Functional census of mutation sequence spaces: the example of p53 cancer rescue mutants

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]  C. Harris,et al.  The IARC TP53 database: New online mutation analysis and recommendations to users , 2002, Human mutation.

[2]  Ray Luo,et al.  Accelerated Poisson–Boltzmann calculations for static and dynamic systems , 2002, J. Comput. Chem..

[3]  Thomas Lengauer,et al.  Geno2pheno: Interpreting Genotypic HIV Drug Resistance Tests , 2001, IEEE Intell. Syst..

[4]  K. Vousden,et al.  Minireviewp 53 : Death Star able to induce the defensive p 53 response to oncogene , 2000 .

[5]  B. Foster,et al.  Pharmacological rescue of mutant p53 conformation and function. , 1999, Science.

[6]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[7]  G. Getz,et al.  DNA microarrays identification of primary and secondary target genes regulated by p53 , 2001, Oncogene.

[8]  Bernhard Schölkopf,et al.  Kernel Methods in Computational Biology , 2005 .

[9]  Ying Liu,et al.  Active Learning with Support Vector Machine Applied to Gene Expression Data for Cancer Classification , 2004, J. Chem. Inf. Model..

[10]  Alison L. Cuff,et al.  Integrating mutation data and structural analysis of the TP53 tumor‐suppressor protein , 2002, Human mutation.

[11]  Wei Gu,et al.  Ubiquitination, phosphorylation and acetylation: the molecular basis for p53 regulation. , 2003, Current opinion in cell biology.

[12]  A. Fersht,et al.  Rescuing the function of mutant p53 , 2001, Nature Reviews Cancer.

[13]  Russ B. Altman,et al.  Representing genetic sequence data for pharmacogenomics: an evolutionary approach using ontological and relational models , 2002, ISMB.

[14]  A. Levine p53, the Cellular Gatekeeper for Growth and Division , 1997, Cell.

[15]  C. A. Murthy,et al.  A probabilistic active support vector learning algorithm , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  J. Shay,et al.  A transcriptionally active DNA-binding site for human p53 protein complexes , 1992, Molecular and cellular biology.

[17]  B. Rannala Bioinformatics: The Machine Learning Approach.Second Edition. Adaptive Computation and Machine Learning. ByPierre Baldiand, Sørenv Brunak.A Bradford Book. Cambridge (Massachusetts): MIT Press. $49.95. xxiii + 452 p; ill.; index. ISBN: 0–262–02506‐X. 2001. , 2002 .

[18]  Adrian A Canutescu,et al.  Access the most recent version at doi: 10.1110/ps.03154503 References , 2003 .

[19]  G. Ciccotti,et al.  Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes , 1977 .

[20]  J. E. Glynn,et al.  Numerical Recipes: The Art of Scientific Computing , 1989 .

[21]  Gustavo A. Stolovitzky,et al.  Bioinformatics: The Machine Learning Approach , 2002 .

[22]  A. Fersht,et al.  Binding of Rad51 and Other Peptide Sequences to a Promiscuous, Highly Electrostatic Binding Site in p53* , 2005, Journal of Biological Chemistry.

[23]  M. Karin,et al.  p53-Dependent apoptosis in the absence of transcriptional activation of p53-target genes , 1994, Nature.

[24]  Judith Klein-Seetharaman,et al.  PROTEINS: Structure, Function, and Bioinformatics 58:955–970 (2005) Protein Classification Based on Text Document Classification Techniques , 2022 .

[25]  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.

[26]  E. Appella,et al.  Post-translational modifications and activation of p53 by genotoxic stresses. , 2001, European journal of biochemistry.

[27]  J. Boeke,et al.  Genetic selection of intragenic suppressor mutations that reverse the effect of common p53 cancer mutations , 1998, The EMBO journal.

[28]  C. Prives,et al.  The p53 pathway , 1999, The Journal of pathology.

[29]  W. Fitch,et al.  Predicting the evolution of human influenza A. , 1999, Science.

[30]  Arlo Z. Randall,et al.  Prediction of protein stability changes for single‐site mutations using support vector machines , 2005, Proteins.

[31]  Gunnar Rätsch,et al.  Active Learning with Support Vector Machines in the Drug Discovery Process , 2003, J. Chem. Inf. Comput. Sci..

[32]  Petr Pancoska,et al.  p53 has a direct apoptogenic role at the mitochondria. , 2003, Molecular cell.

[33]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[34]  Ian Witten,et al.  Data Mining , 2000 .

[35]  M Bycroft,et al.  Hot-spot mutants of p53 core domain evince characteristic local structural changes. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[36]  Ting Wang,et al.  Groups of p53 target genes involved in specific p53 downstream effects cluster into different classes of DNA binding sites , 2002, Oncogene.

[37]  Jason Weston,et al.  Mismatch string kernels for discriminative protein classification , 2004, Bioinform..

[38]  Richard H. Lathrop,et al.  Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses , 1999, J. Comb. Optim..

[39]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[40]  D. Case,et al.  Theory and applications of the generalized born solvation model in macromolecular simulations , 2000, Biopolymers.

[41]  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.

[42]  P. May,et al.  Twenty years of p53 research: structural and functional aspects of the p53 protein , 1999, Oncogene.

[43]  Galina Selivanova,et al.  Restoration of the tumor suppressor function to mutant p53 by a low-molecular-weight compound , 2002, Nature Medicine.

[44]  Tatsuya Akutsu,et al.  Protein homology detection using string alignment kernels , 2004, Bioinform..

[45]  K. Kinzler,et al.  Definition of a consensus binding site for p53 , 1992, Nature Genetics.

[46]  Thierry Soussi,et al.  The UMD‐p53 database: New mutations and analysis tools , 2003, Human mutation.

[47]  A. Levine,et al.  Surfing the p53 network , 2000, Nature.

[48]  Yang Xu,et al.  Regulation of p53 responses by post-translational modifications , 2003, Cell Death and Differentiation.

[49]  David Haussler,et al.  LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources , 2005, Bioinform..

[50]  M. Sanner,et al.  Reduced surface: an efficient way to compute molecular surfaces. , 1996, Biopolymers.

[51]  Marie-France Sagot,et al.  An efficient algorithm for the identification of structured motifs in DNA promoter sequences , 2006, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[52]  K. Kinzler,et al.  A model for p53-induced apoptosis , 1997, Nature.

[53]  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.

[54]  Richard H. Lathrop,et al.  Knowledge-Based Avoidance of Drug-Resistant HIV Mutants , 1998, AI Mag..

[55]  Antony M. Carr,et al.  The evolution of diverse biological responses to DNA damage: insights from yeast and p53 , 2001, Nature Cell Biology.

[56]  J. Manfredi,et al.  p53 and apoptosis: it's not just in the nucleus anymore. , 2003, Molecular cell.

[57]  P. Jeffrey,et al.  Crystal structure of a p53 tumor suppressor-DNA complex: understanding tumorigenic mutations. , 1994, Science.