The resting and ligand-bound states of the membrane-embedded human T-cell receptor–CD3 complex

Domain: biology
Published: 2025-12-16
Editorial outcome: major_revision

Abstract
The T-cell receptor (TCR) initiates T-lymphocyte activation, but the mechanism of TCR activation remains uncertain. Here, we present cryogenic electron microscopy structures for the unliganded and human leukocyte antigen (HLA)-bound human TCR–CD3 complex in nanodiscs that provide a native-like lipid environment. Distinct from the open and extended conformation seen in detergent, the unliganded TCR–CD3 in nanodiscs adopts two related closed and compacted conformations that represent its physiologic resting state in vivo. By contrast, the HLA-bound complex adopts the open and extended conformation, and conformation-locking disulfide mutants show that ectodomain opening is necessary for maximal ligand-dependent T-cell activation. These structures also reveal conformation-dependent protein–lipid and glycan–glycan interactions within the TCR. Together, these results establish allosteric conformational change during TCR activation, reveal avenues for immunotherapeutic engineering, and highlight the importance of native-like lipid environments for membrane protein structure determination.

Introduction
T cells are central to both adaptive and pathologic immune responses1. The T-cell receptor (TCR), in association with CD3 proteins, allows T cells to recognize and respond to antigenic peptides presented on human leukocyte antigen (HLA) complexes of other cells. While the constituents of the TCR–CD3 complex have been known for decades, questions remain regarding its activation mechanism2–4.

The TCR–CD3 is a hetero-octameric membrane protein complex5–8. The αβ-type TCR is comprised of two variable chains, TCRα and TCRβ, which directly interface with the HLA–antigen complex. Antigen specificity is generated through somatic recombination of the TCRα and TCRβ variable segment genes. The remainder of the core complex is comprised of six invariant CD3 family chains: the CD3ϵ-CD3γ and CD3ϵ-CD3δ heterodimers (CD3ϵγ and CD3ϵδ, respectively), and a CD3ζ homodimer. Herein, we differentiate the CD3ϵ protomers by the CD3 component with which each pairs (CD3ϵ pairs with CD3γ and CD3ϵ pairs with CD3δ) and the CD3ζ protomers as CD3ζ and CD3ζ’.

While the TCRα and TCRβ chains lack intracellular signaling domains, the CD3 proteins possess cytoplasmic immunoreceptor tyrosine activation motifs (ITAMs) that become phosphorylated upon TCR interaction with HLA–antigen complex and then bind and activate downstream signaling kinases. Dynamic interactions between ITAMs and the membrane regulate signaling9,10, but the mechanism by which ligand binding to the extracellular TCRα and TCRβ variable domains results in phosphorylation of cytoplasmic CD3 ITAMs remains a matter of debate. Specifically, it is unclear whether ligand binding and activation involves conformational change in the TCR–CD3 complex. Reconstitution of TCR–CD3 signaling in vivo with minimal constructs argues against transmembrane conformational change11. However, biochemical evidence in support of conformational change12–15–specifically, for reversible structural extension of the TCR under mechanical force – suggests the opposite16, although the structural details of such conformational transitions remain to be determined.

Structures of the hetero-octameric αβ-type TCR–CD3 have previously been determined by cryogenic electron microscopy (cryo-EM)5–8. These studies revealed an antenna-like structure, with the TCRα and TCRβ variable domains projecting over a ring formed by the CD3 ectodomains and the TCRα and TCRβ constant domains, with numerous interactions between their respective transmembrane (TM) helices (Supplementary Fig. 1a). Addition of antigen–β2-microglobulin–HLA (herein, “HLA”) to this complex did not meaningfully alter the conformation of the TCR–CD37,8, arguing against models of intrinsic conformational change in ligand-dependent TCR–CD3 activation (Supplementary Fig. 1a, b).

However, the TCR–CD3 used in these studies were solubilized in detergent. Biochemical data have shown important roles for the lipid bilayer in TCR–CD3 signaling6,17,18, raising questions as to the physiologic meaning of the determined structures. Membrane protein structure determination is generally preferable in a native-like membrane environment that increases the likelihood that important protein–lipid interactions are captured in the obtained molecular models19,20.

Here, we report cryo-EM structures of a human TCR–CD3 both in detergent micelles and nanodiscs. The structures reveal conformations for the TCR–CD3 in a native-like lipid environment distinct from those previously reported in detergent, and cross-linking data support the notion that these conformations represent the resting state of an unstimulated TCR–CD3 on T cells. Determination of the structure of the same nanodisc-embedded TCR–CD3 bound to HLA reveals substantial conformational change upon physiologic ligand binding, and functional data show that exit from the resting state conformation is required for maximal TCR–CD3 signaling.

Results
The TCR–CD3 complex is in an open and extended conformation in detergent micelles

In this study, we investigated the structure of the 1G4 (LY-variant) TCR–CD3. We chose the 1G4 TCR, which binds to an HLA-A*02:01-restricted 9-amino acid epitope derived from the NY-ESO-1 cancer-testis antigen, because it is both a model immunoreceptor and being developed as an anti-cancer therapeutic21–24. To confirm that 1G4 has a similar structure to the other TCR–CD3 complexes analyzed in detergent, we purified this complex in glyco-diosgenin (GDN) and determined its structure by cryo-EM to a nominal resolution of 3.3 Å (Fig. 1a, g, Supplementary Figs. 1c–h, 3; Supplementary Tables 1–3). Herein, this model will be abbreviated as “GDN.” Overall, GDN was very similar to the structures of three other TCR–CD3 constructs, including those with and without HLA bound (Fig. 1b). This is unsurprising, as their sequences only differ meaningfully in the TCR variable domains (Fig. 1c).

The TCR–CD3 is in a distinct closed and compacted conformation in nanodiscs

To determine the structure of the 1G4 TCR–CD3 in a native-like lipid environment, we reconstituted it into membrane scaffold protein-based nanodiscs loaded with mammalian polar lipids and cholesteryl hemisuccinate (Fig. 1a, d–f). Computational processing of cryo-EM data yielded two distinct maps determined to nominal resolutions of 3.3 to 3.1 Å (Fig. 1g, Supplementary Figs. 2, 3; Supplementary Tables 1–3). These maps, which we denote as nanodisc (ND)-I and ND-II, were clearly different from GDN (Fig. 1g) and the previously published TCR structures. Overall, reconstitution of the TCR–CD3 into nanodiscs shortens the TCR–CD3 by approximately 35 Å along the membrane-normal axis and induces interdomain contacts (Fig. 1g). Attempts to bias the particle search towards the published conformations with projections of the structure in detergent or that same structure docked into a lipid nanodisc model did not yield any maps approximating the previously published conformation (Supplementary Fig. 2c). Further, analysis of 2D classes from the nanodisc dataset does not show a subset of particles in the conformation seen in detergent (Supplementary Fig. 2i, j). Computational modeling of the conformation from detergents into the nanodisc used in these studies reveals no geometric constraints preventing the TCR–CD3 from adopting this conformation in a nanodisc, arguing against a nanodisc geometry-induced artifact (Supplementary Fig. 2k–m).

Despite equal or higher nominal overall resolutions than GDN, the ND-I and ND-II maps were not as high quality (compare local resolution estimations in Supplementary Fig. 2d with Supplementary Fig. 1h). This was likely due to a higher degree of conformational variability, but also due in part to map anisotropy (compare Supplementary Fig. 2f, h with Supplementary Fig. 1g). Nonetheless, with much of the density having estimated local resolution better than 4 Å, these maps were of sufficient quality to build models into the density.

Molecular models were built for ND-I and ND-II by docking the GDN structure into the maps and refining them by standard approaches (see Methods for details; Supplementary Fig. 3c–k; Supplementary Tables 2, 3). These models reveal two distinct but related conformations of the TCR–CD3 (Figs. 1g and 2). The ND structures show two large conformational changes relative to GDN: a closure of the TCR ectodomains against the CD3 ectodomains (Fig. 2a–c) and a compaction of the juxtamembrane (JM) linker peptides with associated tilting of the TM helices (Fig. 2d–h). Closure of the TCR ectodomains (Fig. 2a) results in part from rotation of the variable domains towards their respective constant domains about the interdomain hinge region (Fig. 2b, c). Relative to the extended JM linker conformations seen in GDN, the CD3 JM linkers are compacted, with closer approximation of the CD3 ectodomains to the amino-terminal ends of their TM helices (Fig. 2e–g). The conformational changes in the TCR ectodomains are accompanied by tilting of TM helices. While the in-plane arrangement of the TM helices is largely similar when viewed along the membrane normal (Fig. 2d), examination from orthogonal axes reveals differences in TM helix conformation. Compaction of the CD3 JM linkers requires opening of the angle between the TM helices of CD3ϵδ by 23° (from 31° to 54°) and CD3ϵγ by 28° (from 39° to 67°), when comparing ND-II with GDN (Fig. 2e, f). For ND-I, the TM helices of CD3ϵδ are bent, with the first helical turn displaying a wide splay (76°, Fig. 2g), but the overall angle between the modeled helices (46°) is intermediate to ND-II and GDN. The CD3ϵ TM helix was not modeled, as its register could not be confirmed, and thus the CD3ϵγ TM angle was not measured. Additionally, there is splaying of the TCRβ and distal CD3ζ TM helices away from the membrane normal (Fig. 2h). In GDN, the TCRα TM helix extends outward past the amino-terminal ends of the CD3 TM helices, where it is ensconced by the extended CD3 JM linkers. Compaction of the JM linkers in nanodiscs necessitates reorganization of the first turn of the TCRα TM helix, which is unstructured and displaced towards the lipid bilayer (Fig. 2h). Taken together, these cryo-EM maps reveal marked conformational differences between the TCR–CD3 in detergent micelles and a lipid bilayer.

Additionally, the TM helices of ND-I and ND-II appear shorter than in GDN. These structures lack strong density for the cluster of basic and aromatic amino acids visualized at the carboxy-terminal ends of the GDN TM helices (Fig. 2i). In a membrane, these extensions would likely be interfacial or extend into the cytoplasm25. Although there is additional density beyond the modeled carboxy-termini for some of these shortened helices in ND-I and ND-II, it is not of sufficient quality to model with confidence (Fig. 2j). This suggests the TCR–CD3 TM helices are less rigid in the inner leaflet of a lipid bilayer than in a detergent micelle.

The TCR–CD3 interacts with the lipid bilayer

The TCR–CD3 is known to interface directly with membrane cholesterol26, and binding sites for two cholesterols have been visualized in previously published structures6. In GDN, ND-I, and ND-II, density corresponding to these cholesterols was again apparent (Fig. 3a). The two cholesterol molecules in ND-I and ND-II are oriented tail-to-tail in a groove between CD3ζ’, TCRα, TCRβ, and CD3γ, as in GDN (Fig. 3b). However, the compaction-associated changes in the TM helices causes sliding of the cholesterol molecules towards each other, with deflection of their branched aliphatic tails to prevent clashing (Fig. 3b, insets). We refer to the cholesterol molecules as CholA and CholB, corresponding to the TCR–CD3 TM helix with which each is approximated. The deflected tail of CholB is interposed between adjacent TM helices, accommodated by their increased splay in the compacted conformation (Fig. 3c, insets, dashed black ovals). The cholesterol-binding groove is more open to the membrane bilayer adjacent to CholB in nanodiscs, creating a possible path for cholesterol loading into the assembled TCR–CD3 and/or exchange with the membrane environment (Fig. 3c, bottom right, dashed red oval).

Imaging of the TCR–CD3 in a lipid bilayer revealed additional sites of protein–lipid interaction. Unmodeled tubular densities consistent with phospholipid acyl chains are present at several sites adjacent to the TCR–CD3 TM helices (yellow densities in Fig. 3d–g). Structured lipid is seen parallel to the tail-to-tail cholesterol dimer, filling the aforementioned channel between the cholesterol-binding groove and the bulk membrane lipids (Fig. 3d). Probable sites of structured lipid include a shroud of density over the CD3ζζ’ TM helices (Fig. 3e) and in the cleft over the TCRαβ TM helices between CD3δ and CD3ϵG (Fig. 3f). Splay of the CD3ζ TM helix away from that of CD3ϵD enlarges a cavity, which is filled by several presumptive lipid acyl chains in the nanodisc (Fig. 3g).

The ND-I and ND-II maps show clear density for the proteolipid nanodisc enshrouding not only the TM helices, but also the membrane-proximal faces of the TCR–CD3 ectodomains (Fig. 4a, left). Lipid contacts with the CD3 ectodomains are apparent even in maps contoured at a higher threshold, consistent with high occupancy (Fig. 4a, right). While the CD3 ectodomain surfaces are poorly conserved in comparison to the TM helices, patches on the membrane-proximal surfaces of the CD3 ectodomains do show high sequence conservation (Fig. 4a, c). While patches (black triangles in Fig. 4b–e) interface with the extracellular membrane leaflet and are neutral to electropositive (Fig. 4e), facilitating phospholipid head group interactions. Of note, we observe a similar conserved electropositive patch at the interface of CD79αβ27, the analogous ITAM-bearing chains in the B-cell receptor, raising the prospect that this interface may also interact with membrane lipids and favor a different conformation in the lipid bilayer than that reported in detergent (Supplementary Fig. 4).

The unliganded TCR–CD3 is closed and compacted in vivo

Which conformation of the TCR–CD3 is the physiologic resting state on the cell membrane: the open/extended conformation seen in detergent micelles, or the closed/compacted conformations seen in the lipid bilayer of the nanodisc? Given that the lipid bilayer of a nanodisc more closely approximates the physicochemical milieu of the plasma membrane, we hypothesized that the TCR–CD3 is closed and compacted in the absence of activating ligand in vivo. To test this notion, we engineered a series of disulfide bonds that would only be geometrically favorable in ND-I and/or ND-II but not GDN (or vice versa), and we read out their presence or absence by one of the following approaches: (1) non-reducing sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and Western blot of whole-cell lysates for interchain disulfides, or (2) mass spectrometry on affinity-purified protein for intrachain disulfides. Analysis of disulfide bonds in the Protein Databank have shown that they almost never have a Cys-Cys Cα-Cα distance greater than 7 Å, with most being between 5 and 6.5 Å28. Accordingly, we sought residue pairs in the TCR–CD3 ectodomains with Cα-Cα distances below 7 Å in one conformation and above 7 Å in the other as candidates for disulfide engineering.

The interface between CD3δ and TCRα contains several pairs of residues with geometry favorable for engineered disulfide-bond formation in ND-I but not GDN (Fig. 5a). As TCRα and TCRβ are linked by an interchain disulfide natively (Supplementary Fig. 5a), formation of a TCRα–CD3δ disulfide would result in an upward gel shift of the TCRαβ heterodimer in non-reducing SDS-PAGE. For all three tested engineered disulfide pairs meeting the 7 Å Cα-Cα distance constraint, a gel shift consistent with TCRα–CD3δ disulfide formation was observed (Fig. 5b, asterisks; full-length blots in Supplementary Fig. 5b). Confirming that this mass shift was due to disulfide formation, no gel shift was apparent under reducing conditions (Supplementary Fig. 5b). Moreover, the mass shift was due to the formation of the engineered disulfide bond and not an off-target bond, as adduct formation was only seen when both residues in a given target pair were mutated to cysteines (Fig. 5b). As an internal negative control, a permutation of cysteine mutants at this interface not meeting geometric criteria for disulfide-bond formation in ND-I (TCRα S78C–CD3δ N38C, Fig. 5a, right, orange labels) was produced. It did not form a disulfide bond in vivo (Fig. 5b, orange) despite S78C and N38C successfully pairing with cognate cysteine mutants that did meet the inter-Cα threshold. This control shows the structural precision and specificity of the engineered disulfide-bond approach. These experiments were performed using whole-cell lysates, but we obtained similar results with NiNTA-affinity-purified protein (Supplementary Fig. 5c), and we were able to reconstitute the two G79C-crosslinked mutants into nanodiscs (Supplementary Fig. 5d, e), suggesting proper folding and assembly of TCR–CD3.

The region around the TCRα and TCRβ interdomain hinges is also notably different in the closed/compacted conformation compared to the open/extended one, with the variable domains coming into closer apposition with their respective constant domains, allowing the engineering of conformation-specific disulfide pairs in this region (Fig. 5c, d). We probed formation of these intrachain disulfides by mass spectrometry. Proteolytic digestion of the TCR–CD3 with trypsin and chymotrypsin yielded distinct peptides, several of which were linked by disulfide bonds. Electron-transfer/higher-energy collision dissociation (EThcD) fragmentation was used to fragment the disulfide-bonded peptides providing in vacuo reduction to the linear peptide constituents along with peptide backbone fragmentation, thereby allowing unambiguous identification of the engineered disulfides29. All native TCR–CD3 disulfides spanning two proteolytic fragments (Supplementary Fig. 5a) were observed in the wild-type 1G4 TCR–CD3 by this approach (Supplementary Table 4). For both ND conformation-compatible engineered disulfides tested (Fig. 5c, d), we observed formation of the engineered disulfide (Fig. 5e, f and Supplementary Table 4). In contrast, an engineered disulfide that would only be present in the open/extended conformation (Supplementary Fig. 5f) did not form in vivo (Supplementary Table 4). Taken together, these data support the conclusion that TCR–CD3 in the unliganded, resting state adopts the closed/compacted conformation in vivo.

The unliganded TCR–CD3 is dynamic in a lipid bilayer

To allow direct comparison of the two distinct, but related, closed/compacted structures (ND-I and ND-II), they were aligned by their TCRα TM helices. TCRα was chosen because it is relatively perpendicular to the membrane plane and thus might be less affected by differences in TM helix splay. Indeed, this alignment strategy brought the nanodisc density of their maps into alignment as well (Supplementary Fig. 6a), giving both models the same orientation relative to the membrane bilayer. In contrast to the strong overall alignment of the TCR–CD3 TM helices by this approach (Supplementary Fig. 6b, c), the ectodomains of ND-I and ND-II have marked conformational differences, as seen in the aligned cryo-EM densities (Supplementary Fig. 6a, b) and the resulting protein models (Fig. 6a). Comparison of the structures reveals the motions allowing exchange between these conformations (Fig. 6b, c and Supplementary Movie 1). ND-I is the most closed of the three structures, and transition to ND-II requires several motions: the depression of the CD3ϵD (Fig. 6b) and CD3γ ectodomains (Fig. 6c) away from the TCR variable domains and towards the bilayer outer leaflet; rotation of the TCRα variable domain about the interdomain hinge (Fig. 6b); and retraction of the TCRβ variable domain (Fig. 6c). This is accompanied by sliding of CD3ζ along its helical axis towards the ectodomains (Fig. 6b). While the TCRα and CD3ζ‘ TM helices remain relatively fixed in this frame of reference, the TCRβ and CD3γ TM helices shift away from the cholesterol-binding groove (Fig. 6d and Supplementary Fig. 6c). To accommodate this movement, there is rotation of CholA and elevation of CholB (Fig. 6d), showing cholesterol to be an integral and dynamic component of the TCR–CD3 structure.

The GDN map displays densities for many of the known asparagine (N)-linked glycans in the TCR–CD3 (Supplementary Fig. 6d). Fewer densities could be modeled as N-linked glycans in the ND-I and ND-II maps (Supplementary Fig. 6d). However, all three maps show an array of densities lateral to the CD3δ and TCRα ectodomains, corresponding to sites of known N-linked glycosylation. In ND-II, TCRα (linkage unclear) and CD3δ N74-linked glycans are visible, emerging radially from the TCR–CD3, as in GDN (Fig. 6e). By comparison, ND-I reveals clear reorganization of the glycans (Fig. 6e), with stacking of adjacent glycans brought into close proximity by tight ectodomain closure. This arraying of N-linked glycans is reminiscent of that seen on human immunodeficiency virus-1 Env30, where interacting adjacent glycans form a structured arcade (Supplementary Fig. 6e). Two densities in the array are attributable to CD3δ N74- and TCRα N82-linked glycans, with a third density emerging between them without definitive attribution (purple question mark in Fig. 6e, right inset). In GDN, as in other structures of TCR–CD3 in detergent, the CD3δ N38-linked glycan is oriented inward toward the cleft between the TCR and CD3 ectodomains (Fig. 6f, top). In ND-II, CD3δ N38 is unstructured (Fig. 6f, middle), but is grossly positioned at the entrance to the cleft, and the linked glycan could conceivably orient either into or away from the cleft. In ND-I, CD3δ N38 is structured (Fig. 6f, bottom), but its glycan cannot be confidently modeled. However, density protrudes from N38 into the open cleft (Fig. 6f, asterisk) where additional unmodeled density is seen, consistent with the glycan remaining within the cleft in the ND-I conformation.

The different degrees of ectodomain closure between ND-I and ND-II could affect TCR variable domain accessibility to HLA. To assess this possibility, we modeled the binding geometry of HLA onto GDN, ND-I, and ND-II by alignment of the TCR–HLA crystal structure to the TCR variable domains in our models (Fig. 6g). There appears to be adequate clearance for the TCR variable domains to interact with HLA in GDN and ND-II. For ND-I, the modeled fit is tighter, with the CD3ϵD ectodomain predicted to be in close contact with HLA. However, on assessment of space-filling model cross-sections, no overt steric clashes are evident (Fig. 6h). Because a portion of a potentially clashing surface loop in the CD3ϵD ectodomain is not modeled in ND-I, the TCR-aligned HLA model was docked into the ND-I cryo-EM density (Fig. 6i), but no unmodeled density appears to clash with HLA. The CD3ϵD ectodomain from GDN is completely modeled and aligns closely with that from ND-I (Supplementary Fig. 6f, inset). We modeled the GDN CD3ϵD ectodomain into ND-I and again assessed the HLA interaction. While there is a small overlap in their space-filling models (Supplementary Fig. 6f), no large clashes are observed. Thus, while it remains unclear what quantitative effect ectodomain cleft closure might have on HLA affinity, particularly in the physiologic context of a glycosylated HLA molecule, it is at least plausible that both ND-I and ND-II may be receptive to HLA binding.

While our modeling data suggest that HLA can bind to TCR–CD3 in the closed/compacted resting state conformations, TCR–CD3 opening and/or extension may be necessary for coreceptor interaction with HLA bound to the TCR–CD3. Modeling the HLA class II–CD4 complex31 onto ND-I and ND-II reveals an unfavorable geometry for CD4 interaction with HLA because of the proximity of the TCR variable regions to the membrane (Supplementary Fig. 6g). In contrast, CD4–HLA can be accommodated in the open/extended GDN conformation (Supplementary Fig. 6g). The full ectodomain structure of the CD8 coreceptor has not been described, precluding a similar analysis applicable to TCRs recognizing HLA class I-restricted epitopes, like the one used in this study.

HLA binding is sufficient to trigger TCR–CD3 conformational change

Whether or not TCR–CD3 undergoes conformational change upon binding to HLA remains an open question. To determine the structure of the HLA-liganded TCR–CD3 in a lipid bilayer, we reconstituted TCR–CD3 in nanodiscs as above, incubated the complex with an excess of single-chain trimeric NY-ESO-1(C9V)–β2-microglobulin–HLA, determined the structure by single-particle cryo-EM (Fig. 7a and Supplementary Fig. 7; Supplementary Tables 1–3). Despite picking particles using a template model of the closed/compacted TCR–CD3 complex bound to HLA, cryo-EM reconstruction showed the HLA-bound TCR–CD3 to be open and extended (Fig. 7a). Indeed, the HLA-bound conformation was nearly identical to that seen in GDN (Fig. 7b).

Comparison of the TCR variable domains in the HLA-bound nanodisc and GDN structures showed them to be nearly identical, despite the absence of ligand in the GDN structure, and both were similar to the crystal structure of the 1G4 TCRαβ ectodomain heterodimer bound to HLA (Fig. 7b). There was subtle tilting of HLA relative to the TCR when comparing the HLA-bound nanodisc structure to the crystal structure, but the binding interface was otherwise similar (Fig. 7c). By contrast, ligand binding induced several structural changes in the TCR variable domains relative to ND-I and ND-II. Specifically, in ND-I and ND-II, the amino-terminal strands and several surface loops in the variable domains were unstructured, but these were ordered in the HLA-bound structure (Supplementary Table 3). Qualitatively, the HLA-bound map showed better resolved protein density throughout (compare Fig. 7a with Fig. 1g), consistent with a general rigidification of the TCR structure upon HLA binding, as has been observed previously for the TCR ectodomains by NMR15 and hydrogen-deuterium exchange32. This rigidification was apparent in the density corresponding to the TCR variable domains, particularly for TCRβ (Fig. 7d, e).

Comparison of the resting and HLA-bound TCR–CD3 in lipid bilayers demonstrates that HLA binding is sufficient to induce substantial conformational changes in TCR–CD3. Our data support a model in which the unliganded resting TCR–CD3 explores a thermally accessible spectrum of closed/compacted conformations from the most closed ND-I to the more open ND-II, with HLA binding triggering a change to the open/extended conformation (Fig. 7f). We favor this transition occurring via the ND-II conformation, which is more open than ND-I and thus closer to the HLA-bound state along this trajectory. Morphing the closed/compacted conformation into the open/extended one (Supplementary Movie 2) highlights the straightening of the TM helices towards the membrane normal (that is, decreased splay, Fig. 7g). These changes in TM conformation add to the plausibility of the model, as they provide a means to transmit conformational changes in the ectodomains to the cytoplasmic ITAMs.

While some published data support allosteric activation models for the TCR, in which HLA binding is sufficient to induce TCR activation33, other data support models in which ligand-induced conformational change is necessary but not sufficient, requiring subsequent receptor clustering12 or mechanical force application34. However, all of these models propose that TCR–CD3 conformational change is a necessary step in the activation process, and thus we hypothesized that constraining conformational change would impair ligand-induced T-cell activation. Transition from the closed/compacted to the open/extended conformation requires further ectodomain opening from the ND-II state (Fig. 7f). We hypothesized that the intra-TCRα disulfide bond-forming double mutation S104C–V182C (characterized above, Fig. 5c, e) would prevent ectodomain opening to the HLA-bound conformation, as the Cα-Cα distance between these residues in that conformation is outside the limits of a disulfide bond (Fig. 7h). To investigate the impact of restrained TCR–CD3 opening on ligand-dependent T-cell activation, we introduced either wild-type or TCRα S104C–V182C mutant TCR constructs into TCRαβ-negative Jurkat T cells bearing an NFAT-responsive fluorescent reporter. TCR–CD3 assembly and surface localization (as assessed by anti-CD3ϵ antibody binding) as well as HLA-binding competence were confirmed by flow cytometry. These parameters were found to be comparable for cells transduced with the mutant and wild-type 1G4 (Supplementary Fig. 8a). In the absence of stimulation, there was no detectable reporter fluorescence in these cells; however, upon exposure to HLA–antigen tetramers, cells expressing wild-type TCR–CD3 became activated, with reporter fluorescence peaking at 16 h (Supplementary Fig. 8b). Reporter fluorescence was significantly decreased in the TCRα S104C–V182C mutant, with peak activation attenuated by 26% relative to wild type on exposure to 8 μg/mL of tetramers (P < 0.05, Supplementary Fig. 8b; source data in Supplementary Table 5). Similarly, using peptide pulsed COS7-A2 cells, we observed a 42% reduction in activated T cells transduced with the TCRα S104C–V182C mutant compared to wild-type TCR (Fig. 7i, P=0.00009; Supplementary Fig. 8c; source data in Supplementary Tables 6 and 7). This finding confirms that the conformation-constraining double mutant also restrains T-cell activation when antigen is presented in a cellular context. In contrast, both the TCRα S104C–V182C mutant and wild-type controls were equally responsive to phorbol 12-myristate 13-acetate and ionomycin (PMA/IMY), which bypass TCR–CD3 signaling to activate T cells, showing this phenotype is specific to ligand-induced TCR–CD3 activation. Similar results were observed for the TCRβ T130C/H172C mutant (characterized above, Fig. 5d, f), which would also be hypothesized to prevent TCR–CD3 opening. For this mutant, peak response to HLA–antigen tetramers was reduced by approximately 50%, despite surface expression of TCR–CD3 complex and PMA/IMY responsiveness greater than those of wild type (Supplementary Fig. 8e–g; source data in Supplementary Tables 8 and 9). Efforts were undertaken to constrain TCR–CD3 extension with two additional disulfide mutants in TCRβ; however, the surface expression of these mutants was significantly lower than that of wild type, precluding unambiguous analysis of their ligand-dependent activation (Supplementary Fig. 8e, f; source data in Supplementary Table 8). Taken together, these data indicate that TCR–CD3 conformational change is associated with optimal receptor signaling following binding to peptide-HLA.

Methods
Plasmids and sequences
A modified version of the BacMam expression system47 was used for the expression of the TCR–CD3 complex in HEK cells with furin cleavage and P2A ribosomal skipping sites separating each protein sequence (see below, Protein expression and purification and Engineered disulfide crosslinking). The modified viral vector also expressed the VSV-g sequence (kind gift from Titia de Lange, Rockefeller University) under a baculoviral promoter to enhance mammalian cell tropism. A self-inactivating retroviral plasmid encoding the 1G4(LY) TCRαβ sequence (wild type for this study) was a kind gift from S.A. Rosenberg, NIH. CD3ζ was cloned from Jurkat cell cDNA using standard restriction enzyme-based approaches. Similarly, CD3ϵ, CD3γ, CD3δ, EGFP, mCherry, and 8xHis tags were cloned from synthetic double-stranded DNA blocks (IDT DNA). Mutant TCR–CD3 sequences were produced by overlap-extension PCR or synthesized de novo (Genscript). Construct sequences can be found in and following Supplementary Table 10.

E. coli strains
Bacteria were grown under standard conditions: 37°C, ambient CO2; LB broth shaking 200 rpm for liquid cultures, LB agar for plates. DH5α (Thermo Fisher Scientific, 18265017) were used for cloning and amplification of plasmid DNA, and BL21(DE3) (Thermo Fisher Scientific, EC0114) were used for MSP expression. DH10Bac cells (Thermo Fisher Scientific, 10361012) were grown as per manufacturer instructions for the recombination of plasmid DNA into bacmids.

Eukaryotic cell lines
Insect and human cells lines were maintained under standard sterile conditions using aseptic technique with the following parameters: SF-9 cells (ATCC, CRL-1711) were maintained at 5×105-1×106 cells/mL in Sf-900 II SFM medium (Thermo Fisher Scientific) supplemented with antibiotic-antimycotic solution (Thermo Fisher Scientific) at 27°C, ambient CO2. HEK293GP cells (Takara Bio, 631458) and derivatives were maintained at 10-90% confluency in RPMI medium 1640 (RPMI, Thermo Fisher Scientific) supplemented with 10% fetal bovine serum (FBS, Sigma Aldrich), 10 mM HEPES, pH 7.5, and penicillin/streptomycin at 37°C, 5% CO2. HEK293F FreeStyle cells (Thermo Fisher Scientific, R79007) were maintained at 5×105 to 2×106 cells/mL in FreeStyle 293 expression medium (Thermo Fisher Scientific) supplemented with antibiotic-antimycotic solution at 37°C, 8% CO2. Modified Jurkat T cells (see below, Generation of an NFAT-reporter cell line) and COS-7-A2 cells were maintained at 2×105 to 2×106 cells/mL in RPMI medium supplemented with 10% FBS and penicillin-streptomycin solution at 37°C, 5% CO2. HEK cells are of female human origin and Jurkat cells are of male human origin. COS-7-A2 cells was gifted by Dr. Steven Rosenberg (NIH) and derived from the male African green monkey cell line COS-7 with stable transduction of human HLA-A*02:01. Cell line identify was validated by fluorescence-activated cell sorting (FACS).

Baculovirus production
Bacmid recombineering was performed as per manufacturer specifications with the FastBac system in DH10Bac cells (Thermo Fisher Scientific). Bacmid was purified by phenol/chloroform/isoamyl alcohol extraction, and approximately 25 μg bacmid DNA was transfected into SF-9 cells adherent in 6-well plates using Cellfectin II reagent (Thermo Fisher Scientific) in Grace’s Insect Medium (Thermo Fisher Scientific) for 6 h at 27°C before transfer to Sf-900 II SFM medium supplemented with antibiotic-antimycotic solution. After 5 days, the cell supernatant (P1 virus) was removed and used to inoculate a 40-mL culture of SF-9 cells at 1×106 cells/mL. After >50% of the cells had died (~day 5), supernatant (P2 virus) was harvested by centrifugation of the culture at 3500×g for 20 min, decanted, supplemented with 2% FBS, and stored in the dark at 4°C. For large-scale expression, P3 virus was produced by inoculating 40-mL cultures of SF-9 cells at 3×106 cells/mL with 2 mL P2 virus and cultured, harvested, and stored as for P2 virus.

Protein expression, purification, and nanodisc reconstitution
See overview schematic in Fig. 1a. For structural studies, ~250-mL cultures of HEK293F FreeStyle cells at 2×106 cells/mL shaking in 1-L baffled polycarbonate flasks were inoculated with 15 mL each of P3 virus, and sodium butyrate (Alfa Aesar) was added to a final concentration of 10 mM to enhance expression. For preparation of the TCR–CD3 in detergent for cryo-EM, TCR–CD3 was expressed using two baculoviruses: (1) TCRα-[furin/P2A]-TCRβ::mCherry, and (2) CD3ϵ-[furin/P2A]-CD3ζ-[furin/P2A]-CD3δ-[furin/P2A]-CD3γ::8HIS. Cells were incubated at 30°C, 5% CO2 for 3 days to allow expression and harvested by centrifugation at 4000×g for 10 min at 4°C. Cell pellets were resuspended in 25 mM HEPES (Sigma Aldrich), pH 7.5, 1 mM phenylmethanesulfonyl fluoride (Sigma Aldrich) and lysed with a Dounce homogenizer. Lysate was clarified by centrifugation at 4000×g for 10 min at 4°C, and the supernatant was decanted and ultracentrifuged at 100,000×g for 1 h at 4°C to recover the membrane fraction. Membrane pellets were resuspended in 25 mM HEPES (Sigma Aldrich), pH 7.5, 150 mM NaCl (Thermo Fisher Scientific), herein HBS, and n-dodecyl-β-D-maltoside (DDM, Anatrace) and cholesteryl hemisuccinate (CHS, Anatrace) were added to final concentrations of 1% and 0.2%, respectively. The membrane pellets were solubilized on a rotator for 2 h at 4°C. Debris was removed by centrifugation at 4000×g for 30 min at 4°C. The clarified supernatant was loaded onto NiNTA-agarose resin (Qiagen). Resin was washed with 50 column volumes of HBS with 0.02% glyco-diosgenin (GDN) and 30 mM imidazole (Thermo Fisher Scientific). The TCR–CD3 was eluted from the NiNTA-agarose resin with 360 mM imidazole and 0.02% GDN in HBS. The mCherry fusion to TCRβ was removed by incubation with human rhinovirus protease 3C overnight at 4°C. Protein was concentrated with Amicon centrifugal filters (Millipore) and further purified by size-exclusion chromatography in HBS with 0.02% GDN over a Superose 6 column (GE Healthcare).

For expression of the TCR–CD3 for affinity purification and nanodisc reconstitution, we found that co-expression of three viral constructs gave acceptable yields of material for cryo-EM analysis: (1) TCRα-[furin/P2A]-TCRβ::mCherry-[furin/P2A]-CD3δ-[furin/P2A]-CD3γ-[furin/P2A]-CD3ϵ-[furin/P2A]-CD3ζ::EGFP, (2) CD3δ-[furin/P2A]-CD3γ::8HIS, and (3) CD3ϵ-[furin/P2A]-CD3ζ::EGFP. This three-virus expression scheme was later simplified to two viruses for studies with HLA-bound (below), in detergent (above), or TCR–CD3 crosslinking experiments, as tandem affinity purification was not found to be necessary for intact complex recovery. Cells were cultured, infected, harvested, and lysed as above. Lysate was clarified, membranes isolated and extracted, and extracts loaded onto NiNTA-agarose resin as above. Resin was washed with 50 column volumes of HBS with 0.1% DDM, 0.02% CHS, and 30 mM imidazole. The resin was resuspended in HBS with 0.1% DDM, 0.02% CHS for on-bead nanodisc reconstitution. For the TCR–CD3 complex in nanodiscs subsequently incubated with HLA–β2M–NY-ESO-1, a simplified two-virus expression scheme was used: (1) TCRα-[furin/P2A]-TCRβ::EGFP, and (2) CD3ϵ-[furin/P2A]-CD3ζ-[furin/P2A]-CD3δ-[furin/P2A]-CD3γ::8HIS. Protein complexes were otherwise purified as above.

T-cell subsets differ in plasma membrane lipid composition, and individual T cells have spatially heterogenous membrane compositions17. To create a lipid environment with a diversity of lipid constituents, including the negatively charged phosphatidylserine and phosphatidylinositol lipids important for ITAM–membrane interactions9,10, porcine brain polar lipids (Avanti Polar Lipids #141101C) were used to reconstitute the lipid bilayer. Avanti reports the composition of this lipid mixture as follows: 12.6% phosphatidylcholines, 33.1% phosphatidylethanolamines, 4.1% phosphatidylinositides, 18.5% phosphatidylserines, 0.8% phosphatidic acid, 30.9% unknown. The porcine brain polar lipids dissolved in 10% DDM were added to a final concentration of 33 mg/mL (approximately 43 μM using the molecular weight of 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine as a standard phospholipid) and membrane-scaffold protein (MSP) 1D1ΔH5 was added to a final concentration of 20 μM. MSP 1D1ΔH5 was expressed in E. coli BL21(DE3), NiNTA-affinity purified, and affinity tags removed with TEV protease as previously described48 after subcloning from pET28a-MSP1D1ΔH5 (a gift from Gerhard Wagner, Addgene plasmid # 71714)49. Nanodisc reconstitution components were allowed to incubate for 15 min on ice before addition of SM-2 BioBeads (Bio-Rad) to approximately ¼ of the reaction volume. The mixture was incubated overnight on a rotator at 4°C. The reaction mixture was again returned to a disposable plastic column and washed with 20 column volumes of HBS, 50 column volumes of HBS with NaCl increased to 300 mM and 30 mM imidazole, washed with 20 column volumes of HBS, then eluted in HBS with 360 mM imidazole. Protein was concentrated with Amicon centrifugal filters (Millipore) and further purified by size-exclusion chromatography in HBS over a Superose 6 column (GE Healthcare). Peak fractions were collected and their quality ensured by SDS-PAGE followed by silver staining (Invitrogen), Coomassie blue staining, or Acquastain (Bulldog Bio) as per manufacturer specifications, and by negative-stain electron microscopy as described previously50. Briefly, samples were adsorbed for 30 s to glow-discharged, carbon-coated copper grids (Electron Microscopy Sciences), washed three times in deionized water, stained in freshly prepared 0.7% uranyl formate, and air dried before imaging on a CM-10 transmission electron microscope (Philips) equipped with XR-16 detector (Advanced Microscopy Techniques).

Cryo-EM specimen preparation and data collection
For the TCR–CD3 in GDN, Superose 6 peak fractions were concentrated with Amicon centrifugal concentrators to 5 mg/mL as determined with a NanoDrop spectrophotometer (Thermo Fisher Scientific) assuming 1 AU = 1 mg/mL. Aliquots of 4 μL were applied to glow-discharged 400 mesh R1.2/1.3 Cu grids with graphene oxide (Quantifoil) using a Vitrobot Mark VI (Thermo Fisher Scientific) set at 4°C and 100% humidity. After 20 s, grids were blotted for 0.5 s with a blot force of −2 and plunged into liquid nitrogen-cooled ethane.

For the TCR–CD3 in nanodisc sample, Superose 6 peak fractions were concentrated with Amicon centrifugal concentrators to 330 μg/mL as determined with a NanoDrop spectrophotometer assuming 1 AU = 1 mg/mL. Aliquots of 4 μL were applied to glow-discharged 400 mesh R1.2/1.3 Cu grids using a Vitrobot Mark VI set at 4°C and 100% humidity. After 5 s, grids were blotted for 4 s with a blot force of −2 and plunged into liquid nitrogen-cooled ethane. For the TCR–CD3 in nanodiscs with HLA–β2M–NY-ESO-1, the Superose 6 peak fractions were concentrated with Amicon centrifugal concentrators to 600 μg/mL. Lyophilized HLA single-chain trimer (herein, “HLA”; fusion of NY-ESO-1 C9V [SLLMWITQV], β2M I21-M119, and HLA-A*02:01 G25-T305; Kactus Biologics) was dissolved in deionized water per manufacturer instructions and then re-purified by size-exclusion chromatography over a Superose 6 column into HBS to remove PBS and trehalose and concentrated to 1 mg/mL. The TCR–CD3 and HLA single-chain trimer were mixed at a 2:1 volume ratio (1:5 approximate molar ratio). Aliquots of 4 μL were applied to glow-discharged graphene oxide-coated 400 mesh R1.2/1.3 Cu grids (Quantifoil) using a Vitrobot Mark VI (Thermo Fisher Scientific) set at 4°C and 100% humidity. After a wait time of 20 s, grids were blotted for 0.5 s with a blot force of −2 and plunged into liquid nitrogen-cooled ethane.

Cryo-EM imaging was performed in the Cryo-EM Resource Center at The Rockefeller University using SerialEM v4.1.0 beta51. Data collection parameters are summarized in Supplementary Table 1. In brief, for all samples, data were collected on a 300-kV Titan Krios electron microscope at a nominal magnification of 64,000×, corresponding to a calibrated pixel size of 1.08 Å on the specimen level. Images were collected with a K3 direct electron detector (Gatan) in super-resolution counting mode. The superfast mode in SerialEM was used, in which 3×3 holes are exposed using beam tilt and image shift before moving the stage to the next position52.

For the TCR–CD3 in detergent sample, images were collected using a defocus range of −0.8 to −2.5 μm. Exposures of 2.0 s were dose-fractionated into 50 frames (40 ms per frame) with a dose rate of 30 electrons per pixel per s (approximately 1.03 electrons per Å2 per frame), resulting in a total dose of 51 electrons per Å2. For the TCR–CD3 in nanodisc sample, images were collected using a defocus range of −0.8 to −2.0 μm. Exposures of 2.2 s were dose-fractionated into 55 frames (40 ms per frame) with a dose rate of 30 electrons per pixel per s (approximately 1.03 electrons per Å2 per frame), resulting in a total dose of 57 electrons per Å2. For the HLA-bound TCR–CD3 in nanodiscs sample, images were collected using a defocus range of −0.8 to −2.0 μm. Exposures of 2.0 s were dose-fractionated into 50 frames (40 ms per frame) with a dose rate of 30 electrons per pixel per s (approximately 1.03 electrons per Å2 per frame), resulting in a total dose of 51 electrons per Å2.

Cryo-EM image processing for the TCR–CD3 in detergent
The 15,726 collected movie stacks were gain-normalized, motion-corrected, dose-weighted, and binned over 2 × 2 pixels in MotionCor2 v1.1.053 and imported into cryoSPARC v4.2 for all further processing (see data processing overview and motion-corrected micrograph examples in Supplementary Fig. 1). A subset of the larger dataset, comprising 4,278 micrographs, was used to generate an initial model. The contrast transfer function (CTF) parameters were determined with CTFFIND v455 implemented in cryoSPARC. Micrographs were curated by average defocus value, CTF fit resolution and CTF cross correlation, yielding 3,292 motion-corrected micrographs with CTF estimations for downstream processing. Particles were picked with the cryoSPARC blob-picker using a size range of 80 to 180 Å. These initial particles were filtered by power level and normalized cross-correlation score. Particles were subjected to serial rounds of 2D classification, wherein particles with the best alignment, as determined quantitatively by resolution estimate or qualitatively by appearance of fine structure, were advanced to the next round of 2D classification and selection. The best 82k particles were used for ab initio modeling, specifying three models. One model was clearly recognizable as TCR–CD3 in a detergent micelle; the other two models were amorphous blobs. All three models were used to seed 3D heterogeneous refinement using the same 82k particles, and the resulting TCR–CD3 model was used as an initial model in downstream processing.

We returned to the full dataset of 15,726 motion-corrected micrographs. CTF parameters were estimated using Patch CTF in cryoSPARC. Micrographs were curated by average defocus value, defocus range, CTF fit resolution, and relative ice thickness, yielding 15,153 micrographs for particle picking. Particles were picked with 20-Å lowpass-filtered templates from the model of the TCR–CD3 in detergent determined above and filtered by power level and normalized cross-correlation score. Particles were subjected to serial 2D classification as above and 3D classification by multireference refinement (using the heterogeneous refinement job in cryoSPARC). Serial rounds of 3D classification were seeded with the best available model along with decoy noise models to remove poorly aligning particles. Decoy classes were created by aborting ab initio modeling jobs after the first round of modeling before any structure was visible. The best aligning particles emerging from 3D classification were then used to train a Topaz (v 0.2.5a) particle picking model56 in cryoSPARC and the resulting particle stacks subjected to iterative rounds of 2D classification, 3D variability analysis, 3D classification, Topaz retraining, and particle pooling until map quality reached a plateau.

Picking from the 15,153-micrograph set with the best Topaz model yielded 2M particles. These particles were subjected to serial 2D classification, and the best 951k particles were entered into three rounds of serial 3D classification seeded with the best prior model and three noise decoys. This yielded 311k particles which were subjected to an additional round of 2D classification, from which 248k particles were selected. These particles, along with those from the best prior Topaz-picked model (242k), were re-extracted in 300×300-pixel boxes and pooled to ensure that all high-quality particles were captured for final reconstruction. One round of 2D classification was performed on the extracted particles and duplicates removed, yielding 388k unique particles. Of these, the 310k best-aligning particles were selected to advance into a final cascade of five rounds of serial 3D classification seeded with the best prior model and 3 noise decoys. The final 252k particle set emerging from these classifications was subjected to non-uniform refinement, yielding a map with nominal resolution of 3.3 Å. For this and all nominal resolutions listed herein, resolutions are reported from gold-standard Fourier shell correlation (FSC) curves with a cut-off criterion of 0.143. Local refinement with a mask including the protein components but excluding the micelle density further improved map quality and interpretability.

Cryo-EM image processing for the TCR–CD3 in nanodiscs
The 17,766 collected movie stacks were gain-normalized, motion-corrected, dose-weighted, binned over 2 × 2 pixels, and CTF parameters estimated as above for GDN (see data processing streams and motion-corrected micrograph examples in Supplementary Fig. 2). Micrographs were curated by CTF fit resolution and cross correlation, yielding 14,852 motion-corrected micrographs with CTF estimations for downstream processing.

Three parallel processing streams were pursued based on different approaches to particle picking (Supplementary Fig. 2). Particles were picked in cryoSPARC using (1) an unbiased blob-picking approach using a particle size range of 60 to 120 Å, (2) a template-based approach with a model of the TCR–CD3 in detergent (PBD 6JXR5) docked into the MSP1D1DH5 nanodisc using the CHARMM-GUI web server [https://www.charmm-gui.org/]57 nanodisc builder58, and (3) a template-based approach using the structure of the TCR–CD3 in detergent (PBD 6JXR5). For each processing stream, 2D image classification was performed on 4×4 binned particle images to eliminate classes of noise or poorly aligning particles and ab initio modeling was performed. At this stage, it was clear that the particles picked using 6JXR templates were of poor quality and did not yield reconstructions substantially different from those obtained with the other two processing streams (see below) and this avenue was thus aborted. For the other two streams, particle stacks were subjected to iterative rounds of 2D classification, ab initio modeling, 3D variability analysis, and 3D classification until map quality plateaued. The best aligning particles from these two streams were pooled and iterative rounds of 2D classification, 3D classification, Topaz training, and particle pooling was performed until map quality plateaued.

Having now exhaustively sampled the micrographs for well-aligning particles, the best aligning 2.2M unique particles were extracted into 256×256-pixel boxes without binning. After one round of 2D classification and selection, 1.8M particles remained. These particles were subjected to two rounds of serial 3D heterogenous refinement seeded with the best two available maps along with two noise maps, yielding 630k and 809k particles in the two best classes. After re-extraction into 300×300-pixel boxes without binning, these two classes, which we now denote ND-I and ND-II, contained 620k and 797k particles and refined to nominal resolutions of 3.2 Å and 3.0 Å, respectively. Local refinement with a mask including the protein components but excluding the nanodisc density further improved map quality and interpretability.

Cryo-EM image processing for the TCR–CD3 in nanodiscs incubated with HLA
The 13,644 collected movie stacks gain-normalized, motion-corrected, dose-weighted, binned over 2 × 2 pixels, and CTF parameters estimated as above for GDN, ND-I, and ND-II (see data processing stream and motion-corrected micrograph examples in Supplementary Fig. 7). Micrographs were curated by average defocus, astigmatism, CTF fit resolution, and CTF fit cross correlation, yielding 10,610 motion-corrected micrographs with CTF estimations for downstream processing. Particles were initially picked by a template-based approach using a model of the TCR–CD3 in nanodiscs ND-II conformation with HLA from PDB 2BNQ59 docked by alignment of the TCR variable domains. Templates were 20-Å lowpass-filtered prior to particle picking. These initial particles were filtered by power level and normalized cross-correlation score and subjected to iterative 2D classification, ab initio modeling, 3D classification (by heterogeneous refinement and by 3D classification without alignment), and Topaz training.

To ensure exhaustive sampling of the available data, particle picks from the best three Topaz models were pooled and duplicate particles removed, yielding 3.3M unique particles. These particles were extracted in 400×400-pixel boxes (binned 2×2) to ensure adequate accommodation for delocalized signal around the elongated TCR–CD3–HLA particles. One round of 3D heterogenous refinement was performed, seeded with the best two maps (one showing strong HLA density and one showing weaker HLA density) from the prior 3D classifications alongside 2 decoy maps. The 895k particles aligning to the HLA-strong model were subjected to an additional cycle of 3D classification without alignment, seeded by principal component analysis and specifying six classes. The six output volumes were used as the starting classes for one round of heterogeneous refinement with the 895k-particle set. One class comprising 192k particles refined to higher resolution than the others. The same analysis (3D classification without alignment followed by 3D heterogenous refinement) was performed for the HLA-weak set, yielding a single high-resolution class with 215k particles. These two sets of particles were re-extracted into a 400×400-pixel box without binning, and combined with the prior best particle stack, comprising 297k particles, also extracted now in a 400×400-pixel box. Duplicate particles were removed, yielding 587k unique particles. These particles were subjected to four rounds of serial 3D heterogeneous refinement seeded with the HLA-strong and HLA-weak models, with the particles classifying in the HLA-strong class advancing to the next round. This yielded 155k particles in the final set. Non-uniform refinement of this particle set yielded a map with a nominal resolution of 3.6 Å. Local refinement was performed with a custom mask excluding the bulk lipid of the nanodisc, yielding a map with a nominal resolution of 3.5 Å. This map was used for refinement in Phenix. Additional local refinements on subsets of the structure (e.g., TCR variable domains – HLA) were performed and used to guide manual model building but were not used for final refinement or calculation of refinement statistics.

Cryo-EM map assessment and post-processing
Gold-standard FSC curves were generated in cryoSPARC during refinement and nominal resolutions are reported with a cut-off criterion of 0.143. FSC curves for final maps are shown in Supplementary Figs. 1, 2, 7. Map anisotropy was assessed with 3DFSC60 implemented in cryoSPARC, and local resolution analysis was also performed in cryoSPARC (Supplementary Figs. 1, 2, 7). Although all models were refined against unsharpened maps (see Model building and refinement, below), sharpened maps were used for visual interpretation, manual model building, and figure generation. Map sharpening (Supplementary Fig. 3) was performed either by global B-factor approaches (better visualization of lipid densities) or DeepEMhancer v0.1461 (better visualization of protein densities).

Model building and refinement
The model of TCR–CD3 in GDN micelles was built starting with rigid-body docking of prior models in ChimeraX v1.6.1-1.1062. Specifically, PDB 6JXR was used as a starting model for the CD3 proteins and the TCR constant regions and transmembrane helices, PDB 7FJD was used to dock the cholesterols, and PDB 6BNU59 was used as a starting model for the 1G4 TCR variable regions. This starting model readily docked into the map, anchored by the clear alignment of immunoglobulin folds to the map features and continuous density for all chains through the ectodomains, juxtamembrane linkers, and transmembrane helices (see example densities in Supplementary Fig. 3). As with prior TCR–CD3 structures, the cytoplasmic unstructured regions were not resolved in the map. The model was improved by iterative cycles of refinement with phenix.real_space_refine in PHENIX v1.20.163, manual adjustment in Coot v0.9.8.764, and brief map-constrained relaxation in ISOLDE v1.965. Residues without clear side-chain density beyond Cβ were manually modeled as stubs. Cores for the N-linked glycans were built in Coot using tools included in the package66 (modeled glycans indicated graphically in Supplementary Fig. 6d). For all models, refinement data are shown in Supplementary Table 2, and model completeness is shown in Supplementary Table 3. All cryo-EM map and molecular model figures were generated in ChimeraX.

For the ND-I and ND-II models, the TCR–CD3-in-GDN model built above was used as a starting model and domains were docked into the map in ChimeraX. Again, the immunoglobulin folds in the TCR and CD3 ectodomains were readily anchored into the map features, with bulky hydrophobic residues and disulfide bonds periodically confirming register (map density overviews and sections shown in Supplementary Fig. 3). Regions without clear density in the maps were removed and residues without clear side-chain density beyond Cβ were manually modeled as stubs. For the CD3ϵ, CD3γ, and CD3δ proteins, the register for the juxtamembrane linker and transmembrane helices were anchored by the placement of the C-X-X-C liats at the beginning of the juxtamembrane region (Supplementary Fig. 3f). Bulky hydrophobic side chains provided further confirmation of register within the transmembrane helices. In ND-I, the register at the amino-terminus of the CD3ϵG transmembrane helix could not be unambiguously determined, so this helix was left unmodeled. For CD3ζ, the GDN model of these chains was docked in place and manually refit, using the interchain disulfide bond and bulky hydrophobic residues to confirm register (Supplementary Fig. 3g). For TCRα and TCRβ, the linker peptides between their constant domains and the transmembrane helices were poorly structured and left unmodeled. Their transmembrane helices were docked from the GDN structure relative to the surrounding helices and bulky side chains were used to confirm register (Supplementary Fig. 3h). Model building and refinement was performed in Phenix, Coot, ChimeraX, and ISOLDE as described above for the TCR–CD3-in-GDN structure.

For the TCR–CD3–HLA structure, the GDN structure readily docked into the cryo-EM map. The HLA–β2M–NY-ESO-1 structure from PDB 2BNQ59 was docked into the map. The structure was refined in Phenix as described above. Example density with constructed model is shown in Supplementary Fig. 7j.

Sequence conservation analysis
TCR and CD79-BCR sequences were analyzed using the Consurf67 web server [https://consurf.tau.ac.il/] and colored according to relative sequence conservation level in ChimeraX. For computation of sequence identity among GDN and the published TCR–CD3 structures in detergent, sequences were aligned with the Clustal Omega web server [https://www.ebi.ac.uk/jdispatcher/msa/clustalo]68.

Engineered disulfide-bond analysis
Cysteine mutant pairs were designed with the Cα-Cα parameters described in the main text. For Western blot analysis, HEK293F cells were plated at 1.5×106 cells/mL in 6-well plates, 2 mL per well. Cells were infected with baculovirus expressing the indicated TCR–CD3 mutants at a volume 1/30 the culture volume and the indicated CD3 mutants at 1/15 the culture volume. All TCRα mutants were paired with the same wild-type TCRβ sequence. 10 mM sodium butyrate was added at the same time as viral infection to enhance expression. Cultures were incubated for 2 days at 37°C, 8% CO2. Cells were harvested by centrifugation at 20,800×g, and resuspended in Laemmli sample buffer with or without 100 mM dithiothreitol (GoldBio). Samples were heated for 5 min at 95°C prior to separation by SDS-PAGE. Gels were electroblotted to immobilon-P PVDF membranes (Millipore), rinsed in 200 mM NaCl, 20 mM Tris (Research Products International), pH 8 (TBS), supplemented with 0.05% Tween-20 (TBST, Sigma Aldrich), and blocked in neat Intercept Blocking Buffer (Li-Cor) for 1 h at room temperature. Blots were incubated with mouse anti-GFP antibody (1:3,000, Roche/Sigma Aldrich, Cat. # 11814460001) in TBST with 10% block overnight at 4°C. Blots were washed with TBST at 25°C, incubated with Alexa 700-conjugated goat anti-mouse IgG (1:10,000, Invitrogen, Cat. # A-21036) in TBST with 10% block at 25°C for 1 h, and again washed sequentially with TBST and TBS. Blots were imaged on an Amersham Typhoon infrared fluorescence scanner.

To confirm the results seen in whole-cell lysates, mutant TCR–CD3 was also detergent extracted from membrane pellets and NiNTA-affinity purified, as was done for cryo-EM analyses, and NiNTA elutions in DDM/CHS were processed for Western blot analysis. To further confirm that these crosslinks were formed in properly assembled TCR–CD3 complexes, select disulfide-bond mutants were also reconstituted into nanodiscs and purified by size-exclusion chromatography, by the same protocol used for producing TCR–CD3 in nanodiscs for cryo-EM analysis. For these samples, protein constituents were visualized in gels using Acquastain as per manufacturer specifications.

Mass spectrometry
Protein was produced as for structural studies but using the indicated mutant TCR–CD3 constructs with TCRβ fused to EGFP and CD3ζ untagged. The CD3 proteins, including 8xHis-tagged CD3γ were expressed from a single virus. Instead of reconstitution into nanodiscs, the TCR–CD3 complex was eluted from NiNTA-agarose resin in HBS containing DDM/CHS, free cysteines were blocked with 20 mM iodoacetamide (Sigma Aldrich) in the dark for 1 h at room temperature. The reaction was quenched, and detergent was removed by acetone precipitation. Protein complexes were deglycosylated with PNGase F and sequentially digested using sequencing-grade trypsin for 3 h, followed by a second digestion step using chymotrypsin for another 3 h. Enzymatic activity was quenched by acidification using trifluoroacetic acid and the resulting peptides were purified using in-house constructed C18 micropurification tips prior to LC-MS MS analysis69. Peptides were separated across a 70-min linear gradient, in which the fraction of solvent B (80% acetonitrile, 0.1% formic acid) over solvent A (1% acetonitrile, 0.1% formic acid) was increased from 2% to 35% using an Easy nLC 1200 Ultra-High Performance Liquid Chromatography system (Thermo Fisher Scientific) equipped with a 100 μm-by-120 μm pulled-emitter column packed with 3-μm C18 material (Nikkyo Technos). Analysis was carried out using a Fusion Lumos Tribrid mass spectrometer (Thermo Fisher Scientific) operating in positive-ion DDA, FT-mode. Ions were fragmented using EThcD. Disulfide bonds were identified using the XlinkX software through Proteome Discoverer v. 2.5 (Thermo Fisher Scientific).

Generation of an NFAT-reporter cell line
The J8Zb2m-α-β- cell line for TCR–CD3 display was generated as follows: The Jurkat-derived cell line J.RT3-T3.5 (ATCC, TIB-153) lacking endogenous TCRβ chain was modified to express an NFAT reporter and CD8 via transduction with retroviral supernatant from HEK293GP-GLV9 producer cells transfected with the 8xNFAT-ZsG-hCD8 plasmid (a gift from Maki Nakayama, Addgene plasmid # 153417)70,71. 48 h post transduction, transductants were stimulated with PMA/Ionomycin overnight and sorted on CD8+ZsGreen+ cells using a Sony SH800. Sorted cells were allowed to recover in vitro for 5 days and subsequently nucleofected using a Nucleofector IIb device (Program X-001) with β2M sgRNA (5’-CGCGAGCACAGCUAAGGCCA-3’; Synthego) pre-complexed with TrueCut Cas9 V2 (Invitrogen, A26498) according to the manufacturer’s recommendation. Nucleofected cells were allowed to recover in RPMI + 2 mM L-glutamine + 20% FCS without antibiotics for 2 days and thereafter HLA-ABC- live cells were sorted using a Sony SH800.

To then render the resulting cell line deficient for the TCRα chain, TRAC gene sgRNA (5’-TCTCTCAGCTGGTACACGGC-3’; IDT) and Alt-R S.p. Cas9 Nuclease V3 (IDT 1081058) were introduced using a Lonza 4D-Nucleofector. TRAC knock-out was confirmed by ICE CRISPR Analysis Tool v3 (Synthego) using Sanger sequencing data flanking the edited cut site. Primer pair: Forward 5’-AGCTTGTGCCTGTCCCTGAG-3’, Reverse 5’-GGGCTGGGGAAGAAGGTGT-3’. The sorted cell line, termed J8Zb2m-α-β-, was expanded and maintained in vitro in RPMI + 2 mM L-glutamine + 10% FCS + 100 μg/mL penicillin/streptomycin.

Viral transduction of T-cell lines
SFG-1G4 wild-type and mutant plasmids were synthesized by Genscript. For transfection of the plasmids mentioned above, 293GP cells (Takara Bio, 631458) were co-transfected with 4.5 μg SFG plasmid and 2.25 μg RD114 feline envelope plasmid (provided by S.A. Rosenburg, NIH) using Lipofectamine™ 3000 Transfection Reagent (Thermo Fisher Scientific, L3000075). Viral supernatant was harvested on day 2 post transfection and always used freshly.

T-cell line transduction was mediated by retronectin (Takara, T100B). In brief, retronectin was coated on a non-tissue-culture-treated plate at 10 μg/mL in PBS overnight. Coated plates were washed by PBS and blocked by 2% FBS in PBS for 30 min at room temperature. The blocking solution was aspirated, viral supernatant was added to the coated plates, and the plates were centrifuged at 2500×g for 2 h at 32°C. Viral supernatant was aspirated after centrifugation. Cells were loaded on plates and spun at 300 g for 30 min at 32°C. On day 2 post transduction, transduction rates were measured by flow cytometry (see below) after staining with phycoerythrin-labeled anti-human TCR Vβ13.1 antibody (3% v/v, Biolegend, Cat. # 362410). For the TCRα mutant and matching wild-type controls, cells were stained by iTAg tetramer/APC – HLA-A*02:01 NY-ESO-1 (SLLMWITQC) (5% v/v, MBL, Cat. # TB-M011-2; prior to HLA-tetramer activation assay) or APC anti-human CD3 antibody (5% v/v, Biolegend, 317318; prior to NY-ESO-1 peptide-pulsed COS-7-A2 cell activation assay) and enriched using the EasySep™ Human APC Positive Selection Kit II (Stemcell Technologies, 17661). TCRβ mutants showed high transduction efficiency and were not immuno-enriched (Supplementary Fig. 8e). Data shown in Supplementary Fig. 8c, f are mean +/- standard deviation using biologic triplicates. Raw data are provided in Supplementary Tables 6 and 8.

Flow cytometry
For cell-surface staining, cells were stained with antibodies for 45 min in PBS + 0.5% FBS at 4°C. Cells were washed twice with PBS + 0.5% FBS and flow cytometry data were acquired on an LSR Fortessa X-20 using FACSDIVA v9.2 (BD) and analyzed in FlowJo v10.6.2 (BD). Antibodies used in our surface staining are Brilliant Violet 785™ anti-human CD3 antibody (5% v/v, Biolegend, 317330), and PE anti-human TCR Vβ13.1 antibody (3% v/v, Biolegend, 362410). To visualize HLA binding, iTAg tetramer/APC-HLA-A*02:01 NY-ESO-1 (SLLMWITQC) (5% v/v, MBL, TB-M011-2) was used. Cells were counterstained with the viability reagent LIVE/DEAD Fixable Aqua Dead Cell Stain Kit as per manufacturer’s specifications (0.7% v/v, Thermo Fisher Scientific, L34966). Singlet, live cells were identified for further analysis by a standard gating hierarchy (Supplementary Fig. 8d).

NY-ESO-1–HLA tetramer activation assay
J8Zb2m-α-β- cells with or without transduction were loaded onto 96-well plates (Corning, 353072) at 2e5 cells per 100 μL complete medium in triplicates. Plates were left at room temperature for 20 min and transferred to Incucyte SX1 system (Sartorius) for baseline images at 37°C and 5% CO2. The plate was then removed from Incucyte and loaded with 100 μL of iTAg tetramer/APC-HLA-A*02:01 NY-ESO-1 at 8 μg/mL, 4 μg/mL or 0 μg/mL as designated. Cell Stimulation Cocktail (PMA and ionomycin, Thermo Fisher Scientific, 00-4970-93) was added at 1:1000 as a positive control. Again, the plate was left at room temperature for 20 min and transferred to Incucyte system for imaging at 37°C and 5% CO2. Instrumental setting: 20x objective lens, 9 images per well, 2-h scan interval, using Sartorius Incucyte v2023A. Analysis was done using Incucyte Basic Analysis Software v2023A. Data shown in Supplementary Fig. 8b are mean +/- standard deviation using biologic triplicates. Raw data are provided in Supplementary Table 5. P values were determined by unpaired, two-tailed t-tests using Prism 10 software (GraphPad), and the predetermined significance threshold was P < 0.05.

NY-ESO-1 peptide-pulsed COS-7-A2 activation assay
COS-7-A2 cells were treated with NY-ESO-1(157-165) peptide (customized synthesis, Genscript, purity 96.4%) at designated concentrations at 37°C and 5% CO2 for 1 h and washed twice with PBS. J8Zb2m-α-β- cells were mixed with COS-7-A2 cells at 1:2 ratio or treated with Cell Stimulation Cocktail (PMA and ionomycin, Thermo Fisher Scientific, 00-4970-93) at 1:1000 as positive control. Cells were incubated at 37°C and 5% CO2 for 18 h and NFAT activity (ZsGreen positive rate) was readout by flow cytometry. ZsGreen positivity was gated against the untransduced controls. Data shown in Fig. 7i are mean +/- standard deviation using biologic triplicates. Raw data are provided in Supplementary Table 7. P values were determined by unpaired, two-tailed t-tests without adjustment for multiple comparisons using Prism v10 software (GraphPad) or Excel v16.102 (Microsoft), and the predetermined significance threshold was P < 0.05.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Over the past few decades, many studies have elucidated structural data regarding the TCR–CD3 complex in both unbound and ligand-bound states. However, the mechanism through which ligand binding to the extracellular domains induces TCR conformational change and activation remains enigmatic. In this manuscript, Notti and colleagues seek to address this important question by employing cryo-electron microscopy (cryo-EM). They present the structures of the unliganded TCR–CD3 complex in detergent micelles and nanodiscs, along with the HLA-bound human TCR–CD3 complex in nanodiscs. This study identifies two previously unobserved conformations of the unliganded TCR–CD3 complex in nanodiscs, termed “closed and compacted” conformations. Notably, these appear distinct from the “open and extended” conformations reported in detergent environments. Cross-linking data seem to corroborate the idea that these conformations represent the resting state of an unstimulated TCR–CD3 complex on T cells. Furthermore, the structure of the nanodisc-embedded TCR–CD3 complex bound to HLA showcases significant conformational changes upon ligand binding, adopting an “open and extended” conformation. My major concerns are regarding the map quality of the “closed and compacted” conformations, which exhibit significant preferred orientation that could substantially impact the structural model. Given the authors’ aim to discuss the conformational differences between the resting and activated states, these issues are particularly relevant. 1. The ND-I and ND-II maps appear abnormal (Supplementary Figs. 2c, d), which is also indicated by the 3D FSC analyses of these maps (Supplementary Figs. 2f, h). These analyses suggest severe preferred orientation, with missing orientations parallel to the cell membrane. A representative 2D classification image should be included for further evaluation. 2. The authors claim that much of the density in the ND-I and ND-II maps has an estimated local resolution better than 4 Å, asserting that the maps are of sufficient quality to build models. However, more representative local density maps with the corresponding models are needed to validate this claim. In fact, the deposited model does not satisfactorily match the deposited map. 3. It is important to note that preferred orientation may also lead to the density of cholesterol molecules clustering together. 4. Have the authors considered using graphene oxide or some other detergents to improve preferred orientations? Alternatively, tilting the sample stage could be an effective approach. Overall, while the manuscript presents intriguing findings, addressing these concerns regarding map quality and model validation is essential for supporting the authors’ conclusions.", "reviewer_2": "The authors in this research article solve the cryo-EM structure of the αβTCR-CD3 complex in ligand-free and ligand-bound states in lipid nanodiscs. The structures presented indicate a ‘closed’ structure in the unliganded state and an ‘open’ structure in the liganded pMHC bound state thereby suggesting allosteric conformational changes occurring in the complex upon activation. As an initial result, the authors solve the cryo-EM structure of the αβTCR-CD3 complex in detergent micelles (GDN) which indicated a structure very similar to the previous cryo-EM TCR-CD3 structures obtained in detergents. They subsequently solved the structure in native lipid-like environment using nanodiscs. They obtained 2 distinguishable structures of the complex, both much shorter than the structure obtained with GDN, representing a closed-form conformation. The authors then identify that the compaction of the structure in nanodiscs causes changes in the associated cholesterol molecules and further reveal different TCR-CD3 ectodomain-lipid interactions. To identify the physiological resting state of the complex, the authors engineered disulfide linkages based on the ‘closed’ nanodiscs and ‘open’ detergent-based structures. SDS-PAGE and Western blot analysis revealed disulfide linkages based on ‘closed’ nanodiscs structures which meant that the closed conformation is the resting structure of the αβTCR-CD3 complex. Finally, the authors solved the cryo-EM structure of αβTCR-CD3 in complex with single-chain-trimeric-pMHC and identified that pMHC-bound conformation was nearly identical to the detergent-derived αβTCR-CD3 structure, which would represent the ‘open’ active conformation. Further, locking the TCR in the ‘closed’ state using disulfide linkage reduced TCR signaling. Overall, the work presented is extensive with multiple cryo-EM structures of the αβTCR-CD3 complex with some biochemical analysis. However, questions remain about the validity and physiological relevance of the ‘closed’ nanodiscs based conformations. The following concerns must be addressed before considering the manuscript for publication in Nature Communications. Major concerns: 1) An important concern is the physiological relevance of the 2 nanodiscs-based ‘closed’ conformations. There is a concern that the 2 conformations are not due to heterogeneity arising from reconstituting the αβTCR-CD3 complex in nanodiscs. A recent study by Dalal et al., Nature Commun., 2024 found that reconstituting pentameric ligand-gated ion channel (pLGIC) in different nanodiscs produces distinct structures by cryo-EM. The study also suggested the use of larger nanodiscs mimics lipid membrane environment better. Based on this, did the authors try different nanodiscs based on size to select the best environment to study the αβTCR-CD3 complex? If so, these results should be discussed. 2) There’s a recent study by Natarajan et al., EMBO Rep., 2024 that studied the in situ cell-surface conformation of the αβTCR-CD3 complex using photo-crosslinkers. This study determined that there were no significant differences between the cell surface and detergent-purified conformation of the αβTCR-CD3 complex. Further, they identified that there were no large conformational changes in the αβTCR-CD3 structure in the presence and absence of pMHC tetramers. The study, based on information from the current manuscript, attempted to detect changes in crosslinks between TCRα-CD3δ region in the presence and absence of pMHC tetramers. However, they couldn’t infer any conformational changes occurring in this region based on their evidence. The Natarajan et al. study needs to be extensively discussed in the manuscript especially since it was conducted on mammalian cells which is much more physiologically relevant than detergents or nanodiscs. 3) In the result section ‘The unliganded TCR-CD3 is closed and compacted in vivo’, multiple cysteine mutants were created to establish disulfide linkages which were tested by SDS-PAGE analyses and Western blotting. However, these mutants must be tested for viability by using pMHC tetramers and compared to the wild type. In Figure 7, S104C/V182C mutant is tested for NY-ESO-1-MHC-tetramer binding. Similarly, all mutants generated should be tested for its viability. In the same experiment, Western blot analyses were done for identifying disulfide linkages between TCRα and CD3δ. However, the blots were stained only for EGFP which is conjugated with TCRβ. To rule out any artifacts, a construct containing a tag should be conjugated to CD3δto aid in CD3δ-TCRαβ crosslinks detection like the one used in Natarajan et al., EMBO Rep., 2024. 4) In the result section ‘The unliganded TCR-CD3 is closed and compacted in vivo’, SDS-PAGE and Western blot analyses were done to identify crosslinks in the unliganded closed conformation. A similar experiment in the presence of pMHC should be done with the same mutant pairs (G79C-D77C, G79C-N38C and S78C-D77C). TCR αβ-CD3δcrosslinks should be absent in the blots for these mutants as pMHC bound form would lead to an ‘open’ conformation of the αβTCR-CD3 complex. We feel this experiment is crucial to support their overall result and conformation-change hypothesis. 5) As indicated by the authors, in the ND-I and ND-II closed conformations, docking of MHC-II-CD4 complex lead to hindrance with the lipid bilayers. This again brings into question the physiological viability of the nanodiscs-bound complexes. 6) In the result section ‘HLA binding is sufficient to trigger TCR-CD3 conformational change’, the authors use S104C-V182C mutant to lock the TCR in the ‘closed’ conformation which resulted in reduced signaling when compared to the WT upon activation. The reason why the signal was not completely abolished is unclear. Perhaps including multiple mutant pairs - G79C-D77C, G79C-N38C or S78C-D77C along with S104C-V182C could completely lock the TCR in the closed conformation and thereby totally abolish signaling. Minor changes: 1) In line 53, ‘TCR-HLA interaction’ should be ‘TCR-pMHC interaction’. 2) The structure illustration in Figure 1b and the color scheme used is indecipherable.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "This study from Notti and colleagues provides structural insights into the inter-subunit conformational relationships that can be assumed in the extracellular and transmembrane domains within TCR-CD3 complexes when solubilized in detergent, or when embedded within artificial membranes after reconstitution within nanodiscs. It makes important additions to the growing library of available cryoEM structures of TCR-CD3 complexes by providing examples embedded within membranes, which are expected to be more physiological than detergent solubilized complexes. Unlike prior detergent-soluble structures, showing the ectodomain of the TCR situated more upright relative to the CD3 heterodimers, the two nanodisc-embedded structures (NDI and NDII) adopt more closed arrangements with the TCR variable regions nearly touching the tops of the CD3 heterodimers. In contrast, nanodisc-embedded TCR-CD3 bound to pHLA adopt a more extended open structure in the ectodomain. The juxtamembrane connecting peptides in the extracellular space, as well as the transmembrane domains of the subunits, also show changes in the unliganded NDI/II conformations relative to the liganded states. Notably, the transmembrane regions of the subunits adopt a splayed relationship in the unliganded state that straightens in the liganded structures, consistent with prior functional data. The authors conclude that the more compact NDI and NDII complexes represent the resting state whereas the open configuration represents a liganded signaling conformation, with changes in the orientation of the constituent subunits in the ectodomain, extracellular juxtamembrane connecting peptides, and transmembrane domains providing a mechanism by which pMHC engagement leads to signaling across the membrane. Experimentally, the study relies largely on cryoEM with some supporting biochemical and functional data: 1. The investigators have the required expertise for the study. The structures appear to be reasonably presented and interpreted given the maps, which highlight residues that could not be built for the NDI and NDII structures. a. The lack of order in some regions creates some problems for insights into allosteric changes for NDI and NDII. 2. The methods indicate that different combinations of constructs were used to purify the TCR-CD3 complexes incorporated into nanodiscs for determination of unliganded structures in NDI and ND2 than were used for the liganded complexes. a. While unlikely, it is formally possible that changes in subunit tags, be they HIS or EGFP or 2A tags left on the subunits, could differentially impact the spatial relationship of the subunits within assembled complexes. What motivated the change in approach is unclear but this caveat should be noted. i. Determination of the structures of the unliganded complexes within the same preparation from which particles were picked and refined for determination of the liganded structures would address this caveat and lend confidence to the approach. The expectation is that the unliganded particles would assume the more closed orientation. ii. Providing the complex construct sequences in the supplemental information would be beneficial to the field. 3. The methods would benefit from better referencing and description of the TCR-HLA paired used and the choice thereof. 4. The manuscript would benefit from an explanation as to why, for the liganded structures, the investigators are confident that the ND-embedded complexes underwent an allosteric change from NDI or NDII. Is there a chance that a subset of complexes existed in an open conformation that favored binding, and that the process of picking and refining particles preferentially enriched liganded particle? 5. The biochemical and functional experiments are performed by conventional techniques, are well-controlled, and are reasonably well-presented for interpretation but they do not add much to the study. a. One caveat is that there is no indication that the disulphide blots or functional assays were performed more than once to ensure reproducibility. b. The methods do not indicate the temperature at which the functional assays were performed (presumably 37ºC). i. This detail is important to ensure others in the field could reproduce the work. c. It would aid in evaluation to add the molecular weights to the blots in Sup Fig 5 rather than color coding the marker and requiring the reader to cross reference the figure legend each time. d. The full gel for Sup Fig 5d and 5e would be helpful for evaluating the extent to which mature complexes are enriched through purification. The zeta subunits are below the cropping of the gel. e. The conclusions regarding Fig 7i and 7j are questionable given the data. i. The histograms in Fig 7i clearly show lower expression of the S104C/V182C mutant than the WT (note that the x-axis is on a log scale). The difference in tetramer staining, as well as the difference in signaling in Fig 7j, could easily be due to differences in surface expression of the WT and mutant TCRs. ii. I recommend removing these data as they do not add to the study and could be misinterpreted. iii. For these data to be convincing, the investigators would need to make multiple independently generated sets of Jurkat lines with matched TCR expression and show that they consistently produced functional differences between the WT and Mut. iv. The investigators should also show that the disulphide bonds are formed on TCR-CD3 complexes that have translocated to the cell surface. Title, Abstract, and Discussion level conclusions: 6. The structures provided herein are interesting and make an important contribution to the field. They provide strong evidence that different inter-subunit conformational relationships can be adopted by the transmembrane and connecting peptides of the TCR-CD3 complex subunits. That alone is important information for the field. 7. The title and abstract level conclusions cannot fully be substantiated by the data and should be calibrated to better reflect the limitations of the data. a. The conclusion that the more closed NDI/II structures represent the resting state are one of several plausible interpretations of the data. As the authors note, the T cell membrane has variable subdomains with different lipid compositions, and it is unclear if porcine neuron membranes are analogous to the membrane subdomain inhabited by TCR-CD3 complexes on the T cell membrane. b. Different environments will have different membrane curvatures (e.g., during transport to the cell membrane or when localized at the tip of a filapodia). Given that the complex is membrane-anchored during assembly and transport to the cell surface, the different conformations may represent assembly or vesicular transport conformations necessitated by the nature of those membrane. c. The effort to present functional data in Fig 7 to support their conclusions is not convincing for the reasons outlined above. These data should be removed. 8. The Discussion level conclusion “…that intrinsic conformational change in the TCR-CD3 is necessary for early TCR-CD3 activation” is not fully substantiated by the data for the reasons outlined above. However, discussing the structures presented here within the context of biochemical and functional data from other studies that support their interpretation would allow the authors to offer a plausible molecular mechanism for those prior observations and lend confidence to the author’s contention that intrinsic changes within the complex occur in the absence of intracellular kinase signaling. a. In addition to the paper cited by the authors (PMID: 34260912), it would be relevant to discuss studies showing exposure of cytoplasmic juxtamembrane proline rich sequence on CD3e can occur in the absence of kinase signaling (even at 4ºC) (PMID: 12110186, 15728235). b. Data that more directly complements the ligand-induced transmembrane allosteric changes observed here, and which should also be noted for completeness, comes from a study indicating that the zeta transmembrane domains are splayed in TCR-CD3 in the resting state and move together in the liganded state in the absence of intracellular signaling (PMID: 26231119). 9. The discussion regarding the potential role of glycosylation in regulating signaling is interesting. a. There is evidence for regulation of gdTCR-CD3 through differential glycosylation that may be worth considering (PMID: 12438426). 10. Evidence that TCR-CD3 inhabits specific membrane sub-domains that concatenate with other distinct sub-domains (e.g., LAT containing domains) are worth consideration (PMID: 20010844). If distinct membrane sub-domains have distinct thicknesses and composition, this could force changes in intra- and inter-subunit transmembrane orientations. They could also exchange sterols at the subdomain interface. They could also potentially impact lipid-protein interactions at the conserved patches noted on CD3. Regarding novelty, extensibility, broad interest and overall impact: 11. This study highlights the value of determining the structures of multi-subunit complex in the membrane environment . a. Doing so for the TCR-CD3 complex is highly novel. 12. This study provides key evidence that inter-subunit allosteric changes in the transmembrane and juxtamembrane extracellular connecting peptides of the TCR-CD3 complex can occur upon ligand engagement. a. This finding is not novel in the broad sense that evidence already exists for allosteric changes in TCR-CD3 upon ligand engagement in the absence of other signaling events, as noted above; nevertheless, it offers important molecular insights into how these changes could occur. b. A key strength of this study, for which it should make an important impact on the field, is therefore derived from contributing to a growing body of evidence that such conformational changes are the earliest events in TCR-CD3 signaling upon engagement of cognate ligands. 13. This study is likely to be of broad interests to people who study multi-subunit signaling receptors, lymphocyte activation, and receptor biology more broadly. 14. This study will also be of impact for its extensibility. The author’s present many key findings that are idea generating, and these ideas are worth testing to work towards a better understanding of this complex."}, "author_response": "Notti et al. Response to Reviewer Comments\nReviewer #1 (Remarks to the Author):\nOver the past few decades, many studies have elucidated structural data regarding the\nTCR–CD3 complex in both unbound and ligand-bound states. However, the mechanism\nthrough which ligand binding to the extracellular domains induces TCR conformational\nchange and activation remains enigmatic. In this manuscript, Notti and colleagues seek\nto address this important question by employing cryo-electron microscopy (cryo-EM).\nThey present the structures of the unliganded TCR–CD3 complex in detergent micelles\nand nanodiscs, along with the HLA-bound human TCR–CD3 complex in nanodiscs.\nThis study identifies two previously unobserved conformations of the unliganded TCR–\nCD3 complex in nanodiscs, termed “closed and compacted” conformations. Notably,\nthese appear distinct from the “open and extended” conformations reported in detergent\nenvironments. Cross-linking data seem to corroborate the idea that these conformations\nrepresent the resting state of an unstimulated TCR–CD3 complex on T cells.\nFurthermore, the structure of the nanodisc-embedded TCR–CD3 complex bound to\nHLA showcases significant conformational changes upon ligand binding, adopting an\n“open and extended” conformation.\nMy major concerns are regarding the map quality of the “closed and compacted”\nconformations, which exhibit significant preferred orientation that could substantially\nimpact the structural model. Given the authors’ aim to discuss the conformational\ndifferences between the resting and activated states, these issues are particularly\nrelevant.\n1. The ND-I and ND-II maps appear abnormal (Supplementary Figs. 2c, d), which is\nalso indicated by the 3D FSC analyses of these maps (Supplementary Figs. 2f, h).\nThese analyses suggest severe preferred orientation, with missing orientations parallel\nto the cell membrane. A representative 2D classification image should be included for\nfurther evaluation.\nAuthors’ response:\nWe thank the referee for reviewing the maps and models. As we described in main text\nlines 121-128 and showed in Supplementary Fig. 2f,h, preferred particle orientation and\nits effect on 3D reconstruction (i.e., map anisotropy) likely impacted reconstruction\nquality, alongside other factors like conformational dynamism. However, efforts to\nameliorate particle orientation preference with graphene oxide grids or fluorinated\ndetergents did not improve map quality (lines 125-126). Examination of 2D classes for\nthe nanodisc dataset do show a preference for “top” (membrane normal) views, but\nviews off this axis are also seen, including views orthogonal or near-orthogonal to the\ntop view. These data are now included as Supplementary Fig. 2i. Comparison of these\n“(tilted)-side” views with those from the GDN dataset (Supplementary Fig. 2j) clearly\nshow differences corresponding to opening-closing and extension-compaction.\nReviewer #1:\n2. The authors claim that much of the density in the ND-I and ND-II maps has an\nestimated local resolution better than 4 Å, asserting that the maps are of sufficient\nquality to build models. However, more representative local density maps with the\ncorresponding models are needed to validate this claim. In fact, the deposited model\ndoes not satisfactorily match the deposited map.\nAuthors’ response:\nWe refer the reviewer to Supplementary Fig. 3, where the fit of the models to\nDeepEMhancer-sharpened maps is shown at the level of whole-density slices (panels c-\ne) and zoomed in to areas of interest with specific features serving as intrinsic fiducial\nmarkers across maps (panels f-h). Additional sections through the density maps with\nthe corresponding models have been added as requested (Supplementary Fig. 3i-k).\nWe also refer the reviewer to Supplementary Table 2, where model validation statistics\nare shown, and highlight that holistic metrics of model quality (e.g., Molprobity score)\nand fit (e.g., dFSCmodel) at the standard 0.143 cutoff are in general agreement with (or\nin some cases exceed) the nominal resolutions.\nTo be as transparent and reproducible as possible, we chose the raw,\nunsharpened maps to serve as the “primary” maps in the PDB/EMDB, because we\nconservatively performed all statistical analyses of map-to-model correlation using these\nmaps. However, we did also deposit standard global B-factor-sharpened and\nDeepEMhancer-sharpened maps in the unified PDB/EMDB deposition system, and\nthese maps can be accessed in the EMDB (Additional maps 1 and 2 under the\n“Additional data” tab for each dataset). For ease of review, we are also uploading all\nsharpened maps and the corresponding models to Figshare. We recommend the use of\nthese sharpened maps for visual inspection, as is standard in single-particle cryo-EM\n(see also Methods section, lines 879-888 for a description of map assessment and post\nprocessing). We do note the assessment of reviewer #5, that “[t]he structures appear to\nbe reasonably presented and interpreted given the maps, which highlight residues that\ncould not be built for the NDI and NDII structures.”\nLastly, the effect of conformational dynamics on local map quality and its\nfunctional implications merits brief discussion. The local resolution maps for ND-I and\nND-II are consistent with specific regions of disorder and other regions that are well\nordered, suggesting significant conformational dynamics. This interpretation is\nconsistent with the observation of more than one conformational state (ND-I and ND-II)\nwithin this dataset. The ordering of previously disordered or poorly ordered domains\nupon ligand binding is consistent with prior studies showing that ligand binding induces\nTCR rigidification (see main text lines 346-351) and suggests that these disorder-to-\norder transitions are functionally important. Moreover, the in vivo crosslinking data\npresented here (and that of the Krogsgaard group, discussed below) provide support for\nthe nanodisc conformations as the true physiologic resting state for the TCR.\nTaken together, we interpret our ND-I and ND-II maps not as “abnormal,” but as more\nbiologically accurate reflections of the TCR–CD3’s dynamic resting conformation, which\nis reflected in areas of lower local resolution in these maps.\nReviewer #1:\n3. It is important to note that preferred orientation may also lead to the density of\ncholesterol molecules clustering together.\nAuthors’ response:\nWhile map anisotropy might cause “smearing” or “stretching” of the density along axes\nperpendicular to missing views, this would not affect solely the density attributed to\ncholesterol, and well-ordered regions of the sharpened maps (e.g., ectodomain\nimmunoglobulin folds) do not suggest a global compression of the reconstruction along\nthe membrane-normal axis to cause artifactual clustering of the cholesterol density in\nthe map.\nReviewer #1:\n4. Have the authors considered using graphene oxide or some other detergents to\nimprove preferred orientations? Alternatively, tilting the sample stage could be an\neffective approach.\nAuthors’ response:\nPlease see main text lines 125-126 for discussion of these efforts. We note also that\nDeepEMhancer post-processing has been shown to reduce map anisotropy (Sanchez-\nGarcia et al., 2024, PMID: 38612769), which may in part explain the improvement in\nmap interpretability with application of this post-processing strategy. We have also\ntested a recently developed deconvolution-based algorithm for reduction of map\nanisotropy (AR-Decon; Li et al., 2025; PMID: 40060637), but this algorithm did not\nimprove map interpretability or map-to-model correlation, nor did it meaningfully change\nthe models built from the maps. Indeed, the RMSD between models refined on the\nunsharpened maps and the AR-Decon-processed maps were less than 1 Å. As there\nare no discernible changes in the models, we do not include these data.\nReviewer #1:\nOverall, while the manuscript presents intriguing findings, addressing these concerns\nregarding map quality and model validation is essential for supporting the authors’\nconclusions.\nAuthors’ response:\nSee comments above.\nReviewer #2 (Remarks to the Author):\nThe authors in this research article solve the cryo-EM structure of the αβTCR-CD3\ncomplex in ligand-free and ligand-bound states in lipid nanodiscs. The structures\npresented indicate a ‘closed’ structure in the unliganded state and an ‘open’ structure in\nthe liganded pMHC bound state thereby suggesting allosteric conformational changes\noccurring in the complex upon activation.\nAs an initial result, the authors solve the cryo-EM structure of the αβTCR-CD3 complex\nin detergent micelles (GDN) which indicated a structure very similar to the previous\ncryo-EM TCR-CD3 structures obtained in detergents. They subsequently solved the\nstructure in native lipid-like environment using nanodiscs. They obtained 2\ndistinguishable structures of the complex, both much shorter than the structure obtained\nwith GDN, representing a closed-form conformation. The authors then identify that the\ncompaction of the structure in nanodiscs causes changes in the associated cholesterol\nmolecules and further reveal different TCR-CD3 ectodomain-lipid interactions. To\nidentify the physiological resting state of the complex, the authors engineered disulfide\nlinkages based on the ‘closed’ nanodiscs and ‘open’ detergent-based structures. SDS-\nPAGE and Western blot analysis revealed disulfide linkages based on ‘closed’\nnanodiscs structures which meant that the closed conformation is the resting structure\nof the αβTCR-CD3 complex. Finally, the authors solved the cryo-EM structure of\nαβTCR-CD3 in complex with single-chain-trimeric-pMHC and identified that pMHC-\nbound conformation was nearly identical to the detergent-derived αβTCR-CD3 structure,\nwhich would represent the ‘open’ active conformation. Further, locking the TCR in the\n‘closed’ state using disulfide linkage reduced TCR signaling.\nOverall, the work presented is extensive with multiple cryo-EM structures of the αβTCR-\nCD3 complex with some biochemical analysis. However, questions remain about the\nvalidity and physiological relevance of the ‘closed’ nanodiscs based conformations. The\nfollowing concerns must be addressed before considering the manuscript for publication\nin Nature Communications.\nMajor concerns:\n1) An important concern is the physiological relevance of the 2 nanodiscs-based\n‘closed’ conformations. There is a concern that the 2 conformations are not due to\nheterogeneity arising from reconstituting the αβTCR-CD3 complex in nanodiscs. A\nrecent study by Dalal et al., Nature Commun., 2024 found that reconstituting pentameric\nligand-gated ion channel (pLGIC) in different nanodiscs produces distinct structures by\ncryo-EM. The study also suggested the use of larger nanodiscs mimics lipid membrane\nenvironment better. Based on this, did the authors try different nanodiscs based on size\nto select the best environment to study the αβTCR-CD3 complex? If so, these results\nshould be discussed.\nAuthors’ response:\nWhen reconstituting an integral membrane protein in lipid nanodiscs, it is important to\nensure that the MSP size (and thus nanodisc diameter) is sufficient to allow for\nincorporation of the membrane with a buffer layer of lipids to minimize obligate MSP-\ntarget protein interactions. The most likely explanation for the different structures\nobserved for pLGIC in different nanodiscs is that some of the nanodiscs used were too\nsmall in this regard (Hiotis et al., 2025; PMID: 40203538). However, for single-particle\ncryo-EM of relatively small membrane protein complexes like the TCR–CD3, it is\nnecessary to use the smallest possible nanodisc to facilitate particle alignment on\nordered protein density (and not MSP/lipid amorphous density). In this work, we used\nthe MSP1D1ΔH5 nanodisc system. The referee seems to question whether this\nnanodisc might introduce artifacts by being too small for the system of interest. If this\nnanodisc were too small, we would expect the TCR to favor an open/extended\nconformation, by virtue of insufficient lipid-protein interaction area to promote\ncompaction and by favoring the smaller TM cross-sectional profile of this “less splayed”\nconformation. That is to say, we would expect an inadequately sized nanodisc to\ngenerate an artifactually open/extended TCR–CD3, not to induce the novel\nconformations we report here. Nonetheless, prior to pursuing single-particle cryo-EM,\nwe did simulate the incorporation of the open/extended conformation of the TCR–CD3\n(PDB 6JXR at the time) into various sized nanodiscs using the CharmmGUI molecular\ndynamics preparation interface. This approach reassured us that the MSP1D1ΔH5\nnanodisc ( ~8nm diameter) would have adequate cross-sectional area to incorporate the\nonly known conformation of the TCR–CD3 at that time with a buffer of lipids between\ntarget protein and MSP. We show these data in Supplementary Fig. 2k-m, and briefly\ndiscuss this in main text lines 117-120.\nReviewer #2:\n2) There’s a recent study by Natarajan et al., EMBO Rep., 2024 that studied the in situ\ncell-surface conformation of the αβTCR-CD3 complex using photo-crosslinkers. This\nstudy determined that there were no significant differences between the cell surface and\ndetergent-purified conformation of the αβTCR-CD3 complex. Further, they identified that\nthere were no large conformational changes in the αβTCR-CD3 structure in the\npresence and absence of pMHC tetramers. The study, based on information from the\ncurrent manuscript, attempted to detect changes in crosslinks between TCRα-CD3δ\nregion in the presence and absence of pMHC tetramers. However, they couldn’t infer\nany conformational changes occurring in this region based on their evidence. The\nNatarajan et al. study needs to be extensively discussed in the manuscript especially\nsince it was conducted on mammalian cells which is much more physiologically relevant\nthan detergents or nanodiscs.\nAuthors’ response:\nWe thank the referees for encouraging discussion of this recent work from Michelle\nKrogsgaard’s group, which was published during revisions of this manuscript. We make\nseveral points in response to their assessment, and as requested, discuss their findings\nextensively in the main text (lines 441-497).\nFirst, we point out that our engineered disulfide crosslinking studies of the human\n1G4 TCR (Figure 5) and functional studies in T cells (Figure 7) were indeed performed\nin human cells. Natarajan et al. studied the heterologous expression of mouse 2B4 TCR\nin human cells, and while the impact of this mismatch on a nuanced understanding of\nTCR conformational dynamics is uncertain, our studies of a human TCR in human cells\nare at least as physiologically relevant.\nNonetheless, we find that our novel resting state structures are in remarkable\nagreement with the data of Natarajan et al., explaining nearly all of their crosslinking\nfindings for the unliganded TCR–CD3 in situ that they could not explain with the\nstructures of the TCR–CD3 in detergent. For example, Natarajan et al. report an\nunexpected crosslink between the mouse TCRβ CC’ loop residues S170 and G171\n(human S186 and G187) and CD3δ. In our models, compaction of the TCR–CD3 in\nconformation ND-I brings CD3δ residues 26-30 in closer proximity to TCRβ residues\n186 and 187 than the GDN conformation (red arrows, Supplementary Fig. 9a), making\ncrosslinking plausible. Similarly, Natarajan et al. report crosslinks between the TCRβ\nFG-loop and CD3γ, which would not be predicted by the conformation of the TCR–CD3\nin detergent (Supplementary Fig. 9b, right). However, closure and compaction in ND-I\nbring the FG-loop into close proximity with the CD3γ amino terminus (Supplementary\nFig. 9b, left, dashed circle). Lastly, they report crosslinks between the helix 4–F-strand\nregion of TCRβ and CD3ϵ and CD3δ, which they wrote could not be explained by the\nstructure of the TCR–CD3 in detergent. The mouse TCRβ R207 (human R223)\ncrosslink to CD3ϵ is indeed incompatible with the GDN conformation (Supplementary\nFig. 9c, right), but this residue is brought into close proximity of CD3ϵ in the ND-I\nconformation (Supplementary Fig. 9c, left). In contrast to their assessment, the mouse\nTCRβ F202 (human F218) crosslink to CD3δ is compatible with both the open/extended\nand ND-II closed/compacted conformations that we report (Supplementary Fig. 9d).\nTaken together, the photo-crosslinking data reported by Natarajan et al. provide strong\nin vivo support for our novel, closed/compacted resting conformation for the TCR–CD3\ncomplex.\nNatarajan et al. went on to analyze the effect of activating ligands (pMHC\ntetramers and activating antibodies) on TCR–CD3 conformation using their photo-\ncrosslinking assay. However, we contend that this analysis was flawed in the following\nways and cannot exclude the possibility of ligand-induced conformational change:\n1. Most p-azido phenylalanine substitution sites used in this study would not be\nexpected to show changes in subunit-level crosslinking pattern on ligand-induced\nreceptor activation. In Figure 6a of their report, Natarajan et al. schematize the\nsites selected for crosslinking readout with and without activating ligands. Most of\nthese sites (exceptions discussed below) are at the core interfaces between the\nCD3 ectodomains and/or the TCR constant domains. Our models do not suggest\nthat there are gross structural changes at these interfaces, and thus we would\nnot predict changes in crosslinking readout from these mutants.\n2. We might have hypothesized that changes in the FG-loop-directed crosslinks\nwould be observed on ligand binding (TCRβ E221, W225 in their study).\nHowever, the only ligand used to stimulate the receptor for these mutants was\nthe FG-loop-binding H57 antibody, and by our read of the report, it is not\napparent that binding of the antibody to the mutants was assessed. For example,\nthe side chain of W225 stabilizes the fold of the FG-loop, forming the\nhydrophobic core of this microdomain. Mutation of this residue would be\nreasonably expected to affect H57 binding. Lastly, without quantitation, it is\ndifficult to assess whether the reporters are not in fact showing any changes in\ncrosslinking efficiency.\n3. Natarajan et al. directly assessed crosslinks based on our reported TCRα and\nCD3δ interface and their crosslink-based computational models. One crosslink\ndesigned from each of these structural hypotheses was expressed on the cell\nsurface. The reporter based on our cryo-EM models formed a crosslink between\nTCRα and CD3δ (see their Appendix Fig. 16c) and the reporter based on their\nopen/extended model did not form a crosslink. As with the other crosslinking data\ndiscussed above, this finding actually supports our novel resting state\nconformations.\n4. They show that qualitatively, this crosslink (human CD3δ D77 p-Aza to TCRα)\npersists after activating ligand exposure. However, this is what we would expect\ngiven the low structural resolution and nonspecificity of the photocrosslinking\napproach. That is to say, the crosslinking readout in their report is unidirectionally\nspecific: while the p-azido phenylalanine mutation site is specified, the site of\ncrosslinking on the target polypeptide is not. A complex in two different\nconformations for which the photocrosslinker remains in close proximity to the\ntarget polypeptide chain (even in proximity of totally different target residues) will\nstill show a crosslink-induced gel shift on Western blot by this assay. For both of\nour open/extended conformations (GDN and ND-HLA), the D77 Cβ remains 8-9\nÅ from TCRα (Supplementary Fig. 9e). This distance is comparable to the 9-10 Å\nradius previously used by the Krogsgaard group as a cutoff for plausible\ncrosslinking (Wang et al., 2014; PMID: 25061810). Modeling the UV-activated\nnitrene intermediate of CD3δ D77 p-azido phenylalanine into ND-HLA as the\nmost likely rotamer of tyrosine places the reactive nitrene less than 4 Å from the\nTCRα backbone (Supplementary Fig. 9e).\n3) In the result section ‘The unliganded TCR-CD3 is closed and compacted in vivo’,\nmultiple cysteine mutants were created to establish disulfide linkages which were tested\nby SDS-PAGE analyses and Western blotting. However, these mutants must be tested\nfor viability by using pMHC tetramers and compared to the wild type. In Figure 7,\nS104C/V182C mutant is tested for NY-ESO-1-MHC-tetramer binding. Similarly, all\nmutants generated should be tested for its viability.\nAuthors’ response:\nThe hypothesis being tested by these crosslinking experiments was structural, not\nfunctional. HLA binding is not germane to the question here, which is strictly whether\nthe residues are present within a specified radius of one another. We make no assertion\nof the HLA-binding competence of these mutants, with the exception of that used for\nfunctional analyses (TCRα S104C/V182C).\nReviewer #2:\nIn the same experiment, Western blot analyses were done for identifying disulfide\nlinkages between TCRα and CD3δ. However, the blots were stained only for EGFP\nwhich is conjugated with TCRβ. To rule out any artifacts, a construct containing a tag\nshould be conjugated to CD3δ to aid in CD3δ-TCRαβ crosslinks detection like the one\nused in Natarajan et al., EMBO Rep., 2024.\nAuthors’ response:\nIt is not clear what specific artifacts are hypothesized by the referee. For each of the\nmutants at the TCRα-CD3δ interface in Figure 5, a control is shown in which the mutant\nis paired with wild-type TCRα or CD3δ to rule out confounding by formation of crosslinks\nto other polypeptides. If a gel shift occurs only when both cysteine mutants in a pair are\npresent, it can be reasonably inferred that the crosslink is between those two species. It\nis important to note that the chemistry of our crosslinking is different from that of\nNatarajan et al., requiring mutations at two defined sites and allowing assessment of\ncrosslink specificity by the control permutations described.\nReviewer #2:\n4) In the result section ‘The unliganded TCR-CD3 is closed and compacted in vivo’,\nSDS-PAGE and Western blot analyses were done to identify crosslinks in the\nunliganded closed conformation. A similar experiment in the presence of pMHC should\nbe done with the same mutant pairs (G79C-D77C, G79C-N38C and S78C-D77C). TCR\nαβ-CD3δcrosslinks should be absent in the blots for these mutants as pMHC bound\nform would lead to an ‘open’ conformation of the αβTCR-CD3 complex. We feel this\nexperiment is crucial to support their overall result and conformation-change\nhypothesis.\nAuthors’ response:\nUnfortunately, the proposed experiment is not compatible with the engineered disulfide\nsystem. The disulfides form on exposure to molecular oxygen during complex\nassembly. Their formation is not temporally controlled.\nWe reiterate that the role of the engineered disulfide experiments was to test\nwhether the conformation of the unliganded TCR–CD3 in the cellular context is\nempirically consistent with the observed conformation in nanodiscs or with that in\ndetergents. The ligand-bound conformation was then observed directly in nanodiscs\nand is consistent with the ligand-bound form observed in detergents. We do not\nhypothesize that the ligand-bound form in vivo would be distinct from that observed in\nthese two separate structural model systems. To reiterate, the point of this experiment\nwas to settle a conflict of physiological relevance between structural models from two\ndifferent systems, and no such conflict exists for the ligand-bound conformation.\nReviewer #2:\n5) As indicated by the authors, in the ND-I and ND-II closed conformations, docking of\nMHC-II-CD4 complex lead to hindrance with the lipid bilayers. This again brings into\nquestion the physiological viability of the nanodiscs-bound complexes.\nAuthors’ response:\nWe disagree with the referee’s interpretation of these modeling results. We predict that\nour ND-I and ND-II conformations are permissive for HLA binding by computational\nmodeling, and when we mix unliganded TCR in nanodiscs with HLA we indeed observe\ncomplex formation (see ND-HLA structure). HLA binding to TCR–CD3 triggers a\nconformational change into the open/extended conformation. We show by modeling that\nthe geometry of this conformation is permissive of TCR-pHLA-coreceptor ternary\ncomplex formation. These results are entirely consistent with a model in which HLA\nbinds to the TCR, inducing a conformational change, which then permits ternary\ncomplex formation and signal transduction.\nReferee #2:\n6) In the result section ‘HLA binding is sufficient to trigger TCR-CD3 conformational\nchange’, the authors use S104C-V182C mutant to lock the TCR in the ‘closed’\nconformation which resulted in reduced signaling when compared to the WT upon\nactivation. The reason why the signal was not completely abolished is unclear. Perhaps\nincluding multiple mutant pairs - G79C-D77C, G79C-N38C or S78C-D77C along with\nS104C-V182C could completely lock the TCR in the closed conformation and thereby\ntotally abolish signaling.\nAuthors’ response:\nWhile we agree that testing multiple pairs is indeed interesting and ongoing work will\naddress similar questions, this work is outside the scope of the current manuscript. We\nshow here a single engineered disulfide mutant pair with surface expression and ligand\nbinding comparable to wild type to avoid confounding from impaired surface expression\nand/or receptor affinity. Identifying double or even triple engineered disulfides that do\nnot compromise these functions is likely to prove challenging. (As it stands, Reviewer\n#5 finds even the single mutant’s surface expression to be unacceptable.)\nThere are several possible reasons for the partial amelioration of signaling. The\ndisulfide may not form reliably in 100% of cell surface-localized receptors, resulting in a\nmixed population on the cell surface. Even if formed, the disulfide may not completely\nprevent conformational change, perhaps allowing formation of intermediate\nconformations, if not the open/extended conformation, per se. Lastly, ligand-induced\nclustering may otherwise change the membrane milieu in ways that “tip the scales” to\nallow for partial (suboptimal) signaling even without conformational change. None of\nthese hypothetical caveats are mutually exclusive. Nonetheless, the ability of this\nmutant to reduce peak signaling with surface expression and ligand binding grossly\ncomparable to wild type is evidence of the importance of conformational change in the\nprocess of ligand-induced receptor activation. We now discuss these caveats explicitly\nin the text (lines 498-510).\nReviewer #2:\nMinor changes:\n1) In line 53, ‘TCR-HLA interaction’ should be ‘TCR-pMHC interaction’.\nAuthor’s response:\nWe have changed “TCR-HLA interaction” in line 51 to “TCR interaction with HLA–\nantigen complex,” consistent with the human nomenclature used in this manuscript.\nReviewer #2:\n2) The structure illustration in Figure 1b and the color scheme used is indecipherable.\nAuthors’ response:\nFigure 1b shows an overlay of backbone representations for the published structures of\nthe TCR–CD3 in detergent. It highlights the near identical conformation of the complex\nin all such structures. The RMSD between these structures is on the order of 1 Å. The\npoint of the figure is exactly as the reviewer observes: That when overlayed, these\nstructures are essentially indistinguishable.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nReviewer #5 (Remarks to the Author):\nThis study from Notti and colleagues provides structural insights into the inter-subunit\nconformational relationships that can be assumed in the extracellular and\ntransmembrane domains within TCR-CD3 complexes when solubilized in detergent, or\nwhen embedded within artificial membranes after reconstitution within nanodiscs. It\nmakes important additions to the growing library of available cryoEM structures of TCR-\nCD3 complexes by providing examples embedded within membranes, which are\nexpected to be more physiological than detergent solubilized complexes. Unlike prior\ndetergent-soluble structures, showing the ectodomain of the TCR situated more upright\nrelative to the CD3 heterodimers, the two nanodisc-embedded structures (NDI and\nNDII) adopt more closed arrangements with the TCR variable regions nearly touching\nthe tops of the CD3 heterodimers. In contrast, nanodisc-embedded TCR-CD3 bound to\npHLA adopt a more extended open structure in the ectodomain. The juxtamembrane\nconnecting peptides in the extracellular space, as well as the transmembrane domains\nof the subunits, also show changes in the unliganded NDI/II conformations relative to\nthe liganded states. Notably, the transmembrane regions of the subunits adopt a\nsplayed relationship in the unliganded state that straightens in the liganded structures,\nconsistent with prior functional data. The authors conclude that the more compact NDI\nand NDII complexes represent the resting state whereas the open configuration\nrepresents a liganded signaling conformation, with changes in the orientation of the\nconstituent subunits in the ectodomain, extracellular juxtamembrane connecting\npeptides, and transmembrane domains providing a mechanism by which pMHC\nengagement leads to signaling across the membrane.\nExperimentally, the study relies largely on cryoEM with some supporting biochemical\nand functional data:\n1. The investigators have the required expertise for the study. The structures appear to\nbe reasonably presented and interpreted given the maps, which highlight residues that\ncould not be built for the NDI and NDII structures.\na. The lack of order in some regions creates some problems for insights into allosteric\nchanges for NDI and NDII.\nAuthors’ response:\nWe thank the reviewer for their careful and thoughtful analysis of the paper. As they\npoint out, we were conservative in our modeling of the structure, omitting regions where\ndisorder prevents reasonable modeling of protein backbone, sidechain, glycan, or lipid\natoms. Disorder-to-order transitions are functionally important and ubiquitous\nthroughout protein biochemistry. While such a transition precludes confident modeling\nof disordered residues in static structures, that does not, in our view, detract from their\nfunctional significance. Much in the way that allostery might be explained\nmechanistically through a transition between two well-ordered states, it can also be\nexplained mechanistically through a disorder-to-order transition. Our data are in\nagreement with the data reported by others showing rigidification of the TCR upon\nligand binding (references 15 and 32 in main text), suggesting this disorder-to-\norder transition is mechanistically important for early TCR activation.\nReviewer #5:\n2. The methods indicate that different combinations of constructs were used to purify\nthe TCR-CD3 complexes incorporated into nanodiscs for determination of unliganded\nstructures in NDI and ND2 than were used for the liganded complexes.\na. While unlikely, it is formally possible that changes in subunit tags, be they HIS or\nEGFP or 2A tags left on the subunits, could differentially impact the spatial relationship\nof the subunits within assembled complexes. What motivated the change in approach is\nunclear but this caveat should be noted.\nAuthors’ response:\nWe agree that differences in carboxy-terminal tags are highly unlikely to explain\ndifferences in conformation of the complex, given that the carboxy-termini of the TCR–\nCD3 constituents are unstructured and thus not observed in the cryo-EM maps. When\nwe began this work, it was anticipated that tandem affinity purification would be needed\nto obtain intact complex. This was fortunately not the case, and we thus simplified our\nconstructs during the course of this investigation. Nonetheless, the position of the His\ntag is the same in all constructs used for cryo-EM, all constructs use furin cleavage site-\nP2A linkers between complex components on the same virus, and all constructs have a\ncarboxy-terminal fluorescent protein fusion to TCRβ. In the three-virus expression\nscheme, CD3ζ carries a carboxy-terminal EGFP fusion, which is separated from the\nstructured components by over 100 disordered amino acids; therefore, we think it\nexceedingly unlikely that this would affect the structural models we report. Indeed, the\ncrosslinking studies were performed without a carboxy-terminal EGFP fusion on CD3ζ\nand are consistent with the cryo-EM data. The rationale for vector simplification is now\nnoted in the text, lines 672-675.\nReviewer #5:\n3. The methods would benefit from better referencing and description of the TCR-HLA\npaired used and the choice thereof.\nResponse:\nWe have added additional references providing the full history of the 1G4(LY) TCR\nspecific for the NY-ESO-1 cancer-testis antigen.\nReviewer #5:\n4. The manuscript would benefit from an explanation as to why, for the liganded\nstructures, the investigators are confident that the ND-embedded complexes underwent\nan allosteric change from NDI or NDII. Is there a chance that a subset of complexes\nexisted in an open conformation that favored binding, and that the process of picking\nand refining particles preferentially enriched liganded particle?\nAuthor’s response:\nAs discussed in the main text, lines 112-117, we attempted to bias data processing of\nthe unliganded TCR–CD3 in nanodisc dataset towards identifying the open/extended\nconformation, but this did not yield any 3D reconstructions consistent with the\nopen/extended conformation. Analysis of 2D classification data (now in Supplementary\nFig. 2i,j) shows no classes of particles specifically suggestive of the open/extended\nconformation. Despite due diligence to identify an open/extended subset of particles in\nthe resting state dataset, we do not detect this in our data.\nReviewer #5:\n5. The biochemical and functional experiments are performed by conventional\ntechniques, are well-controlled, and are reasonably well-presented for interpretation but\nthey do not add much to the study.\na. One caveat is that there is no indication that the disulphide blots or functional assays\nwere performed more than once to ensure reproducibility.\nAuthors’ response:\nFor the studies of T-cell function, the assays were performed in biological triplicate (see\nlines 1026, 1075, 1084, 1097, and 1469) and the raw data were provided in\nSupplementary Table 5 (and now also Supplementary Tables 6 and 7). For the\nqualitative analysis of engineered disulfide formation, the data are indeed reproducible\nand a replicate is now shown in Supplementary Fig. 5b. Moreover, this same analysis\nwas performed separately on whole cell lysates (Supplementary Fig. 5b), NiNTA-affinity\npurified protein (Supplementary Fig. 5c), and for select mutants, TCR–CD3 complex\nreconstituted into nanodiscs (Supplementary Fig. 5d,e), with results reproducible across\nthese different processing streams.\nReviewer #5:\nb. The methods do not indicate the temperature at which the functional assays were\nperformed (presumably 37ºC).\ni. This detail is important to ensure others in the field could reproduce the work.\nAuthors’ response:\nThis is correct and has been added to the methods section.\nReviewer #5:\nc. It would aid in evaluation to add the molecular weights to the blots in Sup Fig 5 rather\nthan color coding the marker and requiring the reader to cross reference the figure\nlegend each time.\nAuthors’ response:\nWe have added the color-coding key to the figure itself to facilitate evaluation. We do\nfeel the color coding is easier to read and less obtrusive over blot and gel images than\ntext, particularly for dense figures, and have kept this scheme, but certainly appreciate\nthe reviewer’s perspective here.\nReviewer #5:\nd. The full gel for Sup Fig 5d and 5e would be helpful for evaluating the extent to which\nmature complexes are enriched through purification. The zeta subunits are below the\ncropping of the gel.\nAuthors’ response:\nThe full-length gels are now shown for Supplementary Fig. 5d,e, revealing the MSP and\nCD3 proteins.\nReviewer #5:\ne. The conclusions regarding Fig 7i and 7j are questionable given the data.\ni. The histograms in Fig 7i clearly show lower expression of the S104C/V182C mutant\nthan the WT (note that the x-axis is on a log scale). The difference in tetramer staining,\nas well as the difference in signaling in Fig 7j, could easily be due to differences in\nsurface expression of the WT and mutant TCRs.\nii. I recommend removing these data as they do not add to the study and could be\nmisinterpreted.\niii. For these data to be convincing, the investigators would need to make multiple\nindependently generated sets of Jurkat lines with matched TCR expression and show\nthat they consistently produced functional differences between the WT and Mut.\niv. The investigators should also show that the disulphide bonds are formed on TCR-\nCD3 complexes that have translocated to the cell surface.\nAuthors’ response:\nRelative to the untransfected control, surface expression of CD3ϵ, surface expression of\nTCRβ, and ligand binding by the mutant are all grossly comparable to the wild type.\nIndeed, this mutant-wild type pair compares quite favorably with other published\nexamples of engineered T-cell panels with purportedly “similar” ligand binding, including\nfrom Nature Communications (e.g., Supplementary Fig. 3C in Finnigan et al., 2024;\nPMID: 38459027). Nonetheless, we now show data for independently transduced TCR\nclones, for which surface CD3ϵ, surface TCRβ, and HLA-tetramer binding in the TCRα\nS104C/V182C mutant is at least numerically equivalent to (if not greater than) the wild\ntype (Supplementary Figure 8c, Supplementary Table 6). For these clones, we assayed\nTCR activation in a more demanding and physiologically relevant system using HLA-\nA*02:01-positive COS-7-A2 cells presenting NY-ESO-1 peptide to stimulate the reporter\ncells instead of HLA-tetramers. In this more rigorous assay, we observed an even more\npronounced phenotype, with the mutant showing a 42% reduction in activated T cells\n(Fig. 7i, Supplementary Table 7). We still include the data from the original clones\nassayed with the HLA-tetramer assay, as they are from an independent clone set and\nare consistent with these new data in a separate experimental system, but we have\nmoved them to the Supplement (Supplementary Fig. 8a,b and Supplementary Table 5).\nRegarding disulfide formation, we demonstrate robust disulfide formation for this\ndouble-cysteine mutation pair by mass spectrometry (Figure 5e and Supplementary\nTable 4). Although this analysis does not allow us to quantify the fraction of disulfide-\ncrosslinked TCRs on the cell surface in the Jurkat model, the mass spectrometry data\nfrom HEK cells make it reasonable to infer that the disulfide is present in some fraction\nof surface-expressed receptor complexes. We discuss in the main text the caveats\ninherent in disulfide formation efficiency being unquantified.\nReviewer #5:\nTitle, Abstract, and Discussion level conclusions:\n6. The structures provided herein are interesting and make an important contribution to\nthe field. They provide strong evidence that different inter-subunit conformational\nrelationships can be adopted by the transmembrane and connecting peptides of the\nTCR-CD3 complex subunits. That alone is important information for the field.\n7. The title and abstract level conclusions cannot fully be substantiated by the data and\nshould be calibrated to better reflect the limitations of the data.\na. The conclusion that the more closed NDI/II structures represent the resting state are\none of several plausible interpretations of the data. As the authors note, the T cell\nmembrane has variable subdomains with different lipid compositions, and it is unclear if\nporcine neuron membranes are analogous to the membrane subdomain inhabited by\nTCR-CD3 complexes on the T cell membrane.\nb. Different environments will have different membrane curvatures (e.g., during\ntransport to the cell membrane or when localized at the tip of a filapodia). Given that the\ncomplex is membrane-anchored during assembly and transport to the cell surface, the\ndifferent conformations may represent assembly or vesicular transport conformations\nnecessitated by the nature of those membrane.\nAuthor’s response:\nWe agree that the membrane subdomains within a given cell are numerous, with\ndiverse physicochemical profiles. To say there is a single “native” membrane\nenvironment, for example, would be overly simplistic. Future efforts are aimed at\ndetermining how these various membrane environments alter TCR structure and\nfunction. However, the data we show here are the first to determine the structure of the\nTCR–CD3 complex in a lipid bilayer of any kind and is thus a priori a more plausible\nmodel of the TCR–CD3 than any of the unliganded TCR–CD3 models from detergent,\nwhich have been – and continue to be – published in the highest impact journals without\napparent concern for the physiological relevance of the micellar environment. Our\nmodel is then supported empirically by the engineered disulfide crosslinking data from\nhuman cells (this manuscript) as well as the independently generated photo-crosslinking\ndata from the Krogsgaard group (see discussion above; Natarajan et al., 2024, EMBO\nReports). Moreover, we show that the TCR–CD3 undergoes large conformational\nchanges in this model membrane environment in response to activating ligands,\nproviding further evidence that the ND-I/II conformational family represent a ligand-\nreceptive resting state. While this model may be nuanced by future studies of\nmembrane composition and geometry on TCR–CD3 structure and function, we argue\nthat our interpretation of the data has adequate empiric support and is the most\nparsimonious explanation for the data at hand.\nc. The effort to present functional data in Fig 7 to support their conclusions is not\nconvincing for the reasons outlined above. These data should be removed.\nAuthors’ response:\nSee above.\nReviewer #5:\n8. The Discussion level conclusion “…that intrinsic conformational change in the TCR-\nCD3 is necessary for early TCR-CD3 activation” is not fully substantiated by the data for\nthe reasons outlined above. However, discussing the structures presented here within\nthe context of biochemical and functional data from other studies that support their\ninterpretation would allow the authors to offer a plausible molecular mechanism for\nthose prior observations and lend confidence to the author’s contention that intrinsic\nchanges within the complex occur in the absence of intracellular kinase signaling.\na. In addition to the paper cited by the authors (PMID: 34260912), it would be relevant\nto discuss studies showing exposure of cytoplasmic juxtamembrane proline rich\nsequence on CD3e can occur in the absence of kinase signaling (even at 4ºC)(PMID:\n12110186, 15728235).\nb. Data that more directly complements the ligand-induced transmembrane allosteric\nchanges observed here, and which should also be noted for completeness, comes from\na study indicating that the zeta transmembrane domains are splayed in TCR-CD3 in the\nresting state and move together in the liganded state in the absence of intracellular\nsignaling (PMID: 26231119).\nAuthors’ response:\nPrevious length limitations had unfortunately prevented us from including discussion of\na number of deserving studies from the vast, over four-decades-long TCR literature.\nHowever, we have now been able to include the supplied references, and appreciate\nthe Reviewer’s assistance in curating these.\nReviewer #5:\n9. The discussion regarding the potential role of glycosylation in regulating signaling is\ninteresting.\na. There is evidence for regulation of gdTCR-CD3 through differential glycosylation that\nmay be worth considering (PMID: 12438426).\nAuthors’ response:\nWe appreciate the reviewer’s interest in the role of glycosylation in regulation of TCR\nfunction. As the γδTCR has notable structural, functional, and biochemical differences\nfrom the αβTCR – including the location of its glycosylation sites – we will\nconservatively limit our discussion to the αβTCR for this manuscript.\nReviewer #5:\n10. Evidence that TCR-CD3 inhabits specific membrane sub-domains that concatenate\nwith other distinct sub-domains (e.g., LAT containing domains) are worth consideration\n(PMID: 20010844). If distinct membrane sub-domains have distinct thicknesses and\ncomposition, this could force changes in intra- and inter-subunit transmembrane\norientations. They could also exchange sterols at the subdomain interface. They could\nalso potentially impact lipid-protein interactions at the conserved patches noted on CD3.\nAuthors’ response:\nWe agree with the reviewer’s inclination that lipid membrane environment may impact\nTCR structure and function, and this work is ongoing, as discussed above.\nReviewer #5:\nRegarding novelty, extensibility, broad interest and overall impact:\n11. This study highlights the value of determining the structures of multi-subunit\ncomplex in the membrane environment .\na. Doing so for the TCR-CD3 complex is highly novel.\n12. This study provides key evidence that inter-subunit allosteric changes in the\ntransmembrane and juxtamembrane extracellular connecting peptides of the TCR-CD3\ncomplex can occur upon ligand engagement.\na. This finding is not novel in the broad sense that evidence already exists for allosteric\nchanges in TCR-CD3 upon ligand engagement in the absence of other signaling events,\nas noted above; nevertheless, it offers important molecular insights into how these\nchanges could occur.\nb. A key strength of this study, for which it should make an important impact on the field,\nis therefore derived from contributing to a growing body of evidence that such\nconformational changes are the earliest events in TCR-CD3 signaling upon\nengagement of cognate ligands.\n13. This study is likely to be of broad interests to people who study multi-subunit\nsignaling receptors, lymphocyte activation, and receptor biology more broadly.\n14. This study will also be of impact for its extensibility. The author’s present many key\nfindings that are idea generating, and these ideas are worth testing to work towards a\nbetter understanding of this complex.\nAuthors’ response:\nWe thank the reviewer for their complementary assessment of our work’s novelty and\nimplications."}, {"round": 2, "reviewer_comments": {"reviewer_1": "My concerns about severe preferred orientation issues in the 'closed and compacted' conformation maps remain unaddressed. Based on our direct experience with membrane protein complexes, such orientation bias can generate artifactual 'conformational differences' similar to those claimed here. Consequently, I find the structural models and the corresponding interpretation unreliable. That said, the functional data appear convincing (though I defer to TCR specialists for final assessment). If other reviewers are satisfied, I support publication, as long as the structural data caveats were mentioned in the paper. This study would be much more impactful if supplemented with higher-quality cryo-EM data.", "reviewer_2": "After review of the revised manuscript, I feel that the authors have addressed all concerns satisfactorily and I recommend this manuscript for publication in Nature Communications. The work presented is extensive and addressed an important problem involving TCR activation. Many findings of Natarajan et al., EMBO Reports, 2024 was corroborated by the present work. We thank the authors for the detailed discussion (with figures in the supplementary section) of the work by Natarajan et al. especially - 1) explaining the compatibility of different crosslinks obtained between Cβ CC’ loop-CD3δ, Cβ FG loop – CD3γ, Cβ helix 4 – F strand- CD3δ/CD3ε and the ND-based open cryo-EM structure; 2) detailing the reason for lack of changes in crosslinks between CD3δ D77-TCRα in the presence and absence of pMHC tetramers. While critiquing the findings of Natarajan et al., the authors incorrectly state that the binding ability of FG loop targeting H57 antibody to W225-pAzpa mutant complex wasn’t assessed in the study. Although lower than other mutant complexes W225-pAzpa showed reasonable H57 antibody binding (Appendix Figure S4 in Natarajan et al.).This statement (line 482-484) should be removed from the manuscript. In Fig 7h and 7i, my previous concern regarding testing only one mutant – S104C-V182C is not addressed satisfactorily. It is completely unclear why testing multiple mutants were outside the scope of the present study. This must be clarified. If not for testing multiple mutant pairs involving G79C-D77C, G79C-N38C and S78C-D77C combinedly, at least one of these alternate pairs could have been tested to strengthen their hypothesis. I understand the concern regarding complex expression which was raised by another reviewer. But the cells can be sorted for comparable expression of the complex to the wild type and tested for activation.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "I think this is a important study that makes valuable contributions to the field. I appreciate and respect the author's carefully considered responses and viewpoints, and agree that their ND structures are more likely to represent the physiological state of TCR-CD3 in a living T cell than detergent structures. I also agree with the authors that their title level conclusion regarding resting-state is the most consistent with the available data, even if alternative explanations remain formally possible. Finally, I think the author's discussion of the Natarajan, et al., data in context with their own is particularly valuable and makes an important addition to the broader discussion within the field regarding T cell activation."}, "author_response": "REVIEWER COMMENTS AND AUTHORS’ RESPONSE\nReviewer #1 (Remarks to the Author):\nMy concerns about severe preferred orientation issues in the 'closed and compacted'\nconformation maps remain unaddressed. Based on our direct experience with\nmembrane protein complexes, such orientation bias can generate artifactual\n'conformational differences' similar to those claimed here. Consequently, I find the\nstructural models and the corresponding interpretation unreliable.\nThat said, the functional data appear convincing (though I defer to TCR specialists for\nfinal assessment). If other reviewers are satisfied, I support publication, as long as the\nstructural data caveats were mentioned in the paper. This study would be much more\nimpactful if supplemented with higher-quality cryo-EM data.\nAuthors’ response:\nTo address more fully the reviewer’s concerns about preferred orientation-induced\nartifacts, we perform several computational analyses, which show that preferred particle\norientation cannot explain the observed closed, compacted conformation seen in 3D\nreconstructions from the ND dataset. Specifically, we test whether the paucity of\nstraight-on “side views” (views parallel to the membrane plane) in the ND\nreconstructions relative to the GDN dataset (Supplementary Figure 2i) could artifactually\nyield a 3D reconstruction in the closed, compacted conformation. To do this, we 2D\nclassified the 251k particles in the final GDN reconstruction into 50 classes and\nremoved classes corresponding to true side views, as well as remaining poorly aligned\nparticles (Response Figure 1a). Tilted views in between a “side” and “top” (membrane\nnormal) view were retained, as analogous views are seen in the ND dataset\n(Supplementary Figure 2i). Using the selected 155k particles, we performed ab initio\nreconstruction, nonuniform refinement, and local refinement as in the main manuscript.\n3D reconstruction from this particle stack showed a similar preferred particle orientation\nto the ND reconstructions, as shown by a similar 3D FSC profile (Response Figure 1b,\nleft), but still generated an unambiguously open, extended 3D reconstruction (Response\nFigure 1b, right). This shows that a preferred particle orientation resulting in a relative\ndeficiency of side views of the TCR–CD3 complex is not sufficient to generate an\nartifactually closed, compacted reconstruction. Moreover, if we seed reconstruction of\nthis 155k particle set with the ND-II conformation as a starting model (instead of an\nunbiased ab initio model), the refinement fails to yield a high-resolution reconstruction\nand the 3D FSC curve declines linearly with a different morphology than that seen in the\nND dataset (Response Figure 1c). Taken together, these results show that deficiency of\nside views from a dataset with an open, extended “ground truth” conformation is not\nsufficient to cause artifactual closure and compaction, even when attempting to bias the\ndata processing towards this result.\nResponse Figure 1: Computational analysis of the impact of preferred orientation\non closure and compaction of TCR–CD3 3D reconstruction\nTo highlight our experimental efforts to improve map quality, we here show density for\nthe TCR–CD3 complex in nanodiscs reconstruction from 425k particles on graphene\noxide coated-grids with fluorinated Fos-choline present. 3D reconstructions still show a\nclosed, compacted complex, but the preferred particle orientation persists, and the\nreconstructions are not of noticeably higher quality (Response Figure 2).\nResponse Figure 2: Example reconstruction of the TCR–CD3 complex in\nnanodiscs from graphene oxide grids with fluorinated Fos-choline present.\nReviewer #2 (Remarks to the Author):\nAfter review of the revised manuscript, I feel that the authors have addressed all\nconcerns satisfactorily and I recommend this manuscript for publication in Nature\nCommunications. The work presented is extensive and addressed an important problem\ninvolving TCR activation.\nMany findings of Natarajan et al., EMBO Reports, 2024 was corroborated by the\npresent work. We thank the authors for the detailed discussion (with figures in the\nsupplementary section) of the work by Natarajan et al. especially - 1) explaining the\ncompatibility of different crosslinks obtained between Cβ CC’ loop-CD3δ, Cβ FG loop –\nCD3γ, Cβ helix 4 – F strand- CD3δ/CD3ε and the ND-based open cryo-EM structure; 2)\ndetailing the reason for lack of changes in crosslinks between CD3δ D77-TCRα in the\npresence and absence of pMHC tetramers.\nAuthors’ response:\nWe appreciate the reviewer’s recognition of the synergy of these two datasets.\nWhile critiquing the findings of Natarajan et al., the authors incorrectly state that the\nbinding ability of FG loop targeting H57 antibody to W225-pAzpa mutant complex wasn’t\nassessed in the study. Although lower than other mutant complexes W225-pAzpa\nshowed reasonable H57 antibody binding (Appendix Figure S4 in Natarajan et al.).This\nstatement (line 482-484) should be removed from the manuscript.\nAuthors’ response:\nWe thank the reviewer for pointing out this figure panel in the Supplementary Data of\nNatarajan et al. (2024) EMBO J. We have removed discussion of the FG-loop mutants\nfrom the manuscript.\nIn Fig 7h and 7i, my previous concern regarding testing only one mutant – S104C-\nV182C is not addressed satisfactorily. It is completely unclear why testing multiple\nmutants were outside the scope of the present study. This must be clarified. If not for\ntesting multiple mutant pairs involving G79C-D77C, G79C-N38C and S78C-D77C\ncombinedly, at least one of these alternate pairs could have been tested to strengthen\ntheir hypothesis. I understand the concern regarding complex expression which was\nraised by another reviewer. But the cells can be sorted for comparable expression of the\ncomplex to the wild type and tested for activation.\nAuthors’ response:\nWe now include additional data for an opening-constraining TCRβ disulfide mutant\n(T130C/H172C). We previously demonstrated disulfide formation for this mutant by\nmass spectrometry (Figure 5 d,f). Here we show that despite greater-than-wildtype\nsurface expression (Supplementary Figure 8e,f and source data in Supplementary\nTable 8), this mutant shows a significantly attenuated response to peptide-HLA\nstimulation (Supplementary Figure 8g and source data in Supplementary Table 9). This\nphenotype is consistent with the results we have already shown for the TCRα disulfide\nmutant (S140C/V182C) and provide further support for the hypothesis that\nconformational change is critical for ligand-dependent TCR activation. We also show\ndata for our efforts to test other conformation-constraining mutants. We include flow\ncytometry characterization for two disulfide mutants predicted to prevent TCR–CD3\nextension (TCRβ Q220C/V274C and TCRβ R223C/L275C; Supplementary Figure 8e,f\nand source data in Supplementary Table 8). Both mutants show a significant reduction\nin surface TCR–CD3 expression and HLA binding, precluding unambiguous functional\nanalysis, as previously pointed out by the reviewers.\nReviewer #5 (Remarks to the Author):\nI think this is a important study that makes valuable contributions to the field. I\nappreciate and respect the author's carefully considered responses and viewpoints, and\nagree that their ND structures are more likely to represent the physiological state of\nTCR-CD3 in a living T cell than detergent structures. I also agree with the authors that\ntheir title level conclusion regarding resting-state is the most consistent with the\navailable data, even if alternative explanations remain formally possible. Finally, I think\nthe author's discussion of the Natarajan, et al., data in context with their own is\nparticularly valuable and makes an important addition to the broader discussion within\nthe field regarding T cell activation.\nAuthors’ response:\nWe thank the reviewer for their careful review and appreciate the positive assessment\nof our revised manuscript."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors present an interesting argument that their 3D reconstruction of the GDN dataset, which exhibits an open, extended conformation, persists even after removing certain side-view particle classes. They note that the preferred orientation profile of their final particle set (n=155k) is similar to that of the ND reconstruction. However, a similar orientation profile does not mean the ND dataset exhibits preferred orientation in the same way. Also, upon close inspection of the 155k-particle stack used for reconstruction, it is evident that particles with side-view orientations are still present. If these residual side-view particles are removed, does the 3D reconstruction become more compact and closed? In fact, is the current reconstruction with 155k particles more compacted than the one reconstructed using all particles?", "reviewer_2": "The authors have satisfactorily addressed all previous concerns, and I recommend the manuscript for publication in Nature Communications. However, one dataset appears to be missing: in the rebuttal, the authors mention testing additional disulfide mutants (TCRβ Q220C/V274C and TCRβ R223C/L275C) with poor TCR-CD3 surface expression and HLA binding. While the expression data are shown in Supplementary Fig. 8e, the corresponding HLA binding data should be included to support these claims fully."}, "author_response": "REVIEWER COMMENTS AND AUTHORS’ RESPONSE\nReviewer #1 (Remarks to the Author):\nThe authors present an interesting argument that their 3D reconstruction of the GDN\ndataset, which exhibits an open, extended conformation, persists even after removing\ncertain side-view particle classes. They note that the preferred orientation profile of their\nfinal particle set (n=155k) is similar to that of the ND reconstruction.\nHowever, a similar orientation profile does not mean the ND dataset exhibits\npreferred orientation in the same way. Also, upon close inspection of the 155k-particle\nstack used for reconstruction, it is evident that particles with side-view orientations are\nstill present. If these residual side-view particles are removed, does the 3D\nreconstruction become more compact and closed? In fact, is the current reconstruction\nwith 155k particles more compacted than the one reconstructed using all particles?\nAuthors’ response:\nThe simulated dataset was constructed to remove the “dead-on” side views in the GDN\ndataset, which are not unambiguously present in the ND dataset, with the goal of\ncreating a qualitatively similar particle orientation profile to that in the ND dataset. Tilted\nviews off the membrane-normal axis were retained as these were also present in the\nND dataset (Supplementary Figure 2i). The independent reconstruction (i.e., computed\nfrom an ab initio starting model) using the 155k particle set is indeed open and\nextended, of course with fewer fine features than that from the full GDN particle stack\n(Response Figure panel a, below).\nThe goal of this simulation was not to create a dataset with a worse preferred\norientation problem than the ND dataset, but one of comparable anisotropy. If only the\ntop views from the 155k particle stack are retained (Response Figure panel b),\nreconstruction from this 98k particle stack yields an uninterpretable density with a\ndubious FSC curve (Response Figure panel c). Here, the degree of anisotropy is\nprohibitive for reliable 3D reconstruction, which is not representative of the ND-I/II\nreconstructions. Of note, this result is also not merely an effect of reduced particle\nnumber, as a random selection of 98k particles from the 155k stack still reliably\nreconstructs in the open/extended conformation (Response Figure panel d).\nResponse Figure. (a) Comparison of the densities from the final GDN particle stack\nand the 155k-particle stack with intentionally introduced anisotropy. (b) Generation of an\nextremely anisotropic 98k-particle stack from within the 155k subset. (c) 3D FSC\ncurve and reconstructed density from the “top views-only” 98k-particle stack. (d) 3D FSC\ncurve and reconstructed density from a random 98k-particle subset of the 155k-particle\nstack.\nReviewer #2 (Remarks to the Author):\nThe authors have satisfactorily addressed all previous concerns, and I recommend the\nmanuscript for publication in Nature Communications. However, one dataset appears to\nbe missing: in the rebuttal, the authors mention testing additional disulfide mutants\n(TCRβ Q220C/V274C and TCRβ R223C/L275C) with poor TCR-CD3 surface\nexpression and HLA binding. While the expression data are shown in Supplementary\nFig. 8e, the corresponding HLA binding data should be included to support these claims\nfully.\nAuthors’ response:\nWe thank the reviewer for identifying a typographical error in our last response to\nreviewers document. The sentence should have ended after “poor TCR and CD3\nsurface expression,” because we did not evaluate their HLA-binding capacity. While we\nstated this incorrectly in our response to the reviewer, the results are described properly\nin the main text and the supplementary materials as just TCR–CD3 surface expression.\nBecause TCR–CD3 surface expression was reduced by ~50% for these mutants, they\nare not a fair comparator with wild-type TCR for activation mechanics and there was no\nutility in assaying their HLA-binding capacity."}]

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