The past 2 yr have been extraordinary in terms of advances in our understanding of how ion channels work. Nowhere has this dizzying progress been more evident than in the study of the voltage-dependent K+ channel family, Kv. The great catalyst has been, of course, the high-resolution structure of the Streptomyces lividans K+ channel KcsA (
Based on sequence similarity (
Two papers in this issue of The Journal (
The method can be extended by searching for a periodic behavior in the perturbations caused by the mutagenesis, as only secondary structure elements with explicit solvent-exposed surfaces will show any periodicity in the response to a mutational scan (Here "solvent" represents either water or lipid molecules). This type of approach has been employed to identify the secondary structure of transmembrane segments in the Mot proton channel of the Escherichia coli flagellar motor (
In an earlier study, Go) calculated from the relative stability of the closed conformation of the channel and referenced to that of the wild-type channel as a
Go. Based on this parameter, residues were classified as either "tolerant" or "high-impact," depending on the degree of functional perturbation caused by the Trp side chain. Their data revealed obvious
-helical periodicity, with tolerant and high-impact residues segregating at opposite sides of a helix projection. Such distribution then allows for a clear definition of a solvent-exposed surface. As expected, the tolerant face of the helix had a striking correspondence with the positions with the highest sequence variability.
Hong and Miller used an identical approach to probe the secondary structure of the S1 and S3 segments (but note that their Go values refer to the stability of the open state). Their results demonstrate quite convincingly the
-helical nature of the S1 segment, where tolerant and high-impact positions are well distributed along two faces of the helix. This distribution is also paralleled in terms of variable and conserved residues at each side of the helix surface. The data for the S3 segment is far more complicated. Viewed from a helical wheel projection, neither high-impact nor tolerant positions tend to segregate into two distinct surfaces as with S1 and S2. Furthermore, variability data derived from sequence alignments shows that a number of tolerant positions are highly conserved, and some high-impact positions fall within a high-variability segment. Further analysis of the data, however, demonstrated that starting from its NH2-terminal end, more than half of S3 displays an apparent
-helical periodicity, with the last third of the segment showing most of the discrepancies.
In a mutational tour de force, Li-Smerin et al. performed an Ala scanning mutagenesis of the entire S1S4 gating domain of drk1 channels (127 residues, including the loops linking the transmembrane segments) to obtain information about the secondary and super-secondary structure of the entire region. As with Hong and Miller, the degree of per-residue perturbation was quantified using an estimate of Go derived from the voltage dependence of the conductance. Also of note is the fact that Li-Smerin et al. used a nonbiased quantitative analysis to determine the quality and extent of the secondary structure estimates. Instead of relying primarily on the apparent segregation of tolerant and high-impact residues in a helical wheel projection, Li-Smerin et al. extracted angular frequency information from the data profile using a discrete Fourier transform method first described by
) identifies the main frequency components from any linear string of data. For example, a perfect
-helix, with a period of 3.6 residues per turn, is expected to have a significant peak at or near 100° (360° of the unitary circle, divided by 3.6), while a strand in a ß sheet is expected to show a large component at 180° (360° divided by two residues per turn). This quantitative approach makes it possible to evaluate the significance of a specific frequency peak by means of the periodicity index (PI;
-helices), relative to the area of the entire power spectrum. For significant frequency components, the value of PI should be >2 (
Based on this type of analysis, results for S1, S2, and S3 agree well with those PI for S1 was 2.3 and for S2 was 1.8 (although it may be actually higher), while Fourier analysis of the
PI value of 2.4. These findings are all in agreement with the S1 and S2 segments having a strong
-helical periodicity and a large solvent-exposed surface. Quantitative analysis of the S3 data turned out to be far more challenging. The power spectra obtained from the entire segment did not show significant
-helix components (
PI = 1.4), while restricting the window of analysis to the NH2-terminal half of S3 generated a large frequency component centered at 122°. In principle, this frequency component would correspond to a 3-10 helix, an unusual type of secondary structure only found in short stretches capping the termini of canonical
-helices. Li-Smerin et al. interpret these results to suggest that the S3 segment is surrounded by a complex environment of multiple proteinprotein contacts, with perhaps a small area accessible to solvent at the NH2-terminal end of the helix.
One of the most interesting, if not unexpected, results from the Li-Smerin et al. study is the uncovering of evidence supporting defined secondary structures in the extracellular "loops" of the channel. By comparing the calculated PI of the entire |
Go| profile using a sliding window (13 or 17 residues;
PI profile with minima in the hydrophobicity profile corresponding to segments connecting S1 with S2 and S3 with S4. When analyzed in detail, the S1S2 loop had a strong
PI = 2.1 and is postulated to lie at the extracellular surface of the protein, while the S3S4 loop had a less-than-ideal
PI = 1.6 and its location relative to the plane of the membrane is still open to interpretation. This surprising finding serves as strong incentive for the use of quantitative data mining in the analysis of scanning mutagenesis experiments.
Figure 1 puts the three data sets in perspective. It shows an overview of all the results for Shaker and drk1 channels, classified according to their functional effects as low or high impact positions. Here, the structural equivalence between specific residue positions is assumed to correspond to that of a straightforward sequence alignment, and the outcome for each position is mapped on a helical wheel projection, as in each of the original manuscripts. Echoing the main conclusion from
|
While both groups regard the S3 segment to be -helical, opinions differ about the details of its packing in the channel. Li-Smerin et al. consider this segment involved in multiple tertiary contacts, with accessibility to solvent provided perhaps by crevasses between helices. Hong and Miller view the S3 segment largely as a lipid-exposed segment, contributing along S1 and S2 to shield S4 and S6 from the lipid environment. In fact, Figure 1 does show that even the positions considered high impact in both studies (Figure 1, check mark) do not segregate into two distinct surfaces, arguing against a simple buried/exposed helix surface. However, as Hong and Miller point out, a helical net diagram suggests that the NH2-terminal portion of S3 show a better segregation between high- and low-impact positions (not shown). This apparent discrepancy in interpretation could be the case of a differential effect of Ala versus Trp scanning in tightly packed architectures. But, since the putative solvent-exposed surface is, in both data sets, fairly small and oriented towards the intracellular end of the helix, this might be the case of a glass half-full/half-emptytype difference in interpretation.
Hong and Miller did not do a similar perturbation analysis in S4, as they argued that the even distribution of charged residues in S4 (every three), and the fact that charge neutralizations may affect channel voltage dependence, could bias the results. Li-Smerin et al. minimized the problem of the electrostatic contribution of charge neutralization along S4 by cleverly (and arbitrarily) assigning the average value of |Go| obtained from the entire S4 segment to each of the basic S4 residues. The direct consequence of this subterfuge was that the
-helix component in the power spectra was dramatically improved, allowing for an univocal
-helical assignment to at least the COOH-terminal half of S4 (
PI = 2.9). Thus, even if S4 seems to be mostly surrounded by other interacting helices, the data from Ala scanning strongly supports the notion that this segment is
-helical.
A very satisfying conclusion from both
Li-Smerin et al. conclude their study by proposing a specific four-helix packing arrangement for the S1S4-gating domain. They base their packing model on three pieces of information: (a) the proposed -helical structure for all transmembrane segments of the S1S4 segment, (b) the relative orientation of a projected net |
Go| calculated as the resultant vector from individual |
Go| values, and (c) the enforcement of the proposed electrostatic interactions between negative charges in S2 and S3 with basic residues in S4. Although speculative, this specific packing model can serve as a blueprint for future mutagenesis studies regarding the voltage-sensing domain of Kv channels. Hong and Miller do not explicitly propose a packing arrangement, but suggest that the outermost portions of the channel must be lined by segments S1 through S3 (surrounding S4 and S6), and based on sequence variability analysis propose that S5 must show some lipid-accessible surface.
The actual placement of the S1S4 four-helix bundle relative to the pore domain is thus far anybody's guess: in fact, the placement of the S1S4 segments relative to the S5S6 core occurs in opposite orientations in Li-Smerin et al. and Hong and Miller's interpretation (Figure 1, respectively). Additional pieces of information, perhaps in the form of double mutant analysis or genetic approaches to identify possible suppression partners, are needed to complete this six-helix packing puzzle.
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