Tunable molecular tension sensors reveal extension-based control of vinculin loading

Molecular tension sensors have contributed to a growing understanding of mechanobiology. However, the limited dynamic range and inability to specify the mechanical sensitivity of these sensors has hindered their widespread use in diverse contexts. Here, we systematically examine the components of tension sensors that can be altered to improve their functionality. Guided by the development of a first principles model describing the mechanical behavior of these sensors, we create a collection of sensors that exhibit predictable sensitivities and significantly improved performance in cellulo. Utilized in the context of vinculin mechanobiology, a trio of these new biosensors with distinct force- and extension-sensitivities reveal that an extension-based control paradigm regulates vinculin loading in a variety of mechanical contexts. To enable the rational design of molecular tension sensors appropriate for diverse applications, we predict the mechanical behavior, in terms of force and extension, of additional 1020 distinct designs.

under tensile loads ( Figure 2B). Importantly, each of these datasets is well-described by the same      To determine which extensible domain length will be optimal for measuring tension across vinculin, we . 178 We assessed the performance of opt-VinTS by evaluating its ability to detect changes in vinculin         Discussion: 260 In this study, we created and characterized a suite of molecular tension sensors with improved, 261 predictable, and tunable force sensitivities. Through these improvements, which included a switch to the 262 Clover-mRuby2 FRET pair, the use of "minimal" FPs, and the modulation of the length and composition   (Saez et al., 2005) and that strain determines the activation of touch-sensitive channels in in 288 vivo models (Eastwood et al., 2015). However, while vinculin appears to be under extension-control in 289 2D culture systems we note that the simple FA structural model demonstrates that the extent of force-290 controlled versus extension-controlled protein loading is highly sensitive to the relative abundance and 291 stiffness of various proteins within the bulk structure. Thus, it seems likely that other load-bearing 292 proteins might be subject to either type of control or that a specific protein in distinct cellular contexts 293 could switch control modalities. The prediction of >1000 possible tension sensor designs should allow 294 the creation of tension sensors for any need in either extension-or force-based control paradigms.

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In sum, this work provides the biophysical foundation for understanding molecular tension sensor 296 function and delivers a suite of in cellulo-calibrated sensors whose distinct and predictable mechanical  Clover as: where is the intensity in the FRET-channel, is the intensity in the donor-channel, and data were 385 binned by donor-channel intensity. Similarly, acceptor bleed-through coefficients ( ) were calculated 386 for Venus and mRuby2 as: where is the intensity in the acceptor-channel, and data were binned by acceptor-channel intensity. To 389 correct for spectral bleed-through in experimental data, corrected FRET images ( ) were generated 390 following the equation: After bleed-through correction, FRET efficiency was calculated following the equation: where is a proportionality constant that describes the increase in acceptor intensity (due to sensitized   FPs highlighting 11 C-terminal residues (donor FP) and 2 N-terminal residues (acceptor FP), which do not contribute to beta barrel structure, but are highly conserved between various FPs; residues not appearing in crystal structures are faded (PDB 2HQK and 1MYW for donor and acceptor FPs, respectively); residues that were removed are underlined. (B, C) Normalized excitation (B) and emission (C) spectra for full length and "minimal" versions of mTFP1, Venus, Clover, and mRuby2.   The issue most pertinent to this work is that Eq. 1 and 2 relate force to the average extension (〈 〉) of the 540 polypeptide chain, which is zero in the absence of force. However, the non-zero polypeptide end-to-end 541 distance ( ), which describes the finite rest length of the extensible domain, is more directly related to 542 the chromophore separation distance ( ), which is the key distance determining FRET efficiency ( ).

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Additionally, the nonlinear relationships between these quantities ( , , and ) prevent the use of 544 simple heuristic correction schemes to relate various distance metrics (Evers et al., 2006a). 545 Previous work has developed formalisms that enable these concerns to be accounted for (Becker et al., Additionally, 0 is a modified Bessel function of the first kind and parameters and are defined as:  sampling method (Titantah et al., 1999). Then, the impact of force on ( ) was accounted for following 680 the Boltzmann relation (Eq. 5). Next, ( ) was first converted to ( ) using the -to-lookup table 681 described by Eq. 14, and then ( ) using the -to-lookup table described by Eq. 17 for TSMod-like

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To evaluate the ability of the calibration model to describe the mechanical sensitivity of TSMods subject 730 to mechanical loads, we examined model fits to previously published fluorescence-force spectroscopy 731 data of (GPGGA)n polypeptides labelled with Cy3 and Cy5 dyes (Brenner et al., 2016). These TSMod- domain. Simulations of Cy3-Cy5 constructs with various polypeptide lengths (10 < < 100 residues) 739 and persistence lengths (0.1 < < 2.0 nm) were then compared to experimentally measured FRET-740 polypeptide length and FRET-force relationships (Figure 2). The simulations where ~1.05 nm agree 741 well with both the FRET-polypeptide length relationship over the range of polypeptide lengths assessed 742 (Figure 2A), as well as the measured relationship between FRET efficiency and force ( Figure 2B). For

IX. Comparison to other descriptions of TSMod mechanical sensitivity
In validating the proposed model, we evaluated its ability to recapitulate published fluorescence force         The number of sensor and linker elements ( and , respectively) and the stiffness of the linker 866 element ( ) were set such that the relative abundance and relative stiffness of the two elements varied between 2 -4 and 2 4 . Note that when calculating the relative stiffness, the three distinct spring constants 868 for the three sensor elements were averaged together. This definition is appropriate as the variation of 869 the stiffness of the sensors springs ( ) is significantly smaller (~30% variation) than the range of 870 that was evaluated. These normalized parameters also allow us to draw conclusions that are 871 independent of the absolute values of and that are simulated.

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With a bulk force input ( 0 ), simple relationships that obey Hooke's Law are observed, and 0 is evenly In response to a bulk extension input ( 0 ), more complex behaviors are observed. We must first 883 determine the total force across the assembly following Hooke's Law for springs in series: where the effective spring constant for the whole assembly is given by: Calculating and as before (Eq. 4 and 5), it becomes apparent that both and will change 888 depending on the relative abundance and stiffness of the sensor and linker elements.