Ensemble-Based Computational Approach Discriminates Functional Activity of p53 Cancer and Rescue Mutants

Autor: Linda V. Hall, Rommie E. Amaro, Richard Chamberlin, Richard H. Lathrop, G. Wesley Hatfield, Özlem Demir, Faezeh Salehi, Peter K. Kaiser, Roberta Baronio, Christopher D. Wassman
Rok vydání: 2011
Předmět:
Models
Molecular

Protein Conformation
genetics [Tumor Suppressor Protein p53]
Mutant
Computational biology
Plasma protein binding
Molecular Dynamics Simulation
Biology
medicine.disease_cause
Physical Chemistry
DNA-binding protein
03 medical and health sciences
Cellular and Molecular Neuroscience
Computational Chemistry
0302 clinical medicine
Protein structure
Neoplasms
Chemical Biology
Medicine and Health Sciences
Genetics
medicine
Humans
lcsh:QH301-705.5
Molecular Biology
Ecology
Evolution
Behavior and Systematics

030304 developmental biology
0303 health sciences
Mutation
Ecology
Point mutation
Computational Biology
Cancer
medicine.disease
Chemistry
lcsh:Biology (General)
Computational Theory and Mathematics
030220 oncology & carcinogenesis
Modeling and Simulation
genetics [Protein Binding]
Tumor Suppressor Protein p53
Carcinogenesis
genetics [Neoplasms]
Research Article
Protein Binding
Zdroj: PLoS Computational Biology
Demir, Özlem; Baronio, Roberta; Salehi, Faezeh; Wassman, Christopher D; Hall, Linda; Hatfield, G Wesley; et al.(2011). Ensemble-based computational approach discriminates functional activity of p53 cancer and rescue mutants.. PLoS computational biology, 7(10), e1002238-e10022e1002238. UC Irvine: Institute for Clinical and Translational Science. Retrieved from: http://www.escholarship.org/uc/item/4nz7j4vb
PLoS Computational Biology, Vol 7, Iss 10, p e1002238 (2011)
ISSN: 1553-7358
Popis: 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 (r2 = 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.
Author Summary p53 is a tumor suppressor protein that controls a central apoptotic pathway (programmed cell death). Thus, it is the most-mutated gene in human cancers. Due to the marginal stability of p53, a single mutation can abolish p53 function (“cancer mutants”), while a second mutation (or several) can restore it (“rescue mutants”). Restoring p53 function is a promising therapeutic goal that has been strongly supported by recent experimental results on mice. Understanding of the effects of p53 cancer and rescue mutations would be helpful for designing drugs that are able to achieve the same goal. The challenge is that cancer and rescue mutations are distributed widely in the protein, and experimental testing of all possible combinations of mutations is not feasible. This paper describes a simple computational metric that reflects the overall stability of the p53 core domain and can discriminate which second-site mutations restore activity to p53 cancer mutants.
Databáze: OpenAIRE