ABSTRACT
This work reports a Markov state model of the conformational dynamics involved in regulating the inner and outer gates of the CLC-2 ion channel. This dynamical model, based on 600 microseconds of molecular dynamics simulation, produces findings in harmony with known properties of CLC-2 gating. The starting conformation of our CLC-2 model is highly similar to the template crystal structure (cmCLC) in the region of the chloride-permeation pathway, where there is a high degree of sequence conservation among all CLC channels and transporters. In this conformation, both outer and inner channel gates are closed. The first conformational change in our dataset involves rotation of the inner-gate backbone along residues S168-G169-I170. This change is strikingly similar to that observed in the recent cryo-EM structure of the bovine CLC-K channel, though the volume of the intracellular (inner) region of the ion conduction pathway is further expanded in our model. From this state (inner gate open and outer gate closed), two additional states are observed, each involving a unique rotameric flip of the outer-gate residue GLUex (E211). Both additional states involve conformational changes that orient GLUex away from the extracellular (outer) region of the ion conduction pathway. In the first additional state, the rotameric flip of GLUex results in an open, or near-open, channel pore. The equilibrium population of this state is low (∼1%), consistent with the low open probability of CLC-2 observed experimentally in the absence of a membrane potential stimulus (0 mV). In the second additional state, GLUex rotates to occlude the channel pore. This state, which has a low equilibrium population (∼1%), is only accessible when GLUex is protonated. Together, these pathways model the opening of both an inner and outer gate within the CLC-2 selectivity filter, as a function of GLUex protonation. Collectively, our findings are consistent with published experimental analyses of CLC-2 gating and provide a high-resolution structural model to guide future investigations.
Introduction
The CLC chloride channel protein family plays a wide variety of physiological functions in organisms ranging from bacteria to humans1-7. This family of membrane proteins is composed of both channels and H+/Cl- exchange transporters that share a structurally unique homodimeric architecture8-9. Each subunit within the homodimer is an independent functional unit10-12 composed of 17 membrane-embedded alpha helices13. These helices coalesce to form a narrow, electropositive ion-conducting pore that is highly selective for Cl-13. Such an architecture is unusual for ion channels and raises many questions about the mechanisms of ion channel opening and closing (gating).
High-resolution CLC structures provide invaluable starting points for understanding the structure-function relationships of CLC protein activation. The first CLC structure solved, a prokaryotic H+/Cl- exchange transporter13, proved relevant for guiding structure-function studies on a wide range of CLCs, including eukaryotic CLC channels14-17. The mechanistic similarities between channels and transporters, which have been discussed and studied extensively, provide strong justification for using CLC transporter structures to understand CLC channel structure and function8-18. The CLC transporter structures represent a closed conformational state in which bound chloride (Cl-) is occluded by proteinaceous “gates” that block exit to either the outer (extracellular) or inner (intracellular) side of the protein. The outer gate is formed in part by a conserved glutamate residue, GLUex19-26. When mutated to GLN (to resemble the protonated GLU), this side chain rotates outwards and partially unblocks the Cl- permeation pathway19. The inner gate is formed by conserved SER and TYR residues13,27-29 that, according to solved CLC structures, physically obstruct the Cl- translocation pathway from the intracellular side. In the recently published structure of the bovine CLC-K channel (the first structure of a CLC channel homolog), the loop containing the SER gate residue is rotated to unblock the intracellular gate30. Despite the advances in structural understanding afforded by these discoveries, additional analysis is required to turn these static conformational snapshots into a complete dynamic picture of CLC gating. Experimental biophysicists have shown that these mechanisms are complex, involving interdependent influences of transmembrane voltage, [Cl-], and [H+]8,31-35. To begin to resolve these complexities and reveal the dynamic, atomistic details of the CLC gating machine, it will be critical to pair structural studies with high-resolution conformational modeling36-45.
In this study, we investigate the molecular dynamics (MD) of CLC-2, a chloride ion channel with high levels of expression in both neurons and glia within the brain3,46. In glia, the importance of CLC-2 was illuminated in studies of knockout mice, which develop white matter vacuolization resembling human leukoencephalopathy47, as well as complementary studies examining the distribution and localization of CLC-2 in astrocytes and oligodendrocytes48,49. Indeed, mutations in human CLC-2 are known to underlie a specific form of white matter degeneration50, although the mechanistic details of causation are still uncertain51. In neurons, CLC-2 has been suggested to play an important role in regulation of excitability and/or chloride homeostasis52-60. However, the details of these roles are controversial3,59,61,62. A precise understanding of the atomic interactions and rearrangements involved in channel activation will be valuable to facilitate understanding of CLC-2 physiological behavior and its role in human disease.
The spatial and temporal resolution afforded by computer simulations allows for the atom-scale examination of protein structure and function. Here we apply MD simulations and Markov State Modeling (MSM) to study the conformational dynamics of CLC-2 gating. MSMs allow for the rigorous statistical characterization of biophysical simulations63-66. Prior to constructing the MSM, the simulation data is first transformed into its kinetically slowest representation via the method of time-structure based independent component analysis (tICA) 67-69. Such a transformation allows identification of the slowest conformational changes in an MD dataset (those most likely to correspond to channel gating). The MSM is then built over these conformational changes, and queried to predict the thermodynamics and kinetics of CLC-2 channel activation. Our CLC MSM suggests that the top activation pathway begins in the closed state, near the template structure on which we based our model. The slowest timescale conformational change involves the rotation of the backbone along the S168-G169-I170 residues at the intracellular entrance to the ion conduction pathway. The second major motion describes the flip of the GLUex side chain on the extracellular vestibule. This flip of the GLUex side chain displays a sensitivity to residue protonation that correlates well with experimental evidence that this conserved glutamate residue regulates the CLC “fast” gate in CLC-270,71. These observed gating motions are all consistent with data in the literature. Our model suggests a stepwise channel activation pathway that fits satisfyingly with published studies on CLC-2 gating. This high-resolution structural model of CLC-2 gating will facilitate experimental design and analysis of future mechanistic studies involving CLC channels.
Results and discussion
Ion channel “gating” involves the conformational rearrangement of the protein, such that transmembrane ion flow through the channel pore is either increased or decreased. These gates are highly regulated so as to avoid dissipation of cellular ion gradients. CLC channels have two types of gating mechanisms, known as slow and fast gating. Slow gating (also known as “common gating”) involves activation of both subunits of the homodimer in unison, requiring global conformational changes and communication between subunits at the dimer interface12,72-74. Fast gating (also known as “pore gating”) acts on each subunit independently and involves much smaller conformational changes, perhaps limited to motions of side chains within the selectivity filter (SF) 8,9,75. Studies based on CLC transporter structures demonstrate similarities between CLC channel fast gating and the “gating” involved in CLC transporter activity. Specifically, high-resolution structures of CLC transporters revealed that a conserved glutamate residue (GLUex) physically occludes the permeation pathway, blocking Cl- access from the permeation pathway from the extracellular side. In transporters, mutation of this residue to glutamine causes the sidechain to swing upward, opening the extracellular gate to the permeation pathway, a structural result also supported by functional studies19,22-24 In channels, mutation of GLUex to a neutral equivalent similarly unblocks the permeation pathway, as revealed by the fast-gate open phenotype observed electrophysiologically19,20,26. Thus, CLC channel fast gating is highly analogous to CLC transporter gating and, to a first approximation, is modeled as the outward movement of GLUex.
Despite the understanding gained from these combined structural and electrophysiological analyses, many questions remain concerning CLC fast (pore) gating. For example, the details of how GLUex moves are not fully resolved. In the transporters, when GLUex is mutated to glutamine, the side chain moves out of the extracellular entrance to the Cl- permeation pathway; however, the pathway remains partially occluded. It is therefore likely that additional conformational changes not observed in known CLC structures are required for both transporter38 and channel activation. Beyond GLUex, CLC pore gating also appears to involve inner-gate residues. This idea is based on electrophysiology studies examining altered state-dependent binding of inhibitors at the intracellular side of a CLC channel. These studies concluded that such behavior could only be explained by conformational changes occurring at the inner gate, in conjunction with the sidechain rotation of GLUex at the outer gate76. Using the CLC transporter structures as a guide, these inner-gate residues correspond to conserved SER and TYR residues that physically occlude Cl- from moving to the intracellular side19,25,27-29. Consistent with the conclusion that these residues must move in order to open the channel, the recent CLC-K channel structures show that these residues are in a more open configuration. However, the dynamic details of how conformational changes at the inner and outer gates are linked to opening CLC channel pores remain unknown. Because CLC channel homologs differ in the degree to which the pore is gated by chloride, protons, transmembrane voltage, and other physiological variables70,71,77-83, the details of pore gating are likely to vary subtly between the homologs, despite the overall similarity in their structures. In this work, we sought specifically to gain insight into pore behavior in the CLC-2 homolog.
We have applied dynamical modeling in order to identify and piece together the major conformational changes characterizing inner- and outer-gate opening in CLC-2. First, a homology model was built for the rCLC-2 sequence, using the 3ORG structure of the cmCLC transporter84 as a template structure. This template was chosen because, at the time of construction, it was the only available eukaryotic CLC structure. This template is 75% identical to CLC-2 within the pore-forming (SF) regions of the protein. The sequence conservation between the target and template85,86 SF is depicted in Fig. 1, bottom right, with conserved outer- and inner-gate residues (E211, Y559, and S168) depicted above. The template and target sequences have a global similarity of 34.4%. Notably, transmembrane regions with sequence similarity in this range have been shown to display high structural similarity87. From this model, two MD datasets were generated, identical with the exception of GLUex protonation. These datasets were analyzed with respect to the SF conformation (Fig. 1 inset). To improve computational sampling, and because each subunit contains an independent and identical pore10-12, only the transmembrane regions of one subunit of a single homodimer subunit were simulated, as depicted in Fig. 1. This simplification is justified based on the fact that fast gating occurs independently within each subunit pore10-12,75.
Selectivity filter conformational dynamics
Markov State Models (MSMs) have emerged as a powerful tool for robustly estimating the statistics of biophysical systems from MD datasets63-66. This technique first partitions a dataset into a set of discrete conformational states, and then parameterizes a transition matrix describing the probability of transition for each state pair. Prior to constructing the MSM, it is advantageous to featurize the data according to a structural metric, and then transform the featurized dataset using time-structure independent component analysis (tICA)67. Here, the structural metric used for featurization was the backbone and side chain dihedral angles of the SF (see the Markov state modeling subsection of the Methods section for more detail). tICA determines the slowest-decorrelating dynamical degrees of freedom88. This improves the MSM statistics, and additionally provides a set of unbiased reaction coordinates that are useful for separating and visualizing important conformational states. These reaction coordinates can be thought of as dynamical modes, and are referred to as “tICs”. A tIC is given by a linear combination of input features, and can be interpreted by identifying the features with the highest linear coefficients (referred to as loadings). Greater detail describing the tIC loadings is given in the supplementary information. After the MSM is constructed, the state representation can be simplified via macrostating. This simplification allows for the identification of the most thermodynamically stable conformational states. Here, we employ these techniques to characterize the conformational dynamics sampled in our datasets.
Our analysis identified four major conformational states: Coi, Co, O, and U. These states were named to reflect whether the channel appears closed (C) or open (O) at the inner (i) or outer (o) gate, with the exception of U, a postulated non-conducting state. A representative conformation for each thermodynamic macrostate is illustrated in Fig. 2. The nomenclature of these states represents the x, y, z-positions of the inner and outer gating residues identified in the MSM analysis, as well as our analysis of the pore radius for each macrostate (see Fig. 5). As such, the names of these four states do not necessarily represent the entire collection of open and closed states possible under physiological conditions, but rather whether the entrance and exit to the pore are physically blocked and the extent to which each conformation can accommodate the volume of a Cl- ion. It is possible that other conformational states (such as a more fully open conducting state) were not sampled in our 600 microsecond experimental timescale due to the extremely long (seconds) timescale required for full CLC-2 channel activation, as well as the limitations imposed by modeling the channel at 0 mV instead of hyperpolarizing potentials (at which the channel has the highest open probability). Despite these limitations, the conformational states that we observe involve motions of SF residues that unblock the pore and widen the anion permeation pathway, suggesting a physiologically relevant mechanism of channel activation. These states are also highly consistent with what is experimentally known about the fast-gate mechanism of CLC channel opening (subsequently discussed in detail). For a detailed labeling of each SF component, see Fig. 1. Each macrostate can be kinetically distinguished from the others by tIC 0, tIC 1, and tIC 3. Note that tIC 2 was found to describe the same conformational change as tIC 1 and hence will not be discussed. This can result from symmetries of the featurized data. These tICs were interpreted by analyzing the input features with the highest loadings. In Table 1 we have detailed how each tIC (dynamical mode) separates a particular two-state kinetic process, and which features distinguish each tIC.
Along each major dynamical mode, we observe two-state behavior. tIC 0 involves the rotation of the backbone dihedrals of the S168, G169, and I170 residues. This implies that the bottom strand of the SF can exist in two unique conformations. tICs 1 and 3 both involve flipping the GLUex χ1 side chain dihedral, a motion that is coupled to the residues involved in the tIC 0 conformational change. Although tICs 1 and 3 begin in the same initial state (Co) and describe the rotameric flipping of the same residue, the end states (O and U) differ in conformation. This suggests that the GLUex rotamer can exist in three possible states.
The thermodynamics of each conformational state was further analyzed using the MSM-derived free energies. This analysis is illustrated by Fig. 3, where the data have been separated by the GLUex protonation state, and projected onto each major dynamical mode. We observe that in all cases, Coi describes the conformational free energy minimum. This state, so named because the channel pore appears closed to both the outer (o) and inner (i) sides of the membrane, is similar to the template cmCLC structure (3ORG) with the exception that GLUex has moved upwards out of the Scen site, towards the Sext site. This positioning at Sext is similar to that observed in all other CLC structures found in the PDB. The Co state is the next most stable state, with a free energy less than 1 kCal/mol greater than the Coi state. This state was labeled Co because the channel pore is closed to the outer side but open towards the inner side. In this state, the S168, G169, and I170 backbone strand has changed conformation, but the GLUex residue remains in the same geometry. As can be seen by the landscapes in Fig. 3, GLUex protonation does not affect the free energies of the Coi and Co states. The transition barrier height for the Coi↔Co conformational change is approximately 2 kCal/mol.
tICs 1 and 3 identify two higher energy conformational states, O and U, each connected to state Co. As discussed previously, these states each represent rotamers of the GLUex residue. In the O (“open”) state, GLUex appears to move out of the way of the Cl- permeation pathway, whereas in the U state, GLUex collapses into the pore, which remains occluded (as discussed below). Both states O and U are between 2 and 3 kCal/mol greater in energy than states Co and Coi. The O state shows a mild sensitivity to protonation, while the U state exists only when GLUex p is protonated. This result can be seen by the absence of a U basin in the GLUex 2D free energy plots of Fig. 3. The transition barrier height of Co → O, along tIC 3, is approximately 4-5 kCal/mol for both protonation states, though there is considerable uncertainty for GLUex p (shown by the error bar along tIC 3). The transition barrier height of Co→U, along tIC 1, for GLUex p is approximately 5 kCal/mol.
Interestingly, TYRcen (Y559) (Fig. 1) was not identified by any of the dynamical modes in our analysis. In prokaryotic CLC transporter structures and in our eukaryotic template structure (cmCLC), this residue, along with SERcen (S168), form a chloride-binding site in the pore. As such, mutation of these residues would be predicted to alter single-channel conductance by changing the binding energy of chloride in the pore. While mutagenesis experiments on SERcen support this prediction, those on TYRcen do not11,17,76,89. In agreement with these findings, our model predicts movement of the SERcen backbone (S168-G169-I170) to be critical for initiation of chloride translocation, while TYRcen does not play a major role.
Kinetic modeling and comparison to experimental data
To examine kinetics in greater detail, continuous-time rate-matrix MSMs were computed for the macrostate model in order to estimate the relative rate of transition between each major conformational state (as the traditional MSM gives transition probabilities, rather than transition rates). The resultant kinetic network graphs are given in Fig. 4. We observe that the Coi state transitions only to the Co state, while the Co state may transition to any of the other states. The forward and backward rate of Coi↔Co are comparable. For both the O and U states, the reverse rates from these states to the Co state are faster than the forward rates.
The hypothesized non-conducting state is consistent with experiments demonstrating the state-dependent effect of pH on the CLC-2 ion channel. Under a constant hyperpolarizing stimulus of −100 mV (to open the channel), application of pH 5.5 external buffer (to favor GLUex protonation) followed by a depolarizing pulse to close the channel (while GLUex remains protonated) renders the channel unable to re-open upon application of a second −100 mV stimulus90. In contrast, the same experiment performed under basic conditions (pH 8.0, favoring GLUex deprotonation) reduces current overall but does not prevent the channel from re-opening, suggesting that GLUex protonation locks the channel in a unique non-conducting conformation that prevents immediate reactivation90. This observation is consistent with our model in which the non-conducting U conformational state of CLC-2 is only accessible when GLUex is protonated.
While the primary effect of GLUex protonation is the addition of the U state, kinetic analysis highlights two additional, mechanistically relevant, features of protonation. First, the connectivity of the modeled closed state, Coi, to the modeled open state, O, suggests that protonation of GLUex is not essential for channel conduction. While this result is in contrast to a computational study of the 3ORG (cmCLC) structure, which found that protonation of the GLUex side chain is required for ion transport to occur39, it is in harmony with electrophysiological studies of CLC-2, which have demonstrated that protonation of GLUex does not affect channel opening70. The second additional highlight of our kinetic analysis is that the transition rate from O to Coi is slowed when GLUex is protonated. While this result is uncertain due to the magnitude of the error bars of our calculations, it is worth highlighting given its consistency with experimental results, which demonstrate that the CLC-2 closing rate decreases with increasing extracellular [H+]71.
Structural analysis and comparison with experimental data
Above, we computed estimates for the thermodynamic and kinetic behavior of four major CLC-2 conformational states. Now, we delve deeper into the structural analysis of these states. To further investigate the geometric structure of each macrostate, the volume of the ion conduction pathway was analyzed. For each macrostate, several conformations were sampled, and the radius of the ion conduction pathway computed. These results are given with respect to distance from the GLUex residue (one path to the extracellular region, one path to the intracellular region), and are illustrated in Fig. 5. It was found that GLUex is the bottleneck of the conduction pathway for all macrostates. States Coi, Co, and O display a comparable bottleneck of ∼1.5 A°. In contrast, state U is more congested in this region, reducing to a radius of only 1 A°. It is unlikely that this especially constricted bottleneck would allow for chloride ion permeation. For comparison, the pore radius of the CLC-K channel structure (5TQQ) is also shown in (Fig. 5 black). This structure appears fairly open (3.0 A° radius) at both the intracellular and extracellular regions of CLC-K, but displays a bottleneck at Sext, where it narrows to a radius of ∼1 A°. Detailed images of the ion conduction pathway and bottleneck, for the 5TQQ structure and the O state, are shown in the supplementary information. Additionally given in the supplementary information is a Ramachandran plot of the S168-G169 backbone for the O macrostate, Coi macrostate, cmCLC (3ORG) structure, and CLC-K (5TQQ) structure.
While states Coi, Co, and O display bottlenecks of ∼1.5 A° in radius (narrower than the ∼1.8 A° radius of a Cl¯ ion), the extent of these bottlenecks along the length of the channel pore varies significantly between the conformations. For Coi and Co, the pore is constricted to less than 1.8 A° for a ∼19 A° stretch, whereas for state O, the pore is constricted for less than half that distance (Fig. 5). Comparing Coi and Co, the most striking difference is along the intracellular pathway, which widens sufficiently to accommodate chloride in Co. This finding aligns with our initial observation that these states are distinguished by the conformational state of the inner gate, while the outer gate remains closed. Across both the extracellular and intracellular region, the O state is the most voluminous. This finding aligns with our observation that both inner-and outer-gate residues (GLUex and SERcen) have rotated away from Scen. The fact that a narrow constriction persists within the putative “open” state (the most open of the set of macrostates) suggests that further conformational change may be involved in reaching a fully open state. Such an additional conformational change (to widen the pore following the rotation of GLUex) has been proposed based on experimental studies38,76,91.
For both extracellular and intracellular pathways, the U state is the most constricted, despite the fact that the inner-gate SER residue is rotated to the “open” position. The GLUex residue is in a unique rotameric state, rotated away from SERcen, but this rotation does not increase the volume of the extracellular region of the channel, in part due to the distortion of helix N in this state (see Fig. 2). The constricted nature of the U state indicates that it is a non-conductive (closed) channel. That the U state is only accessible when GLUex is protonated (neutral) is in agreement with experimental observation that low extracellular pH induces channel closure20,90,92 (though it is known that an extracellular histidine is also involved93).
Comparison to structures in the PDB
Now, we further interpret our model (Figs. 3, 4)structurally by visualizing representative conformations from each macrostate. Superpositions of these macrostate conformations are depicted in Fig. 6A. Observe that the inner residues, S168-G169-I170, assume two major conformational states, while the outer residues, GLUex and those surrounding the Sext binding site, assume three major conformational states.
In Fig. 6B, we compare these structures to a set of relevant structures from the PDB. In the known structures, the GLUex side chain has been observed to adopt three conformations, referred to as “down”, “middle”, and “up” 27. The “down” conformation, with GLUex pointing into Scen, has been seen only in 3ORG (cmCLC), our template structure. In the CLC-2 model based on this structure, we found that GLUex immediately equilibrated away from this conformation to a rotamer where it occupied the Sext binding site (the “middle” position). This positioning is similar to that observed in most CLCs in the PDB (overlay shown for representative structure 1OTS, CLC-ec1). The GLUex “up” position, which has been observed when GLUex is mutated to GLN (PDB 1OTU19) has been considered to represent a more open pore conformation; however this conformation is more occluded than the O macrostate38. The O state demonstrates a SF-conformation similar to that of the 5TQQ (CLC-K channel) structure, especially with respect to the intracellular region.
Analysis of reaction coordinates
Next, we analyze these conformations along each major dynamical mode (tIC). This is done by examining the structures along each tIC projection, as shown in Fig. 7. Here, the free energy surfaces for each two-state process are projected onto the major reaction coordinates (tICs, or dynamical modes) described earlier, and separated by GLUex protonation state. The shading surrounding the free energy curves represents the error (1 standard deviation) calculated from statistical bootstrapping of the MSM. The gray structure along each tIC represents the process transition state.
By examining the most significant tIC 0 feature loadings, we find that the backbone of the S168, G169, and I170 residues determines the conformational states of the Co↔Coi process. This is illustrated in Fig. 7, top. The Coi state (yellow) positions the S168 side chain toward the ion conduction pathway. The Co state positions the S168 side chain to the cytosol. In the tIC 0 transition state, the S168 side chain is oriented halfway between the Coi and Co macrostate structures. Hence, we observe a full 180 degree intracellular rotation of the S168-G169-I170 backbone. Recently, a cryo-EM structure of the bovine CLC-K channel (5TQQ) was found to uniquely (compared to previous CLC structures) display a cytosolic facing SERcen residue30, similar to the conformation found here. The GLUex residue adopts the same rotameric state in Coi and Co, positioned directly in the way of the ion conduction pathway, as is found commonly in known CLC structures. This observation suggests that the outer gate is “closed” for the Coi and Co states.
Together, tIC 1 and tIC 3 separate two unique rotameric states of the GLUex residue. Note that along these tICs, the S167-G169-I170 backbone is “out”, as can be seen by the outward orientation of the S168 side chain. As revealed by our model, accessibility to the U rotameric state is possible only when the GLUex residue is protonated. As visualized in Fig. 7 middle and bottom, states O and U describe rotamers of GLUex where the side chain is no longer resting in the exterior ion binding site (this ion binding site is occupied by Cl for the O state). The structures of the O and U states therefore represent two unique outer gate conformations.
The structures identified for the major conformational states were arranged into a schematic network mechanism, given in Fig. 8. This network diagram summarizes the results presented in Fig. 7. We observe that there is a dominant motion from the Coi to Co state, which involves the rotation of the S168-G169-I170 backbone. Following this motion, the GLUex side chain may rotate away from the ion conduction pathway. This GLUex flip has a low equilibrium population under these conditions (0 mV), such that return to the resting GLUex conformation is favored. The O rotamer possibly activates the channel to the full conducting state, while the protonation-dependent U rotamer constricts the conduction pathway to a non-conducting state.
Summary and conclusions
We have applied computational modeling methodologies to predict the conformational dynamics of CLC-2 activation. Four major conformational states were identified and characterized with respect to thermodynamics, kinetics, and structure. Of the major states, two were found to be highly stable, and two to be less stable but still detectably populated the simulation conditions (0 mV). Progression through these states was found to follow a particular sequence. First, from the Coi, or closed state, there is flip of the S168-G169-I170 backbone. This flip occurs via an intracellular rotation and results in a volume increase in the inner regions of the ion conduction pathway. Once this occurs, the GLUex side chain is able to flip. Our model suggests that intracellular gate opening (mediated by the intracellular rotation of SERcen) must occur prior to opening of the extracellular gate (rotation of GLUex). Such a mechanism is consistent with the picture emerging from mechanistic electrophysiological studies, which indicate that intracellular Cl− is the main factor driving CLC-2 channel opening70. The intracellular rotation of the S168-G169-I170 backbone that we observe would allow intracellular chloride to access the inner channel pore and facilitate opening of the outer glutamate gate by forcing GLUex into its open conformation as chloride traverses the membrane.
Our studies of GLUex protonation reveal consistencies with the existing literature, and suggest potential areas of new investigation. Our observation that protonation of GLUex is not required for opening is consistent with functional studies of CLC-270, as is our observation that protonation of GLUex decreases the rate of transition from open to closed conformations71. Our observation that protonation of GLUex introduces the ability to adopt an alternative rotamer orientation suggests a potential link between CLC-2 inhibition by extracellular protons (which also involves an extracellular histidine residue93). Given the complex relationship between H+, Cl-, and voltage in regulating CLC-2 gating, many questions remain. This CLC-2 activation model will complement current and future efforts toward furthering the biophysical understanding of CLC proteins.
Methods
Molecular dynamics
The initial structures consisted of a CLC-2 homology model built using the software Modeller94. The rat CLC-2 monomer sequence was mapped onto a monomer of the 3ORG structure. The membrane system was assembled via CHARMMGUI95. The simulated system consisted of the homology model structure, 2 leaflets of 64 3:1 POPE:POPG lipid molecules, explicit water, and 0.15 M H+Cl-. The TIP3P96, lipid1497 and AMBER14SB98 forcefields were used. Protonation of the GLUex residue was accomplished using CHARMMGUI. Equilibration was done using AMBER 14*, with restraints on the protein and lipids, which were slowly released as the system was heated gradually from 0 to 300 K. An additional 100 ns simulation was run, from which initial system configurations were sampled. The AMBER prmtop and inpcrd files of these structures were used to set up OpenMM99 simulations in the NPT ensemble. From these initial structures, 100 unique velocity distributions were assigned. Simulations were performed at 0 mV. This is due to the novelty of applying membrane potentials in simulation and concern about the accurate response of MD force fields to applied external electric fields. The distributed computing platform folding@home100 was then used to perform 600 µs of aggregate MD simulation. The UCSF Chimera program101 was used to visualize the MD trajectories and generate figures.
Markov state modeling
MSM analysis was accomplished using MSMBuilder 3.7102. The MD datasets were featurized using the signed ϕ, ?, χ1, and χ2 dihedral angles for all residues in the CLC-2 SF. The SF residue numbers are as follows (using the rCLC-2 primary sequence numbering): 167, 168, 169, 170, 171, 209, 210, 211, 212, 213, 463, 464, 465, 466, 467, 468, 559. Note that the MSM was constructed strictly over protein conformation. Additional details such as chloride and water dynamics stand as future avenues for analysis. The featurized datasets were transformed using kinetic-mapping of all tICA tICs. The transformed data was clustered into 324 microstates via the mini-batch kmeans method. The same tICA model and state decomposition was used for both datasets. 100 bootstrap MSMs were constructed (with a lag-time of 28.8 ns, see supplementary information for justification), from which the maximum likelihood estimate model has been highlighted. The state equilibrium populations were derived from the first eigenvector of the MSM transition matrix. Error bars for the free energy estimates were calculated using all MSM models. Macrostates were determined using the PCCA+ algorithm. A continuous time rate-matrix MSM was used to estimate the kinetic rates of macrostate conversion. The Caver 3.0 software package103 was used to calculate the pore radii for each macrostate.
Author contributions statement
All authors designed the experiments. K.A.M. performed the research. M.M. and V.S.P. supervised the research. All authors edited and reviewed the manuscript.
Additional information
VSP is a consultant and SAB member of Schrodinger, LLC and Globavir, sits on the Board of Directors of Apeel Inc, Freenome Inc, Omada Health, Patient Ping, Rigetti Computing, and is a General Partner at Andreessen Horowitz. Other authors declare no competing financial interest.
Acknowledgements
We acknowledge Professor Justin Du Bois for helpful discussion. We acknowledge funding from NIH grants U19 AI109662 and 2R01GM062868 (VSP), from a Stanford Bio-X seed grant (MM, and VSP), and support from the Center for Molecular Analysis and Design (CMAD). In addition to CMAD, AK acknowledges support from a Stanford Interdisciplinary Graduate Fellowship (SIGF) through the Stanford ChEM-H Institute (Chemistry, Engineering, & Medicine for Human Health). We acknowledge the Folding@home donors who contributed to this project (PROJ9625-9643).
Footnotes
↵* pande{at}stanford.edu
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