A novel binding pocket in the D2 domain of protein tyrosine phosphatase mu (PTPmu) guides AI screen to identify small molecules that modulate tumour cell adhesion, growth and migration

J Cell Mol Med. 2023 Nov;27(22):3553-3564. doi: 10.1111/jcmm.17973. Epub 2023 Oct 20.

Abstract

Approximately 40% of people will get cancer in their lifetime in the US, and 20% are predicted to die from the condition when it is invasive and metastatic. Targeted screening for drugs that interact with proteins that drive cancer cell growth and migration can lead to new therapies. We screened molecular libraries with the AtomNet® AI-based drug design tool to identify compounds predicted to interact with the cytoplasmic domain of protein tyrosine phosphatase mu. Protein tyrosine phosphatase mu (PTPmu) is proteolytically downregulated in cancers such as glioblastoma generating fragments that stimulate cell survival and migration. Aberrant nuclear localization of PTPmu intracellular fragments drives cancer progression, so we targeted a predicted drug-binding site between the two cytoplasmic phosphatase domains we termed a D2 binding pocket. The function of the D2 domain is controversial with various proposed regulatory functions, making the D2 domain an attractive target for the development of allosteric drugs. Seventy-five of the best-scoring and chemically diverse computational hits predicted to interact with the D2 binding pocket were screened for effects on tumour cell motility and growth in 3D culture as well as in a direct assay for PTPmu-dependent adhesion. We identified two high-priority hits that inhibited the migration and glioma cell sphere formation of multiple glioma tumour cell lines as well as aggregation. We also identified one activator of PTPmu-dependent aggregation, which was able to stimulate cell migration. We propose that the PTPmu D2 binding pocket represents a novel regulatory site and that inhibitors targeting this region may have therapeutic potential for treating cancer.

Keywords: artificial intelligence; cell adhesion; cell-cell adhesion; drug design; glioblastoma; glioma; protein tyrosine phosphatase mu; receptor protein tyrosine phosphatase; tyrosine phosphatase.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artificial Intelligence
  • Cell Adhesion
  • Glioblastoma* / drug therapy
  • Glioblastoma* / pathology
  • Glioma*
  • Humans
  • Protein Tyrosine Phosphatases / metabolism
  • Receptor-Like Protein Tyrosine Phosphatases, Class 2 / genetics
  • Receptor-Like Protein Tyrosine Phosphatases, Class 2 / metabolism

Substances

  • Receptor-Like Protein Tyrosine Phosphatases, Class 2
  • Protein Tyrosine Phosphatases

Grants and funding