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      Targeting p53 pathways: mechanisms, structures, and advances in therapy

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          Abstract

          The TP53 tumor suppressor is the most frequently altered gene in human cancers, and has been a major focus of oncology research. The p53 protein is a transcription factor that can activate the expression of multiple target genes and plays critical roles in regulating cell cycle, apoptosis, and genomic stability, and is widely regarded as the “guardian of the genome”. Accumulating evidence has shown that p53 also regulates cell metabolism, ferroptosis, tumor microenvironment, autophagy and so on, all of which contribute to tumor suppression. Mutations in TP53 not only impair its tumor suppressor function, but also confer oncogenic properties to p53 mutants. Since p53 is mutated and inactivated in most malignant tumors, it has been a very attractive target for developing new anti-cancer drugs. However, until recently, p53 was considered an “undruggable” target and little progress has been made with p53-targeted therapies. Here, we provide a systematic review of the diverse molecular mechanisms of the p53 signaling pathway and how TP53 mutations impact tumor progression. We also discuss key structural features of the p53 protein and its inactivation by oncogenic mutations. In addition, we review the efforts that have been made in p53-targeted therapies, and discuss the challenges that have been encountered in clinical development.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            Ferroptosis: an iron-dependent form of nonapoptotic cell death.

            Nonapoptotic forms of cell death may facilitate the selective elimination of some tumor cells or be activated in specific pathological states. The oncogenic RAS-selective lethal small molecule erastin triggers a unique iron-dependent form of nonapoptotic cell death that we term ferroptosis. Ferroptosis is dependent upon intracellular iron, but not other metals, and is morphologically, biochemically, and genetically distinct from apoptosis, necrosis, and autophagy. We identify the small molecule ferrostatin-1 as a potent inhibitor of ferroptosis in cancer cells and glutamate-induced cell death in organotypic rat brain slices, suggesting similarities between these two processes. Indeed, erastin, like glutamate, inhibits cystine uptake by the cystine/glutamate antiporter (system x(c)(-)), creating a void in the antioxidant defenses of the cell and ultimately leading to iron-dependent, oxidative death. Thus, activation of ferroptosis results in the nonapoptotic destruction of certain cancer cells, whereas inhibition of this process may protect organisms from neurodegeneration. Copyright © 2012 Elsevier Inc. All rights reserved.
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              AMPK and mTOR regulate autophagy through direct phosphorylation of Ulk1.

              Autophagy is a process by which components of the cell are degraded to maintain essential activity and viability in response to nutrient limitation. Extensive genetic studies have shown that the yeast ATG1 kinase has an essential role in autophagy induction. Furthermore, autophagy is promoted by AMP activated protein kinase (AMPK), which is a key energy sensor and regulates cellular metabolism to maintain energy homeostasis. Conversely, autophagy is inhibited by the mammalian target of rapamycin (mTOR), a central cell-growth regulator that integrates growth factor and nutrient signals. Here we demonstrate a molecular mechanism for regulation of the mammalian autophagy-initiating kinase Ulk1, a homologue of yeast ATG1. Under glucose starvation, AMPK promotes autophagy by directly activating Ulk1 through phosphorylation of Ser 317 and Ser 777. Under nutrient sufficiency, high mTOR activity prevents Ulk1 activation by phosphorylating Ulk1 Ser 757 and disrupting the interaction between Ulk1 and AMPK. This coordinated phosphorylation is important for Ulk1 in autophagy induction. Our study has revealed a signalling mechanism for Ulk1 regulation and autophagy induction in response to nutrient signalling.
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                Author and article information

                Contributors
                hudiewei18@163.com
                yonghenc@163.com
                Journal
                Signal Transduct Target Ther
                Signal Transduct Target Ther
                Signal Transduction and Targeted Therapy
                Nature Publishing Group UK (London )
                2095-9907
                2059-3635
                1 March 2023
                1 March 2023
                2023
                : 8
                : 92
                Affiliations
                GRID grid.452223.0, ISNI 0000 0004 1757 7615, Department of Oncology, NHC Key Laboratory of Cancer Proteomics, Laboratory of Structural Biology, National Clinical Research Center for Geriatric Disorders, , Xiangya Hospital, Central South University, ; Changsha, Hunan 410008 China
                Author information
                http://orcid.org/0000-0001-8139-6892
                Article
                1347
                10.1038/s41392-023-01347-1
                9977964
                36859359
                1e720419-ba15-4fea-ba6e-1479c74d2ffc
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 August 2022
                : 19 December 2022
                : 7 February 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 81570537
                Award ID: 81974074
                Award ID: 82172654
                Award ID: 82273496
                Award ID: 31900880
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100002767, Hunan Provincial Science and Technology Department (Department of Science and Technology of Hunan Province);
                Award ID: 2018RS3026
                Award ID: 2021RC4012
                Award Recipient :
                Categories
                Review Article
                Custom metadata
                © The Author(s) 2023

                drug development,structural biology
                drug development, structural biology

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