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AI-based Structural Study of CDKN2A Inhibiting MDM2-p53 via AlphaFold2
- 1 School of Pharmacy, Dalian Medical University, Detailed Address: Dalian Medical University, No. 9, West Section of Lushun South Road, Lushunkou District, Dalian, Liaoning, China
* Author to whom correspondence should be addressed.
Abstract
The potential role of CDKN2A in substituting MDM2 in its binding with p53 is investigated in this study, emphasizing the significance of wild-type p53 in cancer suppression and its potential contribution to reducing cancer incidence. The protein sequences of CDKN2A, MDM2, and p53 were obtained from the UniProt database and input into AlphaFold2 to predict their three-dimensional structures. Subsequently, potential binding sites within these structures were analyzed using PLIP software. The results provide new insights into the role of CDKN2A in regulating the stability of p53, suggesting that CDKN2A may substitute for MDM2 in its interaction with p53. This research advances the field of structural biology and offers new tools and perspectives for drug discovery and biomedical research.
Keywords
AI (AlphaFold)-based, protein structure prediction, CDKN2A, MDM2, target study
[1]. Indeglia, Leung, Miller et al. An african-specific variant of tp53 reveals padi4 as a regulator of p53-mediated tumor suppression [J]. Cancer Discov, 2023, 13(7): 1696-1719.
[2]. Wang, Guo, Wei et al. Targeting p53 pathways: Mechanisms, structures, and advances in therapy [J]. Signal Transduct Target Ther, 2023, 8(1): 92.
[3]. Liu, Su, Tavana et al. Understanding the complexity of p53 in a new era of tumor suppression [J]. Cancer Cell, 2024, 42(6): 946-967.
[4]. Hassin, Oren. Drugging p53 in cancer: One protein, many targets [J]. Nat Rev Drug Discov, 2023, 22(2): 127-144.
[5]. Stott, Bates, James et al. The alternative product from the human cdkn2a locus, p14(arf), participates in a regulatory feedback loop with p53 and mdm2 [J]. Embo j, 1998, 17(17): 5001-5014.
[6]. Jumper, Evans, Pritzel et al. Highly accurate protein structure prediction with alphafold [J]. Nature, 2021, 596(7873): 583-589.
[7]. Berman, Westbrook, Feng et al. The protein data bank [J]. Nucleic Acids Research, 2000, 28(1): 235-242.
[8]. Burley, Bhikadiya, Bi et al. Rcsb protein data bank (rcsb.Org): Delivery of experimentally-determined pdb structures alongside one million computed structure models of proteins from artificial intelligence/machine learning [J]. Nucleic Acids Research, 2022, 51(D1): D488-D508.
[9]. Berman, Henrick, Nakamura. Announcing the worldwide protein data bank [J]. Nat Struct Biol, 2003, 10(12): 980.
[10]. Adasme, Linnemann, Bolz et al. Plip 2021: Expanding the scope of the protein-ligand interaction profiler to DNA and rna [J]. Nucleic Acids Res, 2021, 49(W1): W530-w534.
[11]. Vassilev, Vu, Graves et al. In vivo activation of the p53 pathway by small-molecule antagonists of mdm2 [J]. Science, 2004, 303(5659): 844-848.
[12]. Salentin, Schreiber, Haupt et al. Plip: Fully automated protein-ligand interaction profiler [J]. Nucleic Acids Res, 2015, 43(W1): W443-447.
[13]. Kamb, Gruis, Weaver-Feldhaus et al. A cell cycle regulator potentially involved in genesis of many tumor types [J]. Science, 1994, 264(5157): 436-440.
[14]. Serrano, Lin, McCurrach et al. Oncogenic ras provokes premature cell senescence associated with accumulation of p53 and p16ink4a [J]. Cell, 1997, 88(5): 593-602.
[15]. Silver, Schrittwieser, Simonyan et al. Mastering the game of go without human knowledge [J]. Nature, 2017, 550(7676): 354-359.
[16]. Baek, DiMaio, Anishchenko et al. Accurate prediction of protein structures and interactions using a three-track neural network [J]. Science, 2021, 373(6557): 871-876.
Cite this article
Zhang,W. (2025). AI-based Structural Study of CDKN2A Inhibiting MDM2-p53 via AlphaFold2. Theoretical and Natural Science,82,203-212.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Modern Medicine and Global Health
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