Asymmetric framework motion of TCRαβ controls load-dependent peptide discrimination

  1. Ana C Chang-Gonzalez
  2. Robert J Mallis
  3. Matthew J Lang
  4. Ellis L Reinherz
  5. Wonmuk Hwang  Is a corresponding author
  1. Department of Biomedical Engineering, Texas A&M University, United States
  2. Department of Dermatology, Harvard Medical School, United States
  3. Laboratory of Immunobiology, Dana-Farber Cancer Institute, United States
  4. Department of Medicine, Oncology, Dana-Farber Cancer Institute, United States
  5. Department of Chemistry and Biomolecular Engineering, Vanderbilt University, United States
  6. Department of Molecular Physiology and Biophysics, Vanderbilt University, United States
  7. Department of Medicine, Harvard Medical School, United States
  8. Department of Materials Science & Engineering, Texas A&M University, United States
  9. Department of Physics & Astronomy, Texas A&M University, United States

Abstract

Mechanical force is critical for the interaction between an αβ T cell receptor (TCR) and a peptide-bound major histocompatibility complex (pMHC) molecule to initiate productive T-cell activation. However, the underlying mechanism remains unclear. We use all-atom molecular dynamics simulations to examine the A6 TCR bound to HLA-A*02:01 presenting agonist or antagonist peptides under different extensions to simulate the effects of applied load on the complex, elucidating their divergent biological responses. We found that TCR α and β chains move asymmetrically, which impacts the interface with pMHC, in particular the peptide-sensing CDR3 loops. For the wild-type agonist, the complex stabilizes in a load-dependent manner while antagonists destabilize it. Simulations of the Cβ FG-loop deletion, which reduces the catch bond response, and simulations with in silico mutant peptides further support the observed behaviors. The present results highlight the combined role of interdomain motion, fluctuating forces, and interfacial contacts in determining the mechanical response and fine peptide discrimination by a TCR, thereby resolving the conundrum of nearly identical crystal structures of TCRαβ-pMHC agonist and antagonist complexes.

Editor's evaluation

Using extensive atomistic molecular dynamics simulations, the authors analyzed the TCR/pMHC interface with different peptide sequences and protein constructs. The results provide important insights into the catch-bond phenomenon in the context of T-cell activation. In particular, the analysis points to convincing evidence that supports the role of force in further discriminating different peptides during the activation process beyond structural considerations.

https://doi.org/10.7554/eLife.91881.sa0

Introduction

The αβ TCR (αβTCR) consists of the heterodimeric receptor TCRαβ formed by α and β chains each containing the pMHC-binding variable (V) and constant (C) domains (Figure 1 and Figure 2A), and the noncovalently associated cluster of differentiation 3 (CD3) subunits that have cytoplasmic tails containing motifs for downstream signaling (Rudolph et al., 2006; Wang and Reinherz, 2012; Brazin et al., 2018). The TCR recognizes its cognate pMHC on the surface of an antigen-presenting cell (APC) at low or even single copy numbers from a pool of about 105 different self-pMHC molecules (Sykulev et al., 1996; Brameshuber et al., 2018), while it also exhibits reactivity with certain closely related peptide variants, with similar or strikingly altered functional T-cell responses (Ding et al., 1999; Hausmann et al., 1999; Lee et al., 2004; Borbulevych et al., 2009; Baker et al., 2012; Birnbaum et al., 2014). Considering the μM to hundreds of μM TCRαβ-pMHC equilibrium binding affinity (Wang and Reinherz, 2012), several models have been proposed to account for the exquisite specificity and sensitivity of the αβTCR (Chakraborty and Weiss, 2014; Brazin et al., 2015; Schamel et al., 2019; Zhu et al., 2019; Mariuzza et al., 2020; Liu et al., 2021).

Overview of figures and analyses in this work.

Figure 2 introduces the systems studied. WT systems were studied in three structural aspects (Figures 35). Similar analyses were done for the mutant systems (Figures 6 and 7). Appendix 1 and 2 provide simulations of additional mutants that we tested. We further examined the load- and time-dependent changes of the interfacial fit (Figure 8). All of the results collectively lead to the proposed mechanism of catch bond formation and ligand discrimination (Figure 9).

A6 TCRαβ-pMHC complex.

(A) WT (Protein Data Bank, PDB 1AO7). The missing Cα domain was added based on PDB 1QSE (Structure preparation). Blue spheres: terminal Cα atoms held at set extensions during the simulation (Table 1). β2m: β2 microglobulin. (B) Overlay of the X-ray structures of the WT and four point mutants of the Tax peptide at the boxed region of panel A. The CDR loops take nearly identical conformations in different structures. (C) Magnified view of the dashed box in panel B, focusing on the conformation of CDR3β. PDB names for A6 TCRαβ-pMHC complexes containing mutant peptides are listed.

Table 1
Simulations of TCRαβ-pMHC complexes.

Load is average after 500 ns (See Selecting extensions in Computational methods). Parentheses after the average load show standard deviation (std) in forces measured in 40-ns intervals after 500 ns. The std is approximately proportional to the average force, which is a consequence of the positional restraints for applying load being in thermal equilibrium at constant temperature. Exceptions were dFGlow and Y8Alow. They had larger std relative to the average force due to the extra motion caused by the unstable TCRαβ-pMHC interface (see Appendix 3).

PeptidePDBExtension (Å)Time (ns)Load (pN)LabelDescription
Tax (WT)1AO71170WT0wild-type
182.6135013.2 (5.65)WTlow
187.7125018.2 (9.17)WThigh
Tax (dFG-pMHC)1AO7180.5110014.9 (11.2)dFGlowdFG (Table 2) with pMHC
188.9110029.0 (11.8)dFGhigh
Y5F3QFJ1180Y5F0modified agonists
181.412008.24 (2.46)Y5Flow
186.2120023.7 (9.16)Y5Fhigh
V7R1QSE1020V7R0
177.5101210.3 (3.79)V7Rlow
186.2100317.8 (7.55)V7Rhigh
P6A1QRN1090P6A0weak antagonists
175.210188.81 (2.77)P6Alow
186.0102013.5 (6.20)P6Ahigh
Y8A1QSF1020Y8A0
176.5128012.0 (9.79)Y8Alow
187.4133018.1 (6.79)Y8Ahigh
Table 2
Simulations of truncated structures from PDB 1AO7.
LabelModificationTime (ns)
VαβVα-Vβ only (no pMHC)1060
TαβTCRαβ only (no pMHC)1000
Vαβ-pMHCβ with pMHC (no C-module)1020
dFGTαβ without the Cβ FG-loop (no pMHC)1000

A critical factor for peptide discrimination is physiological force applied to the TCRαβ-pMHC complex (Reinherz et al., 2023). A cognate peptide antigen elicits a catch bond behavior where the TCRαβ-pMHC bond lifetime increases with force that peaks in the 10–20 pN range, and is observed with the clonotypic ligand-binding TCRαβ heterodimer in isolation or with the holoreceptor αβTCR including the non-covalently associated CD3 signaling subunit dimers (CD3ϵγ, CD3ϵδ, and CD3ζζ). The catch bond is coupled with a roughly 10 nm structural transition in both (Das et al., 2015; Das et al., 2016; Banik et al., 2021), which supports the notion that the αβTCR acts as a mechanosensor (Kim et al., 2009; Kim et al., 2012; Brazin et al., 2015; Brazin et al., 2018; Choi et al., 2023; Reinherz et al., 2023). In our previous molecular dynamics (MD) study (Hwang et al., 2020), instead of enforcing dissociation of the complex with high force, as done in steered MD simulations (Sibener et al., 2018; Wu et al., 2019), we applied pN-level forces and examined the behavior of the JM22 TCR complexed with an HLA-A*02:01 molecule presenting a peptide from an influenza virus matrix protein. We found that the TCRαβ-pMHC complex is in a loosely-bound state in the absence of load, which allows domain motion. Application of a 16-pN force suppresses the motion and overall enhances the fit among domains. We proposed a model where the TCRαβ-pMHC catch bond arises due to stabilization of the interface by altering the conformational motion of TCRαβ.

An important question regards the generality of this dynamic mechanism in other TCRs. To this end, we study the A6 TCR, which recognizes the Tax peptide (LLFGYPVYV) of the human T lymphotropic virus type 1 (Garboczi et al., 1996b) bound to HLA-A*02:01, the same MHC as for JM22. We perform all-atom MD simulations with the Tax peptide (wild type; WT) (Garboczi et al., 1996a) and four mutant peptides with a single-residue substitution: Y5F (Scott et al., 2011), V7R, P6A, and Y8A (Ding et al., 1999). Below, we call the TCRαβ-pMHC complex by the name of the corresponding peptide. For example, Y5F refers to the complex with the Y5F peptide (PDB 3QFJ, Figure 2C).

While the crystallographic structures of these complexes are very similar (Ding et al., 1999; Scott et al., 2011; Figure 2C), they differ in immunogenicity. P6A and Y8A are weak antagonists because they inhibit T-cell function only at 1000-times higher molar concentration than that needed by the WT for activation (Hausmann et al., 1999; Ding et al., 1999). We refer to them simply as ‘antagonists.’ Y5F is similar to WT in terms of equilibrium binding affinity and T-cell activation in vitro (Hausmann et al., 1999; Scott et al., 2011). V7R induces effector functions comparable to WT at 10- to 100-times higher concentrations (Ding et al., 1999). We call Y5F and V7R as ‘modified agonists.’ There have been several experimental and computational studies comparing the effects of peptide modifications or pMHC binding on A6 TCR (Baker et al., 2000; Michielin and Karplus, 2002; Davis-Harrison et al., 2005; Cuendet and Michielin, 2008; Borbulevych et al., 2009; Cuendet et al., 2011; Scott et al., 2011; Ayres et al., 2016; Fodor et al., 2018), but load was not explicitly considered. To simulate a complex under load, we held the distance between the terminal Cα atoms of the complex (Figure 2A) at a set extension for the duration of the simulation. This was done by applying harmonic positional restraints so that the terminal Cα atoms were allowed to fluctuate in position, hence resulting in instantaneous fluctuation in the applied force akin to loading in experiments. We refer to a simulation as either low or high load based on the average load, which was around the physiological 10–20 pN range (Table 1). To our knowledge, the present study is the first to elucidate the dynamic mechanism of the A6 complex harboring different peptides under load.

We found that differences between the WT and peptide mutants lie in dynamic responses to applied load. In the WT, physiological level load stabilized the TCRαβ-pMHC interface as well as the subdomain motion within TCRαβ. Modified agonists maintained stable contacts, yet high loads led to destabilization. Antagonists had less stable interfaces under load as the mutated residues disrupted surrounding interfacial contacts. Motion within the TCR, such as the Vα-Vβ scissoring as observed in Hwang et al., 2020, and an asymmetric bending of the V-module relative to the C-module, were coupled to the interactions between the variable domains and pMHC such that a single-residue mutation in the peptide affected the conformational behavior of the whole TCR. The present results suggest that the conserved TCRαβ framework motion is leveraged when determining the mechanically matched pMHC, a mechanism that is broadly applicable to different TCRαβ systems.

Results

We first study the WT-based systems to gain insight into the functional implications of TCRαβ-pMHC structural dynamics, followed by point mutations in the Tax peptide. Our analyses below involve time-dependent inter-domain contact dynamics, domain motion, and their dependence on applied load. Figure 1 provides an overview of other figures and analyses, as a guide for navigating this study.

Applying loads to TCRαβ-pMHC complexes

To apply load, we used harmonic positional restraints on the terminal Cα atoms at given extensions (Figure 2A; see Laddered extension with added strands in Computational methods). This was based on the realistic situation of immune surveillance. When a T-cell interacts with an APC, other molecules such as CD2 and CD58 maintain the separation near the ∼120 Å span of the TCRαβ-pMHC complex (Reinherz et al., 2023). The force applied to the complex fluctuates via thermal motion and through cellular activities such as coupling to the actomyosin machinery within the T-cell and APC (Liu et al., 2016; Feng et al., 2017; Reinherz et al., 2023). Restraining terminal Cα atoms in simulation mimics the membrane anchoring of these molecules with a relatively constant spacing and fluctuating force. The measured instantaneous force varies in magnitude and direction across coordinate frames. Averaged over time, it becomes mainly longitudinal (Table 1). Despite the temporal variation in the applied force, the results below show that the response of the complex depends on the average force and the ligand.

In simulation, soft harmonic positional restraint can be used for conformational sampling (Pitera and Chodera, 2012). Rather than positional sampling, our goal is applying force at a given extension, for which we used a stiff 1-kcal/[mol.Å2] potential (Laddered extension with added strands) that yields thermal fluctuations of amplitude ∼0.8 Å (Hwang, 2007). While it is also possible to apply a constant force (Gomez et al., 2021; Stirnemann, 2022), it would not reflect the membrane-anchored state. In addition, the extension of the complex will fluctuate under a constant force, and the actual load that the TCRαβ-pMHC interface experiences will likewise fluctuate. How load propagates through the complex and distributes across the interface is a subject of a future study.

Load stabilizes WT TCRαβ-pMHC interfacial contacts

We assessed the effect of load on WT first by counting high-occupancy contacts with pMHC (Figure 3A; see Contact analysis in Computational methods). WTlow had the least number of contacts, followed by WT0 and WThigh, indicating low and high loads may have opposite effect on the interfacial stability. Vαβ-pMHC without the C-module formed the most contacts. This indicates that without a proper load, the C-module is detrimental to the stability of the interface with pMHC, as we found previously for the JM22 TCR (Hwang et al., 2020).

Figure 3 with 1 supplement see all
Load dependence of the WT TCRαβ-pMHC interface.

(A) Number of high-occupancy contacts (Contact analysis). Bars: std. (B) Hamming distance H over time. Histograms are for the interval after 500 ns. Dashed arrows mark increase in H, corresponding to contacts lost. (C–E) Contact occupancy heat maps for the interface with pMHC. H-bond/hb: hydrogen bonds, including salt bridges, and np: nonpolar (Contact analysis). (F) Location of Cα atoms of the residues whose contacts with pMHC have greater than 80% average occupancy. Cyan spheres: last frame of simulation. Transparent blue: locations rendered every 0.2 ns showing positional variability. (G) Total (pink) and per-residue (blue) BSA for interfacial residues with greater than 80% maximum instantaneous occupancy (BSA). Bars: std. (H) RMSF of backbone Cα atoms of the peptide after 500 ns. For calculation, the Cα atoms were aligned to those at the beginning of the production run.

Time-dependent changes in the interfacial contacts were monitored using the Hamming distance H (Hamming, 1950; Hwang et al., 2020). H is the number of the initial high-occupancy contacts (those with greater than 80% average occupancy during the first 50 ns) that are subsequently lost during the simulation. A low H means that such contacts persist while a high H means the corresponding number of initial high-occupancy contacts are missing. Consistent with the contact count, H remained low for WThigh and Vαβ-pMHC (Figure 3B). In WT0 and WTlow, H increased after about 500 ns and 900 ns, respectively. Thus, the relatively high number of interfacial contacts for WT0 (Figure 3A) is due to the formation of new contacts rather than by maintaining the initial contacts.

Occupancy heat maps provide the time dependence of individual contacts. For WThigh and Vαβ-pMHC, high-occupancy contacts persist throughout the simulation (blue regions in Figure 3C and Figure 3—figure supplement 1) while WT0 or WTlow exhibited breakage of contacts, especially when H increased (dashed arrows in Figure 3D and E). Differences in the interfacial contacts also manifest in their location. We displayed the backbone Cα atoms of Vα and Vβ residues that form contacts with pMHC with greater than 80% average occupancy (averaging was done after the initial 500 ns; Figure 3F). In WT0, contacts are spread apart, and in WTlow they lie mostly along the length of the peptide. These layouts potentially make interfacial contacts more prone to break via easier access by water molecules. In WThigh and Vαβ-pMHC, high-occupancy contacts form more compact clusters. Exposure to water of the TCRαβ residues involved in those contacts was measured by their buried surface area (BSA), which follows the same trend as the number of high occupancy contacts (Figure 3A vs. Figure 3G) Furthermore, this trend also applied to the root-mean square fluctuation (RMSF) of Tax peptide backbone Cα atoms, even though RMSF values were small (Figure 3H).

Experimentally, the WT complex has a relatively strong affinity as a TCRαβ (about 1 μM) (Ding et al., 1999), which may be why the interface with pMHC involved more contacts in WT0 compared to WTlow. At low load, the short distance between restraints on the ends of the complex (Figure 2A) allows wider transverse motion that in turn generates a shear stress or a bending moment at the interface. Transverse stress will be less for WT0 where the end moves freely, and for WThigh where lateral motion is suppressed. The extent of transverse motion can be seen by the RMSF of the center of mass of the Cα atoms of the peptide in the transverse direction, which was 16.3 Å for WTlow and 12.7 Å for WThigh. The high stability of Vαβ-pMHC and WThigh agree well with the results for JM22 (Hwang et al., 2020).

Influence of pMHC and load on Vα-Vβ motion

We analyzed the motion between Vα and Vβ (Vα-Vβ motion) to find its effect on the TCRαβ-pMHC interface. Compared to the unliganded systems (Vαβ and Tαβ; Table 2), the number of high-occupancy Vα-Vβ contacts increased slightly in Vαβ-pMHC (‘V’ in Figure 4A), while it decreased in full TCRαβ-pMHC complexes (‘0’, ’Low’, and ‘High’ in Figure 4A). This shows that the Vα-Vβ interface is difficult to organize with the restrictions imposed by the bound pMHC, except in the absence of the constant domains. The number of Vα-Vβ contacts in the liganded systems is the smallest for WTlow, similar to the case for the number of contacts with pMHC (Figure 3A), again reflecting a destabilizing effect with low load.

Figure 4 with 2 supplements see all
Vα-Vβ motion.

(A) Number of high-occupancy contacts (Contact analysis). Bars: std. (B) Triads {e1,e2,e3} assigned to each domain. Angles between triad arms (e1, e2, and e3) are marked. CDR3 loops are represented as thick tubes. Dashed arrows indicate directions where the CDR3 distance increases via the scissor motion. (C) Histograms of the 3 angles between the triad arms. For WTlow, the smaller peaks in distributions of e2 and e3 arise from simulation trajectories after 1 µs. (D) CDR3 distance vs. the 3 angles. Transparent band: std of the CDR3 distance in each bin. Statistics for bins deteriorate in large- or small-angle tails that contain very few frames.

To measure the Vα-Vβ motion, coordinate trajectories were first aligned to the stably folded β-sheet cores of the two domains. Triads were then assigned to the cores and principal component analysis (PCA) was performed on the triad trajectories (Figure 4B; Variable domain triads and PCA; Hwang et al., 2020). Since triads were assigned in the same way, the relative Vα-Vβ motion in different simulations can be studied by comparing their PCA.

The amplitude of PC1 is lower when the number of Vα-Vβ contacts is higher (Figure 4A vs. Figure 4—figure supplement 1A). Directions of PCs differed to varying extents (arrows in Figure 4—figure supplement 1B). Similarity of the directions was measured by the absolute value of the dot product between PCs as 18-dimensional unit vectors (for the six arms from two triads) in different systems. A value of 1 corresponds to the same direction, and 0 means an orthogonal direction (Figure 4—figure supplement 1C). For PC1, a high degree of similarity was observed between Tαβ and Vαβ, which is consistent with their similarity in the number of Vα-Vβ contacts (Figure 4A) and PC amplitudes (Figure 4—figure supplement 1A). Among triad systems with bound pMHC, WTlow differed significantly in the PC1 direction compared to others (Figure 4—figure supplement 1C, darker colors). The dot products varied more for PC2 and PC3, which capture finer motions with smaller amplitudes (Figure 4—figure supplement 1C).

To determine how the Vα-Vβ motion influences the interface with pMHC, we measured the distance between CDR3 loops (CDR3 distance), which play a central role in peptide discrimination (Figure 2B and C and Figure 4—figure supplement 2A–C). Unliganded Tαβ and Vαβ had greater fluctuation in the CDR3 distance (larger std), as they are unrestrained by pMHC. Among the pMHC-bound systems, WThigh and Vαβ-pMHC had small CDR3 distance (averages of 10.3 and 10.5 Å, respectively; Figure 4—figure supplement 2B C). CDR3 distance was larger for WT0 (11.3 Å), which reflects an altered interface with pMHC. For WTlow, the CDR3 distance varied more widely, with more than a 2-fold increase in standard deviation. The increase in CDR3 distance of WTlow happens after the increase in H (800 ns; Figure 3B and D and Figure 4—figure supplement 2C), suggesting a loss of contacts at the interface is related to the Vα-Vβ motion.

PCA decomposes the Vα-Vβ motion into mutually orthogonal directions. We made 2-dimensional histograms of each of these projections versus the corresponding CDR3 distance (Figure 4—figure supplement 1D). If any of the PC modes is strongly correlated with the CDR3 distance, the corresponding histogram would exhibit a slanted profile. However, no clear correlation could be seen (Figure 4—figure supplement 1D), suggesting that the changes in CDR3 distance may depend on combinations of PCs. We addressed this possibility by considering angles between matching arms of the two triads that do not rely on PCA (Figure 4B). The e1-e1 angle, herein called e1 (and similarly define e2 and e3; Figure 4B), can change either by the e1 arms turning up and down (‘flap’) or in and out of the page (‘twist’) in Figure 4B. Angles e2 and e3 depend primarily on rotation indicated by dashed arrows in Figure 4B (‘scissor’) (Hwang et al., 2020).

Histograms of the three angles (Figure 4C) show a clearer difference than individual PCs among the systems tested, and the CDR3 distance varies with the angles (Figure 4D). The wider distributions for angles of WTlow and WThigh reflect their higher PC amplitudes (Figure 4—figure supplement 1A). The symmetric distributions of e2 and e3 can be seen from the two peaks for WTlow, which is due to the reciprocal behavior of the scissoring motion involving the two angles (Figure 4B, side view, and Figure 4C, open diamonds). The two peaks are also related to the changes in the CDR3 distance (Figure 4D), which reflects an agitating effect of the mild load on the scissor motion. Given the definitions of the angles, the CDR3 distance will increase (dashed arrows in Figure 4B) with larger e1 or e3, or with smaller e2 (Figure 4D). WT0, despite the apparent stability of the interface, had a larger CDR3 distance than WThigh and Vαβ-pMHC, again indicating a disrupted interface. These results show that the CDR3 motion is coupled to the Vα-Vβ motion, especially the scissoring motion.

Asymmetric V-C motion influences the load response of the complex

We next analyzed the motion of the V-module relative to the C-module (V-C motion). The number of high-occupancy Cα-Cβ contacts did not vary significantly (in the 31–34 range) and they were more than the number of Vα-Vβ contacts, similar to the case for the JM22 TCR (Hwang et al., 2020). The C-module thereby influences the V-module as a single unit. The V-C motion was analyzed by performing PCA on the bead-on-chain (BOC) model constructed based on the β-sheet core of each domain, and hinges between V- and C-domains denoted as Hα and Hβ (Figure 5A; V-C BOC and PCA). Coordinate trajectories were aligned to the C-module so that the motion of the V-module relative to the C-module in different simulations can be compared. Across different systems, amplitudes of PCs were similar (Figure 5—figure supplement 1). PC1 (V-C bend; Figure 5A) was similar among systems, as seen by the values of dot products being close to 1.0 (Figure 5B). Directions of higher PCs varied more, similarly as higher PCs for the Vα-Vβ motion.

Figure 5 with 1 supplement see all
WT V-C dynamics.

(A) Average BOC built from the unliganded Tαβ. The C-module is used as orientational reference, hence PC arrows are visible only for the V-module and hinges. (B) Dot products computed between the BOC PCs in listed systems. Values closer to 1.0 denote similar V-C BOC direction of motion. (C) Difference in amplitude between the α and β chain motion measured between Vα and Vβ (top), and Hα and Hβ (bottom). PC amplitudes are proportional to the lengths of the arrows in panel A. (D) Histograms of hinge angles (defined in panel A) for each chain. (E) CDR3 distance vs. hinge angles.

We noticed that Vα bends more compared to Vβ, as can be gleaned from the longer PC arrows for Vα (Figure 5A). We quantified this asymmetry by subtracting the amplitudes of motion for domains in the β chain from those for the matching domains in the α chains, where positive or negative values respectively indicate greater or less motion of the α compared to the β chain (Figure 5C). Compared to Tαβ, binding of pMHC increases the α-chain motion, which is the greatest in WTlow (Figure 5C, PC1 in top row). The greater degree of Vα-Cα motion is consistent with the smaller number of Vα-Cα contacts compared to Vβ-Cβ (Figure 5—figure supplement 1).

The asymmetry was further analyzed by measuring hinge angles ∠TCRα and ∠TCRβ independently of PCA (Figure 5A). Distributions of ∠TCRα varied more compared to ∠TCRβ (Figure 5D). A wide distribution of ∠TCRα for WTlow is related to the increase in the CDR3 distance and concomitant changes in the triad arm angles later during the simulation (Figure 4—figure supplement 2C). For WTlow and WThigh, CDR3 distance decreases with increasing hinge angles, especially with ∠TCRα (Figure 5E), which suggests that unbending of the V-module under load helps with bringing the CDR3 loops closer together. In WTlow, this state is not maintained and ∠TCRα decreases (more bending) as the CDR3 distance increases (Figure 5E) which happens after the increase in H (Figure 3B). These results suggest a mechanism by which the asymmetric response of the whole TCRαβ to load affects the binding with pMHC by controlling the relative positioning between the CDR3 loops via the Vα-Vβ motion. For this, the Cβ FG-loop plays a critical role, as simulations of the bound complex without the Cβ FG-loop resulted in a smaller ∠TCRβ and an over-extended ∠TCRα (see Appendix 1).

Effects of point mutations on the peptide

In the WT crystal structure, the side chain of Y5 in the Tax peptide is located between the CDR3 loops of Vα and Vβ while V7 mainly contacts CDR3β (Figure 2C). P6 makes one contact with CDR3β. The side chain of Y8 is located between CDR3β and the α1 helix of MHC. Crystal structures of point mutants of these four residues are very similar in terms of interfacial contacts, docking angle, and CDR loop conformations, with the only structurally observable difference located at CDR3β (Figure 2B and C; Ding et al., 1999; Scott et al., 2011). However, point mutations profoundly affect dynamics of the complex, as explained below.

Modified agonists Y5F and V7R had about the same number of contacts with pMHC as the WT complexes, but high load resulted in fewer contacts, indicating a potential slip bond behavior (Figure 6A), although loss of contacts in Y5Fhigh might have been due to a higher load experienced compared to other complexes at the same extension (23.7 pN; Table 1). Antagonists P6A and Y8A had overall fewer contacts with pMHC without a consistent load dependence (Figure 6A). This trend was also seen in BSA profiles of residues forming high-occupancy contacts with pMHC (Figure 6B). For modified agonists, higher load also resulted in greater increase of H, whereas the trend was opposite for antagonists (Figure 6—figure supplement 1). The large number of contacts with pMHC for modified agonists (Figure 6A) despite an increase in H suggests an altered binding rather than maintaining the initial contacts.

Figure 6 with 3 supplements see all
Interface with pMHC containing mutant peptides.

The same occupancy cutoffs were used as in Figure 3. (A) Number of contacts with pMHC. Dashed line: count for WThigh in Figure 3A, for reference. (B) Total (pink) and per-residue (blue) BSA. Dashed and dotted lines: values for WThigh (Figure 3G). (C–F) Contact heat maps for peptide residues 5–8. (C) WThigh (included in Figure 3C), and (D) P6A0, (E) P6Alow, and (F) P6Ahigh.

In contact heat maps, the Y5 residue of the WT peptide forms a hydrogen bond with αS31 and nonpolar contacts with a few residues in both Vα and Vβ (Figure 3C–E, Figure 6C). In Y5F, the hydrogen bond with αS31 cannot form, and many of the nonpolar contacts with F5 break under load later during the simulation (Figure 6—figure supplement 2A). The breakage coincides with the increase in H (Figure 6—figure supplement 1A). In addition, contacts involving Y8 and V7 also break in Y5Fhigh (Figure 6—figure supplement 2A). Thus, the Y5-αS31 hydrogen bond may stabilize the interface with pMHC by arranging other nearby residues to form nonpolar contacts in favorable positions; its absence would make the nonpolar contacts more prone to break under load. The relative stability of Y5F0 can also be seen by the similarity in the locations of high-occupancy contact residues between WT and Y5F0 (Figure 3F vs. Figure 6—figure supplement 3A). Experimentally, Y5F has kinetic and cytotoxicity profiles similar to WT (Hausmann et al., 1999; Scott et al., 2011). Its dependence on load needs further experimental analysis. On the other hand, V7 of the WT peptide forms nonpolar contacts with residues in CDR3β (Figure 3C–E, Figure 6C). In V7R, nonpolar contacts with CDR3β form with reduced occupancy, and contacts involving Y8 are also disrupted (Figure 6C vs. Figure 6—figure supplement 2B).

For antagonists, more contacts broke, which again involve non-mutated residues such as Y5 and V7 (Figure 6D–F, Figure 6—figure supplement 2C). The greater number of contacts in Y8Ahigh compared to Y8A0 and Y8Alow (Figure 6A) despite smaller number of contacts involving key peptide residues Y5–A8 (Figure 6—figure supplement 2C) suggests formation of additional contacts with other parts of MHC as a result of an altered interface. Experimentally, binding of the A6 TCR to pMHC containing the P6A or Y8A peptide was not detected in vitro (Ding et al., 1999). Thus, Y8A in principle could exhibit a catch bond, but forming the complex in the loaded state may be kinetically inaccessible.

The modified agonists had more Vα-Vβ contacts than WThigh while the antagonists had fewer, except for Y8Ahigh (Figure 7—figure supplement 1A). While the amplitude of Vα-Vβ motion was generally in a range similar to the WT systems (Figure 4—figure supplement 1A vs. Figure 7—figure supplement 1B), the CDR3 distance was larger for all mutant systems except for P6A, which had a weak dependence on triad angles (Figure 7A, B). The angles in turn varied among systems and loading conditions (Figure 7—figure supplement 2). These results suggest that point mutations to the peptide cause alterations in the load-dependence of the interface and the Vα-Vβ motion.

Figure 7 with 4 supplements see all
Mutant effects on the conformational dynamics of TCRαβ.

(A, B) CDR3 distance versus triad arm angles for (A) modified agonists and (B) antagonists. Plot for WThigh in Figure 4D is reproduced for comparison. (C, D) Average BOCs of labeled complexes oriented to the constant domains of WThigh (V-C BOC and PCA) for (C) modified agonists and (D) antagonists. Average BOCs for WT0 and WThigh are displayed for comparison (marked by angular brackets).

The mutants affected the average V-C BOC similarly as that for dFGhigh (Figure 7C and D vs. Appendix 1—figure 1C). Among them, Y8Ahigh had an average BOC approaching that of WThigh, which aligns with the comparable number of contacts with pMHC (Figure 6A). However, the location of its Hα differed (Figure 7D), and the CDR3 distance was larger (Figure 7B). To quantify deformation of the average BOC, we measured displacements of centroids from the corresponding ones in WThigh, which were overall greater for the α chain than the β chain (Figure 7—figure supplement 3A B). Consistent with this, the mutants had fewer Vα-Cα contacts than WThigh and a similar number of Vβ-Cβ contacts (Figure 7—figure supplement 3C D).

Similar to the WT systems, the greater motion of the α chain than the β chain was observed in the mutant systems, as seen from the differences in V-C PC1 amplitudes (Figure 7—figure supplement 4A B). However, dot products of the BOC PC1 between WT and mutants revealed that the direction of motion differed by varying degrees, which was more for V7Rhigh and Y8A (Figure 7—figure supplement 4C vs. Figure 5B). Thus, point mutations on the WT peptide can affect the conformational motion of the whole TCRαβ, in addition to the average BOC.

To further test effects of point mutations, we introduced in silico point mutations P6A and Y8A to the WT complex (WT to antagonists) and conversely introduced A6P and A8Y mutations to the P6A and Y8A complexes, respectively (antagonists to WT). The in silico antagonists did exhibit reduction in contacts with pMHC while the results were mixed for the in silico WT, especially for A8Y where the introduced tyrosine is bulkier than the original alanine. Nevertheless, these tests support the above results based on the original crystal structures (See Appendix 2 for details).

Load- and time-dependent interfacial response

To probe the dynamic relation between the TCRαβ-pMHC (intermolecular) interface and intra-TCRαβ (intramolecular) interfaces formed between subdomains of the complex, we calculated the total occupancy of the high-occupancy contacts in respective cases (Figure 8A, B). For the intramolecular contacts, we excluded the Cα-Cβ interface contacts since they are larger in number compared to other interfaces and did not differ significantly across different systems. Thus, the C-module moves mostly as a single unit (Hwang et al., 2020).

Figure 8 with 1 supplement see all
Relationship between force and interfacial behavior.

(A, B) The total contact occupancy measured in 40-ns overlapping intervals starting from 200 ns (see Time-dependent behavior in Computational methods). (A) TCRαβ-pMHC (intermolecular) and (B) intra-TCRαβ (intramolecular) contacts excluding Cα-Cβ. Cases without load are shown as horizontal bars below each panel. Plots for low load systems (Table 1) do not have outlines. (C) Angle between antigenic peptide and the line between centroids of the triads for Vα and Vβ (Peptide and V-module angle). Thin lines: values at individual frames. Symbol: 50-ns running average.

For WT0, the intermolecular contact occupancy stayed at around 20 (WT in Figure 8A, horizontal bar on the bottom) and for WTlow, it decreased later in simulation (WT in Figure 8A, darkening of circles without outline). In comparison, the intramolecular contact occupancy remained relatively constant for both WT0 and WTlow (WT in Figure 8B, horizontal bar on the bottom and circles without outline). For WThigh, the intermolecular contact occupancy was steady even with wider fluctuation in force (WT in Figure 8A, outlined circles), and the intramolecular occupancy also remained high, indicating the subdomains are held together tightly (WT in Figure 8B, outlined circles). For dFGlow, the intermolecular contact occupancy stayed low and intramolecular occupancy was relatively constant (dFG in Figure 8A and B, circles without outline). In dFGhigh, the contact occupancy with pMHC increased (dFG in Figure 8A, outlined circles), but the intramolecular contact occupancy became low (dFG in Figure 8B, outlined circles), which suggests that the complex is not as tightly coupled compared to WT.

For modified agonists, the no load and low load cases had overall higher occupancy, both with pMHC and within TCRαβ, but occupancy fluctuated more as can be seen by the changes in colors in the occupancy trajectories (Y5F and V7R in Figure 8A and B, horizontal bars on the bottom and circles without outlines). Under high load, intermolecular contact occupancy decreased over time (Y5F and V7R in Figure 8A, darkening of outlined circles) while intramolecular contact occupancy either increased (Y5Fhigh) or decreased (V7Rhigh) relative to the respective low load cases. For antagonists, both occupancy measures were lower than the WT, and further reduction could be seen over time in some cases (P6A and Y8A in Figure 8A and B, darkening of colors in outlined circles).

The stability of the TCRαβ-pMHC interface also manifested into their relative motion, which was quantified by the angle between the least-square fit line across the backbone Cα atoms of the antigenic peptide and the unit vector formed between the centroids of Vα and Vβ (Figure 8—figure supplement 1A–D). For WT, the peptide angle fluctuated more for WTlow than WThigh (WT in Figure 8C) where 58.4°±3.6° (avg±std after 500 ns) for WThigh reflects a diagonal binding. This is consistent with the greater transverse RMSF of WTlow mentioned earlier which may assist with destabilizing the interface. For dFGlow, the peptide changed orientation by more than 20°, and for dFGhigh, it stabilized, but at a higher value than WThigh, which also was reached in dFGlow later during simulation, suggesting a more orthogonal binding (dFG in Figure 8C). For modified agonists, similar to the behaviors of the total intra- and intermolecular contact occupancy, the peptide angle was affected more under high loads, again becoming more orthogonal compared to WThigh (Y5F and V7R in Figure 8C). For antagonists, the angle overall fluctuated more under no load or settled to different values under high load. Since the antagonists are loosely coupled (low occupancy in Figure 8A and B), settling of the angle does not indicate stabilization of the interface, as evident from the positional shift of the α2 helix of V7Rhigh or Y8Ahigh (Figure 8—figure supplement 1G H) compared to WThigh (Figure 8—figure supplement 1E).

Discussion

The present study elucidates how the load-dependent TCRαβ framework motion influences the dynamics of the TCRαβ-pMHC interface (Figure 9). Instead of using defined conformational changes as seen in other catch bond systems, TCRαβ activates the catch bond via altering its conformational dynamics. A main feature of the TCRαβ framework is the smaller number of contacts for the Vα-Cα compared to the Vβ-Cβ interface. This causes an asymmetric V-C motion, primarily bending, where Vα moves more compared to Vβ relative to the C-module, which serves as a base. This in turn generates relative motion between Vα and Vβ, which can destabilize the contacts with pMHC, especially by affecting the distance between CDR3 loops that play the most direct role for sensing the bound peptide (Figure 9A and B). Applying a physiological level force stabilizes the interface by straining the whole complex into a more tightly coupled state, as can be seen by the increase of both inter- and intramolecular contacts in WThigh (Figures 8 and 9C). This physically plausible mechanism is based on collective analyses of all simulations in this study (Figure 1).

Model for peptide screening.

(A) Non-matching pMHC or (B) matching pMHC but without adequate load do not stabilize the asymmetric V-C framework motion that affects the interfacial stability as measured by the CDR3 distance (CDR3 loops are shown above the V-module). (C) Matching pMHC with adequate load results in an overall tighter fit.

The CDR3 distance of WThigh (10.3±0.3 Å; Figure 4—figure supplement 2C) was shorter than that of WT0 or WTlow (Figure 4D), and it is also shorter than the 10.9 Å CDR3 distance in the crystal structure (PDB 1AO7). The applied load slightly increases the spacing between pMHC and TCRαβ, which provides room for the CDR3 loops to adjust as well as allow other contacts to ‘lock’ into more stable states with higher and more persistent occupancy. Absence of load or low load do not properly channel the framework motion and thereby increase exposure to water (Figure 3F and G) and destabilize the interface.

The Cβ FG-loop stabilizes the Vβ-Cβ interface, thereby contributing to the asymmetric V-C motion. It also controls the relative orientation between Vβ and Cβ, hence it affects the orientation of the CDR loops of the V-module with respect to the loading direction (Appendix 1—figure 1C). The behaviors of the dFG mutant are consistent with the reduced catch bond response observed experimentally (Das et al., 2015). Consistency in these findings between the present study and our previous simulations using JM22 TCR (Hwang et al., 2020) also underscores that the proposed mechanism based on the asymmetric framework motion is applicable to other TCRαβ systems. Furthermore, while the γδ TCR involved in transitional immunity operates as a slip-bond receptor, the Vγδ-Cαβ chimera forms a catch bond with its sulfatide ligand-loaded CD1d molecule (Mallis et al., 2021), which provides additional experimental support for the proposed mechanism.

In the absence of bond dissociation events within the microsecond-long simulation, the catch bond manifests as stabilization of high-occupancy contacts and interfacial fit under load. Conversely, slip bond will exhibit destabilization of the interface under load, as seen for antagonists (Figure 8). In the case of a higher affinity slip bond, the interfacial behavior could be insensitive to the applied load within the finite simulation time. In addition to agreeing with previous experiments mentioned above, our atomistic simulations can be used to design mutant TCRs possessing altered load dependence. For example, the V7-βG100 contact is present mainly in the high-load case (Figure 3C). The V7R peptide is a modified agonist as R7 forms contacts with residues other than βG100, albeit with lower occupancy (Figure 6—figure supplement 2). Point mutations of βG100 may lead to different behaviors depending on the type and size of the mutated residue. Another possibility is placing a disulfide bond to limit the Vα-Cα motion or to alter the relative V-C orientation. Extensive simulations are needed to more accurately predict behaviors of such mutants.

After engagement with a cognate pMHC under load, reversible transition to an extended state is possible. This has been observed both during in vitro single-molecule experiments using TCRαβ and on cells displaying the full αβTCR holoreceptor (Das et al., 2015; Banik et al., 2021). Since Vαβ-pMHC lacking the C-module forms a more stable binding (Figure 3) that was also observed in our previous simulations of the JM22 TCR (Hwang et al., 2020), the C-module likely undergoes partial unfolding in the extended state. Thus, while the folded C-module serves as the base for the asymmetric V-C motion screening for the matching pMHC, once a match is found, the reversible transitioning propelled by the partial unfolding of the C-module may agitate the membrane and activate the cytoplasmic domains of the surrounding CD3 subunits to initiate downstream signaling (Reinherz et al., 2023). A circumstantial evidence for the capacity of the C-module to unfold is that the Cα domain as well as parts of Cβ are occasionally unresolved in crystal structures, as in PDB 1AO7. Catch bond formation and subsequent reversible structural transitioning under applied load indicate that pMHC recognition requires energy input, for example from the actomyosin machinery. Further studies are needed to understand the energetics involved in pMHC recognition, signaling initiation, and ultimately T-cell activation.

In addition to TCRαβ, MHC may also respond to load. Wu et al., 2019 suggested a partial separation of the MHCα1-α2 peptide-binding platform from β2m with the attendant lengthening of pMHC contributing to a longer bond lifetime. Banik et al., 2021 observed a catch bond for CAR-pMHC, where just MHC is being pulled with an antibody. While we did not find a clear load or peptide-dependence in contacts between subdomains of MHC, since the entire TCRαβ-pMHC complex is under load, conformational changes in pMHC may contribute to the extended state of the complex. Yet, for T-cell-based cancer immunotherapy, mechanistic knowledge of the mechanosensing through a TCR has a greater practical significance (Reinherz et al., 2023).

A recent study using a laminar flow chamber assay fit the measured bead survival distribution using Bell’s equation to estimate the zero-force off rate koff and the force sensitivity distance xβ (Pettmann et al., 2023). They found a negative correlation between koff and xβ, to conclude that mechanical forces impair antigen discrimination. However, the force range tested was up to 100 pN, where even systems exhibiting catch bond in the 10–20-pN range will switch to a slip bond behavior. A catch bond exhibits a non-monotonic force versus bond lifetime profile, so that fitting with Bell’s equation, an exponential function, leads to results that do not have a clear physical meaning. For example, xβ in Pettmann et al., 2023 was less than 1 Å in magnitude in all systems, which is shorter than the length of a single covalent bond. They also performed steered MD simulation that applies hundreds of pN forces, which is inadequate for studying behaviors of the system under loads in the 10–20-pN range (Hwang et al., 2020). Use of a coarse grained model without appropriately incorporating atomistic properties of the TCR further makes it difficult to compare their simulation with wet laboratoryexperiments.

We earlier proposed that the residues of the antigenic peptide play a role more as ‘teeth of a key’ for screening the TCRαβ-pMHC interaction fitness rather than bearing applied loads (Hwang et al., 2020; Reinherz et al., 2023; Figure 1, ‘bittings of a key’). The present study confirms this notion through simulations of mutant systems, where several contacts across the interface with pMHC were impaired due to a single-residue mutation on the peptide in ways that reflect the functional outcome of the mutation. In considering how a T-cell may respond to an unknown peptide, the pMHC motion and the asymmetric V-C motion are two points of guidance (Figure 9). Stabilization of the inter- and intramolecular interfaces throughout the whole complex under 10–20-pN load would indicate a cognate TCRαβ-pMHC interaction (Figure 8). Since these features are based on overall TCRαβ-pMHC complex dynamics, rather than changes to specific contacts or a particular conformational change, they can be used to predict fitness of other TCRαβ-pMHC combinations. Such tests involve performing many all-atom MD simulations and trajectory analyses. An in silico method would be needed that efficiently predicts dynamic properties of the complex based on sequence and structural data only. Atomistic insights gained from the present study will be helpful for developing such a method in future studies.

Materials and methods

Structure preparation

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Structure preparation was done using CHARMM (Brooks et al., 2009). Simulation systems were based on PDB 1AO7 (Garboczi et al., 1996a) 1QSE, 1QRN, and 1QSF (Ding et al., 1999) and 3QFJ (Scott et al., 2011). Residues from the TCR α- and β-chains were renumbered sequentially from the original non-sequential numbering in the PDB. Throughout the paper we use the renumbered index to refer to a residue. Residues differing at a few locations in some of the PDB files were converted so that all systems have identical sequences except for point mutations introduced in the Tax peptide (details are given below). Disulfide bonds between cysteine residues were introduced as noted in the PDB file. Histidine protonation sites were determined based on the 1QSE crystal structure to promote hydrogen bond formation with neighboring residues. Where neighboring residues were unlikely to hydrogen bond, we assigned the water-facing nitrogen of histidine as charged. This led to protonation of the Nδ atom for all histidine residues except for MHC H263 and β2m H84, where the Nϵ atom was protonated. For truncated structures, crystal waters within 2.8 Å from the protein atoms were kept in the initially built system. For full structures, all crystal waters were kept.

We extended the termini of the TCRαβ-pMHC complex as handles for applying positional restraints (Figure 2, “added strands”) (Hwang et al., 2020). For MHC, we used the sequence from UniProt P01892, where 276LSSQPTIPI284 was added after E275. For TCRαβ, sequences for the added strands were from GenBank ABB89050.1 (TCRα) and AAC08953.1 (TCRβ), which were 201PESSCDVKLVEKSFETDT218 and 246CGFTSESYQQGVLSA260, respectively. After adding the strands, a series of energy minimization and a short MD simulation in the FACTS implicit solvent environment (Haberthür and Caflisch, 2008) were performed to relax them and bring together the C-terminal ends of the two TCR chains. The first two N-terminal residues of TCRβ were missing in all structures except for 3QFJ, so they were added and briefly energy minimized.

1AO7 (Tax peptide): In the original PDB 1AO7, coordinates for the Cα domain (D116–S204) and parts of Cβ (E130–T143, K179–R188, S219–R228) are missing. The coordinates listed are based on the renumbered indices. These were built using PDB 1QSE. For the Cα domain, we aligned the Vα domain of 1AO7 and 1QSE (K1–P115) based on their backbone Cα atoms and added the missing Cα domain residue coordinates to 1AO7. After this, we performed a brief energy minimization on the added domain while fixing positions of all other atoms of 1AO7. For missing residues in the Cβ domain, we used backbone Cα atoms of two residues each before and after the missing part to align 1QSE to 1AO7 and filled in coordinates, followed by a brief energy minimization of the added part in 1AO7. In this way, the TCRαβ-pMHC interface of the original 1AO7 is preserved. By comparison, previous simulations mutated PDB 1QRN back to WT (Ayres et al., 2016), which corresponds to the A6P in silico WT system (Appendix 2), or converted a high-affinity variant of A6 (PDB 4FTV) by mutating β-chain residues, in particular nearly the entire CDR3 loop (Rangarajan et al., 2018). Compared to our approach, those preparation methods thereby introduce more perturbation to the interface with pMHC.

The β2m residues C67 and C91 were reverted (C67Y, C91K) based on UniProt P61769 referenced in PDB 1AO7. These agree with the β2m sequence in other structures.

1QRN (P6A): Except for the two N-terminal residues of TCRβ, there were no missing coordinates. This also applies to 1QSE and 1QSF. The following conversions were made to match the sequence with other structures: K150S (TCRα), and A133E and E134A (TCRβ).

1QSE (V7R): No residue conversion was made.

1QSF (Y8A): The following conversions were made: A219R (MHC) and A225T (TCRβ).

3QFJ (Y5F): There were no missing residues. We made the D204N conversion in TCRβ.

WT truncated complexes: For truncation, we used the constructed 1AO7 complex.

  • Vαβ: the last residues were αD111 and βE116.

  • Tαβ: the last residues were αD206 and βG247 (no C-terminal strands).

  • Vαβ-pMHC: includes Vαβ, peptide, β2m, and MHC. The last residue of MHC was L276.

  • WT0: WT complex without the added C-terminal strands, as for Tαβ.

  • dFG: residues βL218–βP231 removed from the corresponding WT complex. βG217 and βV232 were covalently joined.

MD simulation protocol

Solvation and equilibration of simulated systems

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We used CHARMM (Brooks et al., 2009) to prepare simulation systems before the production run. The solvation boxes were orthorhombic for systems with pMHC and cubic for those without pMHC. For TCRαβ-pMHC, the size of the initial water box was such that protein atoms were at least 12 Å away from the nearest transverse face of the box and 25 Å from each longitudinal face. The extra space in the longitudinal direction was to initially test and select extensions of the complex for longer simulations in Table 1. For solvation, we used the TIP3P water. Water molecules with their oxygen atoms less than 2.8 Å from protein heavy atoms were removed. Neutralization of the system was done using Na+ and Cl- ions at about 50 mM concentration. Crystal water molecules were kept in this procedure.

After neutralization, a five-stage energy minimization was applied where protein backbone and side chain heavy atoms were progressively relaxed (Hwang et al., 2020). This was followed by heating from 30 K to 300 K for 100 ps and equilibration at 300 K for 200 ps. Backbone heavy atoms were positionally restrained with 5-kcal/[mol·Å2] harmonic spring constant during heating and equilibration, except for structures involving 1AO7 that originally had more missing residues, where a 2-kcal/[mol·Å2] restraint was used. We then performed a 2 ns CPT (constant pressure and temperature) simulation at 1 atm and 300 K. We applied a 0.001-kcal/[mol·Å2] restraint on backbone Cα atoms. The CHARMM DOMDEC module (Hynninen and Crowley, 2014) was used to parallelize the simulation. We applied the SHAKE method to fix the length of covalent bonds involving hydrogen atoms, and used a 2-fs integration time step.

Production runs

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Production runs were performed using OpenMM (Eastman et al., 2017). We used the CHARMM param36 all-atom force field (Huang and MacKerell, 2013) and the particle-mesh Ewald method to calculate electrostatic interactions. We used an Ewald error tolerance of 10-5 which is 1/50 of the default value in OpenMM, for accuracy. The cutoff distance for nonbonded interactions was 12 Å, and the Nose-Hoover integrator of OpenMM at 300 K was used, with a 2-fs integration time step. We ran OpenMM on GPUs with mixed floating point precision. Below are specific steps of the MD protocol relevant to individual systems in Table 1 and Table 2.

TCRαβ-pMHC with load

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Laddered extension with added strands
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To apply load, Cα atoms of the C-terminal ends of the added strands in the complex (Figure 2A, blue spheres) were held by 1-kcal/[mol.Å2] harmonic positional restraint at a given extension during the simulation. Restraints were applied to the Cα atom of MHC I284 and to the center of mass of two Cα atoms: αT218 and βA260. A flat-bottom distance restraint was applied to the latter two atoms to prevent large separation. It was activated when the distance of the two Cα atoms was greater than 10 Å, where a 1.0-kcal/[mol.Å2] harmonic potential was applied. Starting with the initially built complex, we performed a 4-ns run then increased the extension by shifting centers of the positional restraints on terminal atoms by 2 Å at each end, for a total 4 Å added at each extension, for the next 4-ns run. The process continued to yield 4–6 extensions.

After each extension run, we truncated the water box such that the length of the box was 12 Å larger than the maximum span of the complex on each side, and re-neutralized the system. A representative water box size is 218×97×90 Å3 for WThigh, containing 187,250 atoms. Since the system was already equilibrated from the previous run, we used a simpler energy minimization scheme where backbone and side chain heavy atoms were restrained by 10-kcal/[mol·Å2] and 5-kcal/[mol·Å2] harmonic potentials, respectively, and 200 steps of steepest descent followed by 200 steps of adopted-basis Newton-Raphson energy minimization was performed. Heating, equilibration, and the initial 2-ns dynamic runs with positional restraints were carried out as explained above. We then carried out 60–100 ns production runs for each extension and selected two or three extensions to continue for longer than 1000 ns.

Selecting extensions
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We measured the average force on the complex during each 60–100-ns simulation, then selected two extensions where the average force generated was representative of a ‘low’ (around 10 pN) and ‘high’ (over 15 pN) load on the TCR. These values were based on the experimental 10–20-pN catch bond activation force range (Das et al., 2015; Liu et al., 2016).

In some cases, in particular at low extensions, the flexible added strand either folded onto itself or made contacts with the C-module of TCRαβ, effectively shortening the span of the complex. Factors such as this, together with differences in conformational behaviors of the complex, affected the average force for a given extension. Thus we had to test and choose among different extensions for each system. We also ran 1–2 replicate simulations of comparable length (1 µs) at given extensions except for systems involving dFG and in silico mutants. However, even with nearly the same extensions used, measured forces in replicate simulations varied due to the reasons explained above. Additionally, the average force based on the initial 60–100 ns and after 500 ns differed. For detailed analysis, we thus chose sets of runs where higher extension led to higher average force. However, in all runs, we found that the average load rather than extension correlates better with the behavior of the TCRαβ-pMHC interface, which underscores the consistency of the load dependence found in our analysis. The final selection and average forces are in Table 1.

Other systems

TCRαβ-pMHC without load

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These systems include WT0 and the no load complexes with point mutations to the Tax peptide. To prevent the complex from turning transversely in the elongated orthorhombic box, we applied a weak 0.2-kcal/[mol·Å2] harmonic positional restraint on select Cα atoms in the MHC α3 domain that had RMSF below about 0.5 Å in both WTlow and WThigh, which were P185–T187, L201–Y209, F241–V247, and T258–H263.

Vαβ-pMHC

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We applied a 0.01-kcal/[mol·Å2] harmonic restraint to the backbone Cα atoms of the MHC α3 domain (residues P185-L276) to prevent the complex from turning transversely in the orthorhombic box. The restraints are 20 times weaker than those used for TCRαβ-pMHC complexes mentioned above. This was because Vαβ-pMHC is smaller in both size and aspect ratio.

Vαβ, Tαβ, dFG

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No positional restraints were applied. A representative system size is, for Tαβ, a 92.8 Å3 cubic water box containing 75,615 atoms.

dFG-pMHC

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The FG-loop deletion was done after initially preparing (solvation and neutralization) the WT complex in the extended water box. After deletion, the system was re-neutralized. Subsequently, laddered extension, selecting extensions for high and low load cases, and longer production runs were performed as explained above.

In silico mutants

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Each in silico mutation (Appendix 2—table 1) was performed for low and high load extensions of the complex. To use similar extensions as in the original complexes, we used the last frame of the 4-ns laddered extension simulation. After introducing the in silico mutation, we inspected the structure to ensure there was no steric clash with neighboring residues or water molecules. We performed a short energy minimization to relax the modified residue while keeping coordinates of all other residues except for residues immediately before and after the mutated one on the peptide. We then truncated the water box and re-neutralized the system, after which steps from the initial energy minimization up to the final production run followed the same procedure as explained above.

Trajectory analysis

Coordinates were saved every 20 ps (0.02 ns) during production runs, resulting in 50,000 coordinate frames for 1000 ns. We excluded the initial 500 ns when calculating averages and standard deviations in the number of contacts, CDR3 distance, BSA, PCA values, and angle data. Since all systems were simulated for a minimum of 1 µs, this leaves at least 25,000 frames. We report data prior to 500 ns in trajectory plots and contact occupancy heat maps (e.g. Figure 3B–E).

Calculating force

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Force on a restrained atom or the center of mass of the C-terminal atoms of the added strands in TCRαβ was calculated based on the deviation of its average position from the center of the harmonic potential, multiplied by the spring constant used (Hwang, 2007; Hwang et al., 2020). Average force in Table 1 was computed from 500 ns to the end of the simulation. Instantaneous force in Figure 8A and B was computed in 40-ns overlapping intervals starting from 200 ns, i.e., 200–240 ns, 220–260 ns, 240–280 ns, etc.

CDR3 distance

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The CDR3 distance (e.g. Figure 4—figure supplement 2A–C) was measured using the midpoint between backbone Cα atoms of two residues at the base of each CDR3 loop. They were: T92 and K97 for CDR3α and R94 and E103 for CDR3β.

Contact analysis

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We used our previously developed method (Hwang et al., 2020). Briefly, H-bonds (including salt bridges) were identified with the 2.4 Å donor-acceptor distance cutoff. Nonpolar contacts were identified for atom pairs that are within 3.0 Å and both have partial charges less than 0.3e (e=1.6×1019 C) in magnitude. The average occupancy was measured as the fraction of frames over which a bond is present during the measurement period. Instantaneous occupancy was measured as a 40-frame (0.8-ns) rolling average. The average occupancy of a contact represents its abundance during the simulation period while the instantaneous occupancy represents its temporal intensity.

For counting the number of contacts (e.g. Figure 3A, Figure 4A, Figure 5—figure supplement 1), we used contacts with the average occupancy greater than 50% and at least an 80% maximum instantaneous occupancy after the initial 500 ns. Contact occupancy heat maps (e.g. Figure 3C–E) report those with the overall average occupancy greater than 30%, and the maximum instantaneous occupancy during the simulation greater than 80%.

The Hamming distance H (e.g. Figure 3B) was measured using contacts with greater than 80% average occupancy during the first 50 ns.

BSA

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For the BSA calculation (e.g. Figure 3G), we used residues in the V-module with the maximum instantaneous contact occupancy with pMHC greater than 80%. We calculated the surface area for the selected residue contacts and added them to get the total BSA. Per-residue BSA is the total BSA divided by the number of residues forming the contacts in the given time interval. The reported values (e.g. Figure 3G) are respective averages after 500 ns.

Variable domain triads and PCA

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Triads (orthonormal unit vectors) were constructed for Vα and Vβ by modifying the procedure in Hwang et al., 2020 for the A6 V-module. We used the backbone Cα atoms of six residues from the central four β-strands that make up the stably folded β-sheet core of each variable domain: for Vα, S19-Y24, F32-Q37, Y70-I75, Y86-T91 and for Vβ, T20-Q25, S33-D38, F74-L79, V88-S93. The Cα atoms of these residues have RMSF in WThigh near or less than 0.5 Å, and they correspond to two matching segments on each of the inner and outer β-sheets of the immunoglobulin fold. The center of mass of the Cα atoms of the selected residues was used for the centroid of each triad. The e3 arm of the triad was assigned along the major axis of the least-square fit plane of the selected atoms in each domain, which is parallel to the β-strands and points to the CDR3 loop (Figure 4B). The e1 arm was assigned by taking the direction from the center of masses of the selected atoms from the inner to the outer β-sheets of each variable domain and making it perpendicular to e3. The e2 arm was then determined as e2=e3×e1.

PCA was performed on the trajectory of the two triads using a custom FORTRAN95 program (Hwang et al., 2020). The PC amplitude (e.g. Figure 4—figure supplement 1A) corresponds to the rotational motion of these arms in units of radians. The PC vector for the 6 arms of the two triads is an 18-dimensional unit vector. To compare directions of two PCs (e.g. Figure 4—figure supplement 1C), the absolute value of the dot product between them was calculated, which ranges between 0 and 1. To project the Vα-Vβ triad for a given frame to a PC direction (Figure 4—figure supplement 1D), the average triad calculated after the initial 500-ns was subtracted from the triad, then a dot product was formed with the PC vector.

V-C BOC and PCA

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The V-C BOC (Figure 5A) was assigned based on the method we developed previously (Hwang et al., 2020). For beads representing the V-module, centroids of the two triads were used. For the C-module, the center of mass of backbone Cα atoms of the following residues in each domain were used: for Cα, A118–R123, V132–D137, Y153–T158, S171–S176, and for Cβ, T143–A148, L158–N163, S192–V197, F209–Q214. We used αN114 for Hα, and for Hβ, the center of mass between βD117 and βL118 was used, which had large RMSF in WThigh.

We aligned coordinate frames for all simulations to the first frame of WThigh based on atoms used to assign beads for the C-module. In this way, motion of the V-module relative to the C-module can be analyzed. Also, by using a common reference structure (first frame of WThigh), average BOCs can be compared, as in Figure 7C and D. PCA of the V-C BOC was performed using the 6 beads representing the centroids and hinges. PCA for the V-module triads was done separately. Since the reference of motion is the C-module, directions of PCs for the V-module triads indicate motion of the V-module relative to the C-module (arrows on triad arms in Figure 5A), which complements the direction of the V-module centroids obtained from PCA of the V-C BOC (arrows on centroids in Figure 5A).

Time-dependent behavior

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For the total occupancy in Figure 8A and B, we only considered contacts with greater than 50% overall occupancy and over 80% maximum instantaneous occupancy during the entire simulation period. In this way, changes in high-quality contacts under fluctuating force for a given trajectory can be monitored. For each 40-ns window, we calculated the average occupancy of selected contacts and added them to obtain the total occupancy. For intermolecular contacts, interfaces between MHC-Vα, MHC-Vβ, peptide-Vα, and peptide-Vβ were considered. For intramolecular contacts, Vα-Vβ, Vα-Cα, and Vβ-Cβ were considered.

Peptide and V-module angle

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For Figure 8C, at each coordinate frame we calculated the least-square fit line for the peptide backbone Cα atoms and calculated a dot product of its direction with a unit vector pointing from the centroid for the triad of Vα to that of Vβ.

Appendix 1

Allosteric effect of the Cβ FG-loop deletion

The higher number of Vβ-Cβ contacts (Figure 5—figure supplement 1) and smaller range of ∠TCRβ (Figure 5D and E) in the WT TCR suggests that the β chain mainly bears the load while the α chain adjusts to accommodate different loading conditions. Our previous experimental (Das et al., 2015) and computational (Hwang et al., 2020) studies showed that the Cβ FG-loop plays a critical allosteric role for the catch bond formation. To examine its role in A6 TCR, we performed simulations of dFG in isolation (Table 2) and under low and high loads (Table 1). Similar to the WT, the number of contacts with pMHC increased with load (Appendix 1—figure 1A and Appendix 1—figure 2A and B). The BSA for high-occupancy residues contacting pMHC was also greater for high load (Appendix 1—figure 2C). Thus, dFG may also possess a catch bond behavior, which agrees with experiment where a subdued catch bond was observed (Das et al., 2015). However, H increased early on and was slightly larger than that for WThigh (Appendix 1—figure 2), and the CDR3 distance was higher compared to WThigh (Appendix 1—figure 1B), which indicate an altered interface.

The conformation of the whole dFG was also affected. Relative to the C-module, the average BOC for the unliganded dFG was substantially different from those of dFGlow and dFGhigh, where the latter was similar to that of WThigh (Appendix 1—figure 1C). In particular, Vβ of the unliganded dFG is more tilted, as there is a lack of support from the FG-loop (Hwang et al., 2020). When load is applied to the dFG-pMHC complex, dFG becomes less bent. Its tendency to return to the bent conformation would impose a strain on the interface with pMHC. This can be seen by the higher average load on dFG-pMHC complexes than WT-pMHC complexes under similar extensions (Table 1). Comparing between the amplitudes of PCs of α and β chains, a notable difference from the WT systems (Figure 5C) is that Hβ moves more than Hα for loaded dFG systems (Appendix 1—figure 1D, bottom row). Also, distributions of ∠TCRα and ∠TCRβ shift to larger and smaller values, respectively (Appendix 1—figure 1E). These indicate alterations in the conformation and motion of dFG. Furthermore, the CDR3 distance of dFG is elevated regardless of load or V-C angle (Appendix 1—figure 1F), suggesting a reduced allosteric control by the V-C motion.

The altered conformation of dFG causes the interface with pMHC to tilt as observed in our previous study of JM22, which is detrimental to the stability of the complex (Hwang et al., 2020). The increased motion of Hβ may also deliver more agitation to the interface with pMHC. Thus, even though full dissociation with pMHC was not observed within the simulation time, the dFG-pMHC complex is likely to be less stable compared to the WT complex.

Appendix 1—figure 1
Effects of the Cβ FG-loop deletion.

In all panels, the same criteria were used to measure values as for the WT systems in the corresponding figures. For comparison, respective data for WThigh are shown. (A) Number of contacts with pMHC (Figure 3A). (B) CDR3 distance vs. the three Vα-Vβ triad arm angles (Figure 4D). (C) Average BOC of labeled complexes oriented to the C-module of WThigh. The unliganded dFG has notably different average BOC (Figure 7C and D). (D) Differences in amplitudes between respective PC components of the α and β chains (Figure 5C). (E) Histogram of hinge angles and (F) CDR3 distance vs. hinge angles (Figure 5D and E).

Appendix 1—figure 2
Effects of the Cβ FG-loop deletion on the interface with pMHC.

The same criteria were used to plot as for the WT systems in the corresponding figures. (A–B) Contact occupancy heat maps (Figure 3C and D). (C) BSA (Figure 3G). (D) Hamming distance (Figure 3B). (E) Distribution of Vα-Vβ angles. Numbers are avg±std in respective cases (Figure 4C). In (C–E), data for WThigh are shown as reference.

Appendix 2

In silico peptide mutants mimic the behaviors of target systems

We tested whether behaviors of different systems are interchangeable by making point mutations on peptides in silico, for which WT and antagonists were used (Appendix 2—table 1). For example, for Y8AWThigh (Y8A under high load switched to WT), we took Y8Ahigh at the beginning of its production run and mutated A8 to Y8 (see In silico mutants in Computational methods). A main question is whether the in silico mutants attain behaviors of the switched systems during the finite simulation time. We found this to be the case although behaviors were not recapitulated perfectly. Compared to WThigh, the number of contacts with pMHC became lower for WTP6A (Appendix 2—figure 1A). Note that WTP6Alow had a higher force than WTP6Ahigh (24.7 vs 19.5 pN; Appendix 2—table 1). This was because the low extension in the former case allowed the loose C-terminal strands (Figure 2A) to form extensive nonpolar contacts with the C-module, especially with Cβ. This effectively shortened the length of the complex, which led to a higher average force as the extension was kept the same (Selecting extensions). Among the original systems, Y5Fhigh had average force comparable to WTP6Alow (23.7 pN; Table 1), yet it had 1.8-fold more contacts, suggesting that the latter does behave like an antagonist (Figure 6A vs. Appendix 2—figure 1A). For in silico WT, the number of contacts with pMHC was comparable to that of WThigh except for Y8AWThigh (Appendix 2—figure 1A). In the original Y8Ahigh, even though the number of contacts was at the level of WThigh (Figure 6A), the smaller size of A8 caused CDR3β to extend. The altered interface can be seen by the initial rapid increase in H (Figure 6—figure supplement 1D). Mutating A8 to Y8 thereby forces the bulkier Y8 side chain to take an orientation different from that of WT. Thus, an in silico mutation of a residue to a comparable or smaller one is better tolerated than mutating to a bulkier one.

Appendix 2—table 1
Simulations of TCRαβ with in silico mutations on the peptide.

Load reported is average after 500 ns. Parentheses after the average load show standard deviation in forces measured in 40-ns intervals after 500 ns.

PDB IDMutationExtension (Å)Time (ns)Load (pN)Label
1AO7P6A182.9114024.7 (13.9)WTP6Alow
187.5104019.5 (5.67)WTP6Ahigh
Y8A182.3100016.5 (5.02)WTY8Alow
187.1100028.6 (7.87)WTY8Ahigh
1QRNA6P175.2100010.9 (6.09)P6AWTlow
186.2106031.8 (7.57)P6AWThigh
1QSFA8Y176.7100010.0 (5.89)Y8AWTlow
187.4104011.5 (5.85)Y8AWThigh

For WTY8A and P6AWT, a higher load led to more contacts with pMHC (Appendix 2—figure 1A). For WTY8Ahigh, the number was comparable to WThigh, which agrees with the case for the original Y8Ahigh (Figure 6A). The BSA profiles of in silico mutants also followed a trend similar to the number of contacts with pMHC, which was lower for antagonists and higher for WT (Appendix 2—figure 2A). Differences in binding with pMHC can also be seen in the positional distribution of high-occupancy contacts, where P6AWT had relatively compact and evenly distributed contacts (Appendix 2—figure 2B), although not as extensive as Vαβ-pMHC or WThigh (Figure 3F).

Regarding the Vα-Vβ interface, there were overall less contacts in the in silico antagonists than in silico WT (Appendix 2—figure 2C). However, a higher load did not result in greater number of Vα-Vβ contacts. The CDR3 distance was higher for in silico antagonists (Appendix 2—figure 1B, top), while for in silico WT their range became narrower, to 11–12 Å, which is similar to that of the modified agonist Y5F (Figure 7A vs. Appendix 2—figure 1B, bottom row). The CDR3 distance also stabilized over time in all in silico WT, which was less so for in silico antagonists (Appendix 2—figure 2D). However, similarly as the number of Vα-Vβ contacts, there was no consistent load-dependence between the CDR3 distance and triad arm angles. On the other hand, there was a stronger load dependence in the average V-C BOC. The in silico antagonists that were built based on WT bent towards those of the corresponding antagonists, though the extent was not large (Figure 7D vs. Appendix 2—figure 1C). The in silico WT in low load had average BOCs similar to those of the original antagonists whereas average BOCs of high-load in silico WT approached those of the actual WT (Appendix 2—figure 1D). For Y8AWT, this happened even though the forces experienced at the two extensions were only marginally different (10.0 vs 11.5 pN; Appendix 2—table 1). The Vα-Cα and Vβ-Cβ contacts were respectively lower for in silico WT than in silico antagonists, suggesting effects of the in silico mutations of the peptide did not propagate sufficiently to the whole TCRαβ during the simulation time (Appendix 2—figure 2E). The lower number of V-C contacts of in silico WT would have made it easier to unbend under higher load or extension.

The above results suggest that the in silico mutants behave like the target system to varying extents. This is likely because the rearranged interface between the V-module and pMHC of the base system cannot immediately be adjusted upon in silico mutation in loaded states.

Appendix 2—figure 1
Simulations of in silico peptide mutants bound to A6.

(A) Number of contacts with pMHC. Counts were made in the same way as in Figure 3A. Dashed line: average value for WThigh. Bars: std. (B) CDR3 distance vs. triad arm angles (Figure 4D and Figure 7A and B). (C, D) Average BOCs of (C) in silico antagonists, and (D) in silico WT. Average BOCs of WT0 and WThigh are shown as reference, marked by angular brackets (Figure 7C and D).

Appendix 2—figure 2
Behaviors of in silico mutants.

(A) BSA (Figure 3G). (B) Positions of backbone Cα atoms of high contact occupancy residues (Figure 3F). (C) Vα-Vβ contact count (Figure 4A). (D) CDR3 distance trajectory (Figure 4—figure supplement 2A–C). (E) Vα-Cα and Vβ-Cβ contact counts (Figure 5—figure supplement 1).

Appendix 3

Relationship between the average and standard deviation of force

In Table 1, standard deviation (std) of force spans a wide range, from 2.46 pN (Y5Flow) to 11.8 pN (dFGhigh). When it is plotted against the average force, a near-linear relationship can be seen (Appendix 3—figure 1). Thermodynamically, the force and position of the restraint (blue spheres in Figure 2A) form a pair of generalized force and the corresponding spatial variable in equilibrium at temperature 300 K, which is akin to the pressure P and volume V of an ideal gas. If V is fixed, P fluctuates. Denoting the average and std of pressure as P and as ΔP respectively, it has been shown that ΔP/P is a constant (Burgess, 1973). In the case of the TCRαβ-pMHC system, although individual atoms are not ideal gases, since their motion lead to the force fluctuation of the restraints, the situation is analogous to the case of an ideal gas where pressure arises from individual molecules hitting the confining wall as the restraint. Thus, the near-linear behavior is a consequence of the system being many-bodied and at constant temperature. The linearity is also an indirect indicator that sampling of force in our simulation was reasonable.

In addition to the thermodynamic aspect, system-specific aspects influence the std of force. In Appendix 3—figure 1, Y8Alow is an antagonist that had the smallest number of contacts with pMHC except for Y8A0 without load (Figure 6A). dFGlow also had similarly small number of contacts with pMHC (Appendix 1—figure 1A). The weakly held interface likely caused a wider conformational motion, leading to greater fluctuation in force relative to the average (dFGlow and Y8Alow in Figure 8A, symbols without outline).

Above results suggest that the fluctuation of force per se has no direct relation to the catch vs. slip bond mechanisms, although a comparatively larger std is indicative of potential instability.

Appendix 3—figure 1
Standard deviation vs. average force in Table 1.

Two major outliers are marked, that have the lowest number of TCRαβ-pMHC interfacial contacts among all loaded systems.

Data availability

The current manuscript is a computational study, so no experimental data have been generated for this manuscript. Sample analysis scripts are available on GitHub: https://github.com/hwm2746/a6tcr_anal_md/tree/main (copy archived at Chang-Gonzalez and Hwang, 2024).

References

    1. Garboczi DN
    2. Utz U
    3. Ghosh P
    4. Seth A
    5. Kim J
    6. VanTienhoven EA
    7. Biddison WE
    8. Wiley DC
    (1996b)
    Assembly, specific binding, and crystallization of a human TCR-alphabeta with an antigenic Tax peptide from human T lymphotropic virus type 1 and the class I MHC molecule HLA-A2
    Journal of Immunology 157:5403–5410.
    1. Hausmann S
    2. Biddison WE
    3. Smith KJ
    4. Ding YH
    5. Garboczi DN
    6. Utz U
    7. Wiley DC
    8. Wucherpfennig KW
    (1999)
    Peptide recognition by two HLA-A2/Tax11-19-specific T cell clones in relationship to their MHC/peptide/TCR crystal structures
    Journal of Immunology 162:5389–5397.

Decision letter

  1. Qiang Cui
    Senior and Reviewing Editor; Boston University, United States

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Asymmetric framework motion of TCRαβ controls load-dependent peptide discrimination" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Qiang Cui as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) From a technical point of view, discuss carefully the choice of using a restraining approach vs. applying a steady force, and clarify which better mimics the realistic situation. In addition, the principal component analysis can be done differently to ensure the most meaningful comparison between different cases.

2) In terms of results, discuss more explicitly the specific features that discriminate between catch-bond and slip-bond regimes. It is also valuable to explicitly suggest a set of experimentally testable predictions from the simulation study.

Reviewer #1 (Recommendations for the authors):

1. I suggest that the authors discuss why they chose to restrain the TCR/MHC separation, rather than devise an algorithm to apply a steady force. The issue with the restrained distance is that the forces reported for the different mutants are quite variable, and one might even say, not consistent.

2. Uncertainty should be reported as the forces in some form, or the magnitude of their fluctuation.

3. (Table1) it might be interesting to check whether the integral of the force over the three extensions reported in the table correlates with the TCR/pMHC binding strength, if these data are available.

4. A few simulation observations either appear speculative or are not well illustrated, (e.g. on p5), "short distance between restraints … allows wider transverse motion that in turn generates a shear stress or a bending moment at the interface". Given the complexity of this large biomolecular complex and its dynamics, I suggest making a greater effort to distinguish between what is actually observed and what the implications might be.

5. While there are various analyses of the simulation data, it would strengthen the paper greatly if the authors could provide specific experimentally testable hypotheses, eg., in the form of predicted responses to a mutant peptide, or mutations to the variable chains that could alter the fluctuations (e.g. disulfide crosslinking).

Reviewer #2 (Recommendations for the authors):

In terms of presentation, I found the number and extent of data to be a bit overwhelming. If revising this paper, I hope the authors will consider trying to condense each figure to present a single message and summary panel and move data like how the number of contacts changes with time to the supporting information.

Reviewer #3 (Recommendations for the authors):

The authors have carried out a simulation study of the behavior of the TCR-pMHC complex for different peptides with and without load in the physiological range (10-20 pN). The load is calculated by applying harmonic restraints to the ends of the complex and extending their distances iteratively. The analysis of the complex under load seems to be novel.

Recommendations:

1) While the general conclusions regarding the load (e.g., higher number of contacts) seem to be supported by the simulations with different peptides, the conclusions regarding the different behavior of individual peptides (e.g., modified agonist vs. weak antagonist) are not fully supported as only one MD run was carried out for each peptide sequence and value of load. Multiple independent runs should be carried out for each simulation system, i.e., peptide and low/high load.

2) The definition of low and high load (Table 1) seems somewhat arbitrary as a low load of 13.2 pN and 14.9 pN is defined (from averaging over the 2nd half of the MD trajectory) for the WT peptide (full system and dFG, respectively) and these values are similar/higher than the high load of 13.5 pN of the P6A mutant.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Asymmetric framework motion of TCRαβ controls load-dependent peptide discrimination" for further consideration by eLife. Your revised article has been evaluated by Qiang Cui (Senior Editor) and a Reviewing Editor.

The manuscript has been substantially improved and the reviewers appreciated the revision, but there are some remaining issues that need to be addressed, as outlined below:

A new comment concerns the standard deviations of the forces that have been added. The std are quite variable between the various structures (varying from ~2.5A to ~12A). I wonder whether this variability is interpretable in terms of the proposed catch-bond mechanism.

https://doi.org/10.7554/eLife.91881.sa1

Author response

Essential revisions:

1) From a technical point of view, discuss carefully the choice of using a restraining approach vs. applying a steady force, and clarify which better mimics the realistic situation.

Restraining the terminal Cα atoms of the complex (blue spheres in Figure 2A) was done upon considering the realistic situation of immune surveillance. When a T-cell interacts with an antigen presenting cell (APC), other molecules such as CD2 and CD58 maintain the separation near the ∼120-°A span of the TCRαβ-pMHC complex (Reinherz, et al., 2023; see SI Text 3, lines 106–108 of the paper). The force applied to the complex fluctuates via thermal fluctuation and through cellular activities such as coupling to the actomyosin machinery within the T-cell and APC (Figure 3 of Reinherz, et al., 2023). Restraining terminal Cα atoms in simulation mimics the membrane anchoring of these molecules with a relatively constant spacing and fluctuating force. In a constantforce simulation, in addition to the absence of fluctuation in force, the absence of positional restraint would not capture the situation where TCR and pMHC are anchored to the membranes of T-cell and APC, respectively.

Capturing effects of anchoring is important since bending and/or shearing loads can be applied to the TCRαβpMHC interface, which would be much weaker if the whole complex is allowed to move transversely without any restraint (see also our reply R1-4 about this point).

A restrained distance constraint as suggested by Reviewer 1 (R1-6) would allow force to fluctuate, but it would not capture membrane anchoring. In addition, without any positional restraint, the simulation box will have to be made larger to accommodate rotational motion of the whole complex.

Even though applying positional restraints may better capture reality, it has practical issues: It is difficult to find the extension that yields a desired force level, for which we performed ‘laddering’ simulations that scan a range of extensions (page 18, ‘Selecting extension’). Since these initial simulations last only up to 100 ns at each extension, the average force after 500 ns in a 1-µs production run at a selected extension can differ significantly. Issues with the positional restraints are further explained in R3-1.

We are currently testing different strategies of applying loads to find a more reliable and efficient simulation protocol, which would be a subject of a future publication. Despite methodological limitations, the main conclusions of our manuscript are well-supported by the data presented, as noted in the reviewers’ comments.

Changes made: The above are explained in:

– Pages 3–4, newly added Results section ‘Applying loads to TCRαβ-pMHC complexes:’ Explains our choice of applying positional restraints instead of constant forces.

– Page 18, lines 569–574: Expanded discussion about practical issues with applying positional restraints.

In addition, the principal component analysis can be done differently to ensure the most meaningful comparison between different cases.

Our main goal of performing principal component analysis (PCA) based on the Vα-Vβ triads and the V-C beads-on-chain (BOC) models was to examine the relative motion among the 4 subdomains of TCR. To this end, we did apply PCA in different ways. To examine the Vα-Vβ motion (Figure 4B), we aligned coordinate trajectories relative to Cα atoms used for constructing the Vα-Vβ triads (reference residues are listed in lines 650–651). To examine the V-C motion (Figure 5A), we aligned coordinate trajectories relative to the C-module (reference residues are listed in lines 672–673). Since there is very little Cα-Cβ motion, the resulting PCA reveals the motion of the V-module relative to the C-module. Since different simulations involve the same A6 TCR, comparing the PCA across them reveals the effect of load and ligand on the conformational motion of TCR. This point is further addressed in our reply R2-3.

PCA is just one of several analyses we did. For example, the angles between triad arms (Figure 4C,D) and the V-C angle (Figure 5D,E) utilize the triads and BOC models, but they do not involve PCA. By using different approaches in combination, we find a consistent and physically plausible mechanism of the TCR catch bond and its ligand dependence.

Changes made: We added explanations about different ways of aligning coordinate trajectories:

– Page 6, lines 171–175: For Vα-Vβ PCA.

– Page 8, lines 226–227 and caption of Figure 5A: For V-C PCA.

– Page 8, lines 203 and 238–239: Emphasized that angle analyses are independent of PCA.

2) In terms of results, discuss more explicitly the specific features that discriminate between catch-bond and slip-bond regimes. It is also valuable to explicitly suggest a set of experimentally testable predictions from the simulation study.

For TCRαβ-pMHC, catch bond does not activate through any defined conformational transition as observed in other adhesion proteins (e.g., integrin). Instead, our results show that it is achieved by load-dependent changes in the conformational motion of the protein that affect the stability of the interface. We measured the interfacial stability in multiple ways, including: Maintenance of the initial high-occupancy contacts (Figure 3B-E), increase in the buried area of the contact-forming residues (Figure 3G), stabilization of the CDR3 distance (Figure 4D), and related time dependent features (Figure 8). The newly added Figure 1 gives an overview of different measurements. Given the seconds-order bond lifetime in reality, in microseconds-long simulations, slip bonds would manifest as deterioration of the interfacial stability under higher forces but without any dissociation actually occurring during the simulation. The lower stability of the interface observed in the modified agonist or antagonist systems indicate they will exhibit weaker catch bond or slip bond behaviors. Single-molecule experiments of the A6 TCR system, which are currently not available, would be a good test of our simulation.

Other than the relatively straightforward predictions about modified agonists and antagonists, the allosteric catch bond mechanism we found is consistent with available experimental data. They include a reduced catch bond behavior of the Cβ FG-loop deletion mutant (Das et al., 2015), and a chimeric Vγδ-Cαβ receptor that attains catch bond capacity while wild-type TCRγδ works as a slip-bond receptor (Mallis, et al., 2021). Also, given the importance of the asymmetric motion, making mutations to weaken the Vβ-Cβ interface to decouple the V-module from the C-module would render the TCR to exhibit slip bond only. See also our reply R1-0 for additional explanation about making point mutations to TCR. However, making predictions about specific mutations would require additional extensive simulations, which is beyond the scope of the current work.

Changes made:

– Page 2: Figure 1 was added to provide an overview of different simulations and analyses (the rest of the figure numbers shifted by 1).

– Page 13, Concluding Discussion, lines 363–366 and page 14, lines 391–395: We added explanations about the main feature of the TCRαβ catch bond mechanism, as well as expected behaviors of catch- vs. slip bond systems in simulation.

– Page 14, Concluding Discussion, lines 384–385: We added a sentence explaining the agreement between simulation and experiment on the FG-loop deletion mutant.

– Page 14, Concluding Discussion, lines 387–390: Explanation about the Vγδ-Cαβ chimera was added and the reference (Mallis, et al., 2021) was added.

– Page 14, Concluding Discussion, lines 395–402: We added a discussion about using simulations for designing and testing point mutants.

Reviewer #1 (Recommendations for the authors):

1. I suggest that the authors discuss why they chose to restrain the TCR/MHC separation, rather than devise an algorithm to apply a steady force. The issue with the restrained distance is that the forces reported for the different mutants are quite variable, and one might even say, not consistent.

As explained in our reply R0-1, we used positional restraints instead of applying constant forces to better reflect reality where the applied force fluctuates while TCR and pMHC are anchored to respective cell membranes separated by a relatively constant distance. Since fluctuation in the instantaneous force is rapid, the TCRαβpMHC complex does not have time to respond at each moment. Yet, the fluctuating force drives the complex to respond differently depending on the average force, as demonstrated by our simulations. If a constant force were applied, the extension of the complex would fluctuate more widely, and the load actually experienced at the interface between TCR and pMHC may not be the same as the constant force applied to the ends, which would be similar to the case of fluctuating force under constant extension. A more detailed comparison between constant extension vs. constant force methods would be an interesting subject of a future study. For the present manuscript, we believe our main conclusions regarding the TCR catch bond mechanism are well-supported by constant-extension simulations.

Changes made:

Changes are the same as those listed in our response R0-1.

2. Uncertainty should be reported as the forces in some form, or the magnitude of their fluctuation.

We added standard deviations in forces measured in time intervals on page 4, Table 1 and page 27, Appendix 2–table 1.

3. (Table1) it might be interesting to check whether the integral of the force over the three extensions reported in the table correlates with the TCR/pMHC binding strength, if these data are available.

While this is an interesting idea, the work done by force in our simulation and the binding strength have nontrivial relation. For example, the dFG-pMHC system had a greater increase in force (14.9 pN to 29.0 pN) compared to the WT (13.2 pN to 18.2 pN) over similar change in extension (page 4, Table 1). This does not indicate that dFG-pMHC has a higher binding strength. On the contrary, the higher force in dFG-pMHC in the high-load case is due to the conformational change that shortened its longitudinal span, which would have a greater destabilizing effect in a longer simulation, as explained in Appendix 1 (page 27). To quantitatively address energetics, a much more extensive sampling simulation at incremental distances would be needed, with the added complexity of dealing with a catch bond.

Regardless of the practical difficulty, the energetics involved in antigen discrimination and TCR triggering is a fundamental issue, which we briefly mention in the revised manuscript.

Change made:

On pages 14–15, Concluding Discussion, lines 414–417, we mentioned about the energetics issue.

4. A few simulation observations either appear speculative or are not well illustrated, (e.g. on p5), "short distance between restraints … allows wider transverse motion that in turn generates a shear stress or a bending moment at the interface". Given the complexity of this large biomolecular complex and its dynamics, I suggest making a greater effort to distinguish between what is actually observed and what the implications might be.

We believe the revised manuscript incorporating reviewers’ comments have become clearer in providing reasoning behind key points. In particular, the newly added Figure 1 gives an overview of our different simulations and analyses, which helps with following individual Results sections. Regarding the example that the reviewer pointed out, it was based on the expectation that a loosely held molecule will transversely fluctuate more compared to a tightly held one. To check, we measured the root-mean square fluctuation (RMSF) of the center of mass of the Cα atoms of the WT Tax peptide in the transverse direction. It was 16.3 °A for WTlow and 12.7 °A for WThigh.

Mechanically, a wider transverse fluctuation while the ends are restrained is expected to increase the shear stress and/or bending moment, thereby promoting destabilization of the interface. A related measure of the relative motion between TCR and pMHC at the interface is the peptide angle relative to the variable domain which indeed varies more widely for WTlow than WThigh (Figure 8C).

Changes made:

– Page 2: Figure 1 was added to provide an overview of different simulations and analyses (the rest of the figure numbers shifted by 1).

– Page 6, lines 158–160: Transverse RMSF values of the peptide were added.

– Page 12, lines 349–351: A sentence was added to relate the wider fluctuation of the peptide angle in Figure 8C for WTlow to its greater transverse RMSF.

5. While there are various analyses of the simulation data, it would strengthen the paper greatly if the authors could provide specific experimentally testable hypotheses, eg., in the form of predicted responses to a mutant peptide, or mutations to the variable chains that could alter the fluctuations (e.g. disulfide crosslinking).

This is addressed in R0-3 and R1-0.

Reviewer #2 (Recommendations for the authors):

In terms of presentation, I found the number and extent of data to be a bit overwhelming. If revising this paper, I hope the authors will consider trying to condense each figure to present a single message and summary panel and move data like how the number of contacts changes with time to the supporting information.

Given the extent of data and multiple analyses involved, we indeed put much efforts to simplify the data presentation and carefully selected which data to include as the main figures. Figure 8 that the reviewer suggested to move to the supporting information, is particularly important as it shows how forces and contact occupancy fluctuate over time, yet they reflect different loading conditions or ligand. Figure 8 also is relevant to the proposed model in Figure 9. Instead of further reducing figures, we added Figure 1 as the summary panel suggested by the reviewer. We hope it will help with grasping the flow of the manuscript.

Reviewer #3 (Recommendations for the authors):

The authors have carried out a simulation study of the behavior of the TCR-pMHC complex for different peptides with and without load in the physiological range (10-20 pN). The load is calculated by applying harmonic restraints to the ends of the complex and extending their distances iteratively. The analysis of the complex under load seems to be novel.

Recommendations:

1) While the general conclusions regarding the load (e.g., higher number of contacts) seem to be supported by the simulations with different peptides, the conclusions regarding the different behavior of individual peptides (e.g., modified agonist vs. weak antagonist) are not fully supported as only one MD run was carried out for each peptide sequence and value of load. Multiple independent runs should be carried out for each simulation system, i.e., peptide and low/high load.

In addition to the data presented, we did carry out additional 2 replicate runs for the WT and 1 replicate run for each of the mutant systems. However, as was briefly explained in the Methods section of our original manuscript (page 18, lines 563–568) and in Appendix 2 for in silico mutants (page 27, lines 881–886), it was difficult to achieve monotonic increase of force with extension. This was because in some of the runs the Cterminal added strands made contact with the C-module of TCR, effectively shortening the span of the complex during the simulation. This led to the average force comparable to or higher than that for a larger extension. The runs included in Table 1 were those where low and high extensions resulted in low and high average forces (measured after 500 ns), respectively. Importantly, for other replicate simulations, we found that certain measures such as the CDR3 distance and average contact counts, are consistent with the average load rather than average extension, which agrees with the picture that the response at the interface is determined by the applied load.

Aside from replicate runs, simulations of in silico mutants (switching between the WT and mutants; Appendix 2 on pages 27–28) serve as additional tests, which yielded overall consistent results. Most of all, the asymmetric motion observed in all of the simulations and its impact on stability of the interface with pMHC provide a physically plausible explanation for the catch bond mechanism that does not rely critically on details of any single simulation.

Changes made:

– Page 13, lines 372–373 in Concluding Discussion: We added a remark about the generality of our findings obtained by analyzing multiple simulations collectively.

– Page 18, lines 560–574 in Methods: We explained about the issues we found about replicate simulations and the dependence of the responses on load rather than extension per se.

2) The definition of low and high load (Table 1) seems somewhat arbitrary as a low load of 13.2 pN and 14.9 pN is defined (from averaging over the 2nd half of the MD trajectory) for the WT peptide (full system and dFG, respectively) and these values are similar/higher than the high load of 13.5 pN of the P6A mutant.

This partly relates to the difficulty of precisely controlling forces at a given extension, as explained in R0-1 and R3-1. But even when the difference in forces is accounted for, the WT and mutant systems behave distinctly. In the case of the antagonist P6A that the reviewer pointed out, the average force of P6Ahigh (13.5 pN) is the lowest among the high-load simulations shown in Table 1. Both its extension and average force are comparable to those of WTlow. If P6Ahigh were considered in isolation, it could be argued that the instability of the interface observed in simulation would be due to insufficient force, similar to WTlow. However, when P6A0 (no load) and P6Alow are considered together, difference from the WT system emerges. This can be seen by the overall lower stability of the interface and weaker dependence on load (e.g., Figure 3A,G for WT vs. Figure 6A,B for P6A), differences in conformation; in Figure 7D, the average BOC for WTlow lies between those for WT0 and WThigh, all of which differ substantially from the average BOCs for all P6A systems tested. Also, compared to WTlow that gradually loses contacts with pMHC, P6Ahigh has low contact occupancy early on in the simulation (Figure 8A). The lower stability of the interface is also evident for the in silico P6A mutant (WTP6A; see Appendix 2–figure 1A on page 28). As seen in Appendix 2-table 1 (page 27), loads on the in silico P6A are comparable to or higher than those used for WT in Table 1. Also note the average force on WTP6Alow (24.7 pN) is higher than that on WTP6Ahigh because the added strands made contacts with the TCR’s C-module in WTP6Alow (page 27, lines 879–883 in Appendix 2; also explained in R3-1). Since we did not perform any replicate simulations for in silico mutants, we used these data directly.

Changes made:

– Page 4, lines 113–115, newly added Results section ‘Applying loads to TCRαβ-pMHC complexes:’ Added a remark that distinction can be seen between different systems despite variations in the applied load.

– Page 18, lines 569–574: Expanded discussion about practical issues with applying positional restraints.

[Editors’ note: what follows is the authors’ response to the second round of review.]

The manuscript has been substantially improved and the reviewers appreciated the revision, but there are some remaining issues that need to be addressed, as outlined below:

A new comment concerns the standard deviations of the forces that have been added. The std are quite variable between the various structures (varying from ~2.5A to ~12A). I wonder whether this variability is interpretable in terms of the proposed catch-bond mechanism.

This is a very interesting observation. We examined the data more carefully, and found that there are both thermodynamic and system specific aspects to the large variation in the std of force. Regarding the thermodynamic aspect, the std tends to be larger for larger average forces. This can be seen more clearly by plotting all the values in Table 1 (Appendix 3-figure 1).

Except for Y8Alow and dFGlow, all other data points lie on nearly a straight line. Thermodynamically, the force and position of the restraint (blue spheres in Figure 2A) form a pair of generalized force and the corresponding spatial variable in equilibrium at temperature 300 K, which is akin to the pressure P and volume V of an ideal gas.

If V is fixed, P fluctuates. Denoting the average and std of pressure as ⟨P⟩ and as ∆P respectively, Burgess showed that ∆P/P⟩ is a constant (Eq. 5 of Burgess, Phys. Lett. A, 44:37; 1973). In the case of the TCRαβ-pMHC system, although individual atoms are not ideal gases, since their motion lead to the force fluctuation of the restraints, the situation is analogous to the case of an ideal gas where pressure arises from individual molecules hitting the confining wall as the restraint. Thus, the near-linear behavior in the figure above is a consequence of the system being many-bodied and at constant temperature. The linearity is also an indirect indicator that sampling of force was reasonable.

In addition to the thermodynamic aspect, system-specific aspects influence the std of force. In the above figure, Y8Alow is an antagonist that had the smallest number of contacts with pMHC except for Y8A0 without load (Figure 6A of our manuscript). dFGlow also had similarly small number of contacts with pMHC (Appendix 1figure 1A). The weakly held interface likely caused a wider conformational motion, leading to greater fluctuation in force relative to the average (dFGlow and Y8Alow in Figure 8A, symbols without outline).

The above suggest that the variation in the std of force per se does not provide a clear distinction between catch vs. slip bonds, although a comparatively larger std is indicative of potential instability. Feedback from the reviewers motivates us to carry out a future study focused on fundamental aspects of forces, constraints, and fluctuations in a smaller model system for more thorough analysis.

Changes made:

https://doi.org/10.7554/eLife.91881.sa2

Article and author information

Author details

  1. Ana C Chang-Gonzalez

    Department of Biomedical Engineering, Texas A&M University, College Station, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1517-4172
  2. Robert J Mallis

    1. Department of Dermatology, Harvard Medical School, Boston, United States
    2. Laboratory of Immunobiology, Dana-Farber Cancer Institute, Boston, United States
    3. Department of Medicine, Oncology, Dana-Farber Cancer Institute, Boston, United States
    Contribution
    Conceptualization, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2087-9468
  3. Matthew J Lang

    1. Department of Chemistry and Biomolecular Engineering, Vanderbilt University, Nashville, United States
    2. Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, United States
    Contribution
    Conceptualization, Funding acquisition, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8198-144X
  4. Ellis L Reinherz

    1. Laboratory of Immunobiology, Dana-Farber Cancer Institute, Boston, United States
    2. Department of Medicine, Oncology, Dana-Farber Cancer Institute, Boston, United States
    3. Department of Medicine, Harvard Medical School, Boston, United States
    Contribution
    Conceptualization, Funding acquisition, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1048-5526
  5. Wonmuk Hwang

    1. Department of Biomedical Engineering, Texas A&M University, College Station, United States
    2. Department of Materials Science & Engineering, Texas A&M University, College Station, United States
    3. Department of Physics & Astronomy, Texas A&M University, College Station, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – review and editing
    For correspondence
    hwm@tamu.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7514-3186

Funding

US National Institutes of Health (P01AI143565)

  • Robert J Mallis
  • Matthew J Lang
  • Ellis L Reinherz
  • Wonmuk Hwang

US National Institutes of Health (R01AI136301)

  • Matthew J Lang
  • Ellis L Reinherz

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was funded by US NIH Grants P01AI143565 and R01AI136301. Simulations were performed by using computers at the Texas A&M High Performance Research Computing facility.

Senior and Reviewing Editor

  1. Qiang Cui, Boston University, United States

Version history

  1. Received: August 14, 2023
  2. Preprint posted: September 13, 2023 (view preprint)
  3. Accepted: December 21, 2023
  4. Accepted Manuscript published: January 3, 2024 (version 1)
  5. Version of Record published: February 15, 2024 (version 2)

Copyright

© 2024, Chang-Gonzalez et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Ana C Chang-Gonzalez
  2. Robert J Mallis
  3. Matthew J Lang
  4. Ellis L Reinherz
  5. Wonmuk Hwang
(2024)
Asymmetric framework motion of TCRαβ controls load-dependent peptide discrimination
eLife 13:e91881.
https://doi.org/10.7554/eLife.91881

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https://doi.org/10.7554/eLife.91881

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