Literature DB >> 16595554

Computational recognition of potassium channel sequences.

Burkhard Heil1, Jost Ludwig, Hella Lichtenberg-Fraté, Thomas Lengauer.   

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.

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Year:  2006        PMID: 16595554     DOI: 10.1093/bioinformatics/btl132

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  4 in total

1.  A single conserved basic residue in the potassium channel filter region controls KCNQ1 insensitivity toward scorpion toxins.

Authors:  Zongyun Chen; Youtian Hu; Bin Wang; Zhijian Cao; Wenxin Li; Yingliang Wu
Journal:  Biochem Biophys Rep       Date:  2015-07-21

2.  Conduction and Gating Properties of the TRAAK Channel from Molecular Dynamics Simulations with Different Force Fields.

Authors:  Riccardo Ocello; Simone Furini; Francesca Lugli; Maurizio Recanatini; Carmen Domene; Matteo Masetti
Journal:  J Chem Inf Model       Date:  2020-12-09       Impact factor: 4.956

3.  TransportTP: a two-phase classification approach for membrane transporter prediction and characterization.

Authors:  Haiquan Li; Vagner A Benedito; Michael K Udvardi; Patrick Xuechun Zhao
Journal:  BMC Bioinformatics       Date:  2009-12-14       Impact factor: 3.169

4.  Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes.

Authors:  Antonius P A Janssen; Sebastian H Grimm; Ruud H M Wijdeven; Eelke B Lenselink; Jacques Neefjes; Constant A A van Boeckel; Gerard J P van Westen; Mario van der Stelt
Journal:  J Chem Inf Model       Date:  2018-11-08       Impact factor: 4.956

  4 in total

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