Associated Papers
TP53 variant clusters stratify phenotypic diversity in germline carriers and reveal an osteosarcoma-prone subgroup.
Fischer NW et al.
Nature Communications, 2025
41022739 (PubMed )
Supplement: 41467_2025_63528_MOESM7_ESM.xlsx › Sheet1
TP53_PROF: a machine learning model to predict impact of missense mutations in TP53.
Ben-Cohen G et al.
Briefings in Bioinformatics, 2022
35043155 (PubMed )
Supplement: supplementary_table_s1_bbab524.xlsx › S1B._Negative_Set
Using yeast to determine the functional consequences of mutations in the human p53 tumor suppressor gene: An introductory course-based undergraduate research experience in molecular and cell biology.
Hekmat-Scafe DS et al.
Biochemistry and Molecular Biology Education : a Bimonthly Publication of the International Union of Biochemistry and Molecular Biology, 2017
TP53 mutations in human cancer: database reassessment and prospects for the next decade.
Leroy B et al.
Human Mutation, 2014
24665023 (PubMed )
Supplement: supplement_1.pdf
Cancer-associated p53 tetramerization domain mutants: quantitative analysis reveals a low threshold for tumor suppressor inactivation.
Kamada R et al.
The Journal of Biological Chemistry, 2011
Evaluation of transcriptional activity of p53 in individual living mammalian cells.
Imagawa T et al.
Analytical Biochemistry, 2009
The relationship among p53 oligomer formation, structure and transcriptional activity using a comprehensive missense mutation library.
Kawaguchi T et al.
Oncogene, 2005
Nine hydrophobic side chains are key determinants of the thermodynamic stability and oligomerization status of tumour suppressor p53 tetramerization domain.
Mateu MG et al.
The Embo Journal, 1998
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