Ensemble-Based Computational Approach Discriminates Functional Activity of p53 Cancer and Rescue Mutants
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Peter Kaiser | Richard H. Lathrop | G. Wesley Hatfield | Rommie E. Amaro | Richard Chamberlin | Özlem Demir | G. W. Hatfield | Roberta Baronio | Christopher D. Wassman | Linda Hall | Faezeh Salehi | R. Lathrop | R. Amaro | Roberta Baronio | Ö. Demir | Linda Hall | F. Salehi | P. Kaiser | Richard Chamberlin
[1] J. Levine,et al. Surfing the p53 network , 2000, Nature.
[2] Vincent B. Chen,et al. Correspondence e-mail: , 2000 .
[3] A. Fersht,et al. Mechanism of rescue of common p53 cancer mutations by second‐site suppressor mutations , 2000, The EMBO journal.
[4] P. Jeffrey,et al. Crystal structure of a p53 tumor suppressor-DNA complex: understanding tumorigenic mutations. , 1994, Science.
[5] A. Fersht,et al. Structures of p53 Cancer Mutants and Mechanism of Rescue by Second-site Suppressor Mutations* , 2005, Journal of Biological Chemistry.
[6] T. Halazonetis,et al. Structure–based rescue of common tumor–derived p53 mutants , 1996, Nature Medicine.
[7] Laxmikant V. Kalé,et al. Scalable molecular dynamics with NAMD , 2005, J. Comput. Chem..
[8] A. Levine,et al. The P53 pathway: what questions remain to be explored? , 2006, Cell Death and Differentiation.
[9] V. Hornak,et al. Comparison of multiple Amber force fields and development of improved protein backbone parameters , 2006, Proteins.
[10] Yuan-Ping Pang,et al. Novel Zinc Protein Molecular Dynamics Simulations: Steps Toward Antiangiogenesis for Cancer Treatment , 1999 .
[11] A. Fersht,et al. The tumor suppressor p53: from structures to drug discovery. , 2010, Cold Spring Harbor perspectives in biology.
[12] Frank M Boeckler,et al. Targeted rescue of a destabilized mutant of p53 by an in silico screened drug , 2008, Proceedings of the National Academy of Sciences.
[13] B. Brooks,et al. Constant pressure molecular dynamics simulation: The Langevin piston method , 1995 .
[14] B. Foster,et al. Pharmacological rescue of mutant p53 conformation and function. , 1999, Science.
[15] Sebastian Mayer,et al. Effects of Common Cancer Mutations on Stability and DNA Binding of Full-length p53 Compared with Isolated Core Domains* , 2006, Journal of Biological Chemistry.
[16] T. Jacks,et al. Restoration of p53 function leads to tumour regression in vivo , 2007, Nature.
[17] Gerard I. Evan,et al. Modeling the Therapeutic Efficacy of p53 Restoration in Tumors , 2006, Cell.
[18] X. Daura,et al. Peptide Folding: When Simulation Meets Experiment , 1999 .
[19] D. E. Griffiths,et al. DMSO-enhanced whole cell yeast transformation. , 1991, Nucleic acids research.
[20] Alan R. Fersht,et al. Solution structure of p53 core domain: Structural basis for its instability , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[21] Galina Selivanova,et al. Restoration of the tumor suppressor function to mutant p53 by a low-molecular-weight compound , 2002, Nature Medicine.
[22] T Darden,et al. New tricks for modelers from the crystallography toolkit: the particle mesh Ewald algorithm and its use in nucleic acid simulations. , 1999, Structure.
[23] Sophie North,et al. Restoration of wild‐type conformation and activity of a temperature‐sensitive mutant of p53 (p53V272M) by the cytoprotective aminothiol WR1065 in the esophageal cancer cell line TE‐1 , 2002, Molecular carcinogenesis.
[24] A. Fersht,et al. Semirational design of active tumor suppressor p53 DNA binding domain with enhanced stability. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[25] A. Fersht,et al. Crystal Structure of a Superstable Mutant of Human p53 Core Domain , 2004, Journal of Biological Chemistry.
[26] R. Sikorski,et al. A system of shuttle vectors and yeast host strains designed for efficient manipulation of DNA in Saccharomyces cerevisiae. , 1989, Genetics.
[27] Ting Wang,et al. A global suppressor motif for p53 cancer mutants. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[28] A. Fersht,et al. Structural basis for understanding oncogenic p53 mutations and designing rescue drugs , 2006, Proceedings of the National Academy of Sciences.
[29] G. Ciccotti,et al. Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes , 1977 .
[30] Youngho Seo,et al. Selective activation of p53-mediated tumour suppression in high-grade tumours , 2010, Nature.
[31] Xin Lu,et al. Live or let die: the cell's response to p53 , 2002, Nature Reviews Cancer.
[32] J. Boeke,et al. Genetic selection of intragenic suppressor mutations that reverse the effect of common p53 cancer mutations , 1998, The EMBO journal.
[33] Carsten Kutzner,et al. GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. , 2008, Journal of chemical theory and computation.
[34] P. Kollman,et al. Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models , 1992 .
[35] A. Levine,et al. Surfing the p53 network , 2000, Nature.
[36] A. Fersht,et al. Quantitative analysis of residual folding and DNA binding in mutant p53 core domain: definition of mutant states for rescue in cancer therapy , 2000, Oncogene.
[37] M. Klein,et al. Constant pressure molecular dynamics algorithms , 1994 .
[38] Francisco J. Sánchez-Rivera,et al. Stage-specific sensitivity to p53 restoration during lung cancer progression , 2010, Nature.
[39] A. Fersht,et al. Thermodynamic stability of wild-type and mutant p53 core domain. , 1997, Proceedings of the National Academy of Sciences of the United States of America.
[40] Hideyuki Tsuboi,et al. A graph theoretical approach to the effect of mutation on the flexibility of the DNA binding domain of p53 protein , 2009 .
[41] W. L. Jorgensen,et al. Comparison of simple potential functions for simulating liquid water , 1983 .
[42] Richard H. Lathrop,et al. All-codon scanning identifies p53 cancer rescue mutations , 2010, Nucleic acids research.
[43] Galina Selivanova,et al. Mutant p53-dependent growth suppression distinguishes PRIMA-1 from known anticancer drugs: a statistical analysis of information in the National Cancer Institute database. , 2002, Carcinogenesis.
[44] Tirso Pons,et al. Homology modeling, model and software evaluation: three related resources , 1998, Bioinform..
[45] Peter Kaiser,et al. Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning , 2009, PLoS Comput. Biol..