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Bioinformatics Advance Access originally published online on March 29, 2005
Bioinformatics 2005 21(11):2651-2656; 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{at}oupjournals.org

Genetic algorithm-based optimization of hydrophobicity tables

Moti Zviling , Hadas Leonov and Isaiah T. Arkin *

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.

Summary: 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methods based on the physicochemical principle of a sliding window of hydrophobicity (hydropathy analysis) have been replaced by approaches based on hidden Markov models or neural networks which prevail due to their probabilistic orientation.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 physicochemically 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 is 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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Contact: arkin{at}cc.huji.ac.il


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