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Cited by 158

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Dominant-negative p53 mutations selected in yeast hit cancer hot spots.

Open access
Brachmann, R K, Vidal, M, Boeke, J D·1996·Proceedings of the National Academy of Sciences

Abstract

Clinically important mutant p53 proteins may be tumorigenic through a dominant-negative mechanism or due to a gain-of-function. Examples for both hypotheses have been described; however, it remains unclear to what extent they apply to TP53 mutations in general. Here it is shown that the mutational spectrum of dominant-negative p53 mutants selected in a novel yeast assay correlates tightly with p53 mutations in cancer. Two classes of dominant-negative mutations are described; the more dominant one affects codons that are essential for the stabilization of the DNA-binding surface of the p53 core domain and for the direct interaction of p53 with its DNA binding sites. These results predict that the vast majority of TP53 mutations leading to cancer do so in a dominant-negative fashion.

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Metadata

Authors
Brachmann, R. K., Vidal, M., & Boeke, J. D.
Venue
Proceedings of the National Academy of Sciences
Published
1996-04-30
Volume
93
Issue
9
Pages
4091-4095
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2151037269
Cited by
158
References
3
FWCI
5.70
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Dominant-negative p53 mutations selected in yeast hit cancer hot spots. https://app.cochat.ai/papers/10.1073/pnas.93.9.4091
The paper
Brachmann, R. K., Vidal, M., & Boeke, J. D. (1996). Dominant-negative p53 mutations selected in yeast hit cancer hot spots. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.93.9.4091

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.