Title |
Ensemble-Based Computational Approach Discriminates Functional Activity of p53 Cancer and Rescue Mutants
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Published in |
PLoS Computational Biology, October 2011
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DOI | 10.1371/journal.pcbi.1002238 |
Pubmed ID | |
Authors |
Özlem Demir, Roberta Baronio, Faezeh Salehi, Christopher D. Wassman, Linda Hall, G. Wesley Hatfield, Richard Chamberlin, Peter Kaiser, Richard H. Lathrop, Rommie E. Amaro |
Abstract |
The tumor suppressor protein p53 can lose its function upon single-point missense mutations in the core DNA-binding domain ("cancer mutants"). Activity can be restored by second-site suppressor mutations ("rescue mutants"). This paper relates the functional activity of p53 cancer and rescue mutants to their overall molecular dynamics (MD), without focusing on local structural details. A novel global measure of protein flexibility for the p53 core DNA-binding domain, the number of clusters at a certain RMSD cutoff, was computed by clustering over 0.7 µs of explicitly solvated all-atom MD simulations. For wild-type p53 and a sample of p53 cancer or rescue mutants, the number of clusters was a good predictor of in vivo p53 functional activity in cell-based assays. This number-of-clusters (NOC) metric was strongly correlated (r(2) = 0.77) with reported values of experimentally measured ΔΔG protein thermodynamic stability. Interpreting the number of clusters as a measure of protein flexibility: (i) p53 cancer mutants were more flexible than wild-type protein, (ii) second-site rescue mutations decreased the flexibility of cancer mutants, and (iii) negative controls of non-rescue second-site mutants did not. This new method reflects the overall stability of the p53 core domain and can discriminate which second-site mutations restore activity to p53 cancer mutants. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 4 | 6% |
Korea, Republic of | 1 | 1% |
Italy | 1 | 1% |
France | 1 | 1% |
Canada | 1 | 1% |
India | 1 | 1% |
Japan | 1 | 1% |
Argentina | 1 | 1% |
Unknown | 60 | 85% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 17 | 24% |
Researcher | 13 | 18% |
Professor > Associate Professor | 6 | 8% |
Student > Master | 6 | 8% |
Professor | 4 | 6% |
Other | 12 | 17% |
Unknown | 13 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 17 | 24% |
Agricultural and Biological Sciences | 13 | 18% |
Chemistry | 12 | 17% |
Engineering | 6 | 8% |
Pharmacology, Toxicology and Pharmaceutical Science | 2 | 3% |
Other | 7 | 10% |
Unknown | 14 | 20% |