Literature DB >> 17048398

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

Samuel A Danziger1, 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.   

Abstract

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).

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Year:  2006        PMID: 17048398      PMCID: PMC2748235          DOI: 10.1109/TCBB.2006.22

Source DB:  PubMed          Journal:  IEEE/ACM Trans Comput Biol Bioinform        ISSN: 1545-5963            Impact factor:   3.710


  42 in total

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Authors:  R M Bush; C A Bender; K Subbarao; N J Cox; W M Fitch
Journal:  Science       Date:  1999-12-03       Impact factor: 47.728

2.  Pharmacological rescue of mutant p53 conformation and function.

Authors:  B A Foster; H A Coffey; M J Morin; F Rastinejad
Journal:  Science       Date:  1999-12-24       Impact factor: 47.728

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Authors:  Adrian A Canutescu; Andrew A Shelenkov; Roland L Dunbrack
Journal:  Protein Sci       Date:  2003-09       Impact factor: 6.725

4.  A probabilistic active support vector learning algorithm.

Authors:  Pabitra Mitra; C A Murthy; Sankar K Pal
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2004-03       Impact factor: 6.226

5.  Protein homology detection using string alignment kernels.

Authors:  Hiroto Saigo; Jean-Philippe Vert; Nobuhisa Ueda; Tatsuya Akutsu
Journal:  Bioinformatics       Date:  2004-02-26       Impact factor: 6.937

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Journal:  Mol Cell Biol       Date:  1992-06       Impact factor: 4.272

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Journal:  Nat Genet       Date:  1992-04       Impact factor: 38.330

8.  Reduced surface: an efficient way to compute molecular surfaces.

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Journal:  Biopolymers       Date:  1996-03       Impact factor: 2.505

9.  p53-dependent apoptosis in the absence of transcriptional activation of p53-target genes.

Authors:  C Caelles; A Helmberg; M Karin
Journal:  Nature       Date:  1994-07-21       Impact factor: 49.962

10.  Crystal structure of a p53 tumor suppressor-DNA complex: understanding tumorigenic mutations.

Authors:  Y Cho; S Gorina; P D Jeffrey; N P Pavletich
Journal:  Science       Date:  1994-07-15       Impact factor: 47.728

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  8 in total

1.  Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants.

Authors:  Samuel A Danziger; Jue Zeng; Ying Wang; Rainer K Brachmann; Richard H Lathrop
Journal:  Bioinformatics       Date:  2007-07-01       Impact factor: 6.937

2.  Structure-based predictions broadly link transcription factor mutations to gene expression changes in cancers.

Authors:  Justin Ashworth; Brady Bernard; Sheila Reynolds; Christopher L Plaisier; Ilya Shmulevich; Nitin S Baliga
Journal:  Nucleic Acids Res       Date:  2014-11-05       Impact factor: 16.971

3.  Heterogeneous biomedical database integration using a hybrid strategy: a p53 cancer research database.

Authors:  Vadim Y Bichutskiy; Richard Colman; Rainer K Brachmann; Richard H Lathrop
Journal:  Cancer Inform       Date:  2007-02-20

4.  Predicting transcriptional activity of multiple site p53 mutants based on hybrid properties.

Authors:  Tao Huang; Shen Niu; Zhongping Xu; Yun Huang; Xiangyin Kong; Yu-Dong Cai; Kuo-Chen Chou
Journal:  PLoS One       Date:  2011-08-08       Impact factor: 3.240

5.  Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science.

Authors:  Decebal Constantin Mocanu; Elena Mocanu; Peter Stone; Phuong H Nguyen; Madeleine Gibescu; Antonio Liotta
Journal:  Nat Commun       Date:  2018-06-19       Impact factor: 14.919

6.  Predicting positive p53 cancer rescue regions using Most Informative Positive (MIP) active learning.

Authors:  Samuel A Danziger; Roberta Baronio; Lydia Ho; Linda Hall; Kirsty Salmon; G Wesley Hatfield; Peter Kaiser; Richard H Lathrop
Journal:  PLoS Comput Biol       Date:  2008-09-04       Impact factor: 4.475

7.  Prediction of P53 mutants (multiple sites) transcriptional activity based on structural (2D&3D) properties.

Authors:  R Geetha Ramani; Shomona Gracia Jacob
Journal:  PLoS One       Date:  2013-02-13       Impact factor: 3.240

8.  Computational identification of a transiently open L1/S3 pocket for reactivation of mutant p53.

Authors:  Christopher D Wassman; Roberta Baronio; Özlem Demir; Brad D Wallentine; Chiung-Kuang Chen; Linda V Hall; Faezeh Salehi; Da-Wei Lin; Benjamin P Chung; G Wesley Hatfield; A Richard Chamberlin; Hartmut Luecke; Richard H Lathrop; Peter Kaiser; Rommie E Amaro
Journal:  Nat Commun       Date:  2013       Impact factor: 14.919

  8 in total

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