Divining the design principles of voltage sensors

The ability of membrane proteins to sense and respond to changes in membrane voltage is critical for a vast array of biological processes, from generating and propagating the nerve impulse to excitation–secretion coupling. Hodgkin and Huxley (1952) were the first to characterize voltage-activated potassium (Kv) and sodium (Nav) currents, but the common architecture of voltage-activated cation channels only became apparent after the genes encoding these proteins were identified (Noda et al., 1986; Tanabe et al., 1987; Timpe et al., 1988). We now appreciate that the voltage sensitivity of these channels can be ascribed to modular voltage-sensing domains comprised of S1–S4 helices (Lu et al. Bioinformatic searches subsequently identified S1–S4 voltage-sensing domains in other protein families, including voltage-sensitive phosphatases (VSPs; Kumánovics et al., 2002; Murata et al., 2005), where the S1–S4 domain regulates an intracellular enzyme , and voltage-activated proton channels (Hv1; Ramsey et al., 2006; Sasaki et al., 2006), in which the S1–S4 domain forms a stand-alone pore. Although the sequences of S1–S4 domains vary considerably, the mechanisms of these domains appear to be so highly conserved that Kv channels from humans and hyperthermophilic archebacteria are both sensitive to voltage sensor toxins from tarantula venom (Swartz, 2008). S1–S4 domains also adopt similar structures in the few Kv and Nav channels that have been successfully crystalized (Jiang et al., 2003; Long et al., 2007; Payandeh et al., 2011), suggesting that there is a common blueprint, or design principle, for constructing a voltage sensor. In this issue of The Journal of General Physiology, Palovcak et al. computationally analyze the thousands of examples of S1–S4 domains present in all three kingdoms of life to identify the key design features common to all S1–S4 domains. Palovcak et al. (2014) begin by identifying S1–S4 domain sequences in the National Center for Biotechnology Information database using a hidden Markov model (HMM; Eddy, 2004) trained on an initial set, or seed, of well known, phylogenetically diverse sequences. probability distribution of amino acids at each position, and because the HMM accounts for differences in evolutionary pressure, it is typically better able to detect distantly related sequences than a simple BLAST (Basic Local Alignment Search Tool) search. Additionally, an HMM can automatically provide the most probable alignment, and its likelihood, for each residue in the sequence, which is especially helpful when the alignment appears ambiguous. Using this approach, the authors' HMM identified >6,600 sequences from all known branches of …