## Abstract

The voltage dependence of charges in voltage-sensitive proteins, typically displayed as charge versus voltage (*Q-V*) curves, is often quantified by fitting it to a simple two-state Boltzmann function. This procedure overlooks the fact that the fitted parameters, including the total charge, may be incorrect if the charge is moving in multiple steps. We present here the derivation of a general formulation for *Q-V* curves from multistate sequential models, including the case of infinite number of states. We demonstrate that the commonly used method to estimate the charge per molecule using a simple Boltzmann fit is not only inadequate, but in most cases, it underestimates the moving charge times the fraction of the field.

Many ion channels, transporters, enzymes, receptors, and pumps are voltage dependent. This voltage dependence is the result of voltage-induced translocation of intrinsic charges that, in some way, affects the conformation of the molecule. The movement of such charges is manifested as a current that can be recorded under voltage clamp. The best-known examples of these currents are “gating” currents in voltage-gated channels and “sensing” currents in voltage-sensitive phosphatases. The time integral of the gating or sensing current as a function of voltage (V) is the displaced charge Q(V), normally called the *Q-V* curve.

It is important to estimate how much is the total amount of net charge per molecule (*Q _{max}*) that relocates within the electric field because it determines whether a small or a large change in voltage is necessary to affect the function of the protein. Most importantly, knowing

*Q*is critical if one wishes to correlate charge movement with structural changes in the protein. The charge is the time integral of the current, and it corresponds to the product of the actual moving charge times the fraction of the field it traverses. Therefore, correlating charge movement with structure requires knowledge of where the charged groups are located and the electric field profile. In recent papers by Chowdhury and Chanda (2012) and Sigg (2013), it was demonstrated that the total energy of activating the voltage sensor is equal to

_{max}*Q*, where

_{max}V_{M}*V*is the median voltage of charge transfer, a value that is only equal to the half-point of activation

_{M}*V*for symmetrical

_{1/2}*Q-V*curves.

*V*is easily estimated from the

_{M}*Q-V*curve, but

*Q*must be obtained with other methods because, as we will show here, it is not directly derived from the

_{max}*Q-V*curve in the general case.

The typical methods used to estimate charge per molecule *Q _{max}* include measurements of limiting slope (Almers, 1978) and the ratio of total charge divided by the number of molecules (Schoppa et al., 1992). The discussion on implementation, accuracy, and reliability of these methodologies has been addressed many times in the literature, and it will not be discussed here (see Sigg and Bezanilla, 1997). However, it is worth mentioning that these approaches tend to be technically demanding, thus driving researchers to seek alternative avenues toward estimating the total charge per molecule. Particularly, we will discuss here the use of a two-state Boltzmann distribution for this purpose. Our intention is to demonstrate that this commonly used method to estimate the charge per molecule is generally incorrect and likely to give a lower bound of the moving charge times the fraction of the field.

The two-state Boltzmann distribution describes a charged particle that can only be in one of two positions or states that we could call *S*_{1} and *S*_{2}. When the particle with charge *Q _{max}* (in units of electronic charge) moves from

*S*

_{1}to

*S*

_{2}, or vice versa, it does it in a single step. The average charge found in position

*S*

_{2},

*Q*(

*V*), will depend on the energy difference between

*S*

_{1}and

*S*

_{2}, and the charge of the particle. The equation that describes

*Q*(

*V*) is:

*V*is the potential at which the charge is equally distributed between

_{1/2}*S*

_{1}and

*S*

_{2}, and

*k*and

*T*are the Boltzmann constant and absolute temperature, respectively. The

*Q(V)*is typically normalized by dividing Eq. 1 by the total charge

*Q*. The resulting function is frequently called a “single Boltzmann” in the literature and is used to fit normalized, experimentally obtained

_{max}*Q-V*curves. The fit yields an apparent

*V*

_{1/2}(

*Q*

_{MAX}(

*Q*

_{max}. Indeed, this is correct but only for the case of a charge moving between two positions in a single step. However, the value of

To demonstrate the above statement and also estimate the possible error in using the fitted *n* steps between *n* + 1 states, each step with a fractional charge *z _{i}* (in units of electronic charge

*e*) that will add up to the total charge

_{0}*Q*.

_{max}*S*is labeled as

_{i}*P*, and the equilibrium constant of each step is given by

_{i}*z*is the charge (in units of

_{i}*e*) of step

_{0}*i*, and

*V*is the membrane potential that makes the equilibrium constant equal 1. In steady state, the solution of

_{i}*P*can be obtained by combining

_{i}*q*as

*Q-V*curve is defined as

*P*

_{i}yields

*V*:

Eq. 2 is a general solution of a sequential model with *n* + 1 states with arbitrary valences and *V _{i}*’s for each transition. We can easily see that Eq. 2 has a very different form than Eq. 1, except when there is only a single transition (

*n*= 1). In this latter case, Eq. 2 reduces to Eq. 1 because

*z*and

_{1}*V*

_{1}are equal to

*Q*and

_{max}*V*

_{1/2}, respectively. For the more general situation where

*n*> 1, if one fits the

*Q*(V) relation obeying Eq. 2 with Eq. 1, the fitted

*z*values (see examples below and Fig. 1). A simple way to visualize the discrepancy between the predicted value of Eqs. 1 and 2 is to compute the maximum slope of the

_{i}*Q-V*curve. This can be done analytically assuming that

*V*

_{i}=

*V*

_{o}for all transitions and that the total charge

*Q*is evenly divided among those transitions. The limit of the first derivative of the

_{max}*Q*(

*V*) with respect to

*V*evaluated at

*V*=

*V*

_{o}is given by this equation:

*Q-V*curve decreases with the number of transitions being maximum and equal to

*Q*when

_{max}/(4kT)*n*= 1 (two states) and a minimum equal to

*Q*when

_{max}/(12kT)*n*goes to infinity, which is the continuous case (see next paragraph).

### Infinite number of steps

Eq. 2 can be generalized to the case where the charge moves continuously, corresponding to an infinite number of steps. If we make*µ _{i}* =

*µ*, and we can write Eq. 2 as the normalized Q(V) in the limit when

*n*goes to infinity:

### Examples

We will illustrate now that data generated by Eq. 2 can be fitted quite well by Eq. 1, thus leading to an incorrect estimate of the total charge moved. Typically, the experimental value of the charge plotted is normalized to its maximum because there is no knowledge of the absolute amount of charge per molecule and the number of molecules. The normalized *Q-V* curve, *Q*_{nor}, is obtained by dividing *Q*(V) by the sum of all the partial charges.

Fig. 1 shows *Q*_{nor} computed using Eq. 2 for one, two, three, four, and six transitions and for the continuous case using Eq. 5 (squares) with superimposed fits to a two-state Boltzmann distribution (Eq. 1, lines). The computations were done with equal charge in each step (for a total charge *Q _{max}* = 4e

_{0}) and also the same

*V*= −25 mV value for all the steps. It is clear that fits are quite acceptable for cases up to four transitions, but the fit significantly deviates in the continuous case.

_{i}Considering that experimental data normally have significant scatter, it is then quite likely that the experimenter will accept the single-transition fit even for cases where there are six or more transitions (see Fig. 1). In general, the case up to four transitions will look as a very good fit, and the fitted _{i}’s are the same.

It should be noted that if *µ _{i}* of the rightmost transition is heavily biased to the last state (

*V*is very negative), then the

_{i}*V*→∞ and

_{1}*V*→−∞ because in that case, it converts into a two-state model. Although these values of

_{2}*V*are unrealistic, the fitted value of

*V*is much more negative than

_{2}*V*(that is,

_{1}*V*>>

_{1}*V*). On the other hand, If

_{2}*V*<<

_{1}*V*, the

_{2}*Q-V*curve will exhibit a plateau region and, as the difference between

*V*and

_{1}*V*decreases, the plateau becomes less obvious and the curve looks monotonic. These cases have been discussed in detail for the two-transition model in Lacroix et al. (2012).

_{2}We conclude that it is not possible to estimate unequivocally the gating charge per sensor from a “single-Boltzmann” fit to a *Q-V* curve of a charge moving in multiple transitions. The estimated *Q _{max}*, except in the case of the two-state model or the case of a heavily biased late step, which are rare occurrences. It is then safer to call “apparent gating charge” the fitted

### Addendum

The most general case in which transitions between states include loops, branches, and steps can be derived directly from the partition function and follows the general thermodynamic treatment by Sigg and Bezanilla (1997), Chowdhury and Chanda (2012), and Sigg (2013). The reaction coordinate is the charge moving in the general case where it evolves from *q* = *0* to *q* = *Q _{max}* by means of steps, loops, or branches. In that case, the partition function is given by

*Q-V*curve, as

*Q-V*is obtained by taking the derivative of 〈

*q*〉 with respect to

*V*:

Let us now consider the gating charge fluctuation. The charge fluctuation will depend on the number of possible conformations of the charge and is expected to be a maximum when there are only two possible charged states to dwell. As the number of intermediate states increases, the charge fluctuation decreases. Now, a measure of the charge fluctuation is given by the variance of the gating charge, which can be computed from the partition function as:*Q(V*) (Eq. 8). This implies that the slope of the *Q-V* is maximum when there are only two states.

## Acknowledgments

We thank Dr. Daniel Sigg for the contribution of the general derivation in the Addendum and all reviewers for pointing out several important improvements in the presentation. We also thank Dr. Rebecca Segal (Department of Mathematics at VCU) for her insightful comments.

This work was supported by National Institutes of Health grant GM030376 (to F. Bezanilla) and Clinical and Translational Science Award KL2TR000057 (to C.A. Villalba-Galea).

Kenton J. Swartz served as editor.

- Submitted: 30 June 2013
- Accepted: 28 October 2013

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