.143 thresholds are marked by dashed lines. The half-maps FSC crosses the 0.143 threshold at 2.8925 Å resolution, and the map-model FSC crosses the 0.5 threshold at 3.01 Å resolution. e, Surface model of unbound SHOC2, PP1CA, and MRAS. Grey indicates the interacting surfaces. f, MRAS cartoon representation with secondary structured labelled. g, PP1CA cartoon representation with secondary structured labelled. h, SHOC2 LRR and PP1C interactions i, SHOC2 N-term region and PP1C j, PP1C and MRAS k, SHOC2 LRR and MRAS shown in local electron density map corresponding to protein–protein interaction sites in Fig. 4. SHOC2 is shown in teal, PP1CA in yellow and MRAS in magenta. The map (2Fo-Fc) is at 4.5 sigma.
Extended Data Fig. 2 MD simulation (200 ns) of the SHOC2 complex, cryo-EM electron density maps of SHOC2-PP1C contacting surfaces of T411 and N-terminal region RVXF motif, and AUC analysis of PP1C pairwise interactions with complex members.
a, An overview of the MD simulation system for the SHOC2 complex. b, Root-mean-square-deviation (RMSD) of the protein α-carbon throughout the simulation. c, Interaction fraction of contacting residue pairs between SHOC2 and PP1C. d, Interaction fraction of contacting residue pairs between SHOC2 and MRAS. e, Interaction fraction of contacting residue pairs between MRAS and PP1C. f, Local electron density map for T411 of SHOC2 (teal) and K147 of PP1CA (orange) and their neighbouring residues (left) and SHOC2 N-terminal residues interacting with RVXF binding pocket of PP1c (right). The map (2Fo-Fc) is at 4.5 sigma. g, SV-AUC analysis of PP1C binding to SHOC2 or MRAS–GppCp compared to PP1C alone, and with the presence of SHOC2 and MRAS–GppCp.
Extended Data Fig. 3 Surface view of SHOC2-MRAS binding surface, in silico energy calculations of variant substitutions, and correlation of intrinsic protein stability and interaction energies of SHOC2 M173 mutations with DMS functional scores.
Structural view of a, SHOC2 and b, MRAS with hydrophobicity (yellow) and polar (teal) surfaces coloured, and red outlines indicate hydrophobic interaction surfaces. c, Box and whisker plot for calculated differences of SHOC2-MRAS interaction energy between wild-type M173 and models of variants. In silico mutagenesis modelling grouped based on their hydrophobicity and charge states: hydrophobic (n = 8 residue calculated energies: I, V, L, F, M, A, W, and P); polar uncharged group includes (n = 7 residue calculated energies: C, G, T, S, Y, N, and Q); polar negatively charged (n = 2 residue calculated energies: D, and E); polar positively charged (n = 3 residue calculated energies: H, K, and R). Centre line represents median, whiskers represent the first and fourth quartiles, box edges represent the second and third quartiles. All other observed data points outside the boundary of the whiskers are plotted as outliers. Mean and outliers are shown in crosses and dots respectively. SHOC2 M173 variant fitness scores in the DMS screen are presented on the x-axes and d, calculated protein interaction energy between SHOC2 and complex members; e, effect on intrinsic protein stability; and f, combined multiple linear model (0.12*contact energy + 0.25*intrinsic protein stability − 0.75) are represented on the y-axes. Line (blue) represent linear regression model, 95% confidence interval of best fit line (dashed black lines), R2 (goodness of fit), and linear model p-value (analysis of regression coefficient significantly non-zero) indicated.
Extended Data Fig. 4 Comparison of crystal SHOC2 and SHOC2 in the cryo-EM holoenzyme.
a, Primary sequence analysis of highly conserved SHOC2 LRRs with consensus sequence indicated above. The canonical highly conserved leucine-rich repeat motif is indicated above; boxes of hydrophobic residues based on structure data (teal); and disruption in core hydrophobic core residues within LRR11 and LRR12 are indicated (blue box). b, Measurements of dihedral angles between each two neighbouring LRRs (red text) and the distance between alpha carbons of R104 at LRR1 and I545 on LRR20 for crystal SHOC2 (indicated by line), and c, the SHOC2 in the Cryo-EM holoenzyme. d, Per-residue fluctuation reflected from 200-ns MD simulations for crystal SHOC2 and SMP holoenzyme.
Extended Data Fig. 5 In silico modelling of SHOC2 complex interaction with the RAS–RAF dimeric multimer unit.
a, Rank order of 17 established models with preferred van der Waals, electrostatic, and solvation energies (natural log of negative S-score) were manually annotated for spatial accommodation of RAS members to be oriented/embedded within a plasma membrane (indicated red dots). The top energetically favourable model that accommodates RAS orientation within the membrane (model 6) was selected. b, Structural model of SHOC2 complex interacting with dimeric multimer unit (2x RAS, 2x RAF, 2x MEK, and 2x 14-3-3) is presented with c & d, additional rotational views of the docked complex. Individual protein units are coloured and labelled.
Extended Data Fig. 6 In silico modelling and energy calculation for SHOC2(M173I), PP1C(P50R), and MRAS(Q71L) mutations and evaluation of PP1C isoforms in MRAS complex association.
a, Zoom-in views for SHOC2 M173 and modelled M173I mutation with distance measurement to contacting residues on MRAS. b, Zoom-in views for PP1C P50 and modelled P50R mutation with distance measurement to contacting residues on SHOC2. c, Zoom-in views for MRAS Q71 and modelled Q71L mutation with distance measurement to surrounding residues. d, Predicted interaction energy for the WT and mutated residues. e, SV-AUC analysis of SHOC2 holoenzyme with PP1C isoforms (PP1Cα/β/γ) formation in the presence of MRAS–GppCp. Line trace of 1 technical replicate, representative of 3 biological replicates.
Extended Data Fig. 7 Evaluation of RAS isoforms in the context of SHOC2 holophosphatase complex formation.
a, Multiple sequence alignment analysis (EMBL-EBI ClustalW) of RAS isoforms (MRAS, KRAS, HRAS, NRAS). Switch I (red), Switch II (blue), and P-loop (orange) are annotated. MRAS residues that interact with PP1C (cyan highlighted) and SHOC2 (yellow highlighted) are boxed if they contribute < −1.5kcal/mol of calculated paired interaction. b, Mean interaction energy calculated through molecular dynamic simulation of RAS isoforms (n = average of 5 representative frames/RAS isoform) and error bars represent standard deviation of the mean. c, BLI experimentation of MRAS and KRAS complex with activated SHOC2–PP1C. Mean (n = 3 technical replicates, representative of 3 independent experiments) for binding constants (Ka, kd, KD and kin) and error bars (standard deviation) are presented. d, Immunoprecipitation of various exogenously expressed oncogenic RAS isoforms from 293T cells co-transfected with FLAG-tagged RAS and Myc-tagged SHOC2 (representative of 3 biological replicates).
Extended Data Fig. 8 High-resolution heat map of the SHOC2 DMS screen.
High-resolution heat map representation of log2-fold change (LFC) allele enrichment and depletion between trametinib treatment and vehicle control, centred on mean of wild type (silent mutants) and scaled to mean of nonsense mutants (scaled LFC), providing relative enrichment (red) vs depletion (blue) relative to SHOC2 WT. SHOC2 positional evolutionary sequence variation (Evo Score; higher value = less conserved) and protein–protein interacting residues (PPI) from cryo-EM data are indicated (Methods). An additional heat map is provided below which depicts the average scaled LFC score of residues that have been grouped according to biophysical characteristics (orange = GOF; purple = LOF), including negative-charge (D/E), positive charge (K/R), and hydrophobic (G/A/V/L/I/M), polar uncharged (S/T/C/Y/N/Q), non-polar large aromatic (F/W/Y/H), and helix breaker (P/G).
Extended Data Fig. 9 Analysis of the mutational tolerance of SHOC2 residues based on DMS and residue contact points within the SHOC2 complex, and effect of SHOC2 variants on growth in low attachment, MAPK signalling at baseline and in response to MEK inhibition.
a, Violin plot of SHOC2 mean positional viability for surface-contacting residues between complex members, PP1C (yellow, n = 28 positions) and MRAS (maroon, n = 26 positions), compared to core residues (dark green, n = 198 positions) and surface non-contacting residues (light green, n = 329). Centre line represents median, whiskers represent the first and fourth quartiles, box edges represent the second and third quartiles. b, MIA PaCa-2 with knockout of endogenous SHOC2 and stably re-expressing various SHOC2 gain-of-function (red) and loss-of-function (blue) variants were seeded in ultra-low attachment plates and cultured for 7 days. Viability end-point via Cell-Titer-Glo is presented on x-axis along scaled LFC from fitness screen with PaTu-8902. Error bars represent standard deviation of GILA CTG viability (n=6 technical replicates; representative of 3 biological replicates). Line (green) represent simple linear regression model, 95% confidence interval (black dashed lines), R2 (goodness of fit), and linear model p-value < 0.0001 (analysis of regression coefficient significantly non-zero) indicated. c, Wild type (WT) and gain- or loss-of-function (GOF/LOF) variants were stably expressed in KRAS mutant cell line MIA PaCa-2 with knock-out of endogenous SHOC2. d, Densitometry quantification of P-S259 RAF1 relative to total RAF1 and normalized to levels from wild-type expressing cells. Centre line represents median and whiskers represent interquartile range. ***p<0.001, two-sided t-test between LOF (n = 5 variants) and GOF (n = 6 variants) SHOC2 alleles, representative of 3 biological replicates. e, WT and GOF/LOF variants were stably expressed in KRAS mutant cell line MIA PaCa-2 with knock-out of endogenous SHOC2. Cells were treated with the MEK1/2 inhibitor trametinib (10nM) for 24 h prior to western blot. f, Densitometry quantification of P-S259 RAF1 relative to total RAF1 and normalized to levels from wild-type expressing cells. Centre line represents median and whiskers represent interquartile range. ***p<0.001, two-sided t-test between LOF (n = 6 variants) and GOF (n = 6 variants) SHOC2 alleles, representative of 3 biological replicates. g, Immunoprecipitation of V5-tagged SHOC2 variants in 293T cells co-transfected with HA-MRAS. h, Densitometry analysis of relative prey including endogenous PP1CB (yellow) and MRAS (maroon) normalized to V5 bait (y-axis) and DMS fitness score (LFC Z-score) (x-axis). Lines represent simple linear regression model, R2 (goodness of fit), and linear model p-value < 0.0001 (analysis of regression coefficient significantly non-zero) indicated, representative of 3 biological replicates. i, DMS results for N-terminal region of SHOC2 (residues 60–68) depicted via sequence logo plot per amino acid substitution at respective positions (ggseqlogo).
Extended Data Fig. 10 Functional consequence of mutations in the SHOC2 LRR surface based on biophysical attributes of amino acid substitutions, and in silico mutagenesis study of N434D, and 200-ns MD simulations for SHOC2 T411A, Q249K and G63R mutations.
Three major regions of SHOC2 LRR that mediated complex member binding: (1) C-term PP1C-binding region - left; (2) N-term PP1C-binding region - middle; (3) Concave MRAS binding surface - right are presented in columns. Electrostatic surface depiction of SHOC2 (red = negative; blue = positive) for a & b, SHOC2 LRR region surfaces that bind PP1C and c, MRAS are presented (1st row), along with select protein–protein interacting residues of SHOC2 labelled. Subsequently, the SHOC2 DMS screen functional score (Scaled LFC) was averaged for every surface residue of SHOC2 based on the biophysical characteristics of substituted residues at each given surface position and projected onto the SHOC2 surface with colorimetric scale (orange = GOF; purple = LOF). The average functional effects (mean scaled LFC) of positively charged residues (K/R) are presented (2nd row) for d, C-term PP1C-binding region - left; e, N-term PP1C-binding region - middle; f, Concave MRAS binding surface – right. The average functional effects of negatively charged residues (D/E) are presented (3rd row) for g, C-term PP1C-binding region - left; h, N-term PP1C-binding region - middle; i, Concave MRAS binding surface – right. The average functional effects of hydrophobic residues - non-polar, non-aromatic (G/A/V/L/I/M) are presented (4th row) for j, C-term PP1C-binding region - left; k, N-term PP1C binding region - middle; l, Concave MRAS binding surface. m, Predicted interaction energy towards the K150 on PP1C for the SHOC2 WT and N434D mutation. n, Interaction fraction of contacting residue pairs for WT and the N434D mutation during the 200-ns MD simulation. Zoom-in views for SHOC2 N434 o, and modelled N434D mutation with distance measurement to PP1C K150. p, Interaction fraction of contacting residue pairs for WT and mutations. r, Zoom-in views for SHOC2 T411 and modelled T411A mutation with distance measurement to contacting residues on PP1C. s, Zoom-in views for SHOC2 Q249 and modelled Q249K mutation with distance measurement to contacting residues on PP1C. t, Zoom-in views for modelled SHOC2 G63 and G63R mutation with distance measurement to contacting residues on PP1C. The calculated interaction energy is coloured to the SHOC2 protein surface for visualization. u, Box plot of SHOC2 variants with mutations at protein interaction sites that are stabilizing (ddG < −1), inert (ddG: > −1 and < 1), destabilizing (ddG > 1) by FoldX computations. Centre line represents median, whiskers represent the first and fifth quartiles, box edges represent the second and fourth quartiles of SHOC2 variants with mutations in residues interacting with PP1C that are stabilizing (n = 18 variants), inert (n = 912 variants), destabilizing (n = 134 variants) or interacting with MRAS that are stabilizing (n = 13 variants), inert (n = 779 variants), and destabilizing (n = 196 variants) that were functionally evalulated in the DMS screen.
Extended Data Fig. 11 Druggability analysis of the SHOC2 holophosphatase complex and schematic diagram of proposed model for SHOC2 holophosphatase complex assembly.
a, SiteMap analysis of SHOC2 complex identifying druggable binding pockets between SHOC2–PP1C, b, SHOC2-MRAS and c, PP1C-MRAS. d, The SiteScore is capped at 1.0 to limit the effect of hydrophilicity in charged and highly polar sites. A SiteScore of 0.80 has been found to accurately distinguish between drug-binding and non-drug-binding sites. For Druggability score, the hydrophilic score is not capped. e, Hypothesized model of the SHOC2 holophosphatase complex. While MRAS is GDP bound, PP1C and SHOC2 exist in bound/unbound equilibrium in cytoplasm. After RTK stimulation and MRAS–GTP activation, the SHOC2–PP1C complex binds with MRAS at the plasma membrane to produce stable complex formation, and is likely to localize the SHOC2 holophosphatase to lipid domains with concentrated RAS-bound RAF1 to dephosphorylate ‘S259’ on RAF and enable MAPK signalling.
Supplementary Figure 1
Uncropped immunoblots from Extended Data Figures 7 and 9. a, Immunoblots for Extended Data Fig 7 showing expression of exogenously expressed oncogenic RAS isoforms in 293T cells co-transfected with FLAG-tagged RAS and Myc-tagged SHOC2. b, Immunoblots for Extended Data Fig 9 showing stable expression of SHOC2 variants in KRAS mutant cell line MIA PaCa-2 with knockout of endogenous SHOC2. Beta-actin, sample processing control. c, Immunoblots for Extended Data Fig 9 showing stable expression of SHOC2 variants in KRAS mutant cell line MIA PaCa-2 with knockout of endogenous SHOC2 and treatment with MEK1/2 inhibitor trametinib. Beta-actin, sample processing control. d, Immunoblots for Extended Data Fig 9 showing immunoprecipitation of V5-tagged SHOC2 variants in 293T cells co-transfected with HA-MRAS. Beta-actin, loading control. Proteins were detected by western blotting, using the indicated antibodies and the orange rectangles show the cropping location.
Supplementary Table 1
SHOC2–PP1C-MRAS Complex Interaction Energy. Summary of all residue interaction energy calculations of SHOC2–PP1C-MRAS complex members, including mutant models.
Supplementary Table 2
SHOC2 Deep Mutational Scanning (DMS) Screen Data. Data from DMS screen (per variant level scaled LFC and mean positional scaled LFC scores).
Supplementary Table 3
Integration of DMS and clinically relevant datasets. Summary files for cancer (COSMIC) and Noonan-like Syndrome (ClinVar) of SHOC2, MRAS, and PP1C.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kwon, J.J., Hajian, B., Bian, Y. et al. Structure–function analysis of the SHOC2–MRAS–PP1C holophosphatase complex. Nature 609, 408–415 (2022). https://doi.org/10.1038/s41586-022-04928-2
This article is cited by
SHOCing RAF into action
Nature Structural & Molecular Biology (2022)
Structural keys unlock RAS–MAPK cellular signalling pathway
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.