An evolutionarily conserved network of amino acids mediates gating in voltage-dependent potassium channels

A novel sequence-analysis technique for detecting correlated amino acid positions in intermediate-size protein families (50-100 sequences) was developed, and applied to study voltage-dependent gating of potassium channels. Most contemporary methods for detecting amino acid correlations within proteins use very large sets of data, typically comprising hundreds or thousands of evolutionarily related sequences, to overcome the relatively low signal-to-noise ratio in the analysis of co-variations between pairs of amino acid positions. Such methods are impractical for voltage-gated potassium (Kv) channels and for many other protein families that have not yet been sequenced to that extent. Here, we used a phylogenetic reconstruction of paralogous Kv channels to follow the evolutionary history of every pair of amino acid positions within this family, thus increasing detection accuracy of correlated amino acids relative to contemporary methods. In addition, we used a bootstrapping procedure to eliminate correlations that were statistically insignificant. These and other measures allowed us to increase the method's sensitivity, and opened the way to reliable identification of correlated positions even in intermediate-size protein families. Principal-component analysis applied to the set of correlated amino acid positions in Kv channels detected a network of inter-correlated residues, a large fraction of which were identified as gating-sensitive upon mutation. Mapping the network of correlated residues onto the 3D structure of the Kv channel from Aeropyrum pernix disclosed correlations between residues in the voltage-sensor paddle and the pore region, including regions that are involved in the gating transition. We discuss these findings with respect to the evolutionary constraints acting on the channel's various domains. The software is available on our website http://ashtoret.tau.ac.il/-sarel/CorrMut.html (C) 2004 Elsevier Ltd. All rights reserved.