Deep mutational scanning of the multi-domain phosphatase SHP2 reveals mechanisms of regulation and pathogenicity

Multi-domain enzymes can be regulated both by inter-domain interactions and structural features intrinsic to the catalytic domain. The tyrosine phosphatase SHP2 is a quintessential example of a multi-domain protein that is regulated by inter-domain interactions. This enzyme has a protein tyrosine phosphatase (PTP) domain and two phosphotyrosine-recognition domains (N-SH2 and C-SH2) that regulate phosphatase activity through autoinhibitory interactions. SHP2 is canonically activated by phosphoprotein binding to the SH2 domains, which causes large interdomain rearrangements, but autoinhibition is also disrupted by disease-associated mutations. Many details of the SHP2 activation are still unclear, the structure of the active state remains elusive, and hundreds of human variants of SHP2 have not been functionally characterized. Here, we perform scanning mutagenesis on both full-length SHP2 and its isolated PTP domain to examine mutational effects on inter-domain regulation and catalytic activity. Our experiments provide a comprehensive map of SHP2 mutational sensitivity, both in the presence and absence of interdomain regulation. Coupled with molecular dynamics simulations, our investigation reveals novel structural features that govern the stability of the autoinhibited and active states of SHP2. Our analysis also identifies key residues beyond the SHP2 active site that control PTP domain dynamics and intrinsic catalytic activity. This work expands our understanding of SHP2 regulation and provides new insights into SHP2 pathogenicity.

Methods
The molecular dynamics data were generated using the Amber Molecular Dynamics Package, as described in the associated manuscript. Data were processed using the CPPTRAJ program within AmberTools. The AlphaFold2 model for SHP2 was generated using ColabFold with the default settings (https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb). Deep sequencing data are the result of high-throughput peptide display screens, conducted as described in the manuscript. Data were generated using an Illumina MiSeq or NextSeq instrument. Data were processed in three steps: (1) FLASh (https://ccb.jhu.edu/software/FLASH/(opens in new window)) was used for paired-end read merging, (2) CutAdapt (https://cutadapt.readthedocs.io/en/stable/)(opens in new window) was used to trim flanking sequences, and (3) trimmed sequences were translated and counted using in-house Python scripts (https://github.com/nshahlab/2024_Jiang-et-al_SHP2-DMS).