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      Roles of computational modelling in understanding p53 structure, biology, and its therapeutic targeting

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          Abstract

          The transcription factor p53 plays pivotal roles in numerous biological processes, including the suppression of tumours. The rich availability of biophysical data aimed at understanding its structure–function relationships since the 1990s has enabled the application of a variety of computational modelling techniques towards the establishment of mechanistic models. Together they have provided deep insights into the structure, mechanics, energetics, and dynamics of p53. In parallel, the observation that mutations in p53 or changes in its associated pathways characterize several human cancers has resulted in a race to develop therapeutic modulators of p53, some of which have entered clinical trials. This review describes how computational modelling has played key roles in understanding structural-dynamic aspects of p53, formulating hypotheses about domains that are beyond current experimental investigations, and the development of therapeutic molecules that target the p53 pathway.

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          Cancer. p53, guardian of the genome.

          D P Lane (1992)
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            Live or let die: the cell's response to p53.

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              Impact of mutant p53 functional properties on TP53 mutation patterns and tumor phenotype: lessons from recent developments in the IARC TP53 database.

              The tumor suppressor gene TP53 is frequently mutated in human cancers. More than 75% of all mutations are missense substitutions that have been extensively analyzed in various yeast and human cell assays. The International Agency for Research on Cancer (IARC) TP53 database (www-p53.iarc.fr) compiles all genetic variations that have been reported in TP53. Here, we present recent database developments that include new annotations on the functional properties of mutant proteins, and we perform a systematic analysis of the database to determine the functional properties that contribute to the occurrence of mutational "hotspots" in different cancer types and to the phenotype of tumors. This analysis showed that loss of transactivation capacity is a key factor for the selection of missense mutations, and that difference in mutation frequencies is closely related to nucleotide substitution rates along TP53 coding sequence. An interesting new finding is that in patients with an inherited missense mutation, the age at onset of tumors was related to the functional severity of the mutation, mutations with total loss of transactivation activity being associated with earlier cancer onset compared to mutations that retain partial transactivation capacity. Furthermore, 80% of the most common mutants show a capacity to exert dominant-negative effect (DNE) over wild-type p53, compared to only 45% of the less frequent mutants studied, suggesting that DNE may play a role in shaping mutation patterns. These results provide new insights into the factors that shape mutation patterns and influence mutation phenotype, which may have clinical interest.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                J Mol Cell Biol
                J Mol Cell Biol
                jmcb
                Journal of Molecular Cell Biology
                Oxford University Press
                1674-2788
                1759-4685
                April 2019
                06 February 2019
                06 February 2019
                : 11
                : 4 , Special Issue: p53: Updates on Mechanisms, Biology and Therapy (II)
                : 306-316
                Affiliations
                [1 ]Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore
                [2 ]School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore
                [3 ]Department of Biological Sciences, National University of Singapore, 14 Science Drive 4, Singapore
                Author notes
                Correspondence to: Yaw Sing Tan, E-mail: tanys@ 123456bii.a-star.edu.sg ; Chandra S. Verma, E-mail: chandra@ 123456bii.a-star.edu.sg
                Article
                mjz009
                10.1093/jmcb/mjz009
                6487789
                30726928
                f3cb9e2c-cd91-4db9-8cfb-55b1837f266d
                © The Author(s) (2019). Published by Oxford University Press on behalf of Journal of Molecular Cell Biology, IBCB, SIBS, CAS.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 14 November 2018
                : 14 December 2018
                : 31 January 2019
                Page count
                Pages: 11
                Funding
                Funded by: A*STAR 10.13039/501100001348
                Award ID: IAF-PP H17/01/a0/010
                Funded by: Y.S.T.
                Funded by: SINGA
                Funded by: Y.M.
                Categories
                Invited Review

                p53,structure,computational modelling,therapeutic targeting

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