T cells recognize peptide antigens presented by major histocompatibility complexes (MHC) on apposing cell surfaces as a central step in the initiation of adaptive immune responses (Rossjohn et al., 2015; Smith-Garvin et al., 2009; Dustin, 2014; Pettmann et al., 2018; Belardi et al., 2020). The antigen recognition is performed by the T-cell receptor (TCR) complex, a complex of four dimeric transmembrane proteins. In this complex, the heterodimeric TCRαβ contains the binding site for recognizing peptide antigens, and the associated CD3εδ and CD3εγ heterodimers and the CD3ζζ homodimer contain the intracellular signaling motifs that transmit antigen binding to T cell activation (Wucherpfennig et al., 2010). While this stoichiometry of the complex has been known for nearly two decades (Call et al., 2002), the structure of the TCR – CD3 complex remained a puzzling problem (Fernandes et al., 2012; Birnbaum et al., 2014; Natarajan et al., 2016) that has only been recently solved by Dong et al., 2019 with cryogenic electron microscopy (cryo-EM). To determine the structure, Dong et al. expressed all proteins of the complex in cultured cells, replaced the cell membrane around the assembled TCR – CD3 complex by the detergent digitonin, and stabilized the interactions between the extracellular (EC) domains of TCRαβ, CD3εδ and CD3εγ by chemical crosslinking. In the cryo-EM structure, the EC domains of CD3εδ and CD3εγ are both in contact with TCRαβ and with each other (see Figure 1), which explains the cooperative binding of CD3εδ and CD3εγ to TCRαβ observed in chain assembly (Call et al., 2002), mutational (Kuhns and Davis, 2007; Kuhns and Davis, 2012), and NMR experiments (He et al., 2015). As indicated by mutational experiments (Kuhns and Davis, 2007; Kuhns and Davis, 2012), the DE loop of the membrane-proximal constant domain Cα of TCRα is in contact with the CD3εδ EC domain, and the CC’ loop of the constant domain Cβ of TCRβ is in contact with both the CD3εγ and CD3εδ EC domains in the assembled TCR – CD3 complex (see Figure 1). Outstanding questions concern the orientational variability of the TCRαβ EC domain relative to the membrane, in which the TCR – CD3 complex is embedded in its native environment, and the structural variability of the overall TCR – CD3 complex, which is constrained by the chemical crosslinking of the protein chains in the approach of Dong et al., 2019; Reinherz, 2019. The Cβ FG loop, for example, has been suggested to play a key role in T cell activation (Kim et al., 2010; Touma et al., 2006), but exhibits only rather limited contacts with CD3εγ in the cryo-EM structure (see Figure 1).

Figure 1

Download asset Open asset

In this article, we investigate the structural and orientational variability of the membrane-embedded TCR – CD3 complex in extensive, atomistic molecular dynamics (MD) simulations with a cumulative simulation length of 120 μs. Compared to the cryo-EM structure, significantly more residues of TCRαβ are involved in EC domain contacts along our simulation trajectories, in particular in the Cβ FG loop, and notably also in the variable domain Vα of the TCRα chain. We find that the TCRαβ EC domain is rather variable in its orientation, with tilt angles relative to the membrane normal that range from 15° to 55°. The tilt of the TCRαβ EC domain is both coupled to a rotation of the domain and to characteristic changes in the overall structure of the TCR – CD3 complex. These structural changes include a clear decrease of contacts in the Cβ FG loop and an increase of contacts in the Vα domain with increasing tilt angle of the TCRαβ EC domain, as well as changes in the orientation of the transmembrane (TM) helices of the TCRα and CD3γ chain. The concerted motions of the membrane-embedded TCR – CD3 complex revealed in our simulations provide atomistic insights for force-based models of TCR signaling, which involve structural changes, in particular in the Cβ FG loop, that are induced by transversal, tilt-inducing forces on bound TCRs (Brazin et al., 2015; Feng et al., 2018).

Our computational analysis of the structural and orientational variability of the membrane-embedded TCR – CD3 complex is based on 120 atomistic, explicit-water MD simulation trajectories with a length of 1 μs and, thus, on simulation data with a total length of 120 μs. We have conducted these simulations with the Amber99SB-ILDN protein force field (Lindorff-Larsen et al., 2010) and the Amber Lipid14 membrane force field (Dickson et al., 2014) at a simulation temperature of 30°C on graphics processing units (GPUs). The simulation trajectories start from initial system conformations in which the cryo-EM structure of the TCR – CD3 protein complex is embedded in a membrane composed of 456 POPC lipids and 114 cholesterol molecules. We find that the orientational and conformational ensembles sampled by the 120 trajectories equilibrate within the first 0.5 μs of the simulation trajectories (see Materials and methods) and, therefore, focus on the second 0.5 μs of the MD simulation trajectories in our analysis.

Compared to the cryo-EM structure, a much larger set of residues is involved in contacts between the protein dimers of the TCR – CD3 complex in our MD simulations. Figure 2 illustrates the time-averaged contacts between residues of the TCRαβ, CD3εγ, and CD3εδ EC domains along the equilibrated second halves of the MD simulation trajectories. The fourth protein dimer in the complex, CD3ζζ, has no EC domain. The contact maps of Figure 2 include all residue-residue contacts that occur in the simulations with a probability larger than 0.5%. The probabilities of the contacts are calculated from 6000 simulation structures extracted at intervals of 10 ns from the second 0.5 μs of the 120 simulations, and indicated in grayscale in Figure 2. As in the contact maps for the cryo-EM structure shown in Figure 1, two residues are taken to be in contact in a simulation structure if the minimum distance between non-hydrogen atoms of the residues is smaller than 0.45 nm. The contacts are grouped in clusters (blue numbers) that correspond to interactions between loops and strands of the EC domains, which are labeled according to the standard convention for immunoglobulin(Ig)-like domains (Garcia et al., 1996; Wang et al., 1998; Sun et al., 2004). The EC domains of the proteins TCRα and TCRβ consist of the membrane-proximal constant domains Cα and Cβ and the variable domains Vα and Vβ, which are all Ig-like domains, as are the EC domains of the proteins CD3ε, CD3γ, and CD3δ. In our MD simulations, significantly more loops and strands, and more residues of the protein dimers TCRαβ, CD3εγ, and CD3εδ participate in EC domain interactions, compared to the cryo-EM structure. The Cβ FG loop, for example, exhibits only a single contact with an N-terminal residue of CD3γ in the cryo-EM structure (see Figure 1). In our MD simulations, in contrast, the Cβ FG loop is involved in a large number of contacts with the N-terminus of CD3γ and with several loops and strand in the $\mathrm{ϵ}$ chain of the CD3εγ EC domain. Besides the Cα DE loop, Cβ CC’ loop, Cβ EF loop, Cβ FG loop, and Cβ G strand with contacts in the cryo-EM structure, the MD contacts maps of Figure 2 include also the Cα AB loop and the Cβ A and B strand in the constant domains of TCRαβ and, remarkably, the three loops A’B, C”D, and EF in the variable region Vα of TCRα. Residue-residue contacts between Vα and the δ chain of CD3εδ have probabilities smaller than 3%, but occur in 75 of the 120 trajectories and are, thus, a robust feature of our simulations. These contacts are grouped in four small, correlated contact clusters (see cluster-cluster correlation coefficients in Figure 2—figure supplement 1).

Figure 2 with 1 supplement see all

Download asset Open asset

In our MD simulations, the TCRαβ EC domain is rather variable in its orientation relative to the membrane. The orientation can be quantified by two angles, a tilt angle and a rotation angle. To determine these angles, we choose two axes A and B in the TCRαβ EC domain: Axis A connects the centres of mass of Cαβ and Vαβ, where Cαβ is the dimer of the constant domains Cα and Cβ, and Vαβ is the dimer of the variable domains Vα and Vβ. Axis B connects the centres of mass of the variable domains Vα and Vβ. The tilt angle of the TCRαβ EC domain then is the angle between axis A and the membrane normal, and the rotation angle is the angle between axis B and the normal of the plane spanned by axis A and the membrane normal. The rotation angle describes the rotation of the TCRαβ EC domain around axis A (see Figure 3(a) and (b)). In our MD simulations, the tilt angle of TCRαβ EC domain roughly varies between 15° and 55°, while the rotation angle varies between 0° and 55°. A rotation angle of 0° indicates a TCRαβ EC domain orientation in which the centres of mass of the variable domains Vα and Vβ are equally close to the membrane, and the rotation angle is larger than 0° in conformations in which the variable domain Vα is closer to the membrane than the variable domain Vβ (see Figure 3(a) and (b)).

Figure 3 with 7 supplements see all

Download asset Open asset

The two-dimensional probability distribution of the angles in Figure 3(c) indicates that the rotation of the TCRαβ EC domain is coupled to its tilt: For tilt angles between 15° and 35°, the rotation angle predominantly adopts values between about 5° and 25°. For a tilt angle of 40°, the most probable value of the rotation angle is about 32°, and further increases to 40° for a tilt angle of 50°. The coupling between the tilt and rotation of the TCRαβ EC domain is also illustrated in Figure 3(a) and (b). In the structure of the membrane-embedded TCR – CD3 complex of Figure 3(a), the TCRαβ EC domain has a tilt angle of 32.8° and a rotation angle of 12.8°. In the structure of Figure 3(b) with a larger tilt angle of 50.8°, the rotation angle of the TCRαβ EC domain is 42.9°. The rotation and tilt angle for the TCRαβ EC domain in the cryo-EM structure of the TCR – CD3 complex can be determined by aligning the TM domain of this structure to our simulation conformations. This structural alignment embeds and orients the cryo-EM structure in our simulated membranes. From TM domain alignment to the 120 final simulation conformations of our trajectories, we obtain the tilt angle 31.2±0.4° and the rotation angle 14.7±0.7° for the TCRαβ EC domain of the cryo-EM structure. The errors here have been estimated as the error of the mean of the 120 values obtained after structural alignment to these simulation conformations.

The tilt angle of the TCRαβ EC domain is associated with characteristic changes in the overall structure of the TCR – CD3 complex, in particular with changes in the number of residue-residue contacts of the Vα domain and of the Cβ FG loop (see Figure 3(d)) and in the orientations of the transmembrane (TM) helices of the TCRα and CD3γ chains (see Figure 3(e)). Residue-residue contacts of the A’B, C”D, and EF loops of the variable domain Vα with the protein CD3δ only occur for tilt angles of the TCRαβ EC domain larger than about 30° (see Figure 3(d)). The average number of these residues-residue contacts increases to values around two for tilt angles of 50° and larger. For the Cβ FG loop, in contrast, the average number of residues-residue contacts decreases from a value around 30 at the tilt angle 15° to values close 0 for tilt angles of 50° and larger. A decrease in the average number of contacts with increasing tilt angle can also be observed for the Cα AB loop and the Cβ A strand, CC’ loop, and G strand (see Figure 3—figure supplement 1). Only for the Cα DE loop and Cβ EF loop, the average number of contacts is rather independent of the tilt angle. The tilt of the TCRαβ EC domain also affects the orientation of TM helices. The average inclination of the TCRα TM helix relative to the membrane normal decreases from about 11.5° to values around 8.5° with increasing tilt angle of the TCRαβ EC domain, while the average inclination of the CD3γ TM helix increases from about 15.5° to values around 24° (see Figure 3(e)). An increase from about 19° to values around 22° with increasing tilt angle of the TCRαβ EC domain also occurs for the average inclination angle of the TM helix of the ε chain of CD3εγ (see Figure 3—figure supplement 2). The average orientation of the other five TM helices relative to the membrane normal exhibits only small variations with the tilt angle of the TCRαβ EC domain.

The coupling of the tilt angle of the TCRαβ EC domain to overall conformational changes in the TCR – CD3 complex, which we observe in our MD simulations, provides insights on conformational changes induced by transversal, tilt-inducing forces. Transversal forces acting on the TCRαβ EC domain after binding to MHC-peptide-antigen complexes arise during the scanning of antigen-presenting cells by T cells (Göhring et al., 2020; Cai et al., 2017; Huse, 2017; Rushdi et al., 2020). While experiments indicate that the TCR – CD3 complex responds to mechanical force (Kim et al., 2009; Feng et al., 2017) and that the Cβ FG loop plays a key role in this response (Das et al., 2015), an outstanding question is how this force alters the conformation of the TCR – CD3 complex (Courtney et al., 2018). Our MD simulations show that an increased tilt of the TCRαβ EC domain leads to a marked decrease in the contacts between the Cβ FG loop and the CD3εγ EC domain (see Figure 3(d)), and also to changes in the inclination of TM helices relative to the membrane normal (see Figure 3(e)). Such structural changes in the TM domain of the TCR – CD3 have been suggested to be involved in the transmission of forces from the EC domain to the signaling motifs on the intracellular segments of the CD3 chains (Brazin et al., 2015; Brazin et al., 2018), which are not resolved in the cryo-EM structure. Similar to the Cβ FG loop, we also find a decrease of contacts between the Cα AB loop, which has been implicated in TCR triggering (Beddoe et al., 2009), and the ε chain of the CD3εγ EC domain with increasing tilt angle of the TCRαβ EC domain (see Figure 3—figure supplement 1).

Based on our simulation results for the orientational variations of the unbound TCR EC domain, the tilt of the bound TCR-MHC complex induced by a transversal force $f$ parallel to the membrane can be estimated under the assumption that the membrane anchoring of the MHC EC domain is more flexible than the membrane anchoring of the TCR EC domain within the TCR - CD3 complex. This assumption appears reasonable because MHC class I and MHC class II EC domains are anchored by one and two peptide linkers, respectively, to a pair of transmembrane helices, whereas the three EC domains of the TCR - CD3 complex are jointly anchored by six linkers to a bundle of eight transmembrane helices. The anchoring flexibility of MHC class I molecules is also illustrated by a presumably binding-incompetent, supine conformation observed in two-dimensional crystals in which the MHC EC domains are positioned with their 'sides’ on the membrane, rather than 'standing up' (Mitra et al., 2004). In the absence of transversal forces, the tilt-angle distribution of the TCR-MHC EC domain then can be approximated by the distribution of the TCR EC domain tilt angle observed in our simulations of the TCR – CD3 complex. From the energy contribution $-f\beta h\beta \sin [\tau ]$ associated with a transversal force $f$ on the TCR-MHC EC complex with extension $h\simeq 13$ nm (Wang et al., 2009) along the tilt axis and tilt angle $\tau$ in units of rad, the maximum of the tilt-angle distribution can be estimated to be shifted from 34° for zero force to 41° for a transversal force $f=2$ pN and to 49° for a transversal force $f=5$ pN acting on the EC domain of the TCR-MHC complex (see Figure 3—figure supplement 3(a) and Materials and methods). Such transversal forces on TCR-MHC complexes up to 5 pN are within the range measured with force sensors (Göhring et al., 2020).

An increased tilt of the TCR-MHC complex due to transversal forces also affects the membrane separation at the site of the complex and, thus, the size-based segregation of large surface molecules such as CD45 or of other receptor-ligand complexes from TCR-MHC complexes. In the kinetic segregation mechanism, a key step in T cell activation is the size-based segregation of the inhibitory tyrosine phosphatase CD45 from TCR-MHC complexes (Davis and van der Merwe, 2006; Choudhuri and van der Merwe, 2007; Chang et al., 2016). A change of the tilt angle of the EC domain of the TCR-MHC complex with length $h\simeq 13$ nm from 34° to 49° leads to a decrease of about 2.2 nm in the separation $h\beta \cos [\tau ]$ between the membrane surfaces and, thus, to an increased segregation of large surface molecules. Such force-induced decreases of the membrane separation at the site of TCR-MHC complexes may also be relevant for the the recently observed segregation of CD2-CD58 complexes from TCR-MHC complexes, which both have EC domain lengths of about 13 nm (Demetriou et al., 2019).

The orientations of the TCRαβ EC domain within the TCR – CD3 complex are affected by the low-affinity interactions with the CD3εγ and CD3εδ EC domains (He et al., 2015). Because of the inherent limitations of coarse-grained models to describe such low-affinity interactions (Javanainen et al., 2017), we chose state-of-the-art atomistic force fields for our simulations of the TCR – CD3 complex. Our 120 simulation trajectories with a length of 1 μs provide a cumulative sampling on timescales that exceed the length of the individual trajectories (Pande et al., 2003; Noé et al., 2009) and lead to equilibrated conformational and orientational distributions of the EC domains (see Figure 4). The rather large orientational variations of the TCRαβ EC domain observed in our simulations make it plausible that processes on longer timescales do not contribute significantly to the overall EC domain conformations. Our sampling of the TM domain, in contrast, may be limited to conformations close to the cryo-EM structure of the TM domain, which is embedded in the detergent digitonin in the experiments. Larger conformational rearrangements of the TM helices such as the bending of the TCRα TM helix at a helix hinge observed in NMR experiments (Brazin et al., 2018) may occur on longer timescales, and may also depend on the composition of the lipid membrane. The TCRα TM helix remains intact on the microsecond timescales of our simulations and does not break into two helix halves connected by a hinge. Overall, our simulation result for the TM domain illustrate that the tilt of the TCRαβ EC domain is associated with statistically significant changes in the orientation of TM helices. How these orientational changes are affected by the membrane composition, and whether they can be related to conformational changes in the largely disordered intracellular signaling domains requires further simulations, likely with atomistic resolution because of limitations in modeling secondary structure propensities and disordered protein segments with coarse-grained force fields (Monticelli et al., 2008; Robustelli et al., 2018). In recent modeling based on the cryo-EM structure of the TCR – CD3 complex, the intracellular signaling domains have been included in coarse-grained simulations of the entire complex with a cumulative simulation time of 25 μs (Prakaash et al., 2021), and the conformations of the TM domain in a complex, asymmetric membrane have been explored in atomistic simulations with a cumulative simulation time of about 4 μs (Lanz et al., 2021). Future atomistic simulations of the TCR – CD3 complex bound to the MHC-peptide EC domain may provide insights on the role of binding-induced conformational changes in TCR activation (Hwang et al., 2020; Ayres et al., 2016). The orientational distributions of the TCRαβ EC domain complex obtained from our simulations also provide a basis for the coarse-grained or multiscale modeling of the TCR-MHC complex anchored to apposing membranes (Steinkühler et al., 2019).

Figure 4 with 1 supplement see all

Download asset Open asset

The concerted conformational changes of the TCR – CD3 complex in our simulations are reflected in the correlations of contact clusters in the EC domain interactions. The largest positive correlations of clusters in the TCRαβ/CD3εδ contact map of Figure 2 occur between the contact clusters 1 to 4 (see Figure 2—figure supplement 1, top left), which correspond to interactions of the variable domain Vα of TCRα and the δ chain of CD3εδ. The large positive correlations of these four contact clusters can be understood from the coupling to the tilt of the TCRαβ EC domain, because the contacts of the Vα domain reported by the four contact clusters are only possible at high tilt angles of the TCRαβ EC domain (see Figure 3(d)). Relatively large positive correlations occur also between the clusters 13 to 21 of the TCRαβ/CD3εγ contact map (see Figure 2—figure supplement 1, bottom left). These clusters reflect interactions of the Cβ FG loop and G strand with the $\mathrm{ϵ}$ chain and the N-terminus of the γ chain of CD3εγ, which decrease with increasing tilt angle of the TCRαβ EC domain (see Figure 3(d) and Figure 3—figure supplement 1). Besides these positive correlations that result from the concerted conformational changes coupled to the tilt angle of the TCRαβ EC domain, the overall weak correlations between the majority of the other contact clusters in Figure 2 indicate independent motions of the loops and strands involved in these EC domain contacts. In addition, relatively strongly negative correlations of several pairs of contact clusters point to alternative EC domain contacts. For example, the overall rather negative correlations between the clusters 13 to 21 and the cluster 22 of the TCRαβ/CD3εγ contact map show that the interaction of the Cβ G strand and the γ AB loop reported by cluster 22 is not compatible with the interactions of the Cβ FG loop and G strand to the ε chain and the N-terminus of the γ chain of CD3εγ, which are reflected by the clusters 13 to 21.

Overall, our simulations reveal that the orientation of the TCR EC domain relative to the membrane is coupled to structural changes throughout the TCR – CD3 complex. Besides this concerted structural motion, the overall weak correlations of the majority of EC domain interactions indicate additional independent motions of the loops and strands that are involved in these interactions.

All 6000 molecular dynamics structures of the membrane-embedded TCR-CD3 complex used in the analysis have been deposited in the Edmond Open Research Data Repository under https://doi.org/10.17617/3.5m.

1. 1. Pandey PR
   2. Weikl TR

   (2021) Edmond Open Research Data Repository of the Max Planck Society

   MD simulation structures of the membrane-embedded TCR - CD3 complex.

   https://doi.org/10.17617/3.5m

1. 1. Ayres CM
   2. Scott DR
   3. Corcelli SA
   4. Baker BM

   (2016) Differential utilization of binding loop flexibility in T cell receptor ligand selection and cross-reactivity

   Scientific reports 6:25070.

   https://doi.org/10.1038/srep25070

   • PubMed
   • Google Scholar
2. 1. Beddoe T
   2. Chen Z
   3. Clements CS
   4. Ely LK
   5. Bushell SR
   6. Vivian JP
   7. Kjer-Nielsen L
   8. Pang SS
   9. Dunstone MA
   10. Liu YC
   11. Macdonald WA
   12. Perugini MA
   13. Wilce MC
   14. Burrows SR
   15. Purcell AW
   16. Tiganis T
   17. Bottomley SP
   18. McCluskey J
   19. Rossjohn J

   (2009) Antigen ligation triggers a conformational change within the constant domain of the alphabeta T cell receptor

   Immunity 30:777–788.

   https://doi.org/10.1016/j.immuni.2009.03.018

   • PubMed
   • Google Scholar
3. 1. Belardi B
   2. Son S
   3. Felce JH
   4. Dustin ML
   5. Fletcher DA

   (2020) Cell-cell interfaces as specialized compartments directing cell function

   Nature reviews. Molecular cell biology 21:750–764.

   https://doi.org/10.1038/s41580-020-00298-7

   • PubMed
   • Google Scholar
4. 1. Birnbaum ME
   2. Berry R
   3. Hsiao YS
   4. Chen Z
   5. Shingu-Vazquez MA
   6. Yu X
   7. Waghray D
   8. Fischer S
   9. McCluskey J
   10. Rossjohn J
   11. Walz T
   12. Garcia KC

   (2014) Molecular architecture of the αβ T cell receptor-CD3 complex

   PNAS 111:17576–17581.

   https://doi.org/10.1073/pnas.1420936111

   • PubMed
   • Google Scholar
5. 1. Brazin KN
   2. Mallis RJ
   3. Das DK
   4. Feng Y
   5. Hwang W
   6. Wang JH
   7. Wagner G
   8. Lang MJ
   9. Reinherz EL

   (2015) Structural Features of the αβTCR Mechanotransduction Apparatus That Promote pMHC Discrimination

   Frontiers in immunology 6:441.

   https://doi.org/10.3389/fimmu.2015.00441

   • PubMed
   • Google Scholar
6. 1. Brazin KN
   2. Mallis RJ
   3. Boeszoermenyi A
   4. Feng Y
   5. Yoshizawa A
   6. Reche PA
   7. Kaur P
   8. Bi K
   9. Hussey RE
   10. Duke-Cohan JS
   11. Song L
   12. Wagner G
   13. Arthanari H
   14. Lang MJ
   15. Reinherz EL

   (2018) The T cell antigen receptor α transmembrane domain coordinates triggering through regulation of bilayer immersion and CD3 subunit associations

   Immunity 49:829–841.

   https://doi.org/10.1016/j.immuni.2018.09.007

   • PubMed
   • Google Scholar
7. 1. Cai E
   2. Marchuk K
   3. Beemiller P
   4. Beppler C
   5. Rubashkin MG
   6. Weaver VM
   7. Gérard A
   8. Liu TL
   9. Chen BC
   10. Betzig E
   11. Bartumeus F
   12. Krummel MF

   (2017) Visualizing dynamic microvillar search and stabilization during ligand detection by T cells

   Science 356:eaal3118.

   https://doi.org/10.1126/science.aal3118

   • PubMed
   • Google Scholar
8. 1. Call ME
   2. Pyrdol J
   3. Wiedmann M
   4. Wucherpfennig KW

   (2002) The organizing principle in the formation of the T cell receptor-CD3 complex

   Cell 111:967–979.

   https://doi.org/10.1016/s0092-8674(02)01194-7

   • PubMed
   • Google Scholar
9. Book

   1. Case DA
   2. Cerutti DS
   3. Iii TC
   4. Darden T
   5. Duke R
   6. Giese T
   7. Gohlke H
   8. Goetz A
   9. Greene D
   10. Homeyer N
   11. Izadi S
   12. Kovalenko A
   13. Lee T
   14. LeGrand S
   15. Li P
   16. Lin C
   17. Liu J
   18. Luchko T
   19. Luo R
   20. Mermelstein D
   21. Merz K
   22. Monard G
   23. Nguyen H
   24. Omelyan I
   25. Onufriev A
   26. Pan F
   27. Qi R

   (2017)

   AMBER 2017

   San Francisco: University of California.

   • Google Scholar
10. 1. Chang VT
    2. Fernandes RA
    3. Ganzinger KA
    4. Lee SF
    5. Siebold C
    6. McColl J
    7. Jönsson P
    8. Palayret M
    9. Harlos K
    10. Coles CH
    11. Jones EY
    12. Lui Y
    13. Huang E
    14. Gilbert RJC
    15. Klenerman D
    16. Aricescu AR
    17. Davis SJ

    (2016) Initiation of T cell signaling by CD45 segregation at 'close contacts'

    Nature immunology 17:574–582.

    https://doi.org/10.1038/ni.3392

    • PubMed
    • Google Scholar
11. 1. Choudhuri K
    2. van der Merwe PA

    (2007) Molecular mechanisms involved in T cell receptor triggering

    Seminars in immunology 19:255–261.

    https://doi.org/10.1016/j.smim.2007.04.005

    • PubMed
    • Google Scholar
12. 1. Courtney AH
    2. Lo WL
    3. Weiss A

    (2018) TCR Signaling: Mechanisms of Initiation and Propagation

    Trends in biochemical sciences 43:108–123.

    https://doi.org/10.1016/j.tibs.2017.11.008

    • PubMed
    • Google Scholar
13. 1. Darden T
    2. York D
    3. Pedersen L

    (1993) Particle mesh Ewald: an nlog(n) method for Ewald sums in large systems

    The Journal of Chemical Physics 98:10089.

    https://doi.org/10.1063/1.464397

    • Google Scholar
14. 1. Das DK
    2. Feng Y
    3. Mallis RJ
    4. Li X
    5. Keskin DB
    6. Hussey RE
    7. Brady SK
    8. Wang JH
    9. Wagner G
    10. Reinherz EL
    11. Lang MJ

    (2015) Force-dependent transition in the T-cell receptor β-subunit allosterically regulates peptide discrimination and pMHC bond lifetime

    PNAS 112:1517–1522.

    https://doi.org/10.1073/pnas.1424829112

    • PubMed
    • Google Scholar
15. 1. Davis SJ
    2. van der Merwe PA

    (2006) The kinetic-segregation model: TCR triggering and beyond

    Nature immunology 7:803–809.

    https://doi.org/10.1038/ni1369

    • PubMed
    • Google Scholar
16. Preprint

    1. Demetriou P
    2. Abu-Shah E
    3. McCuaig S
    4. Mayya V
    5. Valvo S
    6. Korobchevskaya K
    7. Friedrich M
    8. Mann E
    9. Lee LY
    10. Starkey T
    11. Kutuzov MA
    12. Afrose J
    13. Siokis A
    14. Meyer-Hermann M
    15. Depoil D
    16. Dustin ML

    (2019) CD2 expression acts as a quantitative checkpoint for immunological synapse structure and T-cell activation

    bioRxiv.

    https://doi.org/10.1101/589440

    • Google Scholar
17. 1. DeMond AL
    2. Mossman KD
    3. Starr T
    4. Dustin ML
    5. Groves JT

    (2008) T cell receptor microcluster transport through molecular mazes reveals mechanism of translocation

    Biophysical journal 94:3286–3292.

    https://doi.org/10.1529/biophysj.107.119099

    • PubMed
    • Google Scholar
18. 1. Dickson CJ
    2. Madej BD
    3. Skjevik AA
    4. Betz RM
    5. Teigen K
    6. Gould IR
    7. Walker RC

    (2014) Lipid14: The Amber Lipid Force Field

    Journal of chemical theory and computation 10:865–879.

    https://doi.org/10.1021/ct4010307

    • PubMed
    • Google Scholar
19. 1. Dong D
    2. Zheng L
    3. Lin J
    4. Zhang B
    5. Zhu Y
    6. Li N
    7. Xie S
    8. Wang Y
    9. Gao N
    10. Huang Z

    (2019) Structural basis of assembly of the human T cell receptor-CD3 complex

    Nature 573:546.

    https://doi.org/10.1038/s41586-019-1537-0

    • PubMed
    • Google Scholar
20. 1. Dustin ML

    (2014) The immunological synapse

    Cancer immunology research 2:1023–1033.

    https://doi.org/10.1158/2326-6066.CIR-14-0161

    • PubMed
    • Google Scholar
21. 1. Essmann U
    2. Perera L
    3. Berkowitz ML
    4. Darden T
    5. Lee H
    6. Pedersen LG

    (1995) A smooth particle mesh ewald method

    The Journal of Chemical Physics 103:8577–8593.

    https://doi.org/10.1063/1.470117

    • Google Scholar
22. 1. Feng Y
    2. Brazin KN
    3. Kobayashi E
    4. Mallis RJ
    5. Reinherz EL
    6. Lang MJ

    (2017) Mechanosensing drives acuity of αβ T-cell recognition

    PNAS 114:E8204–E8213.

    https://doi.org/10.1073/pnas.1703559114

    • PubMed
    • Google Scholar
23. 1. Feng Y
    2. Reinherz EL
    3. Lang MJ

    (2018) αβ T cell receptor mechanosensing forces out serial engagement

    Trends in Immunology 39:596–609.

    https://doi.org/10.1016/j.it.2018.05.005

    • PubMed
    • Google Scholar
24. 1. Fernandes RA
    2. Shore DA
    3. Vuong MT
    4. Yu C
    5. Zhu X
    6. Pereira-Lopes S
    7. Brouwer H
    8. Fennelly JA
    9. Jessup CM
    10. Evans EJ
    11. Wilson IA
    12. Davis SJ

    (2012) T cell receptors are structures capable of initiating signaling in the absence of large conformational rearrangements

    Journal of Biological Chemistry 287:13324–13335.

    https://doi.org/10.1074/jbc.M111.332783

    • PubMed
    • Google Scholar
25. 1. Garcia KC
    2. Degano M
    3. Stanfield RL
    4. Brunmark A
    5. Jackson MR
    6. Peterson PA
    7. Teyton L
    8. Wilson IA

    (1996) An alphabeta T cell receptor structure at 2.5 A and its orientation in the TCR-MHC complex

    Science 274:209–219.

    https://doi.org/10.1126/science.274.5285.209

    • PubMed
    • Google Scholar
26. Preprint

    1. Göhring J
    2. Kellner F
    3. Schrangl L
    4. René Platzer R
    5. Klotzsch E
    6. Stockinger H
    7. Huppa JB
    8. Gerhard J
    9. Schütz GJ

    (2020) Temporal analysis of T-cell receptor-imposed forces via quantitative single molecule FRET measurements

    bioRxiv.

    https://doi.org/10.1101/2020.04.03.024299

    • Google Scholar
27. 1. He Y
    2. Rangarajan S
    3. Kerzic M
    4. Luo M
    5. Chen Y
    6. Wang Q
    7. Yin Y
    8. Workman CJ
    9. Vignali KM
    10. Vignali DA
    11. Mariuzza RA
    12. Orban J

    (2015) Identification of the docking site for CD3 on the T cell receptor β chain by solution NMR

    Journal of Biological Chemistry 290:19796–19805.

    https://doi.org/10.1074/jbc.M115.663799

    • PubMed
    • Google Scholar
28. 1. Hopkins CW
    2. Le Grand S
    3. Walker RC
    4. Roitberg AE

    (2015) Long-Time-Step molecular dynamics through hydrogen mass repartitioning

    Journal of Chemical Theory and Computation 11:1864–1874.

    https://doi.org/10.1021/ct5010406

    • PubMed
    • Google Scholar
29. 1. Humphrey W
    2. Dalke A
    3. Schulten K

    (1996) VMD: visual molecular dynamics

    Journal of molecular graphics 14:33–38.

    https://doi.org/10.1016/0263-7855(96)00018-5

    • PubMed
    • Google Scholar
30. 1. Huse M

    (2017) Mechanical forces in the immune system

    Nature reviews. Immunology 17:679–690.

    https://doi.org/10.1038/nri.2017.74

    • PubMed
    • Google Scholar
31. 1. Hwang W
    2. Mallis RJ
    3. Lang MJ
    4. Reinherz EL

    (2020) The αβTCR mechanosensor exploits dynamic ectodomain allostery to optimize its ligand recognition site

    PNAS 117:21336–21345.

    https://doi.org/10.1073/pnas.2005899117

    • PubMed
    • Google Scholar
32. 1. Javanainen M
    2. Martinez-Seara H
    3. Vattulainen I

    (2017) Excessive aggregation of membrane proteins in the Martini model

    PLOS ONE 12:e0187936.

    https://doi.org/10.1371/journal.pone.0187936

    • PubMed
    • Google Scholar
33. 1. Jo S
    2. Kim T
    3. Iyer VG
    4. Im W

    (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM

    Journal of computational chemistry 29:1859–1865.

    https://doi.org/10.1002/jcc.20945

    • PubMed
    • Google Scholar
34. 1. Kim ST
    2. Takeuchi K
    3. Sun ZY
    4. Touma M
    5. Castro CE
    6. Fahmy A
    7. Lang MJ
    8. Wagner G
    9. Reinherz EL

    (2009) The alphabeta T cell receptor is an anisotropic mechanosensor

    Journal of Biological Chemistry 284:31028–31037.

    https://doi.org/10.1074/jbc.M109.052712

    • PubMed
    • Google Scholar
35. 1. Kim ST
    2. Touma M
    3. Takeuchi K
    4. Sun ZY
    5. Dave VP
    6. Kappes DJ
    7. Wagner G
    8. Reinherz EL

    (2010) Distinctive CD3 heterodimeric ectodomain topologies maximize antigen-triggered activation of alpha beta T cell receptors

    Journal of immunology 185:2951–2959.

    https://doi.org/10.4049/jimmunol.1000732

    • PubMed
    • Google Scholar
36. 1. Kuhns MS
    2. Davis MM

    (2007) Disruption of extracellular interactions impairs T cell receptor-CD3 complex stability and signaling

    Immunity 26:357–369.

    https://doi.org/10.1016/j.immuni.2007.01.015

    • PubMed
    • Google Scholar
37. 1. Kuhns MS
    2. Davis MM

    (2012) TCR signaling emerges from the sum of many parts

    Frontiers in Immunology 3:159.

    https://doi.org/10.3389/fimmu.2012.00159

    • PubMed
    • Google Scholar
38. Preprint

    1. Lanz A-L
    2. Masi G
    3. Porciello N
    4. Cohnen A
    5. Cipria D
    6. Prakaash D
    7. Bálint Š
    8. Raggiaschi R
    9. Galgano D
    10. Cole DK
    11. Lepore M
    12. Dushek O
    13. Dustin ML
    14. Sansom MSP
    15. Kalli AC
    16. Acuto O

    (2021) Allosteric activation of T-cell antigen receptor signalling by quaternary structure relaxation

    bioRxiv.

    https://doi.org/10.1101/2020.12.02.407882

    • Google Scholar
39. 1. Le Grand S
    2. Götz AW
    3. Walker RC

    (2013) SPFP: speed without compromise—A mixed precision model for GPU accelerated molecular dynamics simulations

    Computer Physics Communications 184:374–380.

    https://doi.org/10.1016/j.cpc.2012.09.022

    • Google Scholar
40. 1. Lee J
    2. Hitzenberger M
    3. Rieger M
    4. Kern NR
    5. Zacharias M
    6. Im W

    (2020) CHARMM-GUI supports the Amber force fields

    The Journal of chemical physics 153:035103.

    https://doi.org/10.1063/5.0012280

    • PubMed
    • Google Scholar
41. 1. Lindorff-Larsen K
    2. Piana S
    3. Palmo K
    4. Maragakis P
    5. Klepeis JL
    6. Dror RO
    7. Shaw DE

    (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field

    Proteins 78:1950–1958.

    https://doi.org/10.1002/prot.22711

    • PubMed
    • Google Scholar
42. 1. Mitra AK
    2. Célia H
    3. Ren G
    4. Luz JG
    5. Wilson IA
    6. Teyton L

    (2004) Supine orientation of a murine MHC class I molecule on the membrane bilayer

    Current biology : CB 14:718–724.

    https://doi.org/10.1016/j.cub.2004.04.004

    • PubMed
    • Google Scholar
43. 1. Miyamoto S
    2. Kollman PA

    (1992) Settle: an analytical version of the SHAKE and RATTLE algorithm for rigid water models

    Journal of Computational Chemistry 13:952–962.

    https://doi.org/10.1002/jcc.540130805

    • Google Scholar
44. 1. Monticelli L
    2. Kandasamy SK
    3. Periole X
    4. Larson RG
    5. Tieleman DP
    6. Marrink SJ

    (2008) The MARTINI Coarse-Grained Force Field: Extension to Proteins

    Journal of chemical theory and computation 4:819–834.

    https://doi.org/10.1021/ct700324x

    • PubMed
    • Google Scholar
45. 1. Mossman KD
    2. Campi G
    3. Groves JT
    4. Dustin ML

    (2005) Altered TCR signaling from geometrically repatterned immunological synapses

    Science 310:1191–1193.

    https://doi.org/10.1126/science.1119238

    • PubMed
    • Google Scholar
46. 1. Natarajan A
    2. Nadarajah V
    3. Felsovalyi K
    4. Wang W
    5. Jeyachandran VR
    6. Wasson RA
    7. Cardozo T
    8. Bracken C
    9. Krogsgaard M

    (2016) Structural model of the extracellular assembly of the TCR-CD3 complex

    Cell Reports 14:2833–2845.

    https://doi.org/10.1016/j.celrep.2016.02.081

    • PubMed
    • Google Scholar
47. 1. Noé F
    2. Schütte C
    3. Vanden-Eijnden E
    4. Reich L
    5. Weikl TR

    (2009) Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations

    PNAS 106:19011–19016.

    https://doi.org/10.1073/pnas.0905466106

    • PubMed
    • Google Scholar
48. 1. Pande VS
    2. Baker I
    3. Chapman J
    4. Elmer SP
    5. Khaliq S
    6. Larson SM
    7. Rhee YM
    8. Shirts MR
    9. Snow CD
    10. Sorin EJ
    11. Zagrovic B

    (2003) Atomistic protein folding simulations on the submillisecond time scale using worldwide distributed computing

    Biopolymers 68:91–109.

    https://doi.org/10.1002/bip.10219

    • PubMed
    • Google Scholar
49. Book

    1. Pandey PR
    2. Weikl TR

    (2021) MD Simulation Structures of the Membrane-Embedded TCR - CD3 CompleX

    Germany: Max Planck Society.

    https://doi.org/10.17617/3.5m

    • Google Scholar
50. 1. Pettmann J
    2. Santos AM
    3. Dushek O
    4. Davis SJ

    (2018) Membrane ultrastructure and T cell activation

    Frontiers in Immunology 9:2152.

    https://doi.org/10.3389/fimmu.2018.02152

    • PubMed
    • Google Scholar
51. Preprint

    1. Prakaash D
    2. Cook GP
    3. Acuto O
    4. Kalli AC

    (2021) Multi-scale simulations of the T cell receptor reveal its lipid interactions, dynamics and the arrangement of its cytoplasmic region

    bioRxiv.

    https://doi.org/10.1101/2021.01.11.425722

    • Google Scholar
52. 1. Reinherz EL

    (2019) The structure of a T-cell mechanosensor

    Nature 573:502–504.

    https://doi.org/10.1038/d41586-019-02646-w

    • PubMed
    • Google Scholar
53. 1. Robustelli P
    2. Piana S
    3. Shaw DE

    (2018) Developing a molecular dynamics force field for both folded and disordered protein states

    PNAS 115:E4758–E4766.

    https://doi.org/10.1073/pnas.1800690115

    • PubMed
    • Google Scholar
54. 1. Rossjohn J
    2. Gras S
    3. Miles JJ
    4. Turner SJ
    5. Godfrey DI
    6. McCluskey J

    (2015) T cell antigen receptor recognition of antigen-presenting molecules

    Annual review of immunology 33:169–200.

    https://doi.org/10.1146/annurev-immunol-032414-112334

    • PubMed
    • Google Scholar
55. 1. Rushdi M
    2. Li K
    3. Yuan Z
    4. Travaglino S
    5. Grakoui A
    6. Zhu C

    (2020) Mechanotransduction in T cell development, differentiation and function

    Cells 9:364.

    https://doi.org/10.3390/cells9020364

    • PubMed
    • Google Scholar
56. 1. Ryckaert J-P
    2. Ciccotti G
    3. Berendsen HJC

    (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes

    Journal of Computational Physics 23:327–341.

    https://doi.org/10.1016/0021-9991(77)90098-5

    • Google Scholar
57. 1. Salomon-Ferrer R
    2. Götz AW
    3. Poole D
    4. Le Grand S
    5. Walker RC

    (2013) Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 2. Explicit Solvent Particle Mesh Ewald

    Journal of chemical theory and computation 9:3878–3888.

    https://doi.org/10.1021/ct400314y

    • PubMed
    • Google Scholar
58. 1. Smith-Garvin JE
    2. Koretzky GA
    3. Jordan MS

    (2009) T cell activation

    Annual review of immunology 27:591–619.

    https://doi.org/10.1146/annurev.immunol.021908.132706

    • PubMed
    • Google Scholar
59. 1. Steinkühler J
    2. Rozycki B
    3. Alvey C
    4. Lipowsky R
    5. Weikl TR
    6. Dimova R
    7. Discher DE

    (2019) Membrane fluctuations and acidosis regulate cooperative binding of 'marker of self' protein CD47 with the macrophage checkpoint receptor SIRPα

    Journal of Cell Science 132:jcs216770.

    https://doi.org/10.1242/jcs.216770

    • Google Scholar
60. 1. Sun ZY
    2. Kim ST
    3. Kim IC
    4. Fahmy A
    5. Reinherz EL
    6. Wagner G

    (2004) Solution structure of the CD3epsilondelta ectodomain and comparison with CD3epsilongamma as a basis for modeling T cell receptor topology and signaling

    PNAS 101:16867–16872.

    https://doi.org/10.1073/pnas.0407576101

    • PubMed
    • Google Scholar
61. 1. Touma M
    2. Chang HC
    3. Sasada T
    4. Handley M
    5. Clayton LK
    6. Reinherz EL

    (2006) The TCR C beta FG loop regulates alpha beta T cell development

    Journal of Immunology 176:6812–6823.

    https://doi.org/10.4049/jimmunol.176.11.6812

    • PubMed
    • Google Scholar
62. 1. Wang J
    2. Lim K
    3. Smolyar A
    4. Teng M
    5. Liu J
    6. Tse AG
    7. Liu J
    8. Hussey RE
    9. Chishti Y
    10. Thomson CT
    11. Sweet RM
    12. Nathenson SG
    13. Chang HC
    14. Sacchettini JC
    15. Reinherz EL

    (1998) Atomic structure of an alphabeta T cell receptor (TCR) heterodimer in complex with an anti-TCR fab fragment derived from a mitogenic antibody

    The EMBO journal 17:10–26.

    https://doi.org/10.1093/emboj/17.1.10

    • PubMed
    • Google Scholar
63. 1. Wang R
    2. Natarajan K
    3. Margulies DH

    (2009) Structural basis of the CD8 alpha beta/MHC class I interaction: focused recognition orients CD8 beta to a T cell proximal position

    Journal of immunology 183:2554–2564.

    https://doi.org/10.4049/jimmunol.0901276

    • PubMed
    • Google Scholar
64. 1. Wu EL
    2. Cheng X
    3. Jo S
    4. Rui H
    5. Song KC
    6. Dávila-Contreras EM
    7. Qi Y
    8. Lee J
    9. Monje-Galvan V
    10. Venable RM
    11. Klauda JB
    12. Im W

    (2014) CHARMM-GUI Membrane Builder toward realistic biological membrane simulations

    Journal of computational chemistry 35:1997–2004.

    https://doi.org/10.1002/jcc.23702

    • PubMed
    • Google Scholar
65. 1. Wucherpfennig KW
    2. Gagnon E
    3. Call MJ
    4. Huseby ES
    5. Call ME

    (2010) Structural biology of the T-cell receptor: insights into receptor assembly, ligand recognition, and initiation of signaling

    Cold Spring Harbor perspectives in biology 2:a005140.

    https://doi.org/10.1101/cshperspect.a005140

    • PubMed
    • Google Scholar

Author details

1. Prithvi R Pandey

   Max Planck Institute of Colloids and Interfaces, Department of Theory and Bio-Systems, Potsdam, Germany

   Contribution

   Software, Formal analysis, Validation, Investigation, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

2. Bartosz Różycki

   Institute of Physics, Polish Academy of Sciences, Warsaw, Poland

   Contribution

   Formal analysis, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5938-7308

3. Reinhard Lipowsky

   Max Planck Institute of Colloids and Interfaces, Department of Theory and Bio-Systems, Potsdam, Germany

   Contribution

   Conceptualization, Supervision, Funding acquisition

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8417-8567

4. Thomas R Weikl

   Max Planck Institute of Colloids and Interfaces, Department of Theory and Bio-Systems, Potsdam, Germany

   Contribution

   Conceptualization, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing - original draft

   For correspondence

   thomas.weikl@mpikg.mpg.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0911-5328

• 1,562

  views

• 217

  downloads

• 8

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.