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• W4312837986 abstract "Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Methods Data availability References Decision letter Author response Article and author information Metrics Abstract N-methyl-D-aspartate receptors (NMDARs) uniquely require binding of two different neurotransmitter agonists for synaptic transmission. D-serine and glycine bind to one subunit, GluN1, while glutamate binds to the other, GluN2. These agonists bind to the receptor’s bi-lobed ligand-binding domains (LBDs), which close around the agonist during receptor activation. To better understand the unexplored mechanisms by which D-serine contributes to receptor activation, we performed multi-microsecond molecular dynamics simulations of the GluN1/GluN2A LBD dimer with free D-serine and glutamate agonists. Surprisingly, we observed D-serine binding to both GluN1 and GluN2A LBDs, suggesting that D-serine competes with glutamate for binding to GluN2A. This mechanism is confirmed by our electrophysiology experiments, which show that D-serine is indeed inhibitory at high concentrations. Although free energy calculations indicate that D-serine stabilizes the closed GluN2A LBD, its inhibitory behavior suggests that it either does not remain bound long enough or does not generate sufficient force for ion channel gating. We developed a workflow using pathway similarity analysis to identify groups of residues working together to promote binding. These conformation-dependent pathways were not significantly impacted by the presence of N-linked glycans, which act primarily by interacting with the LBD bottom lobe to stabilize the closed LBD. Editor's evaluation Activation of NMDA receptors requires two co-agonists: Glutamate that binds to the GluN2 subunit and glycine/D-serine that binds to the GluN1 subunit. In the present manuscript, the authors address the interaction of D-serine, which is a less studied co-agonist than glycine, with the GluN1 and GluN2A subunits using molecular simulations as well as electrophysiology experiments. Surprisingly they find that D-serine interacts with the GluN2 subunit, further expanding our molecular understanding of NMDA receptor structure-function. This paper will be of interest to those who study NMDA receptors and ligand-gated ion channels in general. https://doi.org/10.7554/eLife.77645.sa0 Decision letter Reviews on Sciety eLife's review process Introduction The N-methyl-D-aspartate receptor (NMDAR) is an ionotropic glutamate receptor (iGluR) that uniquely requires the binding of a co-agonist in addition to its primary agonist for activation (Hansen et al., 2021). This heterotetrameric ion channel comprises at least two different subunits, GluN1 (isoforms 1–4 a and 1-4b) and GluN2 (subtypes A-D), assembled as a dimer of GluN1/GluN2 heterodimers (Karakas and Furukawa, 2014; Lee et al., 2014). The GluN2 subunit binds the neurotransmitter glutamate, while the GluN1 subunit can either bind the co-agonists glycine or D-serine. Traditionally, glycine had been considered the major GluN1 agonist (Johnson and Ascher, 1987; Forsythe et al., 1988; Kleckner and Dingledine, 1988), but more recent work has suggested that D-serine may in fact be the dominant co-agonist for synaptic NMDARs in the brain (Papouin et al., 2012). D-serine is synthesized by the enzyme serine racemase expressed in astroglia (Wolosker et al., 1999) and neurons (Miya et al., 2008; Balu et al., 2014) and is released into the postsynapse by the Asc-1 transporter (Rutter et al., 2007; Coyle et al., 2020). D-serine binding to these synaptic NMDARs is responsible for inducing long-term potentiation (LTP), which is critical for memory functions (Henneberger et al., 2010). In addition, recent clinical efforts have indicated that D-serine could be a promising therapeutic for the treatment of neuropsychiatric disorders (Peyrovian et al., 2019; MacKay et al., 2019), most notably schizophrenia (Kantrowitz et al., 2010) and post-traumatic stress disorder (PTSD) (Heresco-Levy et al., 2005). Unlike the more well-studied agonists glutamate and glycine, the role of D-serine is less defined, causing it to be known as the ‘shape-shifting’ agonist (Coyle et al., 2020) that can adopt different roles in neurotransmission. Each NMDAR subunit consists of an amino-terminal domain (ATD), a ligand-binding domain (LBD; also called an agonist-binding domain, ABD), a transmembrane domain (TMD), and a disordered cytoplasmic C-terminal domain (Mayer, 2017). The LBDs adopt a bi-lobed clamshell architecture that closes upon agonist binding (Yao et al., 2013; Jespersen et al., 2014). The conformational transitions of the LBDs from open to closed clamshells result in the generation of tension in the LBD-TMD linkers, which in turn facilitates gating of the TMD channel (Tajima et al., 2016; Chou et al., 2020). The ATDs allosterically regulate channel activities in a subtype-dependent manner via distinct interactions with the LBDs (Yuan et al., 2009; Gielen et al., 2009; Karakas et al., 2011; Tajima et al., 2022). Therefore, the LBDs can be considered the fundamental vehicles for driving ligand gating. Previous computational studies of NMDAR LBDs have indicated that glycine binding to the GluN1 LBD and glutamate binding to the GluN2A LBD drives the conformational equilibrium toward the closed LBD (Yao et al., 2013). While crystallographic studies have determined the binding pose of D-serine in the closed GluN1 LBD (Furukawa and Gouaux, 2003), the molecular mechanisms by which D-serine finds its way into and stabilizes NMDAR LBDs are not well understood. Previous simulation studies have revealed the mechanisms by which glycine and glutamate diffuse into the LBD binding site (Yu and Lau, 2018). Specifically, they found that glycine binds to the GluN1 subunit by freely diffusing into the binding pocket, where it is trapped by energetically favorable interactions with key binding site residues. Glutamate, on the other hand, was found to contact residues along the protein surface that helped guide itself into its binding pocket, positioning it to interact stably with residues in the binding site. These two binding mechanisms were referred to as ‘unguided’ and ‘guided’ diffusion, respectively (Yu et al., 2018). This paradigm established the two extremes by which ligands enter their receptor sites: one in which stable ligand binding only depends upon the identity of the binding site residues and another that also heavily relies on residues outside the binding site to guide the ligand toward its bound pose. Performing multi-microsecond molecular dynamics simulations of the glycosylated GluN1/GluN2A LBD dimer, we identified binding mechanisms and residues critical for promoting D-serine binding and stabilization by developing a new binding pathway clustering workflow. Surprisingly, we observed D-serine binding to both GluN1 and GluN2A LBDs. We determined that D-serine binding to GluN2A partially stabilizes the active LBD conformation. Inspired by these simulation results, we determined that D-serine competes with glutamate for binding to GluN2A via a competitive inhibition mechanism using electrophysiology measurements, where D-serine was found to be inhibitory at high concentrations. Since NMDAR LBDs are glycosylated under physiological conditions (Kaniakova et al., 2016), including N-linked glycans in our simulations revealed that glycans primarily regulate the binding process by stabilizing the active LBD. In total, we investigated the molecular components contributing to D-serine binding and stabilization, highlighting the complex components driving neurotransmission. Results D-serine binding pathways for GluN2A and GluN1 LBDs In simulating the GluN1/GluN2A LBD dimer, which is a physiological NMDAR unit, we intended to focus our attention on the mechanisms by which D-serine binds to the GluN1 LBD, the subunit to which D-serine is a potent agonist. However, in our simulations, we also observed a significant number of D-serine binding events involving the GluN2A LBD, an unexpected finding. Full binding includes both ligand association and LBD closure (Lau and Roux, 2011). Here, binding and unbinding refer only to ligand association and dissociation, respectively. We observe D-serine binding and unbinding multiple times throughout the trajectory (Figure 1—source data 2, Figure 2—source data 1). These binding events are primarily made up of guided-diffusion pathways in which D-serine contacts key residues on the LBD surface to help guide it into or out of the binding cleft. In our aggregate ~51 μs of sampling of the glycosylated GluN1/GluN2A LBD dimer, we identified 99 guided-diffusion pathways for GluN2A and 104 (plus 23 free diffusion events) for GluN1. Due to the stochastic nature of these pathways, we needed to develop a reliable way to identify key features of predominant binding pathways. To address this, we applied pathway similarity analysis (PSA) (Seyler et al., 2015) to quantify the spatial and geometric similarity between pairs of paths (Figure 1A, Video 1). Here, we extend this application to ligand binding pathways by monitoring the change in ligand Cα position throughout each path. This allowed us to cluster paths traversing similar regions of the LBD surface. To aid in describing the different faces of the LBD, we use an order parameter (ξ1,ξ2) defined in previous work (Yao et al., 2013) to describe whether D-serine primarily contacts residues on the ξ1 or ξ2 face of the LBD (Figures 1B and 2A). For GluN2A, cluster analysis revealed four distinct regions of D-serine occupancy. The clusters correspond to the following methods of binding: 1. D-serine approaches the binding pocket from the ξ2 face; 2. D-serine contacts the D1 residues on the ξ1 face; 3. D-serine zigzags between D1 and D2 lobes on the ξ1 face; 4. D-serine primarily contacts residues on the D2 lobe of the ξ1 face (Figure 1C–F). Similarly, for GluN1, cluster analysis revealed four distinct clusters corresponding to similar pathways of binding: 1. D-serine contacts the ξ2 face; 2. D-serine zigzags between D1 and D2 lobes on the ξ1 face; 3. D-serine contacts residues on the N-terminal (top) end of D1 of the ξ1 face; 4. D-serine contacts residues of D1 loop 2 that protrudes from the LBD into solution. We then analyzed the resulting clusters to identify key residues that guide D-serine into the binding site (Figure 2B–E, Video 2). Interestingly, we observed that GluN1 pathways involve fewer interactions between D-serine and D2 residues; most notably, there were fewer contacts with Helix F (Helix E for GluN2A) compared to GluN2A pathways. Figure 1 with 4 supplements see all Download asset Open asset Identifying D-serine binding pathways for GluN2A using pathway similarity analysis (PSA). (A) Overview of the PSA workflow for quantifying similarity between D-serine binding pathways. (B) 2-dimensional order parameter (ξ1,ξ2) that describes the degree of GluN2A LBD closure. For each of the above (C–F), the left image shows D-serine density, while the right image shows the residues most frequently contacted by D-serine as it enters/leaves the binding site for each cluster. Labeled residues demonstrate ≥ 0.2 fractional occurrence defined relative to the most contacted residue in each cluster, but all residues with ≥ 0.1 fractional occurrence are shown in stick representation (see Figure 1—source data 3). (C) Cluster 1 involves residues of the ξ2 face of the LBD. (D) Cluster 2 involves residues of the ξ1 face of the D1 lobe. (E) In Cluster 3, D-serine zigzags between D1 and D2 lobe residues of the ξ1 face. (F) Cluster 4 primarily involves D2 lobe residues on the ξ1 face. Figure 1—source data 1 Simulation summary: overview of simulation systems. https://cdn.elifesciences.org/articles/77645/elife-77645-fig1-data1-v1.xlsx Download elife-77645-fig1-data1-v1.xlsx Figure 1—source data 2 Record of all successful binding pathways in each simulation system for D-serine binding to GluN2A. https://cdn.elifesciences.org/articles/77645/elife-77645-fig1-data2-v1.xlsx Download elife-77645-fig1-data2-v1.xlsx Figure 1—source data 3 Per-residue contact frequency analysis for D-serine binding to GluN2A by cluster identified with PSA. https://cdn.elifesciences.org/articles/77645/elife-77645-fig1-data3-v1.xlsx Download elife-77645-fig1-data3-v1.xlsx Figure 1—source data 4 GluN2A residues most frequently contacted by D-serine given that the pathway results in successful binding – listed for each simulation system. https://cdn.elifesciences.org/articles/77645/elife-77645-fig1-data4-v1.xlsx Download elife-77645-fig1-data4-v1.xlsx Video 1 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Process of D-serine binding to the GluN2A LBD. Video 2 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Process of D-serine binding to the GluN1 LBD. Figure 2 Download asset Open asset Identifying D-serine binding pathways for GluN1 using pathway similarity analysis (PSA). (A) 2-dimensional order parameter (ξ1,ξ2) that describes the degree of GluN1 LBD closure. For each of the above (B–E), the left image shows D-serine density, while the right image shows the residues most frequently contacted by D-serine as it enters/leaves the binding site for each cluster. Labeled residues demonstrate ≥ 0.2 fractional occurrence defined relative to the most contacted residue in each cluster, but all residues with ≥ 0.1 fractional occurrence are shown in stick representation (see Figure 2—source data 2). (B) In Cluster 1, D-Serine contacts residues on the ξ2 face of the LBD. (C) Cluster 2 involves interactions with both D1 and D2 residues of the ξ1 face. (D) Cluster 3 involves contacts with residues at the top of the D1 lobe on the ξ1 face. (E) Cluster 4 is defined by interactions with D1 loop 2 that reaches into solution. Figure 2—source data 1 Record of all successful binding pathways in each simulation system for D-serine binding to GluN1. https://cdn.elifesciences.org/articles/77645/elife-77645-fig2-data1-v1.xlsx Download elife-77645-fig2-data1-v1.xlsx Figure 2—source data 2 Per residue contact frequency analysis for D-serine binding to GluN1 by cluster identified with PSA. https://cdn.elifesciences.org/articles/77645/elife-77645-fig2-data2-v1.xlsx Download elife-77645-fig2-data2-v1.xlsx Figure 2—source data 3 GluN1 residues most frequently contacted by D-serine given that the pathway results in successful binding – listed for each simulation system. https://cdn.elifesciences.org/articles/77645/elife-77645-fig2-data3-v1.xlsx Download elife-77645-fig2-data3-v1.xlsx To quantify the extent to which these clusters involve similar residue contacts, we used a pairwise similarity metric called the overlap coefficient (i.e., Szymkiewicz–Simpson coefficient) that describes agreement between sets of residues (Vijaymeena and Kavitha, 2016). Doing so provides a way to determine whether these spatial clusters are mostly made up of random contacts, or whether groups of residues tend to act together to promote binding, allowing us to quantify the extent to which agonist diffusion is ‘guided’ by contacts along the LBD. For GluN2A, we computed the overlap coefficient for all path pairs in each cluster for comparison with the global mean (global OC = 0.557) (Figure 1—figure supplement 1A). We found that pathway pairs in three of the four clusters yielded an overlap coefficient greater than the mean of all pairs of paths from all clusters, indicating that pathways in each cluster are made up of specific residue contacts (Figure 1—figure supplement 1C). In contrast, for GluN1, a significant cluster (26 paths) involving interactions with residues on the ξ2 face of the LBD has a cluster mean OC much less than the global mean (global OC = 0.671), indicating that this cluster primarily comprises random contacts (Figure 1B, Figure 1—figure supplement 1B,D, Figure 1—figure supplement 4). This suggests that D-serine binding to GluN1 may be more diffusion-driven and less guided than to GluN2A. Therefore, we propose that agonist binding mechanisms exist on a spectrum ranging from unguided to guided diffusion. The difference in the specificity of D-serine contacts along binding pathways for GluN2A and GluN1 suggests that the extent to which agonists rely on pathways of guiding residues depends on LBD architecture and not solely upon the identity of the agonist. Mapping important pathway residues onto the intact GluN1/GluN2A NMDAR (PDB ID: 6MMM Jalali-Yazdi et al., 2018) further enriches our understanding of binding pathways by allowing us to determine whether residues in particular pathways are accessible for binding or obscured by other receptor domains and subunits. For GluN2A, access to residues on the extreme of the ξ2 face is slightly restricted by the presence of the GluN1 subunit of the adjacent LBD dimer (Figure 1—figure supplement 2A). However, this interface does not seem to be near the specific residues identified as critical for binding. Even more restricted is access to residues on the ξ1 face of GluN1, which are obscured by GluN2A of the adjacent LBD dimer, including residues identified as critical for binding pathways (Figure 1—figure supplement 2B). This might bias the pathways observed for the intact receptor by forcing the agonist to favor residues on the ξ2 face of the LBD. Since our overlap coefficient analysis of the cluster that corresponds to the ξ2 face of GluN1 identified more non-specific interactions, it is possible that the D-serine mechanism would be biased to favor unguided diffusion. It is also possible that access to residues near the N-terminal end of D1 would be restricted by the R2 lobe of its own ATD. We next investigated whether a specific LBD conformational state was favored for successful D-serine binding pathways. We computed our (ξ1,ξ2) order parameter to quantify the degree of closure of the LBDs for all trajectory frames identified as part of binding (and unbinding) pathways and found that (ξ1,ξ2) = (16,14) for GluN2A (Figure 1—figure supplement 3A) and (ξ1,ξ2) = (11,13) for GluN1 (Figure 1—figure supplement 3B). These values correspond to a partially open LBD. The LBD needs to be open enough for the ligand to diffuse into the pocket but closed enough to form some stabilizing interactions with the ligand. However, we notice that the ξ1 is smaller for GluN1, indicating that agonist binding can occur at slightly more closed LBD conformations. GluN1 pathways where (ξ1,ξ2) = (11,13) are mostly in the cluster defined by D-serine interactions with Loop 2, highlighting the role of Loop 2 residues in D-serine binding to GluN1. Overall, these results suggest that the degree of LBD closure does influence the likelihood of successful binding. Effects of D-serine binding on the LBD conformational free energy landscapes Since we did not expect to see D-serine binding to the GluN2A LBD, we needed to determine whether these GluN2A D-serine binding events are able to modulate the GluN2A LBD conformation. Since full LBD closure occurs on multi-microsecond to millisecond timescales (Sinitskiy et al., 2017; Dolino et al., 2016; Rajab et al., 2021), direct observation of such a conformational change was not fully captured from our equilibrium binding trajectories. Instead, to ensure we are sampling the full range of LBD conformations, we performed umbrella sampling free energy molecular dynamics simulations to obtain the conformational free energy landscape of GluN2A bound to D-serine (Figure 3A). We used the order parameter (ξ1,ξ2) (Yao et al., 2013) that captures the opening and closing motion of the LBDs observed in crystal structures of these domains. Since no crystal structure exists for D-serine bound to GluN2A, we identified residues critical for stabilizing the agonist in the closed state by analyzing contacts in lowest-energy (≤1 kcal mol–1) conformers extracted from the 2D PMF computed from umbrella sampling simulations of D-serine bound to the GluN2A LBD (Figure 3—figure supplement 1). For reference, we compared the resulting energy landscape to those previously computed for the apo- and glutamate-bound GluN2A monomers (Figure 3C and D; Yao et al., 2013). In the apo PMF, there is a wide energy minimum that accommodates more open LBD conformations; in contrast, the glutamate-bound PMF exhibits a narrow and steep energy minimum at the closed state. We see that, like glutamate, D-serine stabilizes the closed LBD conformation. The D-serine energy landscape has a global minimum corresponding to (ξ1,ξ2) values of (11, 11.5 Å) and a metastable minimum corresponding to (ξ1,ξ2) values of (15.5, 11.5 Å). The presence of a metastable agonist-bound LBD partially open intermediate suggests that D-serine may not stabilize the closed conformation to the same extent as glutamate and generate sufficient force to control channel gating. We then compared different conformers corresponding to these two states to determine residues critical for agonist stabilization. The primary difference between the residue contacts in conformers of the two states is the prevalence of interactions with Thr-690 (Figure 3—figure supplement 1B), which only contacts D-serine in the more closed state centered at (ξ1,ξ2) = (11, 11.5 Å). This is supported by our binding simulations; although we do not fully sample LBD closure, trajectory frames with low (ξ1,ξ2) values involve contacts with Thr-690. This suggests that Thr-690 is critically involved in promoting full GluN2A LBD closure upon agonist binding. Figure 3 with 2 supplements see all Download asset Open asset Conformational free energy landscapes for GluN2A and GluN1 LBDs. Umbrella sampling molecular dynamics simulations were used to compute the potential of mean force (PMF) along the (ξ1,ξ2) order parameter for (A) D-serine bound to GluN2A, (B) glycine bound to GluN2A, (C) apo GluN2A previously computed in [196], (D) glutamate bound to GluN2A in its crystallographic pose previously computed in [196], (E) glutamate bound to GluN2A in the inverted pose identified in [218], (F) D-serine bound to GluN1, (G) glycine bound to GluN1 previously computed in [196], (H) apo GluN1 previously computed in Yao et al., 2013. Figure 3—source data 1 Per-residue contact frequency analysis of the bound state for each agonist computed from lowest-energy conformers extracted from umbrella sampling simulations. https://cdn.elifesciences.org/articles/77645/elife-77645-fig3-data1-v1.xlsx Download elife-77645-fig3-data1-v1.xlsx Experimental binding studies have indicated that D-serine may be a more potent GluN1 agonist than glycine (Mustafa et al., 2004). To better understand the molecular mechanism responsible for this difference in agonist potency, we computed the conformational free energy for the D-serine-bound GluN1 LBD (Figure 3F). Compared with the previously computed glycine-bound and apo LBDs (Figure 3G and H; Yao et al., 2013), the presence of D-serine in the binding cleft results in a greater population of conformers in the closed conformation and fewer conformers adopting a more open conformation. Similar to GluN2A Thr-690, GluN1 Asp-732 and (to a lesser extent) Ser-688 help stabilize D-serine in the closed LBD conformation by interacting with the D-serine hydroxyl. For this reason, we propose that D-serine’s high potency is due, at least in part, to its ability to more strongly stabilize a closed LBD through additional interactions with the D2 lobe. D-serine and glutamate compete for binding to the GluN2A LBD Since our simulations revealed that D-serine can enter the GluN2A LBD binding pocket and partially stabilize the active conformation, we hypothesized that D-serine might compete with glutamate for binding to GluN2A. In fact, we observed D-serine binding to GluN2A, even in the presence of glutamate, although glutamate bound more frequently and with longer residence times in the binding site (Figure 1—source data 2, Figure 5—source data 1). Specifically, there are 17 successful associations for glutamate in the 15 μs glycosylated mixed-agonist trajectory compared with 5 for D-serine, and 75 glutamate binding events compared with 6 D-serine binding events for the non-glycosylated mixed-agonist trajectory. In addition, the average time bound for glutamate was 131 ns (glycosylated) and 236 ns (non-glcyolated) compared with 3 ns (glycosylated) and 56 ns (non-glycosylated) for D-serine. Since increasing the D-serine concentration would increase the frequency of D-serine binding to GluN2A, it is possible that D-serine could function as an inhibitor (competitive antagonist) at high concentrations. To probe this behavior experimentally, we measured GluN1-2A NMDAR currents using two-electrode voltage clamp (TEVC) electrophysiology. We observed that at high (~1 mM) D-serine concentrations, NMDAR activity was inhibited (Figure 4A). The inhibition was dependent on glutamate concentrations, implying that the inhibitory effect of D-serine may be competitive (Figure 4B). Furthermore, dose-response curves of glutamate activation were right-shifted in the presence of increasing concentrations of D-serine (Figure 4C). The calculated slope value of the Schild plot at 1.1 ± 0.1 implied that D-serine and glutamate likely compete against each other (Figure 4C). Combined with our simulation results, our electrophysiological data support the hypothesis that D-serine at high concentrations can bind to the GluN2A subunit and compete against glutamate. Figure 4 Download asset Open asset D-serine competes glutamate binding as an antagonist at high concentration. (A) Representative Two-electrode voltage clamp (TEVC) recording on GluN1/GluN2A NMDARs expressing oocytes. The traces show inhibition of the NMDAR current by the GluN1 agonists D-serine (left) and glycine (right) at a high concentration. 6 μM of glutamate is present throughout the recording. (B) D-serine inhibition at various concentrations of glutamate (1, 3, 10, and 30 μM). . (C) Glutamate responses at various concentrations of D-serine (0.123, 0.37, 3.33 and 10 mM) (left). Schild plot analysis of D-serine competition against glutamate (right). The calculated slope of the Schild plot was 1.11 ± 0.13 and the intercept was 2.38 ± 0.26. DR stands for dose ratio. (D) D-serine inhibition curves (left) and IC50 values for various pathway residue mutants on GluN1 and GluN2A LBDs (right). The pairwise comparison shows that the changes in IC50 values of the mutants from the wild type are significant. The statistical analysis was done by two-tail t-test where the p values are GluN1a-R694A = 0.0061, GluN1a-R695A = 0.0065, GluN2A-R692A = 4.1 × 10–9, and GluN2A-R695A = 3.3 × 10–5. All experiments were repeated in at least four independent oocytes. Error bars represent the average current ± SD. Since a similar inhibitory effect was also observed at high glycine concentrations by TEVC electrophysiology (Figure 4A), we repeated our umbrella sampling simulations with glycine bound to the GluN2A LBD. We see that glycine also favors the closed LBD (Figure 3B). The lowest-energy conformers of GluN2A with glycine are fastened shut by contacts between the N-terminal amine of glycine and Tyr-730. Although glutamate still stabilizes the closed GluN2A LBD to the greatest extent, comparable thermodynamics between different agonists suggest that kinetics of agonist binding and unbinding is a critical driver of agonist-induced activation. The GluN2A LBD likely never closes around glycine because glycine does not remain bound long enough to induce LBD closure. Previous binding studies Mayer, 2017 have indicated that glutamate, the primary GluN2A agonist, similarly relies on LBD surface residues to promote binding. To determine whether D-serine and glutamate binding are guided by similar residue contacts, we computed the overlap coefficient between residues in D-serine and glutamate pathways to be 0.964 for the glycosylated GluN2A LBD, corresponding to a significant overlap in agonist occupancy (Figure 5A). This high degree of overlap between glutamate and D-serine pathway residues indicates that they bind through similar mechanisms. To assess the importance of pathway residues for D-serine binding to the LBD dimer, we performed TEVC electrophysiology to obtain D-serine dose-response curves for various pathway mutants for GluN1 and GluN2A (Figure 4D). Notably, the GluN2A mutants Arg692Ala and Arg695Ala showed two to three-fold decreased D-serine inhibition potency. This result suggests that these two GluN2A residues play a role in D-serine inhibition and D-serine guided-diffusion pathways. Since these residues are also involved in glutamate binding pathways, this finding more generally supports the guided-diffusion mechanism by which agonists bind to GluN2A. The absence of this effect on the two GluN1 pathway mutants supports the increased diffusive behavior of D-serine binding to GluN1. Figure 5 Download asset Open asset Comparison of D-serine and glutamate binding to GluN2A. (A) Overlay of D-serine (teal) and glutamate (gray) density. (B) Residues that distinguish D-serine (teal) from glutamate (gray) binding pathways (see Figure 5—source data 2). Figure 5—source data 1 Record of all successful binding pathways in each simulation system for glutamate binding to GluN2A. https://cdn.elifesciences.org/articles/77645/elife-77645-fig5-data1-v1.xlsx Downloa" @default.
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• W4312837986 title "Decision letter: Excitatory and inhibitory D-serine binding to the NMDA receptor" @default.
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