For decades, naphthyl-acetyl spermine trihydrochloride (NASPM) has been used as a selective inhibitor of calcium-permeable AMPA receptors (CP-AMPAR). In rodents, NASPM is known to suppress seizures in vivo and seizure-like events (SLE) in vitro , suggesting possible involvement of CP-AMPAR in ictogenesis and epileptogenesis. To address whether these findings can be translated to human brain, we investigated the involvement of glutamatergic receptor subclasses in SLE in human cortex ex vivo, demonstrating that glutamatergic receptor antagonists can block (NASPM and APV) or reduce (UBP302, GYKI52466, GYKI53655) SLE. Using a multimethodological approach we were able to demonstrate that both NASPM and APV inhibit human SLE by inhibition of NMDA receptors. Our results further show that the inhibitory effect of NASPM on NMDA receptors is sufficient to explain its inhibition of seizure like activity, rather than its action on CP-AMPA receptors. Thus, our findings challenge previous knowledge on the use of NASPM as a specific CP-AMPAR inhibitor. Some phenomena previously attributed to CP-AMPAR, may need to be re-examined more closely. Overall, our study raises awareness about potential pitfalls in the use of existing pharmacological agents and sets a new paradigm for the use of NASPM in neuroscience research while questioning its therapeutic potential in a clinical context. ### Competing Interest Statement The authors have declared no competing interest. Deutsche Forschungsgemeinschaft, EXC-2049-390688087-NeuroCure

GluA2 is a key subunit of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) ion channels that is abundantly expressed in the vertebrate brain. Posttranscriptional Q/R editing of GluA2 renders AMPARs nearly impermeable to calcium ions, which is crucial for their normal function. Although previous studies have characterized conductivity and selectivity differences between edited and unedited GluA2 variants and heteromeric receptors incorporating GluA2, the consequences of pore editing have not been studied in all-atom simulations, which leave the atomistic mechanisms unclear. In this study, we investigate ion permeation in the context of multiple Ca2+ binding sites along the pore predicted from molecular dynamics (MD) simulations, considering both mutations and co-permeating monovalent ions. Patch clamp electrophysiology recordings confirmed a binding site at the intracellular mouth of the selectivity filter that confers selectivity for calcium over monovalent ions. A patient mutation at the same site has been previously shown to cause neurodevelopmental abnormalities. Furthermore, MD simulations of GluA2 with different arginine copy number at the Q/R site show that Ca2+ conduction is blocked in the presence of two arginines, whereas K+ is only blocked by four arginines, in explaining the results from decades of electrophysiological work. Finally, MD simulations revealed that Ca2+ reduces K+ conduction by preferentially occupying the intracellular selectivity filter binding site, whereas Na+ does not. This result is consistent with electrophysiological results from the D590 mutants and suggests that divalent binding in the selectivity filter is a major determinant of AMPAR conductance.

We developed a system for optogenetic release of single molecules in live cells. We confined soluble and transmembrane proteins to the Golgi apparatus via a photocleavable protein and released them by short pulses of light. Our method allows for the controlled delivery of functional proteins to cytosol and plasma membrane in amounts compatible with single molecule imaging, greatly simplifying access to single molecule microscopy of any protein in live cells. Furthermore, we could reconstitute cellular functions such as ion conductance by delivering BK and VRAC ion channels to the plasma membrane. Finally, we could induce NF-kB signaling in T-Lymphoblasts stimulated by IL-1 by controlled release of a signaling protein that had been knocked-out in the same cells. We observed light induced formation of functional inflammatory signaling complexes that could trigger IKK phosphorylation in single cells. We thus developed an optogenetic method for the reconstitution and investigation of cellular function at the single molecule level.

The dodecameric protein kinase CaMKII is expressed throughout the body. The alpha isoform is responsible for synaptic plasticity and participates in memory through its phosphorylation of synaptic proteins. Its elaborate subunit organization and propensity for autophosphorylation allow it to preserve neuronal plasticity across space and time. The prevailing hypothesis for the spread of CaMKII activity, involving shuffling of subunits between activated and naive holoenzymes, is broadly termed subunit exchange. In contrast to the expectations of previous work, we found little evidence for subunit exchange upon activation, and no effect of restraining subunits to their parent holoenzymes. Rather, mass photometry, crosslinking mass spectrometry, single molecule TIRF microscopy and biochemical assays identify inter-holoenzyme phosphorylation (IHP) as the mechanism for spreading phosphorylation. The transient, activity-dependent formation of groups of holoenzymes is well suited to the speed of neuronal activity. Our results place fundamental limits on the activation mechanism of this kinase.

Full text Figures and data Side by side Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Abstract Receptors signal by switching between resting (C) and active (O) shapes (‘gating’) under the influence of agonists. The receptor’s maximum response depends on the difference in agonist binding energy, O minus C. In nicotinic receptors, efficiency (η) represents the fraction of agonist binding energy applied to a local rearrangement (an induced fit) that initiates gating. In this receptor, free energy changes in gating and binding can be interchanged by the conversion factor η. Efficiencies estimated from concentration-response curves (23 agonists, 53 mutations) sort into five discrete classes (%): 0.56 (17), 0.51(32), 0.45(13), 0.41(26), and 0.31(12), implying that there are 5 C versus O binding site structural pairs. Within each class efficacy and affinity are corelated linearly, but multiple classes hide this relationship. η unites agonist binding with receptor gating and calibrates one link in a chain of coupled domain rearrangements that comprises the allosteric transition of the protein. Editor's evaluation This valuable work investigates the fundamental concept of how the energy of agonist binding is converted into the energy of the conformational change that opens the pore of a ligand-gated ion channel. The conclusions are based on analysis of solid data in terms of a mechanistic model. The findings will be interesting to biophysicists working on ligand-gated ion channels and, more generally, to enzymologists focused on allosteric enzyme regulation. https://doi.org/10.7554/eLife.86496.sa0 Decision letter Reviews on Sciety eLife's review process Introduction The primary job of a receptor is to convert chemical energy from ligand binding into mechanical work of protein conformational change. Each agonist has two affinities that measure how strongly it binds to resting and active target sites, and an efficacy that measures how well it activates once bound (Cecchini and Changeux, 2022; Ehlert, 2015; Foreman et al., 2011). A third agonist property, efficiency (η; eta), is the correlation between efficacy and affinity and is the receptor’s output/input energy ratio (Nayak et al., 2019). Here, we describe and interpret the distribution of η values estimated from concentration(dose)-response curves (CRCs) of adult-type skeletal muscle nicotinic acetylcholine receptors (AChRs) activated by different agonists and having different binding site mutations. AChRs are five subunit, ligand-gated ion channels that have two neurotransmitter sites in the extracellular domain (ECD), and a narrow equatorial gate in the lumen of the transmembrane domain (TMD) that governs the passive flow of cations across the membrane (Gharpure et al., 2020; Unwin, 1995; Zarkadas et al., 2022). AChRs are allosteric proteins that alternate spontaneously between global off (C, closed-channel) and on (O, open-channel) conformations in which each neurotransmitter site binds agonists weakly (with low affinity; LA) or strongly (with high affinity; HA) (Cecchini and Changeux, 2022; Jackson, 2006; Phillips, 2020). The AChR neurotransmitter sites and gate are separated by ~60 Å, but both regions change structure and function within the gating isomerization, CLA⇄OHA. In adult-type AChRs and without any bound agonists, the probability of the O conformation (PO) is small (~10–6) so baseline current is negligible (Jackson, 1986; Nayak et al., 2012; Purohit and Auerbach, 2009). However, when both agonist sites are occupied by the neurotransmitter ACh, the increase in favorable binding free energy realized in the spontaneous isomerization from C to O increases PO substantially (to ~0.96), to generate a cellular current (Figure 1). Figure 1 Download asset Open asset Bind and gate. (A) Potential energy surfaces (landscapes) for receptor activation without (left) and with (right) bound agonists (A). C, closed channel and low affinity (LA); O, open channel and high affinity (HA). Red, without agonists intrinsic C→O gating is uphill; blue lines, agonists increase PO because they bind more favorably to O; green, agonists also stabilize the gating transition state (‡) to increase the opening rate constant because the LA→HA induced fit (‘hold’, Figure 2) occurs at the start of the gating conformational change. Calibration: ACh, adult-type AChRs, –100 mV, 23 °C. (B) Corresponding reaction scheme (2 equivalent and independent neurotransmitter sites). Horizontal, ligand-protein complex formation; KdC and KdO, LA and HA equilibrium dissociation constants to C and O. Vertical, global conformational change; Ln, gating equilibrium constant with n bound agonists. Red dashed line, pathway that determines the CRC when L0 is negligible (Equation 4a, Equation 4b, Equation 4c, Equation 4d, Methods). Our approach to investigating the AChR allosteric transition is to measure free energy changes associated with each component event. Energy and structure are related, and a longer term goal is to associate these energy changes with those in structure. Briefly, we use electrophysiology to compile a CRC and from the minimum, maximum and midpoint calculate the ligand’s LA and HA equilibrium dissociation constants for binding to C and to O (KdC and KdO). The logs of these are proportional to the LA and HA binding free energies, ΔGLA and ΔGHA. As shown below, agonist efficiency depends only on these two values. Previously, it was observed that in AChRs the ratio ΔGLA/ΔGHA is approximately the same for a group of agonists despite wide variation in individual affinities and efficacies (Jadey and Auerbach, 2012). Within this group, whose members included full agonists (like ACh), partial agonists (like nicotine) and extremely weak agonists (like choline), the binding free energy ratio was 0.51±0.01 (mean ± sd). That is, for all members in the group binding to O is ~two-fold stronger than to C, regardless of agonist affinity or efficacy. Additional experiments revealed two more groups having a shared binding energy ratio, either 0.58 (for example, epibatidine) or 0.46 (for example, tetramethylammonium) (Indurthi and Auerbach, 2021; Nayak et al., 2019). In these groups, agonist binding free energy increases by ~1.7 and ~2.2-fold in the channel-opening conformational change. The observation of a common ΔGLA/ΔGHA ratio for even one group of ligands is remarkable because it implies that the only 2 events in receptor activation that involve the agonist directly - LA binding to C and the switch to HA that happens within gating - are not independent. Rather, the constant ratio indicates that the energy (structure) changes that underpin LA binding to C and HA binding to O are related (Auerbach, 2016; Jadey and Auerbach, 2012; Purohit et al., 2014). Below, we define η empirically as the receptor’s output/input energy ratio, and propose that in AChRs it is the fraction of agonist binding energy applied to the mechanical work of a local rearrangement of the neurotransmitter site (an induced fit) that initiates the gating conformational cascade (Nayak et al., 2019). In principle, η can be calculated from any CRC to estimate the degree of coupling between the maxima of receptor output (efficacy) and agonist input (affinity). We measured η for various agonists of AChRs, both wild-type (wt) and following mutation of a binding site residue. Here we report 16 new values (shown in Tables 1 and 2) that, combined with 60 previous measurement describe a spectrum of 5 η classes. The presence of multiple η classes obscures the underlying correlations between affinity and efficacy and, further, suggests that there are multiple C versus O binding site structures. Importantly, the existence of efficiency classes highlights that binding and gating are energy-linked stages of a unified allosteric transition. Table 1 Agonist efficiencies. AgonistEC50 (μM) (sem)POmax (sem)KdC (μM)KdO (nM)cηnBzTMAa1070 (200)0.60 (0.05)930 (112.0)650 (90)1424 (18.0)0.51 (0.01)3Decb190 (20)0.79 (0.03)90 (7.0)140 (10)643 (21)0.41 (0.01)4SCha20 (3)0.84 (0.02)50 (4.0)20 (1)3177 (118)0.45 (0.01)3BzTEAb+c2 (0.1)0.85 (0.02)0.80 (0.3)3 (2)316 (14)0.29 (0.00)3TriMAab16000 (1200)0.78 (0.03)7720 (316.0)12580 (670)615 (18)0.57 (0.01)4 Mean EC50 and Pomax were measured from each CRC (intra-cluster interval duration histograms in Figure 3 and Figure 3—figure supplement 1), with standard error of mean (sem). KdC and KdO were calculated (Equation 4a) after correcting the background mutations that only changed L0. c, coupling constant (KdC/KdO); η, efficiency (Equation 2); error estimates (calculated by error propagation) for calculated KdC, KdO, c, and η values given by (sem); n, number of CRCs. Membrane potential, +70 mV (to minimize channel block by the agonist). Agonist structures are in Figure 5; superscripts indicate mutation backgrounds: aεS450W, bεL269F, cεE181W that increase Lo (increase responses to weak agonists). Table 1—source data 1 Agonist efficiency. https://cdn.elifesciences.org/articles/86496/elife-86496-table1-data1-v1.docx Download elife-86496-table1-data1-v1.docx Results Background and definitions The standard conception of receptor activation incorporates two seemingly disparate events – bind, the formation of a ligand-protein complex, and gate, the global isomerization of the protein (Figure 1). However, as described below (Figure 2), in AChRs these are composite reactions and are connected by a pair of local, induced-fit rearrangements of the agonist site (Jadey and Auerbach, 2012). For clarity, we define the universal agonist attributes affinity and efficacy. Affinity is the strength at which the ligand binds to its target site. In receptors ligands have two affinities, weak binding to C and strong binding to O (Figure 1). The corresponding binding free energies, ΔGLA and ΔGHA, are calculated (in kcal/mol) as +RT times the natural logarithms of the apparent KdC and KdO, where R is the gas constant and T is the absolute temperature (RT = 0.59 at 23 °C). For ACh at adult-type binding sites KdC = 174 μM (ΔGLA = −5.1 kcal/mol) and KdO = 29 nM (ΔGHA = −10.2 kcal/mol) (Jadey and Auerbach, 2012). Efficacy can be defined in several ways. Oftern, it is simply the high-concentration asymptote (maximum response) of an unnormalized CRC that in our experiments is POmax. Considering just bind and gate (Figure 1B), this limit depends only on the fully-liganded gating equilibrium constant L2 so this constant, too, defines agonist efficacy (Equation 4a). Another definition derives from considering the full cycle of receptor activation. In adult-type AChRs the 2 neurotransmitter binding sites are approximately equivalent and independent and there is no significant input of external energy (Nayak and Auerbach, 2017), so (1) L2L0=(KdCKdO)2. The subscripts of the gating equilibrium constants (L) refer to the number of bound agonists, and the equilibrium dissociation constant (Kd) ratio is called the coupling constant (c). L0 is agonist-independent so differences in L2 (efficacy) among agonists depend only on differences in c. Below, we use the logarithm of c (λ) as the index of relative agonist efficacy, λ=ΔGHA−ΔGLA. The relative efficacy of an agonist depends only on the difference between binding free energies, O minus C (blue lines in Figure 1A). Unlike voltage and mechanical stimuli, a small, thermalized ligand can deliver only a small force to a large receptor (Howard, 2001). The tiny momentum imparted to the protein by the ligand is obscured by those from collisions with water molecules. In the absence of external energy, agonists promote conformational change only by providing more favorable (stabilizing) binding energy to active compared to resting states that interconvert spontaneously. This mechanism likely pertains to all large receptors activated by small agonists. Efficiency (η) is defined empirically as the maximum output/input energy ratio (the efficacy/high-affinity energy ratio), (2) η=(ΔGHA−ΔGLA)ΔGHA=1−ΔGLAΔGHA Accordingly, the agonist-dependent free energy changes in gating (ΔGHA-ΔGLA) and in binding (ΔGHA) are interchangeable, with η as the conversion factor. Again, efficacy (λ) is a free energy difference and the maximum amount the agonist can deliver to the receptor’s gating machinery, and efficiency (η) is a free energy ratio and this amount normalized by the ligand’s maximum binding energy. With regard to equilibrium dissociation constants, λ relates to log(KdC/KdO) and η relates to log(KdC)/log(KdO) (Figure 7—figure supplement 2). Insofar as KdC and KdO are universal agonist attributes, so too is η. We propse that an η value can be calculated from the ratio of logarithms of the 2 dissociation constants for every agonist of every receptor. However, this does not imply that η has a physical meaning, or that all agonists of a given receptor have the same (or a unique) η value. Equation 2 only shows how to calculate η from the 2 equilibrium dissociation constants. We estimated these from CRCs, but other experimental approaches would suffice. In AChRs η does have a physiochemical meaning because KdC and KdO derive mainly from a pair of induced fit rearrangements at the ligand site. Although bind and gate are usually denoted as single-step events (Figure 1B), in AChRs both are composite reactions that harbor intermediate states that are too short-lived to be detected individually and directly (Figure 2, Figure 2—figure supplement 1). Induced fits are common, and undetected intermediate states have been invoked with great success previosuly to explain experimental results (for example ES in enzymology, and AC in pharmacology). Here, we invoke intermediate states to deconstruct η (Figure 2). Figure 2 with 1 supplement see all Download asset Open asset Catch and hold. (A) Bind and gate. Top, as one-step reactions. Bottom, as composite reactions. The main undetected intermediate states are AC, the encounter complex (inside bind) and ACHA, a high affinity and closed channel state (inside gate). Black, the 2 agonist-dependent induced fit rearrangements are called catch (AC⇄ACLA) and hold (ACLA⇄ACHA). Gray, diffusion (A+C⇄AC) and receptor conformation changes in other domains (ACHA⇄AO) are approximately agonist-independent (see Figure 2—figure supplement 1). (B) Catch-and-hold free energy landscape. An η class indicates that catch and hold are linked in a linear free energy relationship (LFER; dashed lines) (Howard, 2001). Green, weak agonist; black, strong agonist. Ligand binding energy ‘tilts’ the entire landscape to the total extent ΔGHA (black side bars). For relative agonist actions, the energy change in catch (brown) determines KdC and the energy change in hold (blue) determines the coupling constant. η is the fraction of the total applied to hold (blue/black) and η is the fraction apllied to catch (brown/black). Agonist affinity and efficacy differ substantially, but η is constant and depends only on the left-right position of ACLA in the reaction. In bind, the agonist diffuses to the target and forms an encounter complex (Held et al., 2011; Homans, 2007; Schiebel et al., 2018), after which a local rearrangement (an induced fit) called ‘catch’ establishes the LA complex (A+C⇄AC⇄ACLA). Gate starts with the second stage of the induced fit called ‘hold’ that increases agonist affinity and, after several additional conformational changes in other protein domains, terminates with rearrangements in the pore that allow water and ions to pass (ACLA⇄ACHA⇄…⇄AOHA). According to Equation 2, efficiency depends only on the LA/HA binding energy ratio. Regarding ΔGLA (proportional to logKdC), the agonists we examined (Figure 5) all have approximately the same diffusion constant so we surmise that differences are caused by differences in catch. Regarding ΔGHA (proportional to logKdO), the effect of perturbations away from the neurotransmitter site are agonist independent so we surmise that differences are caused by differences in hold. Therefore, with regard to η and the CRC (red pathway, Figure 1B), we attribute differences between agonists exclusively to differences in the free energy changes associated with induced fit that is the pair of catch-hold rearrangements, AC⇄ACLA⇄ACHA (Figure 2B). The energy change in catch is ΔGLA and in hold is ΔGHA-ΔGLA. Their sum, ΔGHA, is the total free energy change delivered by the ligand. From Equation 2, η is the fraction of this total associated with hold, and 1-η is the fraction associated with catch. Multiplying η by 100% gives the percent of agonist binding energy used for the hold rearrangement of the binding site that jumpstarts the full conformational cascade that connects the neurotransmitter sites with the gate. In a pure binding reaction, for example A+C⇄AC, Kd is the concentration where the energy gained from formation of the complex is equal to the entropy lost from removing a ligand from solution that is indexed to a reference concentration (Phillips, 2020). However, if KdC and KdO are dominated by free energy changes associated with the induced fit, the entropy components become negligible (Figure 7—figure supplement 1). As far as comparative agonist action in AChRs is concerned, it appears that only the energy changes in catch and hold are germane. EC50 and Pomax were measured from each CRC (intra-cluster interval duration histograms in Figure 3 and Figure 3—figure supplement 1). KdC and KdO were calculated (Equation 4a, Equation 4b, Equation 4c, Equation 4d) after correction for background mutations that only changed L0. c, coupling constant (KdC/KdO); η, efficiency (Equation 2); n, number of CRCs. Membrane potential,+70 mV (to minimize channel block by the agonist). Agonist structures are in Figure 5; superscripts indicate mutation backgrounds: aεS450W, bεL269F, cεE181W that increase Lo (to increase responses to weak agonists). Figure 3 with 1 supplement see all Download asset Open asset Measuring η. (A) Top, single-channel current traces, low time-resolution (Vm = +70 mV; O is up). Clusters of openings are bind-and-gate (Figure 1B); silent periods between clusters are desensitized. Bottom, example clusters and intra-cluster interval duration histograms. PO was calculated at each [agonist] from shut- and open-interval time constants. (B) CRCs. PO values were fitted to estimate POmax and EC50 from which KdC and KdO were calculated (Equation 4a, Equation 4b, Equation 4c, Equation 4d, Materials and methods) (Table 1). The logs of these constants are proportional to ΔGLA and ΔGHA, the ratio of which gives η (Equation 2). The profile for SCh is relatively left-shifted because this agonist is ~10% more efficient than Dec. Symbols are mean ± sem (Table 1; see Figure 3—figure supplement 1 for other agonists). The background mutation εS450W compensates for the effects of depolarization on the gating rate constants (see Materials and methods). Agonists We measured η for 5 agonists using adult-type AChRs with wt neurotransmitter binding sites (Table 1). For each, single-channel currents were recorded at different agonist concentrations and PO values calculated from shut and open interval durations were compiled into a CRC. L0 was known a priori (Nayak et al., 2012) so KdC and KdO could be calculated from EC50 and POmax by using Equation 4a, Equation 4b, Equation 4c, Equation 4d (Materials and methods). As shown elsewhere, CRCs compiled from whole-cell currents serve equally well for η estimation (Indurthi and Auerbach, 2021). Figure 3 shows CRCs for succinylcholine (SCh) and decamethonium (Dec). Although POmax is similar for both agonists, EC50 (potency) of SCh is substantially lower than that of Dec. For each agonist, ΔGLA and ΔGHA were calculated to yield an η value (Equation 2) with the result were ηSCh=0.45 and ηDec=0.41 (Table 1). SCh is 10% more efficient than Dec, which is the root cause of the abovementioned mismatch between POmax and potency. Figure 3 shows that given L0, agonist efficiency can be calculated from a single CRC. Because η is the ratio of two logarithms, error arising from errors in POmax and EC50 are small (Indurthi and Auerbach, 2021). CRCs for the other agonists are in Figure 3—figure supplement 1. η values were calculated from previous measurements of KdC for 18 other agonists (Figure 4A). An x-means cluster analysis of all 23 agonist η values indicates that there are 5 groups (mean ± sd): 0.32±0.035, 0.41±0.005, 0.45±0.014, 0.51±.008 and 0.55±0.015. Figure 4 Download asset Open asset Agonist h values. (A) Each symbol represents the average η value of one agonist, calculated from CRCs (Table 1, Table 1—source data 1). The x-means algorithm was used to separate the agonists into 5 η classes (mean, vertical bars are ± sd). (B) Efficiency plots. Each color group was fitted by a straight line (Equation 3) with L0=5.2 × 10–7 (sd of each point smaller than the symbol). η values calculated from the slopes are the same as in panel A. x- and y-axes are proportional to the agonist’s free energy changes in catch (ΔGLA) and hold (ΔGHA-ΔGLA) induced fits (Figure 2B). Figure 5 shows the agonists grouped by η class. The neurotransmitter ACh, its breakdown product choline (Cho), and the partial agonists carbamylcholine (CCh) and nicotine all belong to the η~0.51 class. The second-most common class, η~0.41, includes ligands that have a bridge nitrogen (for instance, Ebt) as well as others that do not (for instance, Dec and TMP). Small ligands (for instance, TriMA, MW 60) appear to be the most efficient and large rigid ligands (for instance, varenicline, MW 211) appear to be the least efficient. Figure 5 Download asset Open asset Agonists grouped by η class (Figure 4). See Materials and methods for abbreviations. It is valuable to combine Equations 1 and 2 (Nayak et al., 2019), (3) logL2=logL0+mlog(1KdC)η=m(m+2). Equation 3 describes an ‘efficiency’ plot, log affinity (1/KdC) versus log relative efficacy (L2). An average η for a group of agonists is estimated from the slope of the straight line fit (m). Equation 4a, Equation 4b, Equation 4c, Equation 4d converts readily measured CRC parameters (POmax and EC50) into equilibrium constants (KdC and L2), and Equation 3 converts these into fundamental constants that pertain to the agonist (η) and the receptor (L0). The value of the efficiency plot is that it increases the accuracy of the η estimates because if L0 is known a priori, the y-intercept can be added as a fixed point to all lines. For receptors in which L0 has not been measured, the efficiency plot offers a convenient way to do so, as was done previously for glutamate, GABA, glycine, and muscarinic recepotors (Nayak et al., 2019). η values estimated from the slopes for the 23 agonists (Figure 4B) are the same as from the x-mean cluster analysis (Figure 4A) (mean ± sd): 0.31±0.018, 0.41±0.002, 0.46±0.004, 0.51±0.002 and 0.56±0.008. Affinity and efficacy are correlated significantly within the 3 classes having >2 members (19 agonists; Pearson’s correlation test p-values for η=0.41 and 0.51,<0.0001, and for η=0.56, 0.019). Also, the slopes for these 3 classes are significantly different (ANCOVA, F=129, p<0.001). The association of the other agonists with a discrete η class is less certain but supported by mutation studies (see below). Figure 4B shows that in AChRs, agonists having the same affinity can have different efficacies, and vice versa. Absent classification of an agonist according to η (imagine all symbols the same color) there is no global correlation between these two agonist properties. However, a linear correlation between log affinity and log efficacy is clear within each class. In AChRs, the presence of multiple η classes precludes a global correlation between efficacy and affinity. Mutations η values were measured previously in adult-type AChRs having one of 42 binding site mutations (Table 2—source data 1). To these we add 11 more (Table 2), for 5 agonists and 3 binding-site mutations (αD200A, αK145A, αG153S; Figure 6, Figure 6—figure supplement 1, Figure 6—figure supplement 2). Figure 6 with 2 supplements see all Download asset Open asset CRCs for αK145A and αD200A. Agonist efficiencies calculated from the fitted CRC parameters are in Table 2. Intra-cluster interval histograms are in Figure 6—figure supplement 1 and Figure 6—figure supplement 2. Inset, α−δ subunit interface of an AChR neurotransmitter binding site occupied by CCh (cyan) (7QL6.pdb; Zarkadas et al., 2022). Table 2 Mutation efficiencies. mutationagonistEC50 (μM)POmaxKdC (μM)KdO (nM)cηmutnηwtD200AACha110 (10)0.80 (0.02)52 (3)150 (5)338 (12)0.37 (0.00)30.50CCha320 (70)0.45 (0.02)138 (18)900 (94)153 (3)0.36 (0.00)40.52TMAa13350 (2970)0.37 (0.03)5683 (740)4382 (890)130 (5)0.48 (0.02)50.54Ebta+b110 (20)0.50 (0.03)47 (5)680 (47)69 (2)0.30 (0.00)30.41Ebxa+b060 (20)0.49 (0.03)26 (5)390 (58)67 (3)0.29 (0.01)30.46K145AACha110 (10)0.95 (0.06)83 (5)50 (3)1672 (82)0.44 (0.00)3-CCha380 (30)0.53 (0.01)165 (8)400 (15)411 (5)0.41 (0.00)4-TMAa2200 (590)0.19 (0.01)922 (125)5010 (712)184 (3)0.43 (0.01)3-Ebta40 (4)0.81 (0.04)20 (2)20 (2)792 (52)0.38 (0.00)3-Ebxa40 (3)0.83 (0.01)20 (1)20 (1)847 (17)0.38 (0.00)3-G153SEbta2 (0.5)0.65 (0.01)2 (0.3)7 (1)313 (4)0.31 (0.01)4- Measured EC50 & Pomax and calculated Kd, c and h, mean (sem). For mutation location see Figure 6, inset. KdC and KdO were calculated from CRC parameters (Figure 6) by using Eq. 4. c, coupling constant (KdC/KdO); n, number of CRCs. L0 was corrected for background mutations (Methods): aεS450W, bεL269F, cεE181W. See Figure 6—figure supplement 1 and Figure 6—figure supplement 2 for intra-cluster interval duration histograms. Table 2—source data 1 Mutation efficiency. https://cdn.elifesciences.org/articles/86496/elife-86496-table2-data1-v1.docx Download elife-86496-table2-data1-v1.docx αD200 and αK145, along with αY190, have been suggested to work together to initiate the channel-opening conformational change (Mukhtasimova et al., 2005). The mutation αY190A reduces ηACh from 0.50 to 0.35, but αY190F is without effect (Bruhova and Auerbach, 2017). However, αY190F does cause substantial losses in LA and HA binding energies for ACh, to an extent that depends on the αK145 side chain (Bruhova and Auerbach, 2017). These results support the suggestion that these side chains work together, but exactly how and to what effect remains unclear. The agonists we tested with αD200A or αK145A were from 4 different η classes (wt class value): TMA (0.54), CCh (0.51), ACh (0.50), Ebx (0.46), and Ebt (0.41). For mutation location see Figure 6, inset. KdC and KdO were calculated from CRC parameters (Figure 6) by using Equation 4c, coupling constant (KdC/KdO); n, number of CRCs. L0 was corrected for background mutations (Materials and methods): aεS450W, bεL269F, cεE181W. See Figure 6—figure supplement 1 and Figure 6—figure supplement 2 for intra-cluster interval duration histograms. The results are in Table 2. The substitution αD200A reduces the efficiency of 4 of the 5 agonists to 0.33±0.04 (mean ± sd), or to about the same level as with αY190A with ACh. The exceptional ligand was TMA for which η remained relatively high at 0.48. In contrast, in αK145A η for all 5 agonists was 0.41±0.03. Interestingly, this mutation reduces η for ACh, CCh and TMA but not significantly for Ebx and Ebt. Note that the mutation αW149A increases η for ACh to 0.60 (Purohit et al., 2014). Table 2—source data 1 shows KdC and KdO values measured previously for 4 different agonists in AChRs having a substitution at αG153 (Jadey et al., 2013), including a Ser that causes a congenital myasthenic syndrome (Engel et al., 1982). After converting the published equilibrium constants to ΔGLA and ΔGHA, the results indicate that on average the mutations reduce η for Cho, DMP, TMA and nicotine to 0.41±0.03 (mean ± sd), or by ~25%. To these we add the new result that αG153S reduces η of Ebt from 0.42 to 0.31, or also by ~25%. Figure 7A shows x-means cluster analysis of efficiencies for 53 AChR binding site mutations. Mutant η values (mean ± sd) segregate into the same 5 η classes that were apparent with agonists: 0.33±0.03, 0.40±0.02, 0.44±0.01, 0.49±0.01 and 0.56±0.03. The η classes that were under-represented and poorly defined with agonists (Figure 4B) are more common and clearer with mutations. Figure 7 with 2 supplements see all Download asset Open asset Mutation η values. Each symbol is η value calculated for an individual mutation (various agonists; Table 2 and Table 2—source data 1). (A) There are 5 efficiency classes (mean ± sd). As with agonists (Figure 4), the ~0.5 and~0.4 classes predominate. (B) Weak (LA) versus strong (HA) binding energies for the mutants (sd of each point is smaller than the symbol). The 5 slopes reflect different correlations between catch and hold free energy changes (Figure 2B). The efficiency distribution for the mutations (η=1-slope; see text for mean ± sd) is the same as for agonists. C. Spectrum of efficiencies, adult AChR neurotransmitter binding sites. The width of each line is proportional to prevalence (agonists and mutations). With mutations, an η-plot is not useful because substitutions can change L0 (see Figure 7—figure supplement 1) and, hence, the y-intercept of each line. Instead, a plot of ΔGLA versus ΔGHA shows the binding energy correlations directly. This plot for mutations (Figure 7B) shows 5 slopes (Pearson’s correlation test p-value <0.0006 for all classes). The distribution of η values (1-slope) is (mean ± sd): 0.32±0.007, 0.40±0.004, 0.44±0.003, 0.49±0.002

Ionotropic glutamate receptors are ligand-gated cation channels that play essential roles in the excitatory synaptic transmission throughout the central nervous system. A number of open-pore structures of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic-acid (AMPA)-type glutamate receptors became available recently by cryo-electron microscopy (cryo-EM). These structures provide valuable insights into the conformation of the selectivity filter (SF), the part of the ion channel that determines the ion selectivity. Nonetheless, due to the moderate resolution of the cryo-EM structures, detailed information such as ion occupancy of monovalent and divalent cations as well as positioning of the side-chains in the SF is still missing. Here, in an attempt to obtain high-resolution information about glutamate receptor SFs, we incorporated partial SF sequences of the AMPA and kainate receptors into the bacterial tetrameric cation channel NaK, which served as a structural scaffold. We determined a series of X-ray structures of NaK-CDI, NaK-SDI and NaK-SELM mutants at 1.42-2.10 Å resolution, showing distinct ion occupation of different monovalent cations. Molecular dynamics (MD) simulations of NaK-CDI indicated the channel to be conductive to monovalent cations, which agrees well with our electrophysiology recordings. Moreover, previously unobserved structural asymmetry of the SF was revealed by the X-ray structures and MD simulations, implying its importance in ion non-selectivity of tetrameric cation channels.

AMPA-type glutamate receptors (AMPARs) mediate fast excitatory neurotransmission, and the plastic modulation of their surface levels determines synaptic strength. AMPARs of different subunit compositions fulfill distinct roles in synaptic long-term potentiation (LTP) and depression (LTD) to enable learning. Largely unknown endocytic mechanisms mediate the subunit-selective regulation of the surface levels of GluA1-homomeric Ca 2+ -permeable (CP) versus heteromeric Ca 2+ -impermeable (CI) AMPARs. Here, we report that the Alzheimer’s disease risk factor CALM controls the surface levels of CP-AMPARs and thereby reciprocally regulates LTP and LTD in vivo to modulate learning. We show that CALM selectively facilitates the endocytosis of ubiquitinated CP-AMPARs via a mechanism that depends on ubiquitin recognition by its ANTH domain but is independent of clathrin. Our data identify CALM and related ANTH domain–containing proteins as the core endocytic machinery that determines the surface levels of CP-AMPARs to bidirectionally control synaptic plasticity and modulate learning in the mammalian brain.

Optical report of neurotransmitter release allows visualisation of excitatory synaptic transmission. Sensitive genetically-encoded fluorescent glutamate reporters operating with a range of affinities and emission wavelengths are available. However, without targeting to synapses, the specificity of the fluorescent signal is uncertain, compared to sensors directed at vesicles or other synaptic markers. We fused the state-of-the-art reporter iGluSnFR to glutamate receptor auxiliary proteins in order to target it to postsynaptic sites. Chimeras of Stargazin and gamma-8 that we named SnFR-γ2 and SnFR-γ8, were enriched at synapses, retained function and reported spontaneous glutamate release in rat hippocampal cells, with apparently diffraction-limited spatial precision. In autaptic mouse neurons cultured on astrocytic microislands, evoked neurotransmitter release could be quantitatively detected at tens of synapses in a field of view whilst evoked currents were recorded simultaneously. These experiments revealed a specific postsynaptic deficit from Stargazin overexpression, resulting in synapses with normal neurotransmitter release but without postsynaptic responses. This defect was reverted by delaying overexpression. By working at different calcium concentrations, we determined that SnFR-γ2 is a linear reporter of the global quantal parameters and short-term synaptic plasticity, whereas iGluSnFR is not. On average, half of iGluSnFR regions of interest (ROIs) showing evoked fluorescence changes had intense rundown, whereas less than 5% of SnFR-γ2 ROIs did. We provide an open-source analysis suite for extracting quantal parameters including release probability from fluorescence time series of individual and grouped synaptic responses. Taken together, postsynaptic targeting improves several properties of iGluSnFR and further demonstrates the importance of subcellular targeting for optogenetic actuators and reporters.

Nine out of every ten synapses in the brain are excitatory. The glutamate receptor ion channels (iGluRs) that reside in these synapses are cornerstones of fast signaling in the brain. They convert the glutamate released by synaptic terminals into depolarization of their host neurons. Certain iGluRs participate in activity-dependent plasticity, thought to underlie learning and memory. The deployment of iGluRs throughout the nervous system implicates the glutamatergic system in a wide range of neurological and developmental disorders.

The α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) are neurotransmitter-activated cation channels ubiquitously expressed in vertebrate brains. The regulation of calcium flux through the channel pore by RNA-editing is linked to synaptic plasticity while excessive calcium influx poses a risk for neurodegeneration. Unfortunately, the molecular mechanisms underlying this key process are mostly unknown. Here, we investigated calcium conduction in calcium-permeable AMPAR using Molecular Dynamics (MD) simulations with recently introduced multisite force-field parameters for Ca2+. Our calculations are consistent with experiment and explain the distinct calcium permeability in different RNA-edited forms of GluA2. For one of the identified metal binding sites, multiscale Quantum Mechanics/Molecular Mechanics (QM/MM) simulations further validated the results from MD and revealed small but reproducible charge transfer between the metal ion and its first solvation shell. In addition, the ion occupancy derived from MD simulations independently reproduced the Ca2+ binding profile in an X-ray structure of an NaK channel mimicking the AMPAR selectivity filter. This integrated study comprising X-ray crystallography, multisite MD, and multiscale QM/MM simulations provides unprecedented insights into Ca2+ permeation mechanisms in AMPARs, and paves the way for studying other biological processes in which Ca2+ plays a pivotal role.

Using unnatural amino acid mutagenesis, we made a mutant of CaMKII that forms a covalent linkage to Calmodulin upon illumination by UV light. Like wild-type CaMKII, the L308BzF mutant stoichiometrically binds to Calmodulin, in a calcium-dependent manner. Using this construct, we demonstrate that Calmodulin binding to CaMKII, even under these stochiometric conditions, does not perturb the CaMKII oligomeric state. Furthermore, we were able to achieve activation of CaMKII L308BzF by UV-induced binding of Calmodulin, which, once established, is further insensitive to calcium depletion. In addition to the canonical auto-inhibitory role of the regulatory segment, inter-subunit crosslinking in the absence of CaM indicates that kinase domains and regulatory segments are substantially mobile in basal conditions. Characterization of CaMKIIL308BzF in vitro, and its expression in mammalian cells, suggests it could be a promising candidate for control of CaMKII activity in mammalian cells with light.

The α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPAR) are neurotransmitter-activated cation channels ubiquitously expressed across vertebrate brains. The regulation of calcium flux through the channel pore by RNA-editing is linked to synaptic plasticity while excessive calcium influx poses a risk of neurodegeneration. Unfortunately, the molecular mechanisms underlying this key process are still unknown. Here, Molecular Dynamics (MD) simulations employing recently reported multi-site force field parameters for Ca2+ revealed abundant calcium permeation through the unedited form of the GluA2 channel that is in good agreement with experimental data. An additional binding site of Ca2+ enabled by the side-chain of glutamine at the Q/R editing site provides an explanation of the calcium permeability difference in different RNA-edited forms of GluA2. For one of the identified metal binding sites, multi-scale Quantum Mechanics/ Molecular Mechanics (QM/MM) simulations reproduced the Ca2+ hydration statistics emerging from MD simulations. Critically, they also revealed small but reproducible charge transfer between the metal ion and its first solvation shell. The resulting polarization of the bound water likely impacts the hydrogen bond network of hydrated calcium ions in the channel. We suggest that the new Ca2+ parametrization paves the way to provide insight also into other proteins where calcium plays a pivotal role and that even more accurate predictions may be obtained by including electronic polarization in the simulations.

Channelrhodopsins are light-gated ion channels used to control excitability of designated cells in large networks with high spatiotemporal resolution. While ChRs selective for H + , Na + , K + and anions have been discovered or engineered, Ca 2+ -selective ChRs have not been reported to date. Here, we analyse ChRs and mutant derivatives with regard to their Ca 2+ permeability and improve their Ca 2+ affinity by targeted mutagenesis at the central selectivity filter. The engineered channels, termed CapChR1 and CapChR2 for ca lcium- p ermeable ch annel r hodopsins, exhibit reduced sodium and proton conductance in connection with strongly improved Ca 2+ permeation at negative voltage and low extracellular Ca 2+ concentrations. In cultured cells and neurons, CapChR2 reliably increases intracellular Ca 2+ concentrations. Moreover, CapChR2 can robustly trigger Ca 2+ signalling in hippocampal neurons. When expressed together with genetically encoded Ca 2+ indicators in Drosophila melanogaster mushroom body output neurons, CapChRs mediate light-evoked Ca 2+ entry in brain explants.

Glutamate is the major fast excitatory neurotransmitter in the nervous system, and researchers have for decades pursued tools for monitoring glutamate release in real time. Optical reporters, primarily genetically-encoded glutamate sensors, have developed into the predominant method, particularly following the advent of the intensity-based glutamate-sensing fluorescent reporter (iGluSnFR). Several variants have been developed to improve the performance of iGluSnFR in brightness, kinetics and spectral range. Following the targeting principle of other sensors for neurotransmission, iGluSnFR mutants tethered to pre- or postsynaptic targeting proteins should show enhanced properties for detecting glutamate release at central synapses.

The dodecameric protein kinase CaMKII is expressed throughout the body. The alpha isoform is responsible for synaptic plasticity and participates in memory through its phosphorylation of synaptic proteins. Its elaborate subunit organization and propensity for autophosphorylation allow it to preserve neuronal plasticity across space and time. The prevailing hypothesis for the spread of CaMKII activity, involving shuffling of subunits between activated and naïve holoenzymes, is broadly termed subunit exchange. In contrast to the expectations of previous work, we found little evidence for subunit exchange upon activation, and no effect of restraining subunits to their parent holoenzymes. Rather, mass photometry, crosslinking mass spectrometry, single molecule TIRF microscopy and biochemical assays identify inter-holoenzyme phosphorylation (IHP) as the mechanism for spreading phosphorylation. The transient, activity-dependent formation of groups of holoenzymes is well suited to the speed of neuronal activity. Our results place fundamental limits on the activation mechanism of this kinase.

BK channels are high-conductance potassium channels that are activated by voltage and Ca 2+ . The pore-forming α-subunits form homotetramers including a membrane-spanning domain and a cytosolic domain with a tandem of RCK-like domains (RCK1 and RCK2) per subunit. The eight RCKs compose high affinity Ca2+-binding sites that drive channel activation. Full-length Cryo-EM structures revealed intersubunit interactions between the RCK domains. Asparagine 449 (N449, human BK channel) is located at the RCK1 domain and coordinates Ca 2+ in the RCK2 of the adjacent subunit. In addition, two Arginines (R786 and R790) of one RCK2 and Glutamate 955 of the adjacent subunit constitute an additional interaction interface. Functional studies on these residues showed that these two interfaces are crucial in Ca 2+ sensitivity. To detect structural rearrangements induced by Ca 2+ during channel activation, we took advantage of the photoactivatable unnatural amino acid p-benzoyl-L-phenylalanine (BzF). Functional channels were obtained with this amino acid inserted at 11 positions. N449 and R786 positions (N449BzF and R786BzF respectively). UV-induced photocrosslinking led to Ca 2+ dependent and voltage-independent effects in both mutants. N449BzF showed a steady-state current reduction at saturating Ca 2+ concentrations. Our data shows that this effect mainly relies on full occupancy of the RCK1 Ca 2+ binding site, since mutation of this site abolished the effect. The R786BzF construct showed a substantial potentiation of the current in the absence of Ca 2+ . In this case, photocrosslinking seems to favor the activation of the channel by voltage. Overall, these results suggest mobile interfaces between RCK domains are key to BK channel activation.

Conotoxins are a large group of naturally occurring toxic peptides produced by the predatory sea snails of the genus Conus. Many of these toxins target ion channels, often with high specificity and affinity. As such, they have proven to be invaluable for basic research, as well as acting as leads for therapeutic strategies. Con-ikot-ikot is the only conotoxin so far identified that targets AMPA-type glutamate receptors, the main mediators of excitatory neurotransmission in the vertebrate brain. Here, we describe how the toxin modifies the activity of AMPA receptors at the single-channel level. The toxin binds to the AMPA receptor with EC50 of 5 nM, and once bound takes minutes to wash out. As shown previously, it effectively blocks desensitization of AMPA receptors; however, compared to other desensitization blockers, it is a poor stabilizer of the open channel because toxin-bound AMPA receptors undergo frequent brief closures. We propose that this is a direct consequence of the toxin's unique binding mode to the ligand-binding domains (LBDs). Unlike other blockers of desensitization, which stabilize individual dimers within an AMPA receptor tetramer, the toxin immobilizes all four LBDs of the tetramer. This result further emphasizes that quaternary reorganization of independent LBD dimers is essential for the full activity of AMPA receptors.

BK channels are high conductance potassium channels synergistically activated by voltage and Ca2+. Their molecular architecture consists of homotetramers of α-subunits containing a membrane-spanning domain and a cytosolic structure including tandems of RCK-like domains (RCK1 and RCK2). These RCKs contain distinct high affinity Ca2+-binding sites mediating channel activation (Horrigan and Aldrich, 2002; Sweet and Cox, 2008). Full-length Cryo-EM structures of the channel revealed intersubunit interactions between these RCKs (Tao et al., 2017; Tao et al., 2019).

Combining crosslinking strategies with electrophysiology, biochemistry, and structural in silico analysis is a powerful tool to study transient movements of ion channels during gating. This chapter describes crosslinking in living cells using cysteine and photoactive unnatural amino acids (UAAs) that we have used on glutamate receptor ion channels. Here, we share the protocol for building a perfusion tool to enable rapid chemical modification of glutamate-gated AMPA receptors, optimized for their fast activation. This system can be used to perform state-dependent crosslinking in receptors modified by cysteines or UAA incorporation on the millisecond timescale. Introducing UAAs results in receptors with lower expression levels relative to the introduction of cysteine residues. Reduced expression is principally a challenge for biochemical studies, and we share here our approach to capture the light driven oligomerization of AMPA receptors containing UAA crosslinkers. Finally, we describe strategies for computational analysis to make sense of the crosslinking results in terms of structure and function.