bioRxivBiochemistryPreprint
Disease-Causing Mutations Alter the Interplay Between Loop Dynamics and Catalysis in the Protein Tyrosine Phosphatases SHP-1 and SHP-2
Alfie-Louise R. BrownlessiD, Michael RobinsoniD, Shina Caroline Lynn KamerliniD
Abstract
The protein tyrosine phosphatases (PTPs) SHP-1 and SHP-2 play complex roles in a variety of signaling pathways, including those involved in cancers and other diseases, making them important drug targets. These two PTPs have superimposable active sites, but different biological functions in vivo, including opposing roles in cancer development. Unique to these PTPs is the presence of two tandem Src homology 2 (SH2) domains, which regulate access to the phosphate-binding site in the catalytic domain through an autoinhibition mechanism. Studies of the allosteric regulation and dynamics of these PTPs, as well as associated drug discovery efforts, typically focus on autoinhibition rather than the dynamics of a catalytic loop in the phosphatase domain, the WPD-loop, which is essential for PTPase activity. However, recent deep mutational scanning data has demonstrated that oncogenic mutations also regulate WPD-loop motion in SHP-2. We provide here a detailed computational study of WPD-loop dynamics and catalysis in wild-type and mutant full-length and catalytic domain only SHP-1 and SHP-2, demonstrating that many oncogenic residues lie on the allosteric pathways regulating WPD-loop dynamics. Mutations at these positions alter WPD-loop dynamics, disrupting the active site and negatively impacting catalysis. Further, our simulations provide molecular insight into the link between the presence of the SH2 domains and loop motion in the catalytic domain, and, importantly, how it differs between the two PTPs. Taken together, our work showcases the impact of altered WPD-loop motion in disease-causing SHP-1 and SHP-2 variants, opening new strategies for selectively targeting these important therapeutic enzymes.
- organism: human
- assay: protein structure
- research focus: protein folding & dynamics
- approach: deep learning
- Citations
- 1
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