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Bioinformatics Advance Access published online on March 29, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti405
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received August 1, 2004
Revised March 22, 2005
Accepted March 22, 2005

Article

Genetic algorithm-based optimization of hydrophobicity tables

Moti Zviling 1, Hadas Leonov 1, and Isaiah T. Arkin 1*

1 The Alexander Silberman Institute of Life Sciences, Department of Biological Chemistry, The Hebrew University, Givat-Ram, Jerusalem, 91904, Israel

* To whom correspondence should be addressed.
Isaiah T. Arkin, E-mail: arkin{at}cc.huji.ac.il


  Abstract

The genomic abundance and pharmacological importance of membrane proteins have fueled efforts to identify them based solely on sequence information. Previous methods based on the physicochemical principle of a sliding window of hydrophobicity (hydropathy analysis) have been replaced by approaches based on hidden Markov models or neural networks which prevail due to their probabilistic orientation. In the current study, an optimization of the hydrophobicity tables used in hydropathy analysis is performed using a genetic algorithm. As such, the approach can be viewed as a synthesis between the physicochemical and statistically based methods. The resulting hydrophobicity tables lead to significant improvement in the prediction accuracy of hydropathy analysis. Furthermore, since hydropathy analysis is less dependent on the basis set of membrane proteins used to hone the statistically based methods, as well as being faster, it may be valuable in the analysis of new genomes. Finally, the values obtained for each of the amino acids in the new hydrophobicity tables are discussed.


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