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
Analysis and Interpretation of the Impact of Missense Variants in Cancer.
Petrosino M et al.
International Journal of Molecular Sciences, 2021
34063805 (PubMed )
Supplement: ijms-22-05416-s001.zip › new_supplementary_file1.xlsx › new_supplementary_file1
Finding driver mutations in cancer: Elucidating the role of background mutational processes.
Brown AL et al.
Plos Computational Biology, 2019
31034466 (PubMed )
Supplement: pcbi.1006981.s010.xlsx › final_benchmark
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
Characterization of p53 mutants identified in human tumors with a missense mutation in the tetramerization domain.
Rollenhagen C et al.
International Journal of Cancer, 1998
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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