Membrane proteins are uniquely placed at the interface of the internal and external
milieu of cells, and many of them unsurprisingly function as signal transducers involved
in pathways critical for normal physiological activity. A majority of these membrane
proteins have a modular architecture with distinct sensing and catalytic domains.
Understanding how information flows from one part of the molecule to another is crucial
for developing a detailed molecular-level understanding of their function. Voltage-gated
ion channels are a class of such integral membrane proteins found ubiquitously in
all kingdoms of life and are involved in both electrical and chemical signaling pathways
(Hille, 2001).
Members of this superfamily share a common overall architecture wherein a functional
unit is made up of four homologous domains arranged symmetrically around a central
axis (Bezanilla, 2000; Long et al., 2005a). Each domain comprises six transmembrane
segments and a reentrant pore loop, which forms the selectivity filter of the channel
(Heginbotham et al., 1994). The segments S1–S4 constitute the voltage-sensing domain,
which, as the name suggests, is the principal element for sensing changes in membrane
potential (Bezanilla, 2000; Swartz, 2008). The exquisite voltage sensitivity of these
proteins is, in large part, due to a distinct cluster of basic residues on the S4
segment (Aggarwal and MacKinnon, 1996; Seoh et al., 1996). The four S5-S6 segments
associate to form the functional pore domain through which ions can flux. The S6 segments
line the ion permeation pathway, and, on the intracellular side, they come together
at the bundle crossing to form the activation gate (Liu et al., 1997; del Camino et
al., 2000).
Although significant progress has been made in developing a structural view of the
gating process, the molecular driving forces that underlie these structural transitions
remain poorly understood. Structures of channels in various states may suggest possible
interactors, but experimental validation of these interactions is essential. In this
perspective, we will principally focus on the thermodynamics of conformational coupling
between the voltage-sensing domain and the gates in the pore domain, a process referred
to as electromechanical coupling in recent literature. We will critically discuss
the current state of knowledge and highlight the challenges in thermodynamic analysis
of complex multistate proteins.
Defining “conformational coupling”
Dependence between two events can be conveniently expressed using conditional probabilities
(Ben-Naim, 2010). For two events, A and B, where P(A) and P(B) are the respective
probabilities of their occurrences, P(A∩B) is the probability that both A and B occur
together, whereas P(A|B) is the conditional probability of event A, given that B has
occurred. P(A|B) is expressed as:
(1)
P
(
A
|
B
)
=
P
(
A
∩
B
)
P
(
B
)
.
In case the two events are independent of each other, P(A∩B) = P(A).P(B), and thus
by Eq. 1, P(A|B) = P(A). For protein systems, the two events would represent conformational
changes occurring in different parts of the protein. If the two conformational changes
are independent of each other, then the conditional probability of each event will
be equal to its innate probability (i.e., P(A|B) = P(A) and P(B|A) = P(B)), whereas
if the two events are “coupled” (dependent), then the probability measures will be
unequal. While mathematically sound and conceptually intuitive, such probability measures
are seldom useful to describe the physical origins of dependence between two processes
at the molecular level. Most often, to understand the molecular underpinnings of conformational
changes within proteins it is necessary to quantify the dependence in terms of accurate
and unambiguous energy measures.
Consider a simple case of two particles, where each can exist in two states, representing
a structural unit of a protein capable of undergoing conformational transitions. Coupling
between two such particles can be fully described by four state-dependent interactions:
the particles can interact with each other when either of them are resting or activated
(Fig. 1). This system can exist in four possible states, with two of them being the
end states, namely, when both particles are in an activated or resting conformation.
Thus, the net coupling between the two particles, θ, can be expressed as:
θ
=
θ
RR
θ
AA
θ
RA
θ
AR
,
Figure 1.
Definition of coupling. (A) Two particles, 1 and 2, each can exist in two states,
R and A. Each of them have an intrinsic preference for one of the two states, which
is determined by its intrinsic free-energy difference between the two states (represented
by the solid arrows). There are four state-dependent interactions between them: the
horizontal broken lines represent the “like state” interactions, θAA and θRR, which
are the interactions when both particles are in the A or R states, respectively; the
diagonal broken lines represent the “unlike state” interactions, θAR and θRA, which
are the interactions when the particles are in dissimilar states. (B) The system in
A is represented in a reaction scheme showing the transitions between the different
states. Alongside each transition, its equilibrium constant is shown. K1 and K2 are
the intrinsic activation constants of the two particles. The presence of the state-dependent
interactions modifies each of the equilibrium constants, but the net free-energy difference
(ΔGnet) between the doubly resting (ground) state, RR, and the doubly activated (final)
state, AA, is determined by the intrinsic equilibrium constants and the like state
interactions.
where θ is defined as the ratio between the “like” state interactions (θRR, θAA) and
the “unlike” state interactions (θRA, θAR). In case the two particles are positively
coupled, θ > 1, and the net like state interactions will be stronger than the net
unlike state interactions. Conversely, when they are “negatively” coupled, θ < 1,
the unlike state interactions will be greater than like state interactions, and the
intermediate states will be stabilized. θ = 1 implies that the two particles are not
coupled to each other but does not necessarily mean that there are no underlying interactions
between these particles. In theory, θ = 1 simply indicates that the net like state
interactions balance the unlike state interactions.
Thus, in terms of energetic effects, coupling by definition alters the stability of
intermediate states. In addition, coupling could also contribute to the net free-energy
difference between the two end states. For instance, let us consider the two-particle
system described earlier but assume that the only interaction between them is θAA
(i.e., when θRA = θAR = θRR = 1). In this case, the presence of interaction alters
the stability of the doubly activated state relative to the ground state. Functionally,
this would be manifested as the difference in net free energy for activation along
with corresponding change in forward and backward rates. If, however, the two structural
units also interact with each other in the resting state and θAA = θRR, the interactions
between the two particles does not produce a “net change” in the energy level of the
final state (Fig. 1). In this scenario, conformational coupling is manifested only
as a “kinetic effect” wherein the forward and backward rates are modified. Note that
the unlike state interactions (θRA, θAR) never contribute to the net energy imbalance
between the two end states even though they determine the coupling strength.
Experimental quantification of coupling interactions
To determine the origins of such coupling interactions at a molecular level, it is
essential to measure interaction energies and identify perturbations, which alter
coupling energies. Wyman in his influential studies on hemoglobin had shown that the
Hill energy extracted from a logarithmic transformation of the direct ligand binding
curve estimates the interaction energy between the ligand binding sites (Wyman, 1967).
Horrigan and Aldrich (1999, 2002) have used similar strategies to estimate the coupling
interactions between the different sensing and catalytic domains of the BK channel,
although they considered a specific allosteric model to derive these relationships.
Most models of coupling use a single coupling parameter to describe energetic linkage
between two structural domains of a protein. The six independent parameters in our
initial model can be normalized to yield a model with three (normalized) energetic
parameters, KV, KP, and θ (Fig. 2 A; Chowdhury and Chanda, 2010). It is to be noted
that KV and KP may be different from the true intrinsic equilibrium constants of the
voltage sensor and the pore, as they were obtained through normalizations that incorporate
multiple state-dependent interactions.
Figure 2.
χ-value analysis for a voltage-dependent system comprising two particles. (A) The
particle diagram for a system comprising a voltage sensor (which can exist in two
states, R and A) and a pore (which exists in two states, C and O). K1 and K2 represent
the intrinsic equilibrium constants of the voltage sensor and the pore in the absence
of any other interactions. There are four state-dependent interaction terms between
the two particles (θRC, θAC, θRO, and θAO; as in Fig. 1 A). The energetic parameters
of this system can be normalized through the relations shown. The voltage dependencies
of KV and KP can be expressed as:
K
i
=
K
i
0
exp
(
z
i
F
V
/
R
T
)
(i = V, P), where
K
i
0
is the voltage-independent part of the equilibrium constant and zi is its voltage
dependence; F, R, and T represent the Faraday constant, the universal gas constant,
and the temperature, respectively. Because the four coupling constants are voltage
independent, KV has the same voltage dependence as K1, and KP has the same voltage
dependence as K2. The coupling parameter in the normalized version, θ, is the ratio
of the like state interactions (θRCθAO) and the unlike state interactions (θACθRO).
(B) Using arbitrary values of the energetic parameters, the ln(P0/1 − P0) vs. voltage
plot for the pore was simulated for the allosteric model in A (closed symbols). The
broken red lines are the extrapolations of the linear regimes (obtained at high and
low voltages). The slope of the red lines is governed by zP. The difference of the
intercepts created by the linear extrapolations on the V = 0 axis is the χdiff for
pore, which is linearly proportional to the difference in the like state interaction
energies (−RTln(θAOθRC)) and the unlike state interaction energies (−RTln(θACθRO)).
(C) The state diagram for an allosteric model comprising two particles, using the
normalized parameters: there are four possible states of the system, depending on
the conformations of each of the two particles. When KP becomes very low, the allosteric
model reduces to a linear sequential scheme representing an obligatorily coupled system.
In terms of the un-normalized parameters, this could occur when K2 << 1, θRO << 1,
and/or θRC >> 1. The ln(P0/1 − P0) vs. V plot for the pore, for the obligatorily coupled
model, shown in B (open symbols), keeps on decreasing steeply at hyperpolarizing voltages.
Recently, we showed that for a generalized voltage-dependent allosteric system, the
interaction energies associated with a specific structural unit can be extracted using
an analytical technique, not limited by the size or symmetry-based constraints (Chowdhury
and Chanda, 2010). The procedure, referred to as the χ-value analysis, involves measuring
the conformational transition of a particle, using a site-specific probe, over a wide
range of voltages. When applied to the pore, we plot ln(PO/1 − PO) versus voltage
(Fig. 2 B). At very low and high voltages, this plot will be linear with a slope proportional
to the intrinsic voltage dependence of the pore. These asymptotes can be extrapolated
to the V = 0 axis to extract the two intercepts, χ− and χ+. The difference between
the two χ values, referred to as the χdiff, is a measure of the interaction energy
of the pore with all the other domains in the system. Indeed, for a general system
consisting of N-interacting particles, the χdiff for a particle is the ratio of all
its like state interactions versus unlike state interactions and therefore is a direct
measure of coupling interaction. This parameter is independent of the intrinsic stabilities
of the different particles and is a linear correlate of the interaction energy of
the particle with the remainder of the protein. To identify the specific residues
of the protein, which mediate such interactions, one measures only the χdiff parameter
for different perturbations. Also, by measuring the effect of the perturbations on
the χ− and χ+, useful information about the state dependence of the interactions can
also be obtained.
The allosteric model will reduce to an obligatory coupled model if state-dependent
coupling interactions are large or the intrinsic stability of the open pore (relative
to the closed state) is low (i.e., KP << 1; Fig. 2 C). In the allosteric scheme considered
here, kinetic modeling can extract three independent equilibrium constants, which
are sufficient to describe the energetics of the coupled system. However, kinetic
analysis of obligatory systems will yield only two independent equilibrium terms,
and consequently, the interaction energy cannot be independently estimated. Furthermore,
the ln(PO/1 − PO) versus voltage plots (Fig. 2 B) show that, for obligatorily coupled
systems, the steepness of the curve is characteristically large at hyperpolarized
potentials and is not equal to that at depolarized potentials. Thus, it is not meaningful
to extract the χ-value for obligatorily coupled systems.
Obligatory and allosterically coupled voltage-sensitive ion channels
The voltage-dependent gating behavior of the BK channels can be mostly accounted by
a Monod-Wyman-Changeux model (MWC)-like allosteric model (Cox et al., 1997; Cui et
al., 1997; Horrigan and Aldrich, 1999; Horrigan et al., 1999). According to these
models, the pore domains can open even when the voltage sensors are not activated,
albeit with a low probability. Activation of the voltage sensors makes the open pore
progressively more stable, resulting in large open probabilities once the voltage
sensors activate. A similar allosteric coupling model has been envisioned for HCN
(Altomare et al., 2001; Chen et al., 2007; Kusch et al., 2010; Ryu and Yellen, 2012)
and KCNQ channels (Osteen et al., 2010, 2012). In contrast, the coupling between voltage
sensor and pore is obligatory in voltage-gated potassium and sodium channels, such
that the pore domain cannot open until the voltage-sensing domains have transferred
all their charges (Hirschberg et al., 1995; Islas and Sigworth, 1999).
Similar to the plots shown in Fig. 2 B, allosteric and obligatory behavior can be
distinguished by plotting the ln PO with respect to voltage. As shown originally by
Almers (1978), in a linear activation scheme involving a single open state, the slope
of ln PO − V curves increases upon hyperpolarization and reaches a limiting value
(Schoppa et al., 1992). This value corresponds to the total gating charge associated
with channel opening. It should be noted that further hyperpolarization is unlikely
to change this limiting slope value because all the gating charges have retracted
to their resting state conformation, as reflected by the saturation of Q-V curves
(Seoh et al., 1996; Sigg and Bezanilla, 1997; Islas and Sigworth, 1999). The implication
of this observation is that the pore opening always requires prior activation of the
voltage sensors. In contrast to the voltage-gated ion channels, the slope of the ln
PO − V curve for BK channels initially increases but upon further hyperpolarization
decreases to a value less than the total gating charge (Horrigan and Aldrich, 1999,
2002; Horrigan et al., 1999; Ma et al., 2006). This indicates that the pore domain
has some intrinsic voltage sensitivity, and at very highly hyperpolarizing voltages
(where PO = ∼10−7), the pore is able to open without the voltage sensor activating.
Molecular determinants of electromechanical coupling
Although it has not been feasible to obtain a direct estimate of the magnitude of
interaction energy between the voltage sensor and pore in an obligatorily coupled
voltage-gated ion channel, in this section we will consider various strategies that
have been applied to identify the molecular mechanisms of this coupling. The search
for molecular determinants of electromechanical coupling have to a large extent focused
on a region of the polypeptide chain, known as the S4-S5 linker, that covalently links
the voltage sensors to the pore domain. Initial evidence of the role of this region
in coupling came from studies on a leucine heptad motif found in this region, which
when perturbed results in large modification of channel gating (McCormack et al.,
1991, 1993). In a remarkable experiment, Lu et al. (2002) were able to confer voltage
sensitivity to channels that natively feature only the pore domains by fusing them
to the voltage-sensing domains derived from the Shaker potassium channels. They showed
that complementarity of the S4-S5 linker region with the C-terminal end of the S6
transmembrane segment was crucial for cross-talk between the voltage sensor and pore.
Various perturbation studies have examined the functional effects of mutations in
the S4-S5 linker regions on the voltage-dependent opening in different members of
this superfamily (Sanguinetti and Xu, 1999; Chen et al., 2001; Tristani-Firouzi et
al., 2002; Decher et al., 2004; Ferrer et al., 2006; Soler-Llavina et al., 2006; Labro
et al., 2008; Muroi et al., 2010; Van Slyke et al., 2010; Labro et al., 2011; Wall-Lacelle
et al., 2011). In many early studies, attributes such as rightward shifts in conductance-voltage
curves, slowed kinetics of channel opening, and increased deactivation rates were
used to identify sites involved in coupling voltage sensor and pore. A reduction in
the slope of the Boltzmann fit to the conductance-voltage curve in response to perturbations
has also been suggested to reflect alterations in coupling (Yifrach, 2004). Although
such functional effects are not unique to perturbations that disrupt coupling, these
studies highlight the importance of various residues in the S4-S5 linker and the S6
tail regions during activation gating in voltage-gated ion channels.
Measurement of charge-voltage curves in addition to conductance-voltage curves can
provide unique insight with regards to the thermodynamics of this process (Chowdhury
and Chanda, 2012). Mutants such as the ILT mutant (Smith-Maxwell et al., 1998a,b;
Ledwell and Aldrich, 1999) in the Shaker potassium channel produce a distinct response
whereby the conductance-voltage curve is shifted rightward but the charge-voltage
curve is left shifted, implying that the mutation stabilizes the activated voltage
sensor while at the same time destabilizing the open pore. Muroi et al. (2010) identified
multiple positions in the S4-S5 linker and S6 region of the Nav 1.4 channel, where
a tryptophan substitution caused such opposite effects on voltage sensor and pore
stability. Response function analysis of simple models showed that these contrasting
shifts are produced when mutations modify interactions in both resting and activated
conformations (Fig. 3). This is holds true for allosteric (Fig. 3 A, Scheme II) as
well as obligatory models (Fig. 3 A, Scheme IV.) Mutagenesis studies in the Shaker
potassium channel (Soler-Llavina et al., 2006; Haddad and Blunck, 2011) also identified
additional sites that have opposite effects on voltage sensor and pore stability.
It is important to note that the increased separation between the F-V (or Q-V) and
G-V curves by itself cannot be used as an indicator of loss of coupling. Separation
of the response curves is empirically related to conformational coupling but is not
uniquely governed by it. As shown in Fig. 3, altering intrinsic stabilities of the
individual structural units also increases the separation between the two response
curves.
Figure 3.
Coupling schemes to explain the opposite shifts in the activation of the voltage sensors
and the pore. (A) Scheme I shows a canonical allosteric scheme showing voltage sensor
activation (intrinsic equilibrium constant:
K
V
0
) and the pore opening (intrinsic equilibrium constant:
K
P
0
) being energetically connected via an interaction in the doubly activated state (where
the voltage sensor is activated and the pore is open), represented by θ. Scheme II
is a noncanonical counterpart of Scheme I, where the two structural units are interacting
in both the doubly activated and the doubly resting state (i.e., the states where
both the conformation are in “like” conformations). Schemes III and IV are obligatory
analogues of Schemes I and II, respectively, where the conformational state with a
resting voltage sensor and an open pore (VRPO) is eliminated. (B and C) The variation
of occupancy of the activated state of the voltage sensor (
P
V
A
at −50 mV) and the open state of the pore (
P
P
O
at −20 mV) due to change in the different model parameters in Scheme I. The values
for the simulations are taken from Muroi et al. (2010). Changing all the model parameters
one at a time affects the two probability estimates similarly, which suggests that
an opposite effect on the voltage sensor and the pore cannot be simply explained by
Scheme I. The colored bars alongside the correspondingly colored curves depict the
range of parameter values for which
P
V
A
is practically constant but
P
P
O
changes significantly. Such an effect would manifest in increased separation between
the two curves. (D and E) The similar curves (as in B and C) obtained for Scheme II
show that only when θ is changed do
P
V
A
and
P
P
O
change in an opposite way. For all other parameters, the two probability terms change
in the same direction. The same observation will hold true for the obligatory schemes
(III and IV); i.e., Scheme III will not yield a situation when the two probability
measures are affected in opposite ways due to alteration of a single thermodynamic
parameter, whereas in Scheme IV, the opposite shifts can be accomplished by altering
θ.
The high-resolution structures of voltage-gated ion channels (Long et al., 2005a,
2007; Payandeh et al., 2011) support the notion that this region involving the S4-S5
linker and S6 tail is important for electromechanical coupling. The voltage-sensing
domain in the structure of the full-length channel forms limited contacts with the
pore domain, except at the tight intracellular interface constituted by the S4-S5
linker, N-terminal end of S5 helix, and S6 tail. Although this contact is primarily
intrasubunit, they are also seen to form intersubunit contacts (Long et al., 2007;
Batulan et al., 2010).
In addition to the S4-S5 linker, another interface toward the external side has been
implicated in electromechanical coupling (Long et al., 2007; Lee et al., 2009). This
extracellular intersubunit gating interface between the S1 and S5 segments has strongly
coevolved interactors and is also found in close proximity in the crystal structure.
Perturbations at these interfaces exert strong effects on the gating process, and
it has been proposed that the integrity of this interface is necessary for efficient
force transmission from the S4 segment to the channel gates. Other studies have corroborated
the functional importance of this interface (Bocksteins et al., 2011), although, at
a mechanistic level, its role remains unclear.
As shown in Fig. 4, a comparison of sites implicated in electromechanical coupling
shows that many of them are at the ends of the S4-S5 linker. This suggests that the
flexible interhelical hinges may have a specific role. For efficient transfer of mechanical
energy from the S4 to the S6 tail, S4-S5 linker and S4 need to move together as a
rigid body. Such a rigid body motion will be aided when the angle between the S4 and
S4-S5 linker is maintained as the channel activates, whereas the angle between the
S4-S5 linker and S5 undergoes easy relaxations. The latter prevents the transfer of
energy to the S5, which remains relatively unchanged in open and closed states. Therefore,
we expect the hinge between the S4 and S4-S5 linker (the proximal hinge) to be relatively
rigid (preventing large angle bending motions), whereas that between the S4-S5 liner
and S5 (the distal hinge) is flexible (allowing relatively free-angle bending). Hinge
flexing has been shown to be crucial for conformational coupling in several soluble
proteins (Colonna-Cesari et al., 1986; Sharff et al., 1992; Kumar et al., 1999), for
example in T4 lysozyme. Future experiments may shed more light on the role of amino
acids at the ends of the S4-S5 linker.
Figure 4.
Important sites in the S4-S5 linker and S6 end region, which are putatively crucial
for electromechanical coupling. (A) The S4-S5 linker and the S6 tail regions of five
different voltage-gated ion channels are aligned. The demarcation of the helical regions
is based on the structure of the KV1.2/2.1 chimera. In blue are the regions of the
protein that have been analyzed through scanning mutagenesis, and the residues marked
in red are the ones which have been proposed to be involved in coupling voltage sensor
and gate motions (Shaker: Ledwell and Aldrich, 1999; Soler-Llavina et al., 2006; Batulan
et al., 2010, Haddad and Blunck, 2011; NaV1.4-Domain III: Muroi et al., 2010; KCNQ1:
Labro et al., 2008, 2011; hERG: Sanguinetti and Xu, 1999; Tristani-Firouzi et al.,
2002, Van Slyke et al., 2010; HCN2: Chen et al., 2001; Decher et al., 2004). For the
Shaker channel, the regions marked by the magenta bars are the complementary regions
posited to be crucial for voltage sensor pore coupling in Lu et al. (2002). In the
NaV sequence, the brown residues are sites that might be involved in coupling but
lack the distinct phenotypic response of opposite shifts in the FV and GV curves (unlike
the positions in red). (B) An orthogonal assay of coupling performed in the NaV1.4
channel (Domain III) using the lock-in strategy (see main text) identified sites (marked
in magenta) within the same regions that are for the voltage sensor pore coupling
(Arcisio-Miranda et al., 2010). Many of these residues coincide with or are close
to sites that perturb coupling, as shown in Muroi et al. (2010; compare with residues
marked in the NaV sequence in A).
Is the coupling interaction “attractive” or “repulsive”?
Coupling between the voltage sensor and the pore can be achieved in two very distinct
ways: the force linking the two domains could be attractive in nature (pulling force)
or repulsive (pushing force). According to the prevalent model of electromechanical
coupling, the electrical force displaces the charged S4 segment, which pulls the covalently
attached S4-S5 linker. The movement of the linker is transmitted to the S6 tail through
the tight noncovalent interaction interface between them (Long et al., 2005b). This
mechanism necessarily requires strong positive interactions to exist between the S4-S5
linker and S6 tail. Alternatively, one can envision that depolarization-induced movement
of the voltage sensors relieves steric inhibition on the pore, which allows it to
naturally open. In this scenario, the voltage sensors in the resting state exert a
pushing force to keep the pore gates closed.
The two gating paradigms can be distinguished by measuring the innate state preference
of the pore domain; i.e., the state (closed or open) that the pore domain “likes”
to be in, in the absence of the voltage sensors. If the pore preferentially remains
closed, electromechanical coupling would involve a pulling force, exerted by the activating
voltage sensors on the channel gates. Conversely, if the pores like to stay open,
the voltage sensors in their resting conformation apply a pushing force on the channel
gates, keeping them closed. Upon depolarization, activation of the voltage sensors
relieves this force, causing channel opening (Fig. 5). Consideration of the intrinsic
stability of the open/closed state of the pore (and voltage sensors) is critical to
understand the nature of forces driving electromechanical coupling.
Figure 5.
Forces governing voltage sensor pore coupling. (A) Voltage sensor pore coupling can
be attractive. The voltage sensor in up conformation can pull the pore gates, which
intrinsically tend to be closed, open. (B) The voltage sensor–pore interaction can
be repulsive where the voltage sensor in resting state prevents the pore from opening.
In this case, the pore intrinsically tends to be open, and repulsive forces between
the two domains keep it closed when the voltage sensor is down conformation. The arrows
show the direction of the forces. Note that these two forces are not necessarily exclusive
because they occur in different conformations, and in theory they can both exist at
the same time.
Our initial insights regarding the innate stability of the pore came from high-resolution
crystal structures of KcsA, a prokaryotic pH-gated potassium selective channel (Schrempf
et al., 1995; Doyle et al., 1998; LeMasurier et al., 2001). These channels are tetrameric
but comprise only two transmembrane segments per subunit. The structure showed a closed
channel and revealed a hydrophobic bundle crossing at the intracellular end of S6.
Functional studies suggested that the hydrophobic bundle crossing observed in KcsA
is also present in the six transmembrane KV channels and that strong hydrophobic interactions
in these regions stabilize the closed state of the channel (Hackos et al., 2002; Yifrach
and MacKinnon, 2002; Sukhareva et al., 2003; Kitaguchi et al., 2004). The implication
of these studies is that the pore likes to stay closed, and upon depolarization, is
pulled open by the voltage sensors. Nevertheless, direct measurements of the intrinsic
stability of this domain by expressing the pore without the voltage-sensing domain
suggest that the open pore might be more stable (Santos et al., 2008; Shaya et al.,
2011). Potassium channels encoded by viruses, comprising only the S5-P-S6 segments
with very short N and C termini, exhibit large open probability even at hyperpolarized
voltages (Pagliuca et al., 2007). Thus, KcsA may be anomalous because it has a network
of salt bridges and hydrogen bonds that stabilize the closed state (Thompson et al.,
2008; Uysal et al., 2011). We should note that these observations were made in atypical
ion channels and that, at present, it remains unclear to what extent these findings
are applicable to the prototypical voltage-gated ion channels.
How does this alternate gating paradigm affect our interpretation of the mutations
that alter electromechanical coupling? Of particular interest are those mutants that
cause opposite shifts in the Q-V (F-V in case of sodium channels) and G-V curves (Ledwell
and Aldrich, 1999; Soler-Llavina et al., 2006; Muroi et al., 2010; Haddad and Blunck,
2011). Mutants, which increase the separation between the Q-V and G-V curves along
the voltage axis, have been interpreted to decrease coupling between the voltage sensor
and pore on the basis of the assumption that the pore intrinsically prefers to be
in a closed conformation. If, however, the isolated pore domain of Shaker or Nav 1.4
prefers to be in an open conformation, then these mutants, which include the ILT mutant
in the Shaker potassium channel, must increase coupling between the pore and voltage
sensor. Although seemingly counterintuitive, it makes sense when one considers that
the voltage sensors and pore are negatively coupled in this gating paradigm. Therefore,
an increase in negative coupling would increase the energy difference between the
activated voltage sensor and pore.
Other strategies to identify interaction networks
Molecular dynamics simulations and free-energy perturbation techniques are frequently
used to computationally predict the strength of interaction between specific sites
of the protein if a high-resolution structure is available. In some cases, the predicted
interactions have been corroborated by functional measurements (Cordero-Morales et
al., 2011). Nonetheless, as experimentalists, we are of the opinion that these energetic
calculations have to be experimentally validated to assuage concerns regarding the
sensitivity of the simulations to force fields and, thereby, the accuracy of calculated
interaction energies.
The relative interaction energies between a pair or specific network of residues can
be calculated accurately using double mutant cycle analysis under certain conditions
(Horovitz and Fersht, 1990; Yifrach and MacKinnon, 2002; Yifrach et al., 2009). In
this method, the nonadditive component when both interactors are mutated defines the
free energy of interaction. In particular, when a system exists in only two states,
this relationship can be easily established. However, in a coupled system, the measured
free energy term depends on multiple parameters, and unless it can be independently
established that the mutations disrupt only pairwise interaction energy, mutant cycle
analysis cannot be applied to estimate relative interaction energies. In the Shaker
potassium channel, for instance, mutations at two distant sites in different subunits
may produce an appearance of interaction in the G-V curves because the final pore
transition is concerted. Also, as pointed out recently by Clapham and Miller (2011),
mutations that convert a closed state to an open state in a multistate gating process
will produce shifts in macroscopic conductance responses, a widely used measure of
free energy of activation, although the underlying coupling energies may not change.
An alternate experimental approach that has been recently proposed to identify the
network of interacting residues between two structural units is what we refer to as
the “lock-in” strategy. In brief, the approach involves locking one structural unit
in a “fixed” conformation and monitoring the energetics of the other structural unit.
Locking one structural unit will have an effect on the free energy of activation of
the other only when the two units are energetically coupled. Mutations that disrupt
the interaction will attenuate the effect of locking one unit on the activity of the
other. Such an approach was first applied to probe coupling in a voltage-gated sodium
channel (Arcisio-Miranda et al., 2010). The pore was locked in a putatively open state
through the application of a use-dependent channel blocker, lidocaine, whereas the
voltage sensor was monitored through a site-specifically labeled fluorescent probe.
The study identified multiple sites in the S4-S5 linker and S6 tail which, when perturbed
(through a tryptophan substitution), result in a reduced effect of the pore lock-in
on the movement of the voltage sensor (Fig. 4). The lock-in strategy identifies a
limited set of state-dependent coupling interactions. For instance, in the lidocaine
experiments, residues that are involved in interactions with the closed pore will
not be identified. Thus, in this respect, χ
diff
is a more comprehensive parameter to measure coupling interactions, although lock-in
strategy is a useful orthogonal approach and has been used in other systems (Kohout
et al., 2010; Ryu and Yellen, 2012).
Concluding remarks
Over the past decade, combined functional and mutagenesis studies have implicated
the residues in the S4-S5 linker and the S6 end segments as the primary molecular
determinants of electromechanical coupling. High-resolution structures of different
voltage-gated ion channels derived from phylogenetically distant organisms show that
the overall architecture and gating interfaces involved in voltage sensor and pore
domain interaction is strongly conserved. However, important details such as the strength
and state-dependence of these interactions remain obscure. This, for the most part,
is due to our inability to obtain a direct estimate of the coupling interaction between
different domains in an obligatorily coupled system, in contrast to allosteric systems.
Although new structures, particularly of those in different states, will undoubtedly
continue to be important, more effort will be required to develop new experimental
and analytical tools to quantify these interactions and identify interactors. The
importance of energetics is underscored by the fact that sometimes a single relatively
conserved mutation can cause dramatic changes in the channel function, but only modest
changes in crystallographically derived protein structures (Gonzalez-Gutierrez et
al., 2012). Understanding the molecular driving forces behind protein conformation
changes, in combination with high-resolution structures, will be the key for developing
novel intervention strategies to treat ion channel pathophysiology.
This Perspectives series includes articles by Andersen, Colquhoun and Lape, and Horrigan.