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Computational Recognition of Potassium Channel Sequences

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Lengauer,  Thomas
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Citation

Heil, B., Ludwig, J., Lichtenberg-Fraté, H., & Lengauer, T. (2006). Computational Recognition of Potassium Channel Sequences. Bioinformatics, 22(13), 1562-1568. doi:10.1093/bioinformatics/btl132.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-225A-1
Abstract
Motivation: Potassium channels are mainly known for their role in regulating
and maintaining the membrane potential. Since this is one of the key mechanisms
of signal transduction, malfunction of these potassium channels leads to a wide
variety of severe diseases. Thus potassium channels are priority targets of
research for new drugs, despite the fact that this protein family is highly
variable and closely related to other channels, which makes it very difficult
to identify new types of potassium channel sequences.
Results: Here we present a new method for identifying potassium channel
sequences (PSM, Property Signature Method), which—in contrast to the known
methods for protein classification—is directly based on physicochemical
properties of amino acids rather than on the amino acids themselves. A
signature for the pore region including the selectivity filter has been
created, representing the most common physicochemical properties of known
potassium channels. This string enables genome-wide screening for sequences
with similar features despite a very low degree of amino acid similarity within
a protein family.