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. Author manuscript; available in PMC: 2022 Jan 1.
Published in final edited form as: J Physiol. 2020 May 20;599(2):417–430. doi: 10.1113/JP278707

Coupling structure with function in acid-sensing ion channels: Challenges in pursuit of proton sensors

Matthew L Rook 1, Maria Musgaard 2, David M MacLean 3,*
PMCID: PMC7869053  NIHMSID: NIHMS1597483  PMID: 32306405

Abstract

Acid-sensing ion channels (ASICs) are a class of trimeric cation-selective ion channels activated by changes in pH within the physiological range. They are widely expressed in the central and peripheral nervous systems where they participate in a range of physiological and pathophysiological situations such as learning and memory, pain sensation, fear and anxiety, substance abuse and cell death. ASICs are localized to cell bodies and dendrites, including the postsynaptic density, and within the last five years several examples of proton-evoked ASIC excitatory postsynaptic currents have emerged. Thus, ASICs have become bona fide neurotransmitter-gated ion channels, activated by the smallest neurotransmitter possible: protons. Here we review how protons are thought to drive the conformational changes associated with ASIC activation and desensitization. In particular, we weigh the evidence for and against the so-called ‘acidic pocket’ being a vital proton sensor and discuss the emerging role of the β11–12 linker as a desensitization switch or ‘molecular clutch’. We also examine how proton-induced conformational changes pose unique challenges to classical molecular dynamics simulations, as well as some possible solutions. Given the emergence of new methodologies and structures, the coming years will likely see many advances in the study of acid-sensing ion channels.

Keywords: gating, desensitization, molecular dynamics, Acid-sensing ion channels, ASICs

Graphical Abstract

graphic file with name nihms-1597483-f0001.jpg

Abstract figure. Acid-sensing ion channels harbor clusters of negatively charged amino acids in their extracellular domains. Protonation neutralizes these charges, allowing conformational changes which trigger transient channel opening and sodium flux before ultimately leading to desensitization.

Changes in extracellular proton concentration are a ubiquitous feature of neurophysiology. These changes range from the brief and small-amplitude acidification of the synaptic cleft during basal neurotransmission to the prolonged acidosis observed during stroke or seizure (Du et al. 2014; DeVries 2001; Chesler 2003). Several channels and receptors respond to such changes in extracellular acidity including the class of acid-sensing ion channels (ASICs) (Pattison, Callejo, and St John Smith 2019). ASICs are expressed widely throughout the central and peripheral nervous systems (Lin, Sun, and Chen 2015), in both excitatory and inhibitory neurons as well as in glial cells (Du et al. 2014; Chiang et al. 2015; Yu et al. 2015; Huang et al. 2010). Evidence from knockout mice and ASIC selective toxins has implicated these channels in noxious pain (Deval and Lingueglia 2015), fear and anxiety (Wemmie, Taugher, and Kreple 2013; Du et al. 2017) as well as ischemic cell death (Xiong et al. 2004; Wang et al. 2015). Interestingly, ASICs have also been reported to participate in basal synaptic transmission in a few synapses (Du et al. 2014; Kreple et al. 2014; Gonzalez-Inchauspe et al. 2017) and participate in synaptic plasticity, especially as related to fear conditioning. Given their involvement in an array of pathological conditions, ASICs are attractive drug targets and an active area of structure-function inquiry. Here we review our current understanding of how protons drive ASIC activation and desensitization at the molecular level. We also explore the opportunities and limitations of using molecular dynamic simulations to address these challenging questions.

The basics of ASICs

In mammals there are four ASIC genes which give rise to at least eight distinct isoforms (Figure 1A). Of these, six have clear proton sensitivity (Waldmann, Champigny, et al. 1997; Chen et al. 1998; Waldmann et al. 1996; Price, Snyder, and Welsh 1996; Lingueglia et al. 1997; Waldmann, Bassilana, et al. 1997; Grunder et al. 2000; Wiemuth, Assmann, and Grunder 2014). ASICs are trimers (although see (van Bemmelen et al. 2015)), with both homo and heterotrimers as feasible arrangements (Bartoi et al. 2014; Benson et al. 2002; Gregory et al. 2018). The ASIC1a subunit is the most thoroughly studied using functional and biophysical methods since it was the first ASIC clone reported to have proton sensitivity, is the most widely expressed and the only for which structural data is available. In response to a rapid drop in extracellular pH (i.e. pH 5), ASIC1a activates within 5–10 ms and desensitizes in hundreds of milliseconds, depending on the species variant (Figure 1B). The half-maximal pH for mammalian ASIC1a activation is approximately 6.5 (MacLean and Jayaraman 2017; Waldmann, Champigny, et al. 1997; Grunder and Pusch 2015). Pre-incubation of ASIC1a with mildly acidic stimuli produces steady-state desensitization, with a half-maximal pH of around 7.25 in murine subunits (7.0 in human ASIC1a) and a very steep pH-dependency (Figure 1B) (Babini et al. 2002; Cristofori-Armstrong et al. 2019; Vaithia et al. 2019). If we consider ASIC1a to be the representative or prototypical family member, then we can grossly simplify the rest of the subunits by saying that ASIC3 is a somewhat faster activating and faster desensitizing version of ASIC1a, while ASIC1b and ASIC2a are slightly slower and lower affinity forms of ASIC1a (Grunder and Pusch 2015).

Figure 1. Phylogeny, function and form of acid-sensing ion channels.

Figure 1

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(A) Phylogenetic tree of human ASIC subunits. hBASIC is also known as ASIC5. Gray colored subunits are not proton sensitive. (B) Representative response of hASIC1a in an outside-out patch recording. (B, inset) Curves for pH activation (blue) and steady-state desensitization (red) generated using the Hill equation with pH50 values (and slopes) of 6.4 (2) and 7.1 (8), respectively. (C) cASIC1 monomer (PDB: 4NYK) with individual domains colored. (D) Close in view of acidic pocket (boxed region in C). (E) cASIC1 trimer (PDB: 4NYK) with subunits tinted in green, orange or blue. Acidic pocket, palm domain and GAS belt are marked. (F) Close in view of palm domain (boxed region in E). Labelled positions for D and F are listed in Table 1 using cASIC1 numbering.

While structural data are only available for chicken ASIC1 (cASIC1), sequence alignments indicate that all ASIC subunits share a similar layout consisting of a large ~300 amino acid residue extracellular domain flanked by two transmembrane helices and short intracellular N and C terminal tails (~40 & 70 amino acids, respectively). This layout is shared by trimeric P2X receptors. However, outside of similarities in the pore, P2X receptors and ASICs have limited structural and functional parallels (Kellenberger and Grutter 2015). Amongst the proton sensitive human ASIC subunits, the second transmembrane helix is the most highly conserved (78–90% sequence identity), followed by the extracellular domain (56 – 83%) while the C terminal tail is quite divergent (16–76%). The second transmembrane helix shows an unusual break where the bottom half swaps over to join up with a neighboring subunits upper half (Figure 1C, E). Linking the upper and lower halves of the second transmembrane helix is a highly conserved GAS belt, which is observed in all functional states including the resting state cryo-electron microscopy structure (Yoder, Yoshioka, and Gouaux 2018). The GAS belt was initially proposed to act as the selectivity filter but subsequent mutagenesis and molecular dynamics studies indicate sodium selectivity arises from conserved glutamate residues lower down in the pore (Lynagh et al. 2017; Lynagh et al. 2020). The proton-sensing extracellular domain of ASICs is often compared to a left hand, with distinct finger, thumb, knuckle, palm and wrist domains, as well as the β-ball (Figure 1C)(Jasti et al. 2007). The interface between the thumb and finger domains contains a high density of acidic amino acid residues (Figure 1C, boxed region, Figure 1D). This so-called ‘acidic pocket’ was initially hypothesized to be the locus of proton-induced gating (Jasti et al. 2007). At the level of the trimeric structure (Figure 1E), the palm domains of individual subunits each contribute several acidic side chains towards a central cavity, forming an additional cluster of closely positioned acidic amino acids (Figure 1F). As discussed below, both the acidic pocket and palm domain play critical roles in ASIC function although the precise division of labor remains to be established. Since the first structure was reported in 2007, additional structures using functional cASIC1 have been solved in the resting (Yoder, Yoshioka, and Gouaux 2018), toxin-stabilized open (Dawson et al. 2012; Baconguis and Gouaux 2012; Baconguis et al. 2014) and low-pH desensitized states (Dawson et al. 2012; Gonzales, Kawate, and Gouaux 2009). Figure 2 depicts two such structures of the resting and toxin-stabilized open state (Figure 2A, B). Thus far, the open state has only been resolved in crystallographic studies in the presence of toxins, raising the question as to whether the conformational changes stem from toxin binding or protonation. However, given the similarities between protonated toxin-stabilized open and desensitized states (see below), the conformational changes are more likely driven by protonation with the toxins stabilizing the resulting open configuration. By measuring differences between these structures, a global outline of ASIC conformational changes emerges with some noteworthy trends (Figure 2C, D). First, the pore-forming transmembrane helices are considerably displaced between the conducting (open) state and non-conducting (desensitized and resting) states, consistent with pore opening (Figure 2C, D). And second, the acidic pocket ‘collapses’ between the alkaline resting state and the acidic open state (Figure 2C, D). While this motion is quite salient, the causal role of acidic pocket collapse in ASIC activation has been questioned.

Figure 2. Structural changes associated with ASIC gating.

Figure 2

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(A, B) Resting and MitTx-bound open states of cASIC1 (PDB: 5WKV and 4NTW, A and B) with single subunit shown in cylinder representation. (C) Overlay of single subunits from A and B. (D) Toxin-bound open state (PDB: 4NTW) colored based on root mean squared difference between the resting state in A and the open state, where warmer colors denote greater differences according to the color key.

The acidic pocket: The red herring of ASIC1a activation?

The first crystal structure of cASIC1 revealed a dense cluster of acidic residues between the thumb and finger regions, specifically Asp238, Glu239, Glu243, Asp346, Asp350 and Asp354 (Figure 1D) as well as Glu220 and Asp408 from the adjacent subunit (all amino acids herein are cASIC1 numbering). This conserved ‘acidic pocket’ was hypothesized to be the proton binding site, with protonation leading to the formation of carboxyl-carboxylate bonds between acidic residue pairs (Jasti et al. 2007). These interactions would then drive the contraction or collapse of the pocket and a resulting conformational cascade through the palm domain to open the pore. This hypothesis has received experimental support from structural studies which show a collapsed acidic pocket in the open and desensitized states compared to the resting state (Dawson et al. 2012; Baconguis and Gouaux 2012; Baconguis et al. 2014; Gonzales, Kawate, and Gouaux 2009; Yoder, Yoshioka, and Gouaux 2018). Further evidence comes from resonance energy transfer experiments which revealed a closure of the acidic pocket, also termed a ‘pinching motion’ between the finger and thumb domains, under acidic stimuli (Ramaswamy et al. 2013). Voltage clamp fluorometry experiments using tryptophan quenching of site-specific fluorophores have also reported motions consistent with collapse of the acidic pocket during activation/desensitization (Vullo et al. 2017), as well as distance increases between the finger and β-ball during acidification (Gwiazda et al. 2015; Bonifacio, Lelli, and Kellenberger 2014). Finally, purely electrophysiological evidence for this hypothesis came in the form of neutralization mutations of residues Asp346 and Asp350. Both neutralization mutations decreased the Hill slopes of activation curves (8.9 to 5.3 and 2.7, respectively) and the pH-response curve of D346N shifted to a more acidic half maximal value (ΔpH50act ± 0.3), suggesting their role in channel activation is significant (Table 1)(Jasti et al. 2007). Despite this support, additional functional studies have found limited effect of acidic pocket mutations. For example, both Paukert et al. and Leichti et al. conducted extensive mutational analyses of nearly all acidic amino acid residues in the extracellular domain, mutating acidic side chains to their uncharged counterparts. Surprisingly, neutralization of individual amino acid residues within the acidic pocket had minimal effect on the pH sensitivity of activation, with the exception of D238N in rASIC1a (D239 in cASIC1) that produced a modest, yet significant acidic shift in the pH50act (ΔpH50act −0.4) (Table 1)(Paukert et al. 2008; Liechti et al. 2010). Instead, neutralizing residues within the palm domain had a more profound effect on the pH50 values for activation. For example, neutralization of two histidine residues, H72N/H73N in rASIC1a (P73 and H74 in cASIC1), produced channels insensitive to pH, and this was subsequently confirmed this is due to the alteration at position 74 (Lynagh et al. 2018). Since this mutation abolishes responses, we cannot exclude that this position plays a critical structural role. But nonetheless, these initial findings raised doubt as to whether the acidic pocket or some other area is the critical ‘proton sensor’. In the ensuing years, these doubts have been amplified by further evidence. For example, Krauson et al. neutralized six acidic residues within the pocket and found no effect on activation curves (Krauson, Rued, and Carattino 2013). A recent paper from the Kellenberger laboratory extended this approach and reported a particularly damning set of experiments where every acidic residue in the acidic pocket was neutralized, up to 16 in total (Vullo et al. 2017). Yet, even channels so profoundly modified could still respond to protons, albeit with a pH50 around 5. For the convenience of the reader, we assembled a number of mutations in the pocket and the palm so the extent of shift can be examined (Table 1). However, we note that pH50 values can vary between laboratories, possibly due to differences in perfusion or expression systems, buffer composition, or temperature which can alter ASIC gating (Blanchard and Kellenberger 2011) as well as buffer pKa.

Table 1.

Mutational effects on pH response curves

Location Residue in cASIC1 Modification according to species position ΔpH50act ΔnHact ΔpH50SSD ΔnHSSD Ref.
Acidic Pocket E220 E219Qr −0.1 - - - Paukert et al., 2008
E219K**m +0.05 - - - Krauson et al., 2013
E236 E235Qr −0.2 - - - Paukert et al., 2008
E235C/Y389Ch No Response - - - Gwiazda et al., 2015
E235C/Y389C#h −3.35 - −0.13 - Gwiazda et al., 2015
D238 D237Nr −0.4 - - - Paukert et al., 2008
D237K**m +0.57 - - - Krauson et al., 2013
D238A/E239A+ −2.5 - - - Ramaswamy et al., 2013
E239 E238Qr <−0.1 - - - Paukert et al., 2008
E238K**m +0.1 - - - Krauson et al., 2013
E238C**m −0.11 - - - Krauson et al., 2013
E238C-MTSET**m −0.78 - - - Krauson et al., 2013
E243 E242Qr −0.1 - - - Paukert et al., 2008
D260 D260A+ −0.21 - - - Ramaswamy et al., 2013
D346 D346N −0.3 −3.4 - - Jasti et al., 2007
D345Nr −0.29 no Δ - - MacLean et al., 2017
D347Nh −0.19 no Δ no Δ - Liechti et al., 2010
D347Ch −0.4 - no Δ - Liechti et al., 2010
D347C-MTSESh −0.75 - −0.1 - Liechti et al., 2010
D347C-MTSETh −0.65 - no Δ - Liechti et al., 2010
D347C-DMBE-MTSh −1.05 - no Δ - Liechti et al., 2010
D345C**m −0.11 - - - Krauson et al., 2013
D345N**m +0.12 - - - Krauson et al., 2013
D345M**m −0.58 - - - Krauson et al., 2013
D345K**m −0.511, −2.362 - - - Krauson et al., 2013
D345R**m −0.671, −2.262 - - - Krauson et al., 2013
D345C-MTSET**m −1.57 - - - Krauson et al., 2013
D345C-MTSEA**m −0.481, −2.252 - - - Krauson et al., 2013
E79K/D345K**m −3.33 - - - Krauson et al., 2013
D350 D350N +0.04 −6.2 - - Jasti et al., 2007
D349Nr −0.17 −0.78 - - MacLean et al., 2017
D349K**m +0.28 - - - Krauson et al., 2013
E354 E355Qh +0.04 - +0.11 - Gwiazda et al., 2015
E355Rh −0.05 - +0.19 - Gwiazda et al., 2015
D408 D407Nr +0.2 - - - Paukert et al., 2008
D407K**m +0.17 - - - Krauson et al., 2013
>2 mutations E219Q/D237N/E238Q/D345N/D349N/D407N**m −0.06 - - - Krauson et al., 2013
E219Q/D237N/E238Q/D345K/D349N/D407N**m −0.831, −2.422 - - - Krauson et al., 2013
D238A/E239A/D260A+ No Response - - - Ramaswamy et al., 2013
E315Q/E344Q/D347N/E355Qh −0.6 −0.3 +0.35 −2.5 Vullo et al., 2017
D183N/D303N/E315Q/E321Q/E344Q/D347N/D351N/E355Qh −0.8 −1.9 +0.58 −0.5 Vullo et al., 2017
E97Q/E177Q/D183N/D237N/E238Q/D303N/E315Q/E321Q/E344Q/D347N/D351N/E355Qh −0.4 −0.5 +0.5 −2.7 Vullo et al., 2017
E97Q/E113Q/E177Q/D183N/E235Q/D237N/E238Q/D259N/D303N/E315Q/E321Q/H329N/E344Q/D347N/D351N/E355Qh −1.2 −1.6 0.15 −4.5 Vullo et al., 2017
Palm N64 E63Qr +0.1 - - - Paukert et al., 2008
E63Q*r No Response - - - Paukert et al., 2008
E63Kr +0.15 - - - Paukert et al., 2008
P73 H72Ar <+0.1 - - - Paukert et al., 2008
H72NH73Nr No Response - - - Paukert et al., 2008
H74 H73Ar −0.7 - - - Paukert et al., 2008
H73Nr No Response - - - Paukert et al., 2008
ΔH73m No Response - - - Lynagh et al., 2018
D79 D78Nr −0.3 - - - Paukert et al., 2008
D78N*r No Response - - - Paukert et al., 2008
D78Kr −1.11 - - - Paukert et al., 2008
E80 E79Qr −0.1 - - - Paukert et al., 2008
E79Qr −0.8 −1.14 - - MacLean et al., 2017
E79Q**m −0.31, −2.252 - - - Krauson et al., 2013
E79K**m −0.451, −2.512 - - - Krauson et al., 2013
E411 E413Qh −0.1 no Δ −0.14 - Liechti et al., 2010
E416 E416Qr −0.15 - - - Paukert et al., 2008
E416Qr −0.81 −1.14 - - MacLean et al., 2017
E418Qh −0.19 no Δ −0.13 - Liechti et al., 2010
E416K**m −0.151, −2.262 - - - Krauson et al., 2013
E426, D433 E425GD432Cr −0.2 - - - Paukert et al., 2008
Miscellaneous D108 D107Qr −1.9 - - - Paukert et al., 2008
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*

denotes E425GD432C background,

**

denotes C70L background,

+

denotes L139C/Q340C background,

#

denotes reducing conditions.

1 and 2

refer to the first and second components of biphasic fits.

m, r and h

refer to mouse, rat and human ASIC1a isoforms and numbering.

Colors indicate the following changes in response curves: blue: left shift, gray: no change, yellow: right shift of 0.1–0.5, yellow-orange: 0.51–1.0, orange: 1.01–1.5, orange-red: >1.51.

Structural biology and fluorescence measurements support the importance of acidic pocket collapse in ASIC activation, yet the functional data outlined above indicate the pocket can be heavily mutated with little impact on activation. How can these data be reconciled? One caveat of the neutralizing substitutions discussed above is that they mimic the properties of protonated acidic residues, essentially tethering a proton in place. Such ‘pre-protonated’ channels might therefore be expected to undergo steady-state desensitization or have a reduced open probability, not necessarily shifted dose-response curves. An alternative test would be to introduce a charge swap mutation, such as a Lys or Arg residue, whose positive charge might be expected to reduce the local affinity for protons. Interestingly, introduction of charge swaps (ex. D345K, D346 in cASIC1) not only produces an acidic shift in the pH response curve but generates biphasic curves, resulting in two separate pH50act values (D345K-pH50act1=5.69, D345K-pH50act2=3.74; D345R-pH50act1=5.53, D345R-pH50act2=3.84 (Krauson, Rued, and Carattino 2013)). A similar biphasic response results when a charge substitution is placed in the palm domain at position 79 (E80 in chicken ASIC1, Figure 1F) (Krauson, Rued, and Carattino 2013). Combining both acidic pocket and palm domain charge swaps produces a huge shift in the dose-response curve to a very acidic pH50 of less than 3. Based on these data, while both the acidic pocket and the palm domain harbor protonatable residues which are important for activation, no single position appears critical, and thus more dramatic interventions may be needed to impair gating. Consistent with this, more potent interventions have been employed using introduced Cys residues. Krauson et al. introduced a Cys residue at Glu238 or Asp345 (Glu239 and Asp346 in cASIC1). These mutations alone do not significantly alter the pH50 of activation, however, allowing the bulky and positively charged MTSET to bind at these sites produces significant shifts in activation curves, especially in the case of D345C (D346 in cASIC1) (Krauson, Rued, and Carattino 2013) (Table 1). Furthermore, Yoder et al. engineered a disulfide bond between position N357C (the lower thumb domain) and T84C (the β1–2 linker) in the neighboring subunit which substantially impaired channel activation under normal conditions (Yoder, Yoshioka, and Gouaux 2018). Importantly, channel activation returned under reducing conditions, indicating this disulfide bond likely prevented the thumb domain from moving inward and triggering acidic pocket collapse. It remains to be determined whether the T84C/N357C impairment of activation can be surmounted by stronger acidic stimuli.

Based on these experiments, we propose that activation (and desensitization) requires collapse of the acidic pocket however this collapse may either be driven by protonation of side chains within the pocket itself, or allosterically by protonation of residues distributed elsewhere in the channel, or some combination. In the case of wild-type channels, protonation of side chains in the pocket and palm domains together drive the conformational changes from resting to open. If the acidic pocket is mildly altered, as in single or even multiple neutralization mutations, then pocket collapse may still be relatively favorable. However, if the acidic pocket is substantially altered, as in the case of lysine or arginine or MTSET, then the closure of the acidic pocket during protonation becomes much less favorable, changing from an energetic source to a sink. Thus, higher ligand concentrations are needed to surmount electrostatic or steric penalties associated with closing an acidic pocket containing charge swapped or MTSET-bound residues. Ultimately testing this proposal would require further use of engineered disulfide, or possibly non-canonical amino acid based crosslinks (Rook et al. 2020), to lock the acidic pocket open (or closed) and test the necessity and sufficiency of this domain in activation.

The structural underpinnings of desensitization

Returning to the general map of conformational changes, what can be gleaned about the transition into the desensitized state? Interestingly, the open and desensitized states of cASIC1 look very similar, other than the changes associated with the pore-forming transmembrane helices (Figure 3A–D). An important exception to this is the linker connecting the 11th and 12th β strands (Figure 1C, Figure 3C, E–G). In cASIC1, this linker has the sequence EALN where the Leu and Asn residues are nearly completely conserved in all ASICs across a variety of lineages. In the open (and resting) state of the channel, the side chain of the leucine residue is pointed upward and away from the central axis of the pore while the Asn415 side chain is oriented downwards (Figure 3F–G)(Yoder, Yoshioka, and Gouaux 2018; Baconguis and Gouaux 2012; Baconguis et al. 2014). However, in the desensitized state these side chains essentially change positions, with the Leu414 pointing downward (Figure 3F–G)(Jasti et al. 2007; Gonzales, Kawate, and Gouaux 2009). This region has been proposed to act as a ‘molecular clutch’, coupling conformational changes in the extracellular domain with the pore in the resting state but then swiveling to a new position where extracellular changes are disengaged from the pore allowing desensitization (Yoder, Yoshioka, and Gouaux 2018). Given a vital role in the desensitization process, one expects this region to be unusually sensitive to mutagenesis. Consistent with this, mutations or manipulations to this area have historically produced profound effects. For example, the N415C mutation shows slower entry into desensitization ((Li, Yang, and Canessa 2010a; Roy et al. 2013) although see (Wu, Chen, and Canessa 2019)) and appending MTSET onto N415C decelerates desensitization even further (Li, Yang, and Canessa 2010a). This effect is even stronger at the neighboring L414 position, where MTSET addition results in a huge suppression of macroscopic desensitization, and mutation to other amino acids substantially impacts acceleration or slowing of desensitization kinetics (Roy et al. 2013; Wu, Chen, and Canessa 2019; Rook et al. 2020). Interestingly, the desensitization process is even sensitive to changes in the area surrounding the β11–12 linker. For instance, Wu et al. reported that alteration of Gln276 (Gln277 in cASIC1), positioned just ‘behind’ the β11–12 linker, can substantially modify desensitization rates, and even produce the largest suppression of desensitization yet reported (Wu, Chen, and Canessa 2019). We recently noted that in the desensitized state, the Leu414 residue is stabilized within a hydrophobic pocket formed by the adjacent subunit. Reducing the hydrophobicity of this pocket by mutagenesis can profoundly accelerate both desensitization and recovery (Rook et al. 2020). Before the arrival of the cASIC1 structure, it was noted that fish and frog ASIC1 desensitized faster than their rat counter parts (Coric et al. 2003; Li, Yang, and Canessa 2010b). Based on sequence alignments, the experimenters swapped positions 83–85 of rat ASIC1a (84–86 in cASIC1) to the fish and frog side chains, and vice versa. This resulted in a ‘kinetic transplant’ with mutant rat ASIC1a channels becoming faster and the fish and frog mutants slowing down (Coric et al. 2003; Li, Yang, and Canessa 2010b). With the benefit of structural hindsight, we know this stretch of peptide lies close to the β11–12 linker and it seems likely these mutations exert their effects by impacting the ease of β11–12 linker rotation.

Figure 3. Structural changes associated with ASIC desensitization.

Figure 3

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(A, B) MitTx-bound open and desensitized states of cASIC1 (PDBs: 4NTW and 4NYK, respectively) with single subunit shown in cylinder representation. (C) Structural alignment of single subunits from A and B. (D) Toxin-bound open state monomer (PDB: 4NTW) colored based on root mean squared difference between the desensitized state in B and the open state, where warmer colors denote greater differences according to the color key. (E) Open state colored based on RMSD from the desensitized state with two chains illustrated. (F) Close up view of the boxed region in E to highlight the β11–12 linker. (G) Rotation and expansion of the boxed region showing the re-orientation of the linker between open and desensitized structures.

Clearly, the β11–12 linker amino acid residues are important for kinetics, but is their rotation required for desensitization to occur? To address this, Yoder et al. engineered a disulfide bridge between Leu414 and Leu86, converting both to Cys residues, and examined the consequences in outside out patches. Consistent with the linker flipping being a requirement for desensitization, this putative disulfide bridge attenuated macroscopic desensitization. Recently, we have extended this work using non-canonical amino acid incorporation. Benzylphenylalanine (Bpa) is a non-canonical amino acid which generates a free radical upon UV exposure, thus it is able to form irreversible crosslinks with adjacent amino acid residues (Klippenstein et al. 2014; Ye et al. 2008). We incorporated Bpa at the L414 position, i.e. L414Bpa, and found that exposing these channels to UV light in the resting state dramatically and irreversibly reduced macroscopic desensitization (Rook et al. 2020). Taken together, all these data constitute compelling evidence that β11–12 linker flipping is needed for channel desensitization, being akin to a switch or molecular clutch which inactivates the channel. The remaining challenge is to determine how protonation of distinct sites drives linker flipping, and to integrate other observations into this emerging picture.

Beyond the β11–12 linker, desensitization can be influenced by other factors. For example, simple anion substitution accelerates or slows desensitization rates in certain ASIC subunits (Kusama, Harding, and Benson 2010; Kusama et al. 2013). Interestingly, the anion binding site is housed at the base of the thumb domain and potentially influences the dynamics of the region surrounding the β11–12 linker through electrostatic interactions. Also, inserting the thumb and/or finger domains of ASIC1a into ASIC2a results in unexpectedly fast desensitization kinetics, hinting at some role for the acidic pocket in regulating desensitization (Krauson and Carattino 2016). The palm domain as well must play some role as neutralization of Glu79 (Glu80 in cASIC1, Figure 1F) markedly accelerates desensitization (MacLean and Jayaraman 2017; Cushman et al. 2007). Finally, pH itself is a strong determinant of desensitized state stability. With mild acidic stimuli (i.e. pH 7.1) ASICs desensitize without apparent activation. This process, termed steady-state desensitization, takes several tens or hundreds of seconds to occur and is accelerated by strong pH stimuli (Babini et al. 2002; Bonifacio, Lelli, and Kellenberger 2014). Desensitization by low agonist concentrations is a common feature of other ligand-gated ion channels, and the time course of this process should accelerate with increasing agonist concentration by virtue of more frequent binding events (Jones et al. 1998; Coombs et al. 2017). Interestingly, with more acidic stimuli that directly activate the channel, desensitization kinetics continue to be pH dependent for some ASIC subtypes, even at apparently saturating concentrations (Hesselager, Timmermann, and Ahring 2004; Krauson and Carattino 2016). That is, currents decay faster with pH 4 application than pH 5, even though both conditions produce the same peak response. This manner of kinetic behavior is not observed with other ‘classical’ ligand-gated ion channels, where desensitization rates plateau once saturation is reached (Coombs et al. 2017; Bianchi et al. 2007). In addition to pH dependent desensitization rates, ASIC deactivation and recovery from desensitization are also strongly pH dependent (MacLean and Jayaraman 2016; MacLean and Jayaraman 2017; Rook et al. 2020; Bonifacio, Lelli, and Kellenberger 2014). Reproducing this unique cluster of agonist-dependent transitions with the sort of Markov models that have been useful for other ligand gated ion channels has proven extremely challenging for ASICs.

Protonating ASICs in computational simulations

Molecular dynamics simulations can predict the single-channel dynamics at atomistic resolution on the nanosecond to microsecond or even millisecond timescale. Over the last decade, molecular dynamics simulations have provided highly detailed insight into the structure-function relationships for different ligand-gated ion channels (Musgaard and Biggin 2017), however, ASIC structure-function relationships remain largely unexplored by molecular dynamics simulations. The main reason is that protons present five particular challenges to molecular dynamics simulations as compared to the traditional small molecule agonist. First, proton affinity strongly depends on local electrostatics which are difficult to estimate. The pKa value of a given side chain is the pH value where 50% of the population is ionized, and at pH = pKa ± 1, the distribution is 90:10. These pKa values are highly affected by the local environment, therefore sidechain pKa values can be heavily perturbed from standard values due to interactions with nearby residues, which change dynamically during the course of a simulation. Second, protons bind covalently. In general, molecular dynamics simulations based on classical force fields preclude the formation or breakage of covalent bonds, thus protonation states must be assigned prior to running simulations. Third, even if simulations allowed for proton transfer from a hydronium ion to a sidechain based on an accurate pKa, the concentration of hydronium ions is very small. A typical molecular dynamics setup of an ASIC in a membrane/water environment includes approximately 150 sodium ions to mimic a physiological concentration of 150 mM. A pH value of 5 is a hydronium ion concentration of 10−5 moles per liter, i.e. orders of magnitude smaller than the sodium ion concentration, and each simulation box would have much less than one hydronium ion. Fourth, protons are hard to ‘see’. For other ligand-gated ion channels, the agonist binding site can generally be located by identifying non-protein electron density when co-crystallizing with the agonist. However, hydrogen atoms have low electron density, making their positions difficult to determine unambiguously. Their positions can be inferred to some degree by analyzing the environment around the individual ionizable side chains and predicting their pKa values using cheap computational methods like PROPKA (Olsson et al. 2011) or H++ (Anandakrishnan, Aguilar, and Onufriev 2012). This approach was adopted by Shaikh and Tajkhorshid shortly after the first structure of ASIC was published (Shaikh and Tajkhorshid 2008) who used PROPKA to determine relevant protonation states and tested a few different combinations of protonation states. In agreement with the hypothesis that the acidic pocket functions as a proton sensor, they found that the acidic pocket would open upon deprotonation (Shaikh and Tajkhorshid 2008). However, as discussed above, the precise role of the acidic pocket awaits clarification. This also highlights the fifth challenge: distinguishing protonation sites which drive conformational change from those that are a byproduct of acidic conditions.

These challenges do not preclude the use of molecular dynamics simulations to study ASICs in general. Indeed, past molecular dynamics work has explored interactions with psalmotoxin (Pietra 2009), investigated a predicted calcium site at the extracellular mouth of the pore for ASIC3 (Zuo et al. 2018), as well as studied the stability of a conserved salt bridge (Yang et al. 2012) and the effects of mutations on dynamics (Roy et al. 2013). Interestingly, unlike pentameric ligand gated ion channels whose open state pore tends to collapse in molecular dynamics simulations (Damgen and Biggin 2020), the open state of cASIC1 is reasonably stable allowing more detailed examination of ion selectivity (Lynagh et al. 2017; Lynagh et al. 2020). However, these studies were not aimed at addressing how protonation drives transitions between functional states. To gain insight into this question using computational methods, we suggest three approaches which may be especially fruitful.

First, rather than using molecular dynamics simulations one can simply identify or classify candidate proton sensors. Some studies have attempted to identify the proton sensors using Poisson-Boltzmann or Poisson-Boltzmann/Debye-Hückel continuum approaches (Sazanavets and Warwicker 2015; Liechti et al. 2010) which are more refined methods for pKa prediction than PROPKA and H++. Liechti et al. calculated pKa values of all extracellular histidine, glutamate and aspartate sidechains based on static structures representing desensitized states, trying to include the effect of protonation of nearby residues on predicted pKa values by using an iterative approach. Candidate pH-sensing residues were mutated and tested in functional experiments. The results suggested that many residues in each subunit change protonation state as a result of a pH reduction, wherefore the pH-sensing may be spread at multiple sites. Because of this, defining ‘pH-sensing’ residues is exceedingly difficult as ionizable residues can change the pKa values of adjacent side chains without themselves being needed for conformational changes. Moreover, Lietchi et al. noted that pKa prediction using homology models based on two different crystal structures of the same functional states showed differences for residues for which the template structures differed (Liechti et al. 2010), illustrating that the method is rather sensitive to the given structure. Molecular dynamics simulations could be included to provide a more realistic description of the different functional states rather than using static structures. This may also reveal the coupling between residues with interdependent pKa values, which to some degree can be thought of as in intricate allosteric network. With the publication of the resting state (Yoder, Yoshioka, and Gouaux 2018), one may extend the candidate sensor approach to include a functional ranking. If one assumes that during the transition from resting to open/desensitized, the residues with the highest pKa get protonated first, these residues may be thought of as driving the conformational change. Further, one can assume that in the open or desensitized state, residues with the highest pKa maintain that particular conformation. Thus, using the methods above to estimate pKa values could provide a ranking of potentially critical positions to be targeted for functional experiments. Such an analysis would be best if the impact of structural dynamics within a functional state were also considered.

A second approach is to modify the molecular dynamics simulations themselves. New algorithms for constant-pH simulations (Chen and Roux 2015; Goh et al. 2014; Radak et al. 2017) have been developed in recent years, and using such methods would allow for better sampling of different protonation states and likely provide a more accurate pKa prediction. However, the methods do require approximate starting values for side chain pKa values for the residues of interest, and efficiency is highly compromised if the provided values are not reasonably close to the actual values. Furthermore, the methods then struggle with highly shifted pKa values and residues in salt bridges. The residues important for acid-sensing in ASICs are likely of the type of residues which would be challenging for the constant-pH methods, therefore these algorithms may not be optimal for ASIC studies just yet.

A third approach is to focus on transitions which occur upon deprotonation and then simply deprotonate residues at the start of the simulation. We recently adopted this approach to capture the flipping of the β11–12 linker when transitioning from the protonated desensitized state to the alkaline resting state in a fast recovering ASIC mutant (Rook et al. 2020). Importantly, no linker flipping was observed when a number of aspartate, glutamate and histidine residues in the ECD were protonated (Rook et al. 2020). Because both channel shutting/deactivation and recovery from desensitization get much faster under alkaline conditions (MacLean and Jayaraman 2017; Rook et al. 2020), this deprotonation tactic also increases the chances that functional important conformational changes will be observed within the timescale of molecular dynamics simulations. Furthermore, a selective de-protonation strategy, ie. deprotonating all but a few key amino acids, may help uncover residues which maintain specific functional states, hopefully leading to clearer delineation of the acid sensors.

Conclusions

In summary, there are several reasons for why the coupling between agonist binding and channel opening in ASICs is still relatively poorly understood. Most of the methodologies that are considered standard methods for studying structure-function relationships for ligand-gated ion channels are complicated by the proton agonist. For instance, protons cannot readily be seen in crystal structures. And site-directed mutagenesis is complicated both by the classical problem of distinguishing a binding effect from a gating effect (Colquhoun 1998) and the additional problem of distinguishing the proton sensors themselves from residues that alter the local electrostatics/pKa values of nearby proton sensing residues. Furthermore, it is not straightforward to implement molecular dynamics simulations with changing protonation states. All of these issues are shared by the bacterial proton-activated pentameric ligand-gated ion channel GLIC and recent progress in the study of GLICs brings optimism for work on ASICs. As with ASICs, every acidic amino acid residue of GLIC has been systematically mutated, either neutralized or converted to Ala (Nemecz et al. 2017). And as with ASICs, no single residues appears to be the critical proton sensor. Rather, protons sensing appears distributed across several clusters of residues at the interface between subunits and the junction between the extracellular domain and transmembrane domains (Nemecz et al. 2017). To narrow in on the vital protonatable residues, the Allen group implemented a string method where a preliminary route between functional states (eg. open to closed transition) is simulated using all-atom molecular dynamics (Lev et al. 2017). This route, or string, linking functional states is broken down into sub-states and clusters or swarms of trajectories starting from these sub-states are then simulated. The average route taken by these swarms can reveal the most probable pathway linking sub-states along the string, and can also be used to further refine the string itself in an iterative fashion. While computationally intensive, this method was able to highlight GLIC global motions associated with the open to closed transition, even proposing a rank order of molecular motions and predicting individual amino acid residues driving or preventing these transitions. Impressively, several of these mutations (eg. E26) were confirmed experimentally by mutational studies from an independent group (Nemecz et al. 2017). The swarm-trajectory string method holds promise, however it still relies on all-atom simulations and hence all the challenges of protonation in molecular dynamic simulations outlined above hold true. More recently, Fourier transform infra-red spectroscopy (FTIR) was able to directly measure the pKa of GLIC residue E35, lending further support to the critical role of this position (Hu et al. 2018). Thus, from the list of 40 protonatable residues in GLIC, electrophysiology, string analysis and FTIR were able to narrow the list to 3–5 core residues. These approaches may help winnow ASICs list of 64 acidic residues down to a more manageable number. With the constant refinement of methodologies, for example in the use of non-canonical amino acid substitution, and the continued development of simulation algorithms, many exciting advances in ASIC structure-function coupling are likely on the horizon. Ultimately, such a detailed knowledge will be needed if we are to rationally develop or refine drugs to target ASICs and treat some of the many conditions in which ASICs are implicated (Wemmie, Taugher, and Kreple 2013; Boscardin et al. 2016).

Funding

This work was funded by NIH R00NS094761 and NARSAD Young Investigator Award (to D.M.M.), NSERC Discovery Grant RGPIN 2019-06864 and the Canada Research Chairs Program 950-232154 (to M.M.), and NIH T32GM068411-15 (to M.L.R).

Footnotes

Conflicts of Interest

The authors declare no financial conflicts of interest.

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