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).

Figure 1

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Figure 2

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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 C_α 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 C_α 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.

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.

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). WT^low had the least number of contacts, followed by WT⁰ and WT^high, 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).

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Time-dependent changes in the interfacial contacts were monitored using the Hamming distance ${\displaystyle \mathcal{H}}$ (Hamming, 1950; Hwang et al., 2020). ${\displaystyle \mathcal{H}}$ 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 ${\displaystyle \mathcal{H}}$ means that such contacts persist while a high ${\displaystyle \mathcal{H}}$ means the corresponding number of initial high-occupancy contacts are missing. Consistent with the contact count, ${\displaystyle \mathcal{H}}$ remained low for WT^high and Vαβ-pMHC (Figure 3B). In WT⁰ and WT^low, ${\displaystyle \mathcal{H}}$ 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 WT^high and Vαβ-pMHC, high-occupancy contacts persist throughout the simulation (blue regions in Figure 3C and Figure 3—figure supplement 1) while WT⁰ or WT^low exhibited breakage of contacts, especially when ${\displaystyle \mathcal{H}}$ increased (dashed arrows in Figure 3D and E). Differences in the interfacial contacts also manifest in their location. We displayed the backbone C_α 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 WT^low 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 WT^high 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 C_α 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 WT^low. 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 WT^high where lateral motion is suppressed. The extent of transverse motion can be seen by the RMSF of the center of mass of the C_α atoms of the peptide in the transverse direction, which was 16.3 Å for WT^low and 12.7 Å for WT^high. The high stability of Vαβ-pMHC and WT^high 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 WT^low, similar to the case for the number of contacts with pMHC (Figure 3A), again reflecting a destabilizing effect with low load.

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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, WT^low 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, WT^high 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 WT^low, the CDR3 distance varied more widely, with more than a 2-fold increase in standard deviation. The increase in CDR3 distance of WT^low happens after the increase in ${\displaystyle \mathcal{H}}$ (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 ${\displaystyle {\mathbf{e}}_1}$-${\displaystyle {\mathbf{e}}_1}$ angle, herein called ${\displaystyle \mathrm{\angle }{e}_1}$ (and similarly define ${\displaystyle \mathrm{\angle }{e}_2}$ and ${\displaystyle \mathrm{\angle }{e}_3}$; Figure 4B), can change either by the ${\displaystyle {\mathbf{e}}_1}$ arms turning up and down (‘flap’) or in and out of the page (‘twist’) in Figure 4B. Angles ${\displaystyle \mathrm{\angle }{e}_2}$ and ${\displaystyle \mathrm{\angle }{e}_3}$ 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 WT^low and WT^high reflect their higher PC amplitudes (Figure 4—figure supplement 1A). The symmetric distributions of ${\displaystyle \mathrm{\angle }{e}_2}$ and ${\displaystyle \mathrm{\angle }{e}_3}$ can be seen from the two peaks for WT^low, 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 ${\displaystyle \mathrm{\angle }{e}_1}$ or ${\displaystyle \mathrm{\angle }{e}_3}$, or with smaller ${\displaystyle \mathrm{\angle }{e}_2}$ (Figure 4D). WT⁰, despite the apparent stability of the interface, had a larger CDR3 distance than WT^high 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.

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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 WT^low (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 WT^low 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 WT^low and WT^high, 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 WT^low, this state is not maintained and ∠TCRα decreases (more bending) as the CDR3 distance increases (Figure 5E) which happens after the increase in ${\displaystyle \mathcal{H}}$ (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 Y5F^high 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 ${\displaystyle \mathcal{H}}$, 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 ${\displaystyle \mathcal{H}}$ suggests an altered binding rather than maintaining the initial contacts.

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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 ${\displaystyle \mathcal{H}}$ (Figure 6—figure supplement 1A). In addition, contacts involving Y8 and V7 also break in Y5F^high (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 Y8A^high compared to Y8A⁰ and Y8A^low (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 WT^high while the antagonists had fewer, except for Y8A^high (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.

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The mutants affected the average V-C BOC similarly as that for dFG^high (Figure 7C and D vs. Appendix 1—figure 1C). Among them, Y8A^high had an average BOC approaching that of WT^high, 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 WT^high, 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 WT^high 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 V7R^high 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).

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For WT⁰, the intermolecular contact occupancy stayed at around 20 (WT in Figure 8A, horizontal bar on the bottom) and for WT^low, 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 WT^low (WT in Figure 8B, horizontal bar on the bottom and circles without outline). For WT^high, 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 dFG^low, the intermolecular contact occupancy stayed low and intramolecular occupancy was relatively constant (dFG in Figure 8A and B, circles without outline). In dFG^high, 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 (Y5F^high) or decreased (V7R^high) 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 C^_α 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 WT^low than WT^high (WT in Figure 8C) where 58.4°±3.6° (avg±std after 500 ns) for WT^high reflects a diagonal binding. This is consistent with the greater transverse RMSF of WT^low mentioned earlier which may assist with destabilizing the interface. For dFG^low, the peptide changed orientation by more than 20°, and for dFG^high, it stabilized, but at a higher value than WT^high, which also was reached in dFG^low 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 WT^high (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 V7R^high or Y8A^high (Figure 8—figure supplement 1G H) compared to WT^high (Figure 8—figure supplement 1E).

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 WT^high (Figures 8 and 9C). This physically plausible mechanism is based on collective analyses of all simulations in this study (Figure 1).

Figure 9

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The CDR3 distance of WT^high (10.3±0.3 Å; Figure 4—figure supplement 2C) was shorter than that of WT⁰ or WT^low (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 ${\displaystyle k_{\text{off}}}$ and the force sensitivity distance ${\displaystyle x_{\beta }}$ (Pettmann et al., 2023). They found a negative correlation between ${\displaystyle k_{\text{off}}}$ and ${\displaystyle x_{\beta }}$, 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, ${\displaystyle x_{\beta }}$ 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.

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).

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

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

   Scientific Reports 6:25070.

   https://doi.org/10.1038/srep25070

   • PubMed
   • Google Scholar
2. 1. Baker BM
   2. Gagnon SJ
   3. Biddison WE
   4. Wiley DC

   (2000) Conversion of a T cell antagonist into an agonist by repairing a defect in the TCR/peptide/MHC interface: implications for TCR signaling

   Immunity 13:475–484.

   https://doi.org/10.1016/s1074-7613(00)00047-9

   • PubMed
   • Google Scholar
3. 1. Baker BM
   2. Scott DR
   3. Blevins SJ
   4. Hawse WF

   (2012) Structural and dynamic control of T-cell receptor specificity, cross-reactivity, and binding mechanism

   Immunological Reviews 250:10–31.

   https://doi.org/10.1111/j.1600-065X.2012.01165.x

   • PubMed
   • Google Scholar
4. 1. Banik D
   2. Hamidinia M
   3. Brzostek J
   4. Wu L
   5. Stephens HM
   6. MacAry PA
   7. Reinherz EL
   8. Gascoigne NRJ
   9. Lang MJ

   (2021) Single molecule force spectroscopy reveals distinctions in key biophysical parameters of αβ T-cell receptors compared with chimeric antigen receptors directed at the same ligand

   The Journal of Physical Chemistry Letters 12:7566–7573.

   https://doi.org/10.1021/acs.jpclett.1c02240

   • PubMed
   • Google Scholar
5. 1. Birnbaum ME
   2. Mendoza JL
   3. Sethi DK
   4. Dong S
   5. Glanville J
   6. Dobbins J
   7. Ozkan E
   8. Davis MM
   9. Wucherpfennig KW
   10. Garcia KC

   (2014) Deconstructing the peptide-MHC specificity of T cell recognition

   Cell 157:1073–1087.

   https://doi.org/10.1016/j.cell.2014.03.047

   • PubMed
   • Google Scholar
6. 1. Borbulevych OY
   2. Piepenbrink KH
   3. Gloor BE
   4. Scott DR
   5. Sommese RF
   6. Cole DK
   7. Sewell AK
   8. Baker BM

   (2009) T cell receptor cross-reactivity directed by antigen-dependent tuning of peptide-MHC molecular flexibility

   Immunity 31:885–896.

   https://doi.org/10.1016/j.immuni.2009.11.003

   • PubMed
   • Google Scholar
7. 1. Brameshuber M
   2. Kellner F
   3. Rossboth BK
   4. Ta H
   5. Alge K
   6. Sevcsik E
   7. Göhring J
   8. Axmann M
   9. Baumgart F
   10. Gascoigne NRJ
   11. Davis SJ
   12. Stockinger H
   13. Schütz GJ
   14. Huppa JB

   (2018) Monomeric TCRs drive T cell antigen recognition

   Nature Immunology 19:487–496.

   https://doi.org/10.1038/s41590-018-0092-4

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

   (2015) Structural features of the αβTCR mechanotransduction apparatus that promote pMHC discrimination

   Frontiers in Immunology 6:441.

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

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

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

   Immunity 49:829–841.

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

   • PubMed
   • Google Scholar
10. 1. Brooks BR
    2. Brooks CL
    3. Mackerell AD
    4. Nilsson L
    5. Petrella RJ
    6. Roux B
    7. Won Y
    8. Archontis G
    9. Bartels C
    10. Boresch S
    11. Caflisch A
    12. Caves L
    13. Cui Q
    14. Dinner AR
    15. Feig M
    16. Fischer S
    17. Gao J
    18. Hodoscek M
    19. Im W
    20. Kuczera K
    21. Lazaridis T
    22. Ma J
    23. Ovchinnikov V
    24. Paci E
    25. Pastor RW
    26. Post CB
    27. Pu JZ
    28. Schaefer M
    29. Tidor B
    30. Venable RM
    31. Woodcock HL
    32. Wu X
    33. Yang W
    34. York DM
    35. Karplus M

    (2009) CHARMM: the biomolecular simulation program

    Journal of Computational Chemistry 30:1545–1614.

    https://doi.org/10.1002/jcc.21287

    • PubMed
    • Google Scholar
11. 1. Burgess RE

    (1973) Pressure fluctuations in an ideal gas

    Physics Letters A 44:37–38.

    https://doi.org/10.1016/0375-9601(73)90949-3

    • Google Scholar
12. 1. Chakraborty AK
    2. Weiss A

    (2014) Insights into the initiation of TCR signaling

    Nature Immunology 15:798–807.

    https://doi.org/10.1038/ni.2940

    • PubMed
    • Google Scholar
13. Software

    1. Chang-Gonzalez AC
    2. Hwang W

    (2024) Sample analysis scripts for molecular Dynamics simulations of A6 T-cell receptor, version swh:1:rev:64d863bf2d4cd385d823c925fdef5e11e0ca7416

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:97cdace93904baefb128589b229b00df657da3b8;origin=https://github.com/hwm2746/a6tcr_anal_md;visit=swh:1:snp:02f4cbbb404b044bde3d9e605b0456a108e4837b;anchor=swh:1:rev:64d863bf2d4cd385d823c925fdef5e11e0ca7416
14. 1. Choi HK
    2. Cong P
    3. Ge C
    4. Natarajan A
    5. Liu B
    6. Zhang Y
    7. Li K
    8. Rushdi MN
    9. Chen W
    10. Lou J
    11. Krogsgaard M
    12. Zhu C

    (2023) Catch bond models may explain how force amplifies TCR signaling and antigen discrimination

    Nature Communications 14:2616.

    https://doi.org/10.1038/s41467-023-38267-1

    • PubMed
    • Google Scholar
15. 1. Cuendet MA
    2. Michielin O

    (2008) Protein-protein interaction investigated by steered molecular dynamics: the TCR-pMHC complex

    Biophysical Journal 95:3575–3590.

    https://doi.org/10.1529/biophysj.108.131383

    • PubMed
    • Google Scholar
16. 1. Cuendet MA
    2. Zoete V
    3. Michielin O

    (2011) How T cell receptors interact with peptide-MHCs: A multiple steered molecular dynamics study

    Proteins 79:3007–3024.

    https://doi.org/10.1002/prot.23104

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

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

    PNAS 112:1517–1522.

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

    • PubMed
    • Google Scholar
18. 1. Das DK
    2. Mallis RJ
    3. Duke-Cohan JS
    4. Hussey RE
    5. Tetteh PW
    6. Hilton M
    7. Wagner G
    8. Lang MJ
    9. Reinherz EL

    (2016) pre-t cell receptors (Pre-Tcrs) leverage vβ complementarity determining regions (CDRS) and hydrophobic patch in mechanosensing thymic self-ligands

    The Journal of Biological Chemistry 291:25292–25305.

    https://doi.org/10.1074/jbc.M116.752865

    • PubMed
    • Google Scholar
19. 1. Davis-Harrison RL
    2. Armstrong KM
    3. Baker BM

    (2005) Two different T cell receptors use different thermodynamic strategies to recognize the same peptide/MHC ligand

    Journal of Molecular Biology 346:533–550.

    https://doi.org/10.1016/j.jmb.2004.11.063

    • PubMed
    • Google Scholar
20. 1. Ding YH
    2. Baker BM
    3. Garboczi DN
    4. Biddison WE
    5. Wiley DC

    (1999) Four A6-TCR/peptide/HLA-A2 structures that generate very different T cell signals are nearly identical

    Immunity 11:45–56.

    https://doi.org/10.1016/s1074-7613(00)80080-1

    • PubMed
    • Google Scholar
21. 1. Eastman P
    2. Swails J
    3. Chodera JD
    4. McGibbon RT
    5. Zhao Y
    6. Beauchamp KA
    7. Wang LP
    8. Simmonett AC
    9. Harrigan MP
    10. Stern CD
    11. Wiewiora RP
    12. Brooks BR
    13. Pande VS

    (2017) OpenMM 7: Rapid development of high performance algorithms for molecular dynamics

    PLOS Computational Biology 13:e1005659.

    https://doi.org/10.1371/journal.pcbi.1005659

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

    (2017) Mechanosensing drives acuity of T-cell recognition

    PNAS 114:E8204–E8213.

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

    • Google Scholar
23. 1. Fodor J
    2. Riley BT
    3. Borg NA
    4. Buckle AM

    (2018) Previously hidden dynamics at the TCR-peptide-MHC interface revealed

    Journal of Immunology 200:4134–4145.

    https://doi.org/10.4049/jimmunol.1800315

    • PubMed
    • Google Scholar
24. 1. Garboczi DN
    2. Ghosh P
    3. Utz U
    4. Fan QR
    5. Biddison WE
    6. Wiley DC

    (1996a) Structure of the complex between human T-cell receptor, viral peptide and HLA-A2

    Nature 384:134–141.

    https://doi.org/10.1038/384134a0

    • PubMed
    • Google Scholar
25. 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.

    • PubMed
    • Google Scholar
26. 1. Gomez D
    2. Peña Ccoa WJ
    3. Singh Y
    4. Rojas E
    5. Hocky GM

    (2021) Molecular paradigms for biological mechanosensing

    The Journal of Physical Chemistry. B 125:12115–12124.

    https://doi.org/10.1021/acs.jpcb.1c06330

    • PubMed
    • Google Scholar
27. 1. Haberthür U
    2. Caflisch A

    (2008) FACTS: Fast analytical continuum treatment of solvation

    Journal of Computational Chemistry 29:701–715.

    https://doi.org/10.1002/jcc.20832

    • PubMed
    • Google Scholar
28. 1. Hamming RW

    (1950) Error detecting and error correcting codes

    Bell System Technical Journal 29:147–160.

    https://doi.org/10.1002/j.1538-7305.1950.tb00463.x

    • Google Scholar
29. 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.

    • PubMed
    • Google Scholar
30. 1. Huang J
    2. MacKerell AD

    (2013) CHARMM36 all-atom additive protein force field: validation based on comparison to NMR data

    Journal of Computational Chemistry 34:2135–2145.

    https://doi.org/10.1002/jcc.23354

    • PubMed
    • Google Scholar
31. 1. Hwang W

    (2007) Calculation of conformation-dependent biomolecular forces

    The Journal of Chemical Physics 127:17.

    https://doi.org/10.1063/1.2784557

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

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

    PNAS 117:21336–21345.

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

    • PubMed
    • Google Scholar
33. 1. Hynninen AP
    2. Crowley MF

    (2014) New faster CHARMM molecular dynamics engine

    Journal of Computational Chemistry 35:406–413.

    https://doi.org/10.1002/jcc.23501

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

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

    The Journal of Biological Chemistry 284:31028–31037.

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

    • PubMed
    • Google Scholar
35. 1. Kim ST
    2. Shin Y
    3. Brazin K
    4. Mallis RJ
    5. Sun ZYJ
    6. Wagner G
    7. Lang MJ
    8. Reinherz EL

    (2012) TCR mechanobiology: torques and tunable structures linked to early T cell signaling

    Frontiers in Immunology 3:76.

    https://doi.org/10.3389/fimmu.2012.00076

    • PubMed
    • Google Scholar
36. 1. Lee JK
    2. Stewart-Jones G
    3. Dong T
    4. Harlos K
    5. Di Gleria K
    6. Dorrell L
    7. Douek DC
    8. van der Merwe PA
    9. Jones EY
    10. McMichael AJ

    (2004) T cell cross-reactivity and conformational changes during TCR engagement

    The Journal of Experimental Medicine 200:1455–1466.

    https://doi.org/10.1084/jem.20041251

    • PubMed
    • Google Scholar
37. 1. Liu Y
    2. Blanchfield L
    3. Ma VPY
    4. Andargachew R
    5. Galior K
    6. Liu Z
    7. Evavold B
    8. Salaita K

    (2016) DNA-based nanoparticle tension sensors reveal that T-cell receptors transmit defined pN forces to their antigens for enhanced fidelity

    PNAS 113:5610–5615.

    https://doi.org/10.1073/pnas.1600163113

    • PubMed
    • Google Scholar
38. 1. Liu B
    2. Kolawole EM
    3. Evavold BD

    (2021) Mechanobiology of T cell activation: to catch a bond

    Annual Review of Cell and Developmental Biology 37:65–87.

    https://doi.org/10.1146/annurev-cellbio-120219-055100

    • PubMed
    • Google Scholar
39. 1. Mallis RJ
    2. Duke-Cohan JS
    3. Das DK
    4. Akitsu A
    5. Luoma AM
    6. Banik D
    7. Stephens HM
    8. Tetteh PW
    9. Castro CD
    10. Krahnke S
    11. Hussey RE
    12. Lawney B
    13. Brazin KN
    14. Reche PA
    15. Hwang W
    16. Adams EJ
    17. Lang MJ
    18. Reinherz EL

    (2021) Molecular design of the γδT cell receptor ectodomain encodes biologically fit ligand recognition in the absence of mechanosensing

    PNAS 118:e2023050118.

    https://doi.org/10.1073/pnas.2023050118

    • PubMed
    • Google Scholar
40. 1. Mariuzza RA
    2. Agnihotri P
    3. Orban J

    (2020) The structural basis of T-cell receptor (TCR) activation: An enduring enigma

    The Journal of Biological Chemistry 295:914–925.

    https://doi.org/10.1074/jbc.REV119.009411

    • PubMed
    • Google Scholar
41. 1. Michielin O
    2. Karplus M

    (2002) Binding free energy differences in a TCR-peptide-MHC complex induced by a peptide mutation: a simulation analysis

    Journal of Molecular Biology 324:547–569.

    https://doi.org/10.1016/s0022-2836(02)00880-x

    • PubMed
    • Google Scholar
42. 1. Pettmann J
    2. Awada L
    3. Różycki B
    4. Huhn A
    5. Faour S
    6. Kutuzov M
    7. Limozin L
    8. Weikl TR
    9. van der Merwe PA
    10. Robert P
    11. Dushek O

    (2023) Mechanical forces impair antigen discrimination by reducing differences in T-cell receptor/peptide-MHC off-rates

    The EMBO Journal 42:e111841.

    https://doi.org/10.15252/embj.2022111841

    • PubMed
    • Google Scholar
43. 1. Pitera JW
    2. Chodera JD

    (2012) On the use of experimental observations to bias simulated ensembles

    Journal of Chemical Theory and Computation 8:3445–3451.

    https://doi.org/10.1021/ct300112v

    • PubMed
    • Google Scholar
44. 1. Rangarajan S
    2. He Y
    3. Chen Y
    4. Kerzic MC
    5. Ma B
    6. Gowthaman R
    7. Pierce BG
    8. Nussinov R
    9. Mariuzza RA
    10. Orban J

    (2018) Peptide-MHC (pMHC) binding to a human antiviral T cell receptor induces long-range allosteric communication between pMHC- and CD3-binding sites

    The Journal of Biological Chemistry 293:15991–16005.

    https://doi.org/10.1074/jbc.RA118.003832

    • PubMed
    • Google Scholar
45. 1. Reinherz EL
    2. Hwang W
    3. Lang MJ

    (2023) Harnessing αβ T cell receptor mechanobiology to achieve the promise of immuno-oncology

    PNAS 120:e2215694120.

    https://doi.org/10.1073/pnas.2215694120

    • PubMed
    • Google Scholar
46. 1. Rudolph MG
    2. Stanfield RL
    3. Wilson IA

    (2006) How TCRs bind MHCs, peptides, and coreceptors

    Annual Review of Immunology 24:419–466.

    https://doi.org/10.1146/annurev.immunol.23.021704.115658

    • PubMed
    • Google Scholar
47. 1. Schamel WW
    2. Alarcon B
    3. Minguet S

    (2019) The TCR is an allosterically regulated macromolecular machinery changing its conformation while working

    Immunological Reviews 291:8–25.

    https://doi.org/10.1111/imr.12788

    • PubMed
    • Google Scholar
48. 1. Scott DR
    2. Borbulevych OY
    3. Piepenbrink KH
    4. Corcelli SA
    5. Baker BM

    (2011) Disparate degrees of hypervariable loop flexibility control T-cell receptor cross-reactivity, specificity, and binding mechanism

    Journal of Molecular Biology 414:385–400.

    https://doi.org/10.1016/j.jmb.2011.10.006

    • PubMed
    • Google Scholar
49. 1. Sibener LV
    2. Fernandes RA
    3. Kolawole EM
    4. Carbone CB
    5. Liu F
    6. McAffee D
    7. Birnbaum ME
    8. Yang X
    9. Su LF
    10. Yu W
    11. Dong S
    12. Gee MH
    13. Jude KM
    14. Davis MM
    15. Groves JT
    16. Goddard WA
    17. Heath JR
    18. Evavold BD
    19. Vale RD
    20. Garcia KC

    (2018) Isolation of a structural mechanism for uncoupling T cell receptor signaling from peptide-MHC binding

    Cell 174:672–687.

    https://doi.org/10.1016/j.cell.2018.06.017

    • Google Scholar
50. 1. Stirnemann G

    (2022) Recent advances and emerging challenges in the molecular modeling of mechanobiological processes

    The Journal of Physical Chemistry. B 126:1365–1374.

    https://doi.org/10.1021/acs.jpcb.1c10715

    • PubMed
    • Google Scholar
51. 1. Sykulev Y
    2. Joo M
    3. Vturina I
    4. Tsomides TJ
    5. Eisen HN

    (1996) Evidence that a single peptide-MHC complex on a target cell can elicit a cytolytic T cell response

    Immunity 4:565–571.

    https://doi.org/10.1016/s1074-7613(00)80483-5

    • PubMed
    • Google Scholar
52. 1. Wang J
    2. Reinherz EL

    (2012) The structural basis of αβ T-lineage immune recognition: TCR docking topologies, mechanotransduction, and co-receptor function

    Immunological Reviews 250:102–119.

    https://doi.org/10.1111/j.1600-065X.2012.01161.x

    • PubMed
    • Google Scholar
53. 1. Wu P
    2. Zhang T
    3. Liu B
    4. Fei P
    5. Cui L
    6. Qin R
    7. Zhu H
    8. Yao D
    9. Martinez RJ
    10. Hu W
    11. An C
    12. Zhang Y
    13. Liu J
    14. Shi J
    15. Fan J
    16. Yin W
    17. Sun J
    18. Zhou C
    19. Zeng X
    20. Xu C
    21. Wang J
    22. Evavold BD
    23. Zhu C
    24. Chen W
    25. Lou J

    (2019) Mechano-regulation of peptide-MHC class i conformations determines TCR antigen recognition

    Molecular Cell 73:1015–1027.

    https://doi.org/10.1016/j.molcel.2018.12.018

    • PubMed
    • Google Scholar
54. 1. Zhu C
    2. Chen W
    3. Lou J
    4. Rittase W
    5. Li K

    (2019) Author Correction: mechanosensing through immunoreceptors

    Nature Immunology 20:1700.

    https://doi.org/10.1038/s41590-019-0545-4

    • PubMed
    • Google Scholar

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

   "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

   "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

   "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

   "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

   "This ORCID iD identifies the author of this article:" 0000-0001-7514-3186

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