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Title: The dual interactions of p53 with MDM2 and p300 : implications for the design of MDM2 inhibitors
Authors: Kannan, Srinivasaraghavan
Partridge, Anthony William
Lane, David P.
Verma, Chandra Shekhar
Keywords: Science::Biological sciences
Issue Date: 2019
Source: Kannan, S., Partridge, A. W., Lane, D. P., & Verma, C. S. (2019). The dual interactions of p53 with MDM2 and p300 : implications for the design of MDM2 inhibitors. International Journal of Molecular Sciences, 20(23), 5996-. doi:10.3390/ijms20235996
Journal: International Journal of Molecular Sciences 
Abstract: Proteins that limit the activity of the tumour suppressor protein p53 are increasingly being targeted for inhibition in a variety of cancers. In addition to the development of small molecules, there has been interest in developing constrained (stapled) peptide inhibitors. A stapled peptide ALRN_6924 that activates p53 by preventing its interaction with its negative regulator Mdm2 has entered clinical trials. This stapled peptide mimics the interaction of p53 with Mdm2. The chances that this peptide could bind to other proteins that may also interact with the Mdm2-binding region of p53 are high; one such protein is the CREB binding protein (CBP)/p300. It has been established that phosphorylated p53 is released from Mdm2 and binds to p300, orchestrating the transcriptional program. We investigate whether molecules such as ALRN_6924 would bind to p300 and, to do so, we used molecular simulations to explore the binding of ATSP_7041, which is an analogue of ALRN_6924. Our study shows that ATSP_7041 preferentially binds to Mdm2 over p300; however, upon phosphorylation, it appears to have a higher affinity for p300. This could result in attenuation of the amount of free p300 available for interacting with p53, and hence reduce its transcriptional efficacy. Our study highlights the importance of assessing off-target effects of peptide inhibitors, particularly guided by the understanding of the networks of protein-protein interactions (PPIs) that are being targeted.
ISSN: 1661-6596
DOI: 10.3390/ijms20235996
Schools: School of Biological Sciences 
Organisations: Bioinformatics Institute, A*STAR
Rights: © 2019 The Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
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