This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Functional Census of Mutation Sequence Spaces: The Example of p53 Cancer Rescue Mutants
April-June 2006 (vol. 3 no. 2)
pp. 114-125
Many biomedical problems relate to mutant functional properties across a sequence space of interest, e.g., flu, cancer, and HIV. Detailed knowledge of mutant properties and function improves medical treatment and prevention. A functional census of p53 cancer rescue mutants would aid the search for cancer treatments from p53 mutant rescue. We devised a general methodology for conducting a functional census of a mutation sequence space by choosing informative mutants early. The methodology was tested in a double-blind predictive test on the functional rescue property of 71 novel putative p53 cancer rescue mutants iteratively predicted in sets of three (24 iterations). The first double-blind 15-point moving accuracy was 47 percent and the last was 86 percent; r = 0.01 before an epiphanic 16th iteration and r = 0.92 afterward. Useful mutants were chosen early (overall r = 0.80). Code and data are freely available (http://www.igb.uci.edu/research/research.html, corresponding authors: R.H.L. for computation and R.K.B. for biology).

[1] D.L. Rubin, F. Shafa, D.E. Oliver, M. Hewett, and R.B. Altman, “Representing Genetic Sequence Data for Pharmacogenomics: An Evolutionary Approach Using Ontological and Relational Models,” Bioinformatics, vol. 18, supplement 1, pp. S207-215, 2002.
[2] R.H. Lathrop and M.J. Pazzani, “Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses,” J. Combinatorial Optimization, vol. 3, pp. 301-320, 1999.
[3] R.H. Lathrop, N.R. Steffen, M. Raphael, S. Deeds-Rubin, M.J. Pazzani, P.J. Cimoch, D.M. See, and J.G. Tilles, “Knowledge-Based Avoidance of Drug-Resistant HIV Mutants,” AI Magazine, vol. 20, pp. 13-25, 1999.
[4] N. Beerenwinkel, T. Lengauer, J. Selbig, B. Schmidt, H. Walter, K. Korn, R. Kaiser, and D. Hoffman, “Geno2pheno: Interpreting Genotypic HIV Drug Resistance Tests,” IEEE Intelligent Systems, vol. 16, pp. 35-41, 2001.
[5] M.R. Bush, C.A. Bender, K.C. Subbarao, J. Nancy, and W.M. Fitch, “Predicting the Evolution of Human Influenza A,” Science, vol. 286, pp. 1921-1925, 1999.
[6] G.M. Wahl and A.M. Carr, “The Evolution of Diverse Biological Responses to DNA Damage: Insights from Yeast and p53,” Natural Cell Biology, vol. 3, pp. E277-286, 2001.
[7] Y. Xu, “Regulation of p53 Responses by Post-Translational Modifications,” Cell Death Differentiation vol. 10, pp. 400-403, 2003.
[8] E. Appella and C.W. Anderson, “Post-Translational Modifications and Activation of p53 by Genotoxic Stresses,” European J. Biochemistry, vol. 268, pp. 2764-2772, 2001.
[9] C.L Brooks and W. Gu, “Ubiquitination, Phosphorylation and Acetylation: The Molecular Basis for p53 Regulation,” Current Opinions in Cell Biology, vol. 15, pp. 164-171, 2003.
[10] W.S. el-Deiry, S.E. Kern, J.A. Pietenpol, K.W. Kinzler, and B. Vogelstein, “Definition of a Consensus Binding Site for p53,” Nature and Genetics, vol. 1, pp. 45-49, 1992.
[11] W.D. Funk, D.T. Pak, R.H. Karas, W.E. Wright, and J.W. Shay, “A Transcriptionally Active DNA-Binding Site for Human p53 Protein Complexes,” Molecular Cell Biology, vol. 12, pp. 2866-2871, 1992.
[12] H. Qian, T. Wang, L. Naumovski, C.D. Lopez, and R.K. Brachmann, “Groups of p53 Target Genes Involved in Specific p53 Downstream Effects Cluster into Different Classes of DNA Binding Sites,” Oncogene, vol. 21, pp. 7901-7911, 2002.
[13] K. Kannan, N. Amariglio, G. Rechavi, J. Jakob-Hirsch, I. Kela, N. Kaminski, G. Getz, E. Domany, and D. Givol, “DNA Microarrays Identification of Primary and Secondary Target Genes Regulated by p53,” Oncogene, vol. 20, pp. 2225-2234, 2001.
[14] K. Polyak, Y. Xia, J.L. Zweier, K.W. Kinzler, and B. Vogelstein, “A Model for p53-Induced Apoptosis,” Nature, vol. 389, pp. 300-305, 1997.
[15] C. Caelles, A. Helmberg, and M. Karin, “p53-Dependent Apoptosis in the Absence of Transcriptional Activation of p53-Target Genes,” Nature, vol. 370, pp. 220-223, 1994.
[16] J.J Manfredi, “p53 and Apoptosis: It's Not Just in the Nucleus Anymore,” Molecular Cell, vol. 11, pp. 552-554, 2003.
[17] M. Mihara, S. Erster, A. Zaika, O. Petrenko, T. Chittenden, P. Pancoska, and U.M. Moll, “p53 Has a Direct Apoptogenic Role at the Mitochondria,” Molecular Cell, vol. 11, pp. 577-590, 2003.
[18] M. Olivier, R. Eeles, M. Hollstein, M.A. Khan, C.C. Harris, and P. Hainaut, “The IARC TP53 Database: New Online Mutation Analysis and Recommendations to Users,” Human Mutations, vol. 19, no. 6, pp. 607-14, June 2002.
[19] C. Beroud and T. Soussi, “The UMD-p53 Database: New Mutations and Analysis Tools,” Human Mutations, vol. 21, pp. 176-181, 2003.
[20] Y. Cho, S. Gorina, P.D. Jeffrey, and N.P. Pavletich, “Crystal Structure of a p53 Tumor Suppressor-DNA Complex: Understanding Tumorigenic Mutations,” Science, vol. 265, p. 346, 1994.
[21] A.N. Bullock, J. Henckel, B.S. DeDecker, C.M. Johnson, P.V. Nikolova, M.R. Proctor, D.P. Lane, and A.R. Fersht, “Thermodynamic Stability of Wild-Type and Mutant p53 Core Domain,” Proc. Nat'l Academy of Science USA, vol. 94, pp. 14338-14342, 1997.
[22] A.N. Bullock, J. Henckel, and A.R. Fersht, “Quantitative Analysis of Residual Folding and DNA Binding in Mutant p53 Core Domain: Definition of Mutant States for Rescue in Cancer Therapy,” Oncogene, vol. 19, pp. 1245-1256, 2000.
[23] K.B. Wong, B.S. DeDecker, S.M. Freund, M.R. Proctor, M. Bycroft, and A.R. Fersht, “Hot-Spot Mutants of p53 Core Domain Evince Characteristic Local Structural Changes,” Proc. Nat'l Academy of Science USA, vol. 96, pp. 8438-8442, 1999.
[24] A.J. Levine, “p53, the Cellular Gatekeeper for Growth and Division,” Cell, vol. 88, pp. 323-331, 1997.
[25] C. Prives and P.A. Hall, “The p53 Pathway,” J. Pathology, vol. 187, pp. 112-126, 1999.
[26] B. Vogelstein, D. Lane, and A.J. Levine, “Surfing the p53 Network,” Nature, vol. 408, pp. 307-310, 2000.
[27] K.H. Vousden, “p53: Death Star,” Cell, vol. 103, pp. 691-694, 2000.
[28] P. May and E. May, “Twenty Years of p53 Research: Structural And Functional Aspects of the p53 Protein,” Oncogene, vol. 18, pp. 7621-7636, 1999.
[29] B.A. Foster, H.A. Coffey, M.J. Morin, and F. Rastinejad, “Pharmacological Rescue of Mutant p53 Conformation and Function,” Science, vol. 286, pp. 2507-2510, 1999.
[30] V.J.N. Bykov, N. Issaeva, A. Shilov, M Hultcrantz, E. Pugacheva, P. Chumakov, J. Bergman, K.G. Wiman, and G. Selivanova, “Restoration of the Tumor Suppressor Function to Mutant p53 by a Low-Molecular-Weight Compound,” Natural Medicine, vol. 8, pp. 282-288, 2002.
[31] R.K. Brachmann, K. Yu, Y. Eby, N.P. Pavletich, and J.D. Boeke, “Genetic Selection of Intragenic Suppressor Mutations that Reverse the Effect of Common p53 Cancer Mutations,” EMBO J., vol. 17, pp. 1847-1859, 1998.
[32] T.E. Baroni, T. Wang, H. Qian, L. Dearth, L.N. Troung, J. Zeng, A.E. Denes, S.W. Chen, and R.K. Brachmann, “A Global Suppressor Motif for p53 Cancer Mutants,” Proc. Nat'l Academy of Sciences, vol. 101 14, pp. 4930-4935, 2004.
[33] A.N. Bullock and A.R. Fersht, “Rescuing the Function of Mutant p53,” Nat'l Rev. Cancer, vol. 1, no. 1, pp. 68-76, 2001.
[34] A.C. Martin, A.M. Facchiano, A.L. Cuff, T. Hernandez-Boussard, M. Olivier, P. Hainaut, and J.M. Thornton, “Integrating Mutation Data and Structural Analysis of the tp53 Tumor-Suppressor Protein,” Human Mutation, vol. 19, pp. 149-164, 2002.
[35] M.K. Warmuth, J. Liao, G. Raetsch, M. Mathieson, S. Putta, and C. Lemmen, “Active Learning with Support Vector Machines in the Drug Discovery Process,” J. Chemistry Information and Computer Science, vol. 43, pp. 667-673, 2003.
[36] Y. Liu, “Active Learning with Support Vector Machine Applied to Gene Expression Data for Cancer Classification,” J. Chemistry Information and Computer Science, vol. 44, pp. 1936-1941, 2004.
[37] P. Mitra, C.A. Murthy, and S.K. Pal, “A Probabilistic Active Support Vector Learning Algorithm,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, pp. 413-418, 2004.
[38] R. Karchin, M. Diekhans, L. Kelly, D.J. Thomas, U. Pieper, N. Eswar, D. Haussler, and A. Sali, “LS-SNP: Large-Scale Annotation of Coding Non-Synonymous SNPs Based on Multiple Information Sources,” Bioinformatics, vol. 21, pp. 2814-2820, 2005.
[39] D.A. Case, T.A. Darden, T.E. Cheatham III, C.L. Simmerling, J. Wang, R.E. Duke, R. Luo, K.M. Merz, B. Wang, D.A. Pearlman, M. Crowley, S. Brozell, V. Tsui, H. Gohlke, J. Mongan, V. Hornak, G. Cui, P. Beroza, C. Schafmeister, J.W. Caldwell, W.S. Ross, and P.A. Kollman, “AMBER 8,” Univ. of California, San Francisco, 2004.
[40] V. Tsui and D.A. Case, “Theory and Applications of the Generalized Born Solvation Model in Macromolecular Simulations,” Biopolymers (Nuclear Academy of Science, vol. 56, pp. 275-291, 2001.
[41] J.-P. Ryckaert, G. Ciccotti, and H.J. C. Berendsen, “Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes,” J. Computational Physics, vol 23, pp. 327-341, 1977.
[42] A.A. Canutescu, A.A. Shelenkov, and R.L. Dunbrack Jr., “A Graph Theory Algorithm for Protein Side-Chain Prediction,” Protein Science, vol. 12, pp. 2001-2014, 2003.
[43] W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery, Numerical Recipes, the Art of Scientific Computing, second ed. Cambridge, U.K.: Cambridge Univ. Press, 1992.
[44] J. Cheng, A. Randall, and P. Baldi, “Prediction of Protein Stability Changes for Single Site Mutations Using Support Vector Machines,” Proteins: Structure, Function, Bioinformatics, 2005.
[45] R. Luo, L. David, and M.K. Gilson, “Accelerated Poisson-Boltzmann Calculations for Static and Dynamic Systems,” J. Computational Chemistry, vol. 23, pp. 1244-1253, 2002.
[46] B. Schlkopf, K. Tsuda, and J. Vert, Kernel Methods in Computational Biology (Computational Molecular Biology). Bradford Books, 2004.
[47] H. Saigo, J. Vert, N. Ueda, and T. Akutsu, “Protein Homology Detection Using String Alignment Kernels,” Bioinformatics, vol. 20, no. 11, pp. 1682-1689, 2004.
[48] C.S. Leslie, E. Eskin, A. Cohen, J. Weston, and W.S. Noble, “Mismatch String Kernels for Discriminative Protein Classification,” Bioinformatics, vol. 20, no. 4, pp. 467-476, 2004.
[49] I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, second ed. San Francisco: Morgan Kaufmann, 2005.
[50] J. Platt, “Fast Training of Support Vector Machines Using Sequential Minimal Optimization,” Advances in Kernel Methods— Support Vector Learning, B. Schoelkopf, C. Burges, and A. Smola, eds., MIT Press, 1998.
[51] P. Baldi and P.S. Brunak, Bioinformatics: The Machine Learning Approach, second ed. Cambridge, Mass.: MIT Press, 2001.
[52] D.B. Friedler, T. Veprintsev, K.I. Rutherford, and A. Fersht, “Binding of RAD51 and Other Peptide Sequences to a Promiscuous, Highly Electrostatic, Binding Site in p53,” J. Biological Chemistry, vol. 280, no. 9, pp. 8051-8059, 2005.
[53] E.W. Weisstein et al., “Mutual Information,” MathWorld— A Wolfram Web Resource, http://mathworld.wolfram.comMutual Information.htm , 2000.
[54] M.F. Sanner, A.J. Olson, and J.C. Spehner, “Reduced Surface: An Efficient Way to Compute Molecular Surfaces,” Biopolymers, vol. 38, no. 3, pp. 305-320, 1996.

Index Terms:
Biology and genetics, feature extraction or construction, machine learning, medicine and science.
Citation:
Samuel A. Danziger, S. Joshua Swamidass, Jue Zeng, Lawrence R. Dearth, Qiang Lu, Jonathan H. Chen, Jianlin Cheng, Vinh P. Hoang, Hiroto Saigo, Ray Luo, Pierre Baldi, Rainer K. Brachmann, Richard H. Lathrop, "Functional Census of Mutation Sequence Spaces: The Example of p53 Cancer Rescue Mutants," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 3, no. 2, pp. 114-125, April-June 2006, doi:10.1109/TCBB.2006.22
Usage of this product signifies your acceptance of the Terms of Use.