bioRxivBiochemistryPreprint
Structural Insights into The Role of MAGEA4 in RAD18 Regulation: Implications for Ubiquitin Ligase-Binding across the MAGE Protein Family
S. Griffith-Jones, L. Alvarez, U. Mukhopadhyay, S. Gharbi, M. Rettel, M. Adams, J. Hennig, S. Bhogaraju
Abstract
Melanoma associated antigen 4 (MAGEA4) is a cancer-testis antigen (CTA) that is primarily expressed in the testes of healthy adults but is aberrantly overexpressed and also a poor prognostic marker in several human cancers. In its cancer-specific role, MAGEA4 interacts with RAD18 and activates trans-lesion DNA synthesis (TLS), potentially favouring tumour evolution. However, the precise mode of interaction between RAD18 and MAGEA4 and its implications on the ubiquitination activity of RAD18 are unknown. Here, we employed NMR and AlphaFold2 (AF) to reveal that the ubiquitin-conjugating enzyme RAD6-binding domain (R6BD) of RAD18 interacts with a groove in the C-terminal winged-helix subdomain (WH) of MAGEA4. Using cross-linking mass spectrometry (XL-MS), we found that MAGEA4 displaces RAD6 from the R6BD of RAD18 and inhibits degradative autoubiquitination of RAD18, which could be countered by a competing short peptide of the RAD18 R6B region. AF and XL-MS also revealed an evolutionary invariant intramolecular interaction within RAD18 between the catalytic RING and the DNA-binding SAP domains that is essential for the ubiquitination of PCNA. Using interaction proteomics, we revealed that another Type-I MAGE, MAGE-C2, interacts with the RING ubiquitin ligase TRIM28 in a similar fashion as the MAGEA4/RAD18 complex. We propose that the peptide-binding groove identified in the C-terminal WH of MAGEA4 exists in other type-I MAGE proteins and serves as a ligase-binding cleft. Our data reveal crucial insights into RAD18-mediated ubiquitination of PCNA and its regulation by MAGE-A4.
- organism: human
- assay: proteomics
- research focus: cell signaling & trafficking
- research focus: chromatin & epigenetic regulation
- research focus: protein folding & dynamics
- approach: experimental assay
- approach: deep learning
- approach: statistical model
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