Mutations in the tyrosine phosphatase SHP2 are associated with various human diseases. Most of these mutations increase the basal catalytic activity of SHP2 by disrupting auto-inhibitory interactions between its phosphatase domain and N-terminal SH2 (phosphotyrosine recognition) domain. By contrast, several other disease-associated mutations located in the ligand-binding pockets of the N- or C-terminal SH2 domains do not increase basal activity and likely exert their pathogenicity through alternative mechanisms. We lack a molecular understanding of how these SH2 mutations impact SHP2 structure, activity, and signaling. Here, we characterize five SHP2 SH2 domain ligand-binding pocket mutants through a combination of high-throughput biochemical screens, biophysical and biochemical measurements, molecular dynamics simulations, and cellular assays. We show that, while several of these mutants alter binding affinity to phosphorylation sites, the T42A mutation in the N-SH2 domain is unique in that it also alters ligand-binding specificity. The functional consequence of this altered specificity is that the T42A mutant has biased sensitivity toward a subset of activating ligands. Our study highlights an example of a nuanced mechanism of action for a disease-associated mutation, characterized by a change in protein-protein interaction specificity that alters enzyme activation.

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. 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/) was used for paired-end read merging, (2) CutAdapt (https://cutadapt.readthedocs.io/en/stable/) was used to trim flanking sequences, and (3) trimmed sequences were translated and counted using in-house Python scripts (https://github.com/nshahlab/2022_Li-et-al_peptide-display) as described in PMID: 36927728.

Molecular dynamics trajectories can be opened/analyzed using AmberTools, VMD, or other common molecular dynamics visualization and analysis software. The *.pse files can be viewed using the PyMOL molecular graphics software. FASTQ sequencing files can be read using any plain text editor and analyzed/processed using any common tool for analyzing FASTQ sequence files.