Genetic algorithm-based optimization of hydrophobicity tables

Moti Zviling, Hadas Leonov, Isaiah T. Arkin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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methods based on the physicochemical principle of a sliding window of hydrophobicity (hydropathy analysis) have been replaced by approaches based on hidden Markov models or neural networks which prevail due to their probabilistic orientation.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.

Original languageAmerican English
Pages (from-to)2651-2656
Number of pages6
Issue number11
StatePublished - 1 Jun 2005

Bibliographical note

Funding Information:
This work was supported in part by a grant from the Israel Science Foundation (784/01) to I.T.A.


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