Conformational Plasticity of Human Protease-Activated Receptor 1 upon Antagonist- and Agonist-Binding

Article

Conformational Plasticity of Human ProteaseActivated Receptor 1 upon Antagonist- and AgonistBinding Graphical Abstract

Authors Patrizia M. Spoerri, K. Tanuj Sapra, Cheng Zhang, Stefania A. Mari, Hideaki E. Kato, Brian K. Kobilka, € ller Daniel J. Mu

Correspondence [email protected]

In Brief

Highlights d

Mechanical, kinetic, and energetic properties of PAR1

d

Properties changing upon binding of an agonist or antagonist

d

Secondary structures change conformational variability and lifetime

d

Secondary structures change free energy and mechanical rigidity

Spoerri et al., 2019, Structure 27, 1517–1526 October 1, 2019 ª 2019 Elsevier Ltd. https://doi.org/10.1016/j.str.2019.07.014

Spoerri et al. determine the mechanical, kinetic, and energetic properties of the human protease-activated receptor 1 (PAR1) in the unliganded state, active state, and inhibited state. Binding of the native peptide-based agonist SFLLRN or of the synthetic peptide-based antagonist BMS modulates the mechanical stiffness, conformational flexibility, lifetime, and free energy of certain structural regions within PAR1. The insights outline a general framework of how GPCRs stabilize functional states.

Structure

Article Conformational Plasticity of Human Protease-Activated Receptor 1 upon Antagonist- and Agonist-Binding Patrizia M. Spoerri,1 K. Tanuj Sapra,1 Cheng Zhang,2,3 Stefania A. Mari,1 Hideaki E. Kato,2,4 Brian K. Kobilka,2 € ller1,5,* and Daniel J. Mu 1Department

of Biosystems Science and Engineering, Eidgeno¨ssische Technische Hochschule (ETH) Zurich, 4058 Basel, Switzerland of Cellular Physiology and Medicine, Stanford University School of Medicine, Palo Alto, CA 94305, USA 3Present address: Department of Pharmacology and Chemical Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15261, USA 4Present address: Komaba Institute for Science, The University of Tokyo, Tokyo, 1538902, Japan 5Lead Contact *Correspondence: [email protected] https://doi.org/10.1016/j.str.2019.07.014 2Department

SUMMARY

G protein-coupled receptors (GPCRs) show complex relationships between functional states and conformational plasticity that can be qualitatively and quantitatively described by contouring their free energy landscape. However, how ligands modulate the free energy landscape to direct conformation and function of GPCRs is not entirely understood. Here, we employ single-molecule force spectroscopy to parametrize the free energy landscape of the human protease-activated receptor 1 (PAR1), and delineate the mechanical, kinetic, and energetic properties of PAR1 being set into different functional states. Whereas in the inactive unliganded state PAR1 adopts mechanically rigid and stiff conformations, upon agonist or antagonist binding the receptor mechanically softens, while increasing its conformational flexibility, and kinetic and energetic stability. By mapping the free energy landscape to the PAR1 structure, we observe key structural regions putting this conformational plasticity into effect. Our insight, complemented with previously acquired knowledge on other GPCRs, outlines a more general framework to understand how GPCRs stabilize certain functional states. INTRODUCTION G protein-coupled receptors (GPCRs) encompass a large family of transmembrane proteins that sense a wide range of extracellular cues including neurotransmitters, peptides, hormones, light, or mechanical forces. Upon sensing the cues, GPCRs trigger specific cellular responses through intracellular signaling pathways (Calebiro and Jobin, 2019; Hilger et al., 2018; Palczewski, 2006; Wootten et al., 2018). Being virtually involved in almost every cellular process, the seven transmembrane a-helical receptors regulate numerous physiological processes, making

them favored drug targets (Campbell and Smrcka, 2018; Hauser et al., 2017). GPCRs are dynamic structures that show a high conformational variability, of which certain conformations induce specific intracellular signaling pathways (Deupi and Kobilka, 2010; Venkatakrishnan et al., 2013; Wingler et al., 2019). The probability to possess one or a set of specific conformations depends on which ligand binds the GPCR and on cofactors including receptor assembly, lipids, or chemical compounds (Katritch et al., 2013; Smith et al., 2018; Weis and Kobilka, 2018; Zocher et al., 2012b). Although high-resolution structures of GPCRs are the pinnacle of corroborating our present understanding of most functional studies (Hilger et al., 2018), static structures only provide insights into a specific stabilized state frozen in time and space (Steyaert and Kobilka, 2011) out of the many different dynamic conformations (Deupi and Kobilka, 2010). Therefore, to understand how the binding of ligands or cofactors can guide GPCRs to adopt certain conformations requires complementary methods (Garcı´a-Nafrı´a et al., 2018; Maeda and Schertler, 2013; Manglik et al., 2017; Rosenbaum et al., 2007; Serrano-Vega et al., 2008). Protease-activated receptors (PARs) form a functionally unique and medically important sub-family among GPCRs. PARs are unique because their N-terminal end, after enzymatic cleavage, exposes a tethered peptide sequence that auto-activates the receptor to induce signal transduction pathways. The PAR family consists of four known members PAR1-4, each of which is activated by a specific set of proteases (Adams et al., 2011; Coughlin, 2000). Of the PAR members, PAR1 is responsible for hemostasis, thrombosis, and inflammation. Its key role in the cardiovascular, musculoskeletal, gastrointestinal, respiratory, and central nervous systems makes PAR1 a sought-after drug target (Hamilton and Trejo, 2017; Wojtukiewicz et al., 2015). PAR1 activation is initiated by serine-proteases including thrombin that cleave a part of the receptor’s extracellular N-terminal end. The truncated N-terminal end exposes an SFLLRN peptide sequence, which binds and activates PAR1 (Coughlin, 2000). This binding of the tethered SFLLRN ligand can be inhibited by an irreversible antagonist, vorapaxar, a US Food and Drug Administration-approved drug for anti-platelet therapy against thrombosis (Hamilton and Trejo, 2017). The vorapaxarinhibited conformation of PAR1 is the only structure solved by Structure 27, 1517–1526, October 1, 2019 ª 2019 Elsevier Ltd. 1517

X-ray crystallography (Zhang et al., 2012). Besides the lack of additional structural models providing insight into other PAR1 conformations, their chemical and physical properties also remain to be characterized. Atomic force microscopy (AFM) is well-established for the high-resolution imaging (%1 nm) of single membrane proteins at physiologically relevant conditions (Dufrene et al., 2017) and for quantifying the structural stability of membrane proteins using AFM-based single-molecule force spectroscopy (SMFS) (Bippes and Muller, 2011; Thoma et al., 2018). The exceptional sensitivity of SMFS enables to map the stability of the structural segments (e.g., single a helices and polypeptide loops) of a membrane protein and to monitor how they change stability in response to ligand or inhibitor binding (Bippes et al., 2009; Kedrov et al., 2006, 2008; Serdiuk et al., 2014; Zocher et al., 2012a), temperature (Janovjak et al., 2007), pH (Damaghi et al., 2010), ions (Park et al., 2007), mutations (Kawamura et al., 2012; Sapra et al., 2008), or membrane lipid composition (Serdiuk et al., 2015; Zocher et al., 2012b). Furthermore, applying SMFS in the dynamic mode can quantify the mechanical, kinetic and energetic properties and contour the free energy landscape of functionally activated or inhibited GPCR structures (Kawamura et al., 2013; Spoerri et al., 2018; Zocher et al., 2012a). So far, however, a common trend describing the intrinsic chemical and physical properties of unliganded, activated, and inhibited GPCR conformations could not be highlighted. Here, we apply SMFS to quantify how the mechanical, kinetic, and energetic properties of unliganded inactive human PAR1 change upon activating the receptor via agonist binding (SFLLRN peptide) or inhibiting the receptor via antagonist binding (peptide-mimic BMS 200261 [BMS]). Using dynamic SMFS we map the parameters characterizing the free energy landscape of PAR1 and structurally delineate the mechanical, kinetic and energetic properties of all three functional states. In the inactive unliganded state PAR1 adopts mechanically rigid and stiff conformations. However, upon agonist or antagonist binding the receptor mechanically softens, while increasing its conformational flexibility, and kinetic and energetic stability. Thereby, we observe key structural regions of PAR1 putting this conformational plasticity into effect. The results highlight how ligand binding tunes the conformational plasticity of human PAR1 in specific ways and show a common trend of how agonist and antagonist binding change conformational properties of GPCRs. RESULTS AND DISCUSSION PAR1 Stabilizes the Same Structural Segments Independently of Ligand Binding For SMFS, purified human PAR1 was reconstituted into liposomes made of 1,2-dioleoyl-sn-glycero-3-phosphocholine and the cholesterol analog cholesteryl hemisuccinate at a 10:1 ratio (w/w) (STAR Methods; Figure S1). The proteoliposomes were adsorbed onto mica (Muller and Engel, 2007; Spoerri et al., 2018), where they collapsed and formed membrane patches exposing the terminal ends of PAR1 in buffer solution (Figures S1C–S1E). To non-specifically attach the stylus of the AFM cantilever to the N terminus of single receptors, the stylus was pushed to a membrane applying a force of 700 pN for 0.5 s. Then, the cantilever was retracted from the membrane at con1518 Structure 27, 1517–1526, October 1, 2019

stant speed. This retraction stretched the N terminus and the pulling force built up during stretching unfolded and extracted a single PAR1 from the membrane (Figure S2A–B). Simultaneously, a force-distance (FD) curve recorded a sequence of force peaks corresponding to the stepwise unfolding and extraction of the receptor (Muller and Engel, 2007; Spoerri et al., 2018). Next, we repeated the SMFS experiments several thousand times to record the common unfolding pattern of human PAR1 in the unliganded inactive state (STAR Methods), the inhibited antagonist-bound state, and the activated agonist-bound state (Figures 1 and S2C). Whereas the antagonist-bound state was measured in the presence of the antagonist peptide-mimic BMS-200261 (BMS) known to inhibit PAR1-mediated platelet aggregation (Bernatowicz et al., 1996), the agonist-bound state was measured in the presence of the synthetic agonist peptide (SFLLRN peptide) corresponding to the native N-terminal SFLLRN sequence that activates PAR1 (Coughlin, 2000). Although the exact structural locations of the agonist and antagonist binding to PAR1 are not known, both binding sites are known to localize on the extracellular side of PAR1 (Zhang et al., 2012). Previously, we have shown that mechanical unfolding of PAR1 by SMFS predominantly occurred by non-specifically attaching the extracellular N terminus to the AFM stylus and retracting the stylus (Spoerri et al., 2018). In this study, we used PAR1 carrying a shorter N-terminal (
35 amino acids [aa]) and a longer C-terminal (+76 aa) end. Nevertheless, unfolding PAR1 with the altered terminal ends, and in the absence and presence of ligands, produced FD curves of similar force peak patterns as obtained previously. This suggests that a shorter N terminus and a longer C terminus and the presence of ligands did not change the preference of the AFM stylus to non-specifically attach to the PAR1 N terminus. To confirm if the preferential attachment to the N terminus was indeed the case, we reduced the highly conserved disulfide bond formed between Cys175 (a helix H3) and Cys254 (extracellular loop E2) of PAR1 with DTT and conducted SMFS (Figure S3). Consequently, the force peak pattern of the FD curves increased distance by about 25 nm (z79 aa), which corresponds to the contour length of the fully unfolded and stretched polypeptide comprising a helices H3 and H4 (Sapra et al., 2006; Spoerri et al., 2018). Thus, the PAR1 construct preferentially attached with the N terminus to the AFM stylus from which it was mechanically extended, unfolded, and extracted. Superimposition of the FD curves recorded for unliganded, antagonist-, and agonist-bound PAR1 showed recurring patterns of force peaks (Figure 1). Fitting each force peak with the worm-like chain model followed by statistical analysis unveiled eight main classes of force peaks corresponding to the contour lengths of the polypeptide stretched in each of the eight unfolding steps (Figure 1) (Bippes and Muller, 2011; Muller and Engel, 2007). The contour lengths of the eight force peak classes were then used to locate the eight structural segments via which PAR1 unfolded (Figure 2): structural segment N1 describes the distal free end of the N terminus; segment N2 the proximal end of N terminus and top of a helix H1; segment H1-C1-H2 describes a helix H1, cytoplasmic loop C1, and a helix H2; segment E1 the extracellular loop E1; segment H3-C2-H4-E2 describes a helix H3, cytoplasmic loop C2, a helix H4, and extracellular loop E2; segment H5-C3-H6-E3 describes a helix H5, cytoplasmic loop C3, a helix H6, and extracellular loop E3; segment H7 describes

Figure 1. Single-Molecule Force Spectroscopy of Unliganded, BMS- and SFLLRNBound Human Protease-Activated Receptor 1 (A–C) Superposition of FD curves recorded upon mechanically unfolding (A) unliganded, (B) BMSbound, and (C) SFLLRN-bound human PAR1. The force peaks of the FD curves reveal a common unfolding pattern. Every force peak recorded of the superimposed FD curves was fitted by the wormlike chain (WLC) model (colored lines) to reveal the contour length (given in amino acids [aa] at the end of each WLC fit) (Marko and Siggia, 1995; Rief et al., 1997) of the stretched polypeptide PAR1 releases in each unfolding step (STAR Methods). (D–F) Probability of contour lengths revealed by fitting the force peaks from each FD curve superimposed in (A–C). Contour length histograms are given for (D) unliganded, (E) BMS-bound, and (F) SFLLRN-bound human PAR1. Applying a multiGaussian mixture model to fit the distribution reveals eight mean contour lengths and thus force peak classes. Contour lengths and SDs are given above each Gaussian distribution in aa. Dashed lines indicate the uniform baseline noise for each fit region of the contour length histograms (STAR Methods). A total of 845 FD curves were recorded, superimposed, and analyzed from unliganded PAR1, 682 from BMS-bound PAR1, and 778 from SFLLRN-bound PAR1. Experiments were conducted in SMFS buffer (300 mM NaCl, 25 mM MgCl2, 25 mM Tris [pH 7.0]) at 27 C. See also Figures S1–S2.

a helix H7; and segment CT the C terminus. The eight structural segments were the same as detected previously for the mechanical unfolding of PAR1 in the inactive and vorapaxar-bound states (Spoerri et al., 2018). We thus conclude that human PAR1, independently of whether it was unliganded or liganded, stabilizes its structure against unfolding using the same structural segments. Properties of Structural Segments Stabilizing Inactive, Active, and Inhibited PAR1 To detect the structural segments stabilizing PAR1 in the inactive, active, and inhibited states, we mechanically pulled and unfolded the receptor (Figure 1; STAR Methods). However, the force required to unfold and extract structural segments of proteins from membranes depends on the loading rate (e.g., pulling speed) applied by the AFM cantilever and is thus less suitable to interpret the stability of the membrane protein (Bippes and Muller, 2011). Yet, measuring the unfolding force at a wide range of loading rates allows to approximate the parameters describing the kinetic stability of the structural segments. To access the parameters describing the free energy landscape of each structural segment of PAR1 (Figure S4), we performed dynamic SMFS (DFS) and mechanically unfolded PAR1 over a wide range of pulling speeds of

300, 600, 900, 1,200, 2,500, and 5,000 nm s
1 (Figure S5; STAR Methods). For every structural segment of PAR1, a DFS plot showed that the mean force required to unfold the segment increased linearly with the logarithm of the mean loading rate (Figures 3 and S6). This behavior was independent of whether PAR1 was liganded or not. However, the unfolding forces characterizing the stability of the structural segments of unliganded and SFLLRN-bound PAR1 were considerably below those of the segments of BMS-bound PAR1. Although such changes in forces may suggest higher or lower mechanical stability of a structural segment at certain applied loading rates, which are far from equilibrium, they do not necessarily reflect the mechanical, kinetic, or energetic stability of the segment at equilibrium. To approach the parameters describing these properties of PAR1 at equilibrium we fitted the Bell-Evans model (Figure S4; STAR Methods) to the slope of each DFS plot (Figure 3). The fits estimated the distance xu a folded structural segment had to be stretched along the pulling direction to reach its transition state toward unfolding (Table 1). The xu values approximate the width of the free energy valley stabilizing a structural segment and thus the conformational flexibility of the segment. The DFS plot also estimated the unfolding rate k0 of a structural segment under zero force, which is reciprocal of the lifetime. Furthermore, the unfolding free energy DGuz, which describes the height of the free energy barrier stabilizing a structural segment, and k, which defines the mechanical Structure 27, 1517–1526, October 1, 2019 1519

Figure 2. Structural Segments Mechanically Stabilizing Human PAR1 (A) Mapping the eight force peak classes detected by SMFS (Figure 1) to the secondary structure of human PAR1. Each color represents one structural segment, which stabilizes the receptor against mechanical unfolding. Red colored aa indicate the end of the preceding and the beginning of the next stable structural segment. Numbers give the mean contour lengths (in aa) of experimentally determined force peak classes (Figure 1), and numbers in brackets annotate the amino acid position as indexed in the PDB structure (PDB: 3VW7) (Zhang et al., 2012). Lighter color shades indicate the SD of the mean contour lengths. (B) Structural segments mapped to the tertiary structure of PAR1. The tertiary structure was taken from PDB: 3VW7 (Zhang et al., 2012). See also Figure S3.

stiffness (i.e., spring constant), were estimated from k0 and xu, respectively (Figure S4). PAR1 Increases Conformational Flexibility upon Ligand Binding An increase in xu in the inhibited and the activated states indicates that the energy wells stabilizing the structural segments widened and that the segments had to be stretched more than in unliganded PAR1 to reach the transition state toward unfolding. It is assumed that this widening allows the structure to increase the number of conformational sub-states on a rough energy landscape and thus the conformational flexibility. Compared with unliganded PAR1, from the eight structural segments, five increased their xu values upon binding of the antagonist BMS (Table 1; Figure 4). Structural segments H1-C1-H2, E1, H3-C2-H4-E2, H7, and CT, considerably increased xu, ranging from 29% to 200%. The largest increase in xu was observed for extracellular loop E1, which moved its transition state from 0.31 nm in unliganded PAR1 to 0.92 nm in inhibited PAR1. a Helix H7 increased xu from 0.56 to 0.95 nm, and the extramembranous cytoplasmic C terminus CT increased xu from 0.18 to 0.32 nm in inhibited PAR1. In the SFLLRN-bound active PAR1, from the five structural segments that changed xu 1520 Structure 27, 1517–1526, October 1, 2019

compared with unliganded PAR1, only four segments showed a considerable change. Segment N2 increased xu by 21% from 0.19 to 0.23 nm in active PAR1. The extracellular loop E1, which showed a 200% increase in xu in BMSbound PAR1, increased xu by 23% changing from 0.31 nm in the unliganded state to 0.38 nm in the SFLLRN-bound state. Segment H3-C2-H4-E2 increased xu by 17% from 0.24 to 0.28 nm, and segment H5-C3-H6-E3 increased xu by 41% from 0.17 to 0.24 nm in active PAR1. This was closely followed by H7 with a 36% increase from 0.56 to 0.76 nm in the SFLLRN-bound state. Generally, upon BMS binding, PAR1 increased xu of the structural segments more compared with upon SFLLRN binding (Figure 4). Previously it had been shown that vorapaxar binding enhances the xu values of the individual structural segments similarly to BMS measured here (Spoerri et al., 2018). However, PAR1 bound to the strong antagonist vorapaxar increases xu more (Spoerri et al., 2018) compared with the weaker antagonist BMS characterized here. Thus, one may speculate that antagonist binding to PAR1 generally increases the conformational flexibility of its structural segments. In summary, the conformational flexibility of the PAR1 structural segments increases in the agonist- and antagonist-bound states. Higher conformational flexibility may be interpreted as an increase in the number of sub-states or conformations (Frauenfelder and McMahon, 2000; Henzler-Wildman and Kern, 2007). The native agonist SFLLRN increases the conformational flexibility marginally; the antagonist BMS increases the flexibility further and inhibits the receptor, and the stronger antagonist vorapaxar increases the flexibility even further and irreversibly inhibits PAR1 (Spoerri et al., 2018). Among the structural segments of PAR1, the conformational flexibility of loop E1 and a helix H7 play specific roles in modulating the receptor’s state and are involved in shaping the

Figure 3. Approaching the Free Energy Landscape Parameters of Unliganded, Antagonist (BMS)-Bound, and Agonist (SFLLRN)-Bound Human PAR1 by Dynamic Force Spectroscopy Mean unfolding force (colored data points) of each structural segment over the mean loading rate. Dynamic force spectroscopy (DFS) plots and their fits (thin lines) are shown for unliganded (dark gray), BMS-bound (pink), and SFLLRN-bound (green) PAR1 (STAR Methods). Slanted ellipses include 1 SEM for the unfolding force and the mean loading rate for each pulling speed. Dark- and light-colored regions along the fit lines indicate confidence intervals of 1 SD (68%) and 2 SD (95%), respectively. xu and k0 values (Figure S4) obtained from fits are given in Table 1. The raw data of the DFS analysis are shown in Figure S6. See also Figure S5.

putative binding sites of agonist and antagonist on the extracellular side (Zhang et al., 2012). Although we lack complementary structural data of all PAR1 states investigated, based on the high conformational plasticity described for other GPCR structures and states (Deupi and Kobilka, 2010) we may assume that the conformational flexibility of the different PAR1 states correlate to different structural sub-states or conformations. Thus, the observed changes in conformational flexibility of structural regions within PAR1, which are specific to agonist or antagonist binding, provide detailed insight of how a ligand tunes the receptor structure to modulate its function. Ligand Binding Increases Kinetic Stability All structural segments in BMS-bound PAR1 decreased the unfolding rate k0 and thus increased the lifetime and kinetic stability between 17% and 100% compared with unliganded PAR1 (Table 1; Figure 4). The kinetic stability, as described by the reciprocal of the unfolding rate, of the structural segments in unliganded PAR1 ranged from k0 z 0.01 to 2.25 s
1 and in BMSbound PAR1 from 9.3 3 10
8 to 0.62 s
1. The largest increase in kinetic stability was observed for the extracellular loop E1, with k0 ranging from 0.6 s
1 in unliganded PAR1 to 1.9 3 10
7 s
1 in BMS-bound PAR1, and for a helix H7 with k0 ranging from 0.01 s
1 in unliganded PAR1 to 9.3 3 10
8 s
1 in BMSbound PAR1. In the SFLLRN-bound active PAR1, six structural segments (all except H1-C1-H2, and CT) increased kinetic stability between 42% and 96% compared with the unliganded

state. As observed for BMS-bound inhibited PAR1, the structural segment a helix H7 increased kinetic stability most from 0.01 s
1 in unliganded PAR1 to 4.1 3 10
4 s
1 in SFLLRN-bound PAR1. In contrast to BMS-bound PAR1, in which H1-C1-H2 and CT formed two kinetically more stable structural segments, both structural segments considerably decreased kinetic stability upon SFLLRN binding. In numbers, the kinetic stabilities of H1-C1-H2 decreased by 81% from 1.71 s
1 in unliganded PAR1 to 3.10 s
1 in SFLLRN-bound PAR1 and of CT decreased by 128% from 0.39 s
1 in unliganded PAR1 to 0.89 s
1 in SFLLRN-bound PAR1. Generally, BMS binding increased the kinetic stability of all structural segments of PAR1 up to 100%. A similar trend was recently described for the binding of the strong antagonist vorapaxar to PAR1 (Spoerri et al., 2018). The extent to which ligand binding changes the kinetic stability of a particular structural segment is apparently specific to the ligand. For example, major differences in kinetic stability induced by agonist and antagonist binding manifested in segment H1-C2-H2 where BMS binding increased the kinetic stability by 90% and SFLLRN binding decreased the stability by 81%. On the other hand, BMS binding to PAR1 increased the kinetic stability of the structural segment H3-C2-H4-E2 by 85%, whereas SFLLRN binding increased the stability of the same segment by only 35%. In another example, BMS binding increased the kinetic stability of the structural segment H5-C3-H6-E3 by only 17%, whereas SFLLRN binding increased the stability of the same segment Structure 27, 1517–1526, October 1, 2019 1521

Table 1. Parameters Characterizing the Properties of Unliganded, BMS-, and SFLLRN-Bound PAR1 Structural Segment

Unliganded PAR1

BMS-Bound PAR1

SFLLRN-Bound PAR1

N1

0.21 ± 0.02

0.23 ± 0.04 (+10%)

0.23 ± 0.04 (+10%)

N2

0.19 ± 0.02

0.20 ± 0.04 (+5%)

0.23 ± 0.03 (+21%)

H1-C1-H2

0.17 ± 0.03

0.22 ± 0.05 (+29%)

0.17 ± 0.01 (0%)

E1

0.31 ± 0.07

0.92 ± 0.23 (+200%)

0.38 ± 0.06 (+23%)

H3-C2-H4-E2

0.24 ± 0.06

0.32 ± 0.07 (+33%)

0.28 ± 0.03 (+17%)

H5-C3-H6-E3

0.17 ± 0.03

0.15 ± 0.01 (
12%)

0.24 ± 0.04 (+41%)

H7

0.56 ± 0.23

0.95 ± 0.25 (+70%)

0.76 ± 0.21 (+36%)

CT

0.18 ± 0.04

0.32 ± 0.07 (+78%)

0.16 ± 0.01 (
11%)

N1

2.25 ± 1.00

0.44 ± 0.53 (
80%)

1.31 ± 1.35 (
42%)

N2

1.32 ± 0.78

0.35 ± 0.45 (
73%)

0.64 ± 0.48 (
52%)

H1-C1-H2

1.71 ± 1.63

0.17 ± 0.34 (
90%)

3.10 ± 1.17 (+81%)

E1

0.60 ± 0.95

1.9 3 10
7 ± 1.6 3 10
6 (
100%)

0.24 ± 0.27 (
60%)

H3-C2-H4-E2

0.26 ± 0.45

0.04 ± 0.01 (
85%)

0.09 ± 0.08 (
35%)

xu ± SD (nm)

k0 ± SD (s–1)

H5-C3-H6-E3

0.75 ± 0.81

0.62 ± 0.20 (
17%)

0.15 ± 0.18 (
80%)

H7

0.01 ± 0.05

9.3 3 10
8 ± 5.6 310
7 (
100%)

4.1 3 10
4 ± 1.6 3 10
3 (
96%)

CT

0.39 ± 0.57

0.004 ± 0.010 (
99%)

0.89 ± 0.45 (+128%)

N1

17.6 ± 0.4

19.2 ± 1.2 (+9%)

18.2 ± 1.0 (+3%)

N2

18.1 ± 0.6

19.5 ± 1.3 (+8%)

18.9 ± 0.8 (+4%)

H1-C1-H2

17.9 ± 1.0

20.2 ± 2.0 (+13%)

17.3 ± 0.4 (
3%)

E1

18.9 ± 1.6

33.9 ± 5.7 (+79%)

19.8 ± 1.1 (+5%)

H3-C2-H4-E2

19.8 ± 1.7

21.6 ± 0.2 (+9%)

20.8 ± 0.9 (+5%)

H5-C3-H6-E3

18.7 ± 1.1

18.9 ± 0.3 (+1%)

20.3 ± 1.3 (+9%)

H7

22.9 ± 4.4

34.6 ± 6.0 (+51%)

26.2 ± 3.9 (+14%)

CT

19.4 ± 1.5

23.9 ± 2.4 (+23%)

18.5 ± 0.5 (
5%)

3.45 ± 0.61

2.91 ± 1.02 (
16%)

2.74 ± 1.00 (
21%)

DGuz ± SD (kBT)

k ± SD (N m–1) N1 N2

4.08 ± 0.89

4.18 ± 1.54 (+2%)

2.90 ± 0.70 (
29%)

H1-C1-H2

4.88 ± 1.80

3.58 ± 1.84 (
28%)

4.95 ± 0.84 (+1%)

E1

1.58 ± 0.80

0.33 ± 0.17 (
79%)

1.12 ± 0.34 (
29%)

H3-C2-H4-E2

2.95 ± 1.44

1.75 ± 0.72 (
41%)

2.16 ± 0.47 (
27%)

H5-C3-H6-E3

5.35 ± 2.03

6.70 ± 0.75 (+25%)

2.87 ± 0.97 (
46%)

H7

0.60 ± 0.50

0.32 ± 0.17 (
47%)

0.38 ± 0.21 (
37%)

CT

4.76 ± 2.17

1.94 ± 0.82 (
59%)

5.75 ± 1.05 (+21%)

Mean values and SDs are obtained from fitting the DFS plots (Figure 3; STAR Methods).

by as much as 80%. These changes in kinetic stability of structural regions within PAR1 are thus specific to whether an agonist or antagonist bound the receptor and provide detailed mechanistic insight of how a ligand tunes the kinetic properties of the receptor to modulate its function. Ligand Binding Increases Energetic Stability The free energy DGuz, which describes the height of the free energy barriers stabilizing the structural segments of PAR1 against unfolding, ranged from 17.6 to 22.9 kBT in the unliganded state (Table 1; Figure 4). Upon BMS binding, every structural segment increased DGuz ranging from 18.9 to 34.6 kBT. The highest increase in the free energy barrier was 79% for the structural 1522 Structure 27, 1517–1526, October 1, 2019

segment E1. In SFLLRN-bound PAR1, DGuz of the structural segments ranged from 17.3 to 26.2 kBT. Whereas segments N1, N2, E1, H3-C2-H4-E2, H5-C3-H6-E3, and H7 increased their free energies up to 14%, segments H1-C1-H2 and CT slightly decreased free energies up to 5%. In summary, antagonist binding enhanced the free energy barrier stabilizing each structural segment of PAR1, whereas agonist binding stabilized six structural segments and marginally destabilized two structural segments. Interestingly, among all structural segments E1 of extracellular loop E1 and H7 of transmembrane a helix H7, which are involved in shaping the putative binding site of antagonist and agonist (Zhang et al., 2012), showed the largest changes. Upon BMS binding, the free energy of E1 and H7 increased by

Figure 4. Mechanical and Energetic Properties of Unliganded, Antagonist BMS-Bound, and Agonist SFLLRN-Bound Human PAR1 Properties of unliganded, BMS-bound, and SFLLRN-bound PAR1 are taken from Table 1 and mapped to the tertiary structure (PDB: 3VW7) (Zhang et al., 2012). The properties mapped are the transition state distance xu, which approximates the width of the free energy valley stabilizing a structural segment and thus the conformational variability of the segment, the unfolding rate k0 of a structural segment at equilibrium, which is reciprocal of its lifetime, the unfolding free energy DGuz, which describes the height of the free energy barrier stabilizing a structural segment, and k, which defines the mechanical stiffness (i.e., spring constant) of a structural segment (Figure S4). See also Figure S6.

79% and 51%, while upon SFLLRN binding they increased by 5% and 14%, respectively. The findings thus highlight that the changes in energetic stability of the structural regions within PAR1 are specific to whether an agonist or antagonist bound the receptor and provide detailed mechanistic insight of how a ligand tunes the energetic stability of the receptor to modulate its function. Ligand Binding Decreases Structural Stiffness The mechanical stiffness k of the structural segments in unliganded PAR1 ranged from 0.60 to 5.35 N m
1 (Table 1; Figure 4). The broad distribution of stiffness among the structural segments shows different structural regions within PAR1 to possess heterogeneous flexibilities. In unliganded PAR1, transmembrane a helix H7 is the softest segment at 0.60 N m
1, and segment H5-C3-H6-E3 is the stiffest at 5.35 N m
1. Upon BMS binding, six of the eight structural segments decreased stiffness considerably. Among these segments, the two established by extracellular loop E1 and by a helix H7 decreased stiffness by 79% and 47%, respectively. One strong exception was

segment H5-C3-H6-E3, which increased stiffness by 25% to z6.70 N m
1. In SFLLRN-bound PAR1, again six of the eight structural segments decreased stiffness with k values ranging from 0.38 to 4.95 N m
1. One exception was segment CT of the C terminus, which increased stiffness by 21% to z5.75 N m
1. Our results show that PAR1 in the inactive state adopts a mechanically stiff and less-flexible conformation, which indicates that it is more difficult to change the receptor structure and functional state. Changing the stiffness or the flexibility of PAR1 upon binding of an agonist or antagonist may serve multiple purposes. First, the soft and flexible activated receptor may easily adopt conformations needed for signal transduction and to bind G proteins or arrestin. Second, it may be easier for a flexible activated GPCR to sample different conformations (Liu et al., 2012), which would explain why, for example, activated GPCRs to some extent also show the functional characteristics of inactive, partially activated, fully or partially inhibited receptors (Deupi and Kobilka, 2010; Kahsai et al., 2011). Third, the data also suggest that, upon mechanically softening the receptor, too much of the receptor becomes functionally impaired. The changes in mechanical stiffness of structural regions within PAR1 are specific to whether an agonist or antagonist bound the receptor and provide detailed insight of how a ligand tunes structural regions of the receptor to modulate its function. Conclusion SMFS experiments of GPCRs thus far have suggested that a rather complex network of interactions stabilizes different functional states of a GPCR (Sapra et al., 2018; Zocher et al., 2013). Generally, we here observe that the binding of the inhibitor BMS or of the activating SFLLRN peptide changes the mechanical, kinetic, and energetic properties of PAR1 and equips each functional state of the receptor with different chemical and Structure 27, 1517–1526, October 1, 2019 1523

Figure 5. Mechanical and Energetic Properties of Human PAR1 Changing upon BMS or SFLLRN Binding The changes of the structural segments of PAR1 are shown with respect to unliganded PAR1. The properties mapped for each structural segment are the transition state distance xu, the unfolding rate k0, the unfolding free energy DGuz, and the mechanical stiffness k (i.e., spring constant) (Figure S4). Values to calculate the differences are taken from Table 1. Tertiary structure taken from PDB: 3VW7 (Zhang et al., 2012).

physical properties. The parameters describing the free energy landscape of individual structural segments of PAR1 change to varying degrees depending on whether PAR1 binds an agonist or antagonist. Although not all of the structural segments change properties upon ligand binding, most segments do and, surprisingly, show similar trends, irrespective of whether the ligand is an agonist or antagonist. The distance of the unfolding transition state from the folded structural segment xu, which describes the structural flexibility, the lifetime (1/k0), and the unfolding free energy barrier DGuz increases, and the mechanical stiffness k decreases. A similar trend in the free energy landscape parameters of PAR1 structural segments was recently observed upon binding of the strong antagonist vorapaxar (Spoerri et al., 2018). Comparing how both antagonists, BMS and vorapaxar, change the free energy landscape parameters (Table 1) unravels how they can tune the properties of PAR1 differentially to reach the inhibited state. Although the PAR1 constructs previously used to study the effects of vorapaxar binding (Spoerri et al., 2018) and of BMS binding in this study differed in their N and C termini sequences (STAR Methods), the general trends observed in the change in free energy landscape parameters were similar in both studies. The vorapaxar-inhibited PAR1 structure shows that residues of transmembrane a helices H3, H4, H5, H6, and H7, along with those of extracellular loops E2 and E3 form the vorapaxar-binding pocket (Zhang et al., 2012). We found that, upon binding of an antagonist—BMS or vorapaxar—the conformational flexibility, kinetic stability, and free energy barrier stabilizing the extracellular loop E1 and a helix H7 increase considerably, whereas their mechanical stiffness decreases (Figure 5). The WxFG motif in loop E1 is conserved across all class A GPCRs (WQFG in PAR1), and is important for maintaining receptor topology via long-range interactions (Rizzo et al., 2018). In addition, a helix H7 hosts a network of ordered water molecules, deforming 1524 Structure 27, 1517–1526, October 1, 2019

the helix, changing the cytoplasmic surface of the receptor, and stabilizing the inactive state of GPCRs (Rosenbaum et al., 2009). This interaction networks were also observed in the inhibited structure of PAR1 (Zhang et al., 2012). In a complementary way, our quantification of how loop E1 and a helix H7 change structural properties upon antagonist binding contributes to understanding the physical and chemical mechanisms of PAR1 inhibition. In summary, the orchestration of molecular changes in BMSand SFLLRN-bound PAR1 modulates the mechanical, kinetic, and energetic properties of selected structural regions. The sensitivity of SMFS measures the extent to which each of the structural regions changes properties and thus the free energy landscape, thereby modulating the functional state of the receptor. The mechanistic insight obtained directly corroborates the current qualitative view of how activated or inhibited GPCRs populate the free energy landscape differently (Deupi and Kobilka, 2010). Future complementary experimental and theoretical approaches will provide a better understanding of how ligand binding modulates a rather complex network of interactions and properties of a GPCR toward tuning its state. Certainly, solving the structure of PAR1 in the inactive and active state will allow to correlate our measurements of the structural properties to the inactive, inhibited, and active structure. Experimentally, one may also think of systematically quantifying a wide variety of ligands and compounds modulating the structural properties and functional states of PAR1 and of other GPCRs. Although with the current SMFS technology acquiring such insight would take years, combining it with structural biology, physiology, and theoretical approaches, including molecular dynamics simulations and big data analysis, holds promise in providing a better understanding of the mechanisms modulating and stabilizing the various functional states of GPCRs. STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: d d

KEY RESOURCES TABLE LEAD CONTACT AND MATERIALS AVAILABILITY

d

d

METHOD DETAILS B Purification and Reconstitution of PAR1 B Single-Molecule Force Spectroscopy (SMFS) B SMFS Data Selection and Analysis B Energy Landscape and Mechanical Properties B Protein Representations DATA AND CODE AVAILABILITY

SUPPLEMENTAL INFORMATION Supplemental Information can be found online at https://doi.org/10.1016/j.str. 2019.07.014. ACKNOWLEDGMENTS We thank T. Serdiuk for discussion and S. Weiser for the SDS gel (Figure S1). This work was supported by ETH Zurich (grant ETH-03 14-1) and Swiss National Science Foundation (SNF) (grant 205320_160199 to D.J.M.). AUTHOR CONTRIBUTIONS P.M.S., K.T.S., C.Z., H.E.K., B.K.K., and D.J.M. discussed and designed the project. C.Z. and H.E.K. engineered, expressed, purified, and reconstituted the PAR1 construct. P.M.S. performed SMFS and analyzed the data. S.A.M. performed AFM and transmission electron microscopy imaging. DECLARATION OF INTERESTS The authors declare no competing interests. Received: May 9, 2019 Revised: July 7, 2019 Accepted: July 23, 2019 Published: August 15, 2019 REFERENCES

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STAR+METHODS KEY RESOURCES TABLE

REAGENT OR RESOURCE

SOURCE

IDENTIFIER

This paper

N/A

Invitrogen

Cat#10359-016

Vorapaxar derivative

Laboratory of Shaun Coughlin, UCSF

N/A

Dodecyl maltoside (DDM)

Anatrace

Cat#D310

Cholesterol hemisuccinate (CHS)

Steraloids

Cat#C6823-000

Antibodies M1 antibodies Bacterial and Virus Strains pFastBac baculovirus system Chemicals, Peptides, and Recombinant Proteins

FLAG peptide

This paper (Stanford facility)

N/A

CNBr-Activated Sepharose 4B

GE healthcare

Cat#17043001

Superdex 200 10/300 column

GE healthcare

Cat#17517501

1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) in chloroform

Avanti Polar Lipids

Cat#850375C

n-Octyl-b-D-Glucopyranoside (octylglucoside)

Anatrace

Cat#O311

Sephadex G-50 fine

Sigma

Cat#G5080

PierceTM disposable 2 ml polystyrene columns (2 ml)

Thermo Fisher Scientific

Cat#29920

Flag tag (DYKDDDDVD) followed by hPAR1 P29 to S395 with Y52 to A86 deleted, two mutated N-linked glycosylation sites in loop E2, and the last C-terminal 30 aa following residue S395 removed followed by a 3C protease cleavage site (LEVLFQGP), a spacer (His10 and the C-terminus of the b2 adrenergic receptor), and a C-terminal rho1D4-tag (ETSQVAPA)

This paper

N/A

BMS-200261 trifluoroacetate

Sigma-Aldrich

Cat#B4188, discontinued

SFLLRN (Thrombin Receptor Activating Peptide 6, TRAP-6)

Sigma-Aldrich

Cat#T1573

Igor Pro 6

WaveMetrics

https://www.wavemetrics.com/index.html

Automated WLC fitting procedure on Igor Pro 6

(Thoma, 2017)

PhD Thesis, ETHZ

R (inc. multi-Gaussian fitting scripts)

(Kawamura et al., 2013)

https://www.r-project.org/

graphPad Prism 7

GraphPad Software

https://www.graphpad.com

vmd 1.9.3

University of Illinois at Urbana-Champaign

http://www.ks.uiuc.edu/Research/vmd/

Software and Algorithms

Other Sf9 cells (for hPAR1 expression)

ExpressionSystems

Cat# 94-001F

PAR1 structure

Protein Data Bank (PDB)

PDB ID 3VW7

LEAD CONTACT AND MATERIALS AVAILABILITY Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Daniel J. €ller ([email protected]). Mu METHOD DETAILS Purification and Reconstitution of PAR1 Human PAR1 was engineered to lack 35 amino acids (aa) at the N-terminus, thereby reducing the N-terminus to half its original length, and adding a C-terminal rho1D4tag for protein purification. The PAR1 sequence was wild type human PAR1 from P29 to S395 with Y52 to A86 deleted, two mutated N-linked glycosylation sites in loop E2, and the last C-terminal 30 aa following residue S395 Structure 27, 1517–1526.e1–e3, October 1, 2019 e1

removed. The final construct consisted of an N-terminal FLAG M1 epitope (DYKDDDDVD), followed by the PAR1 sequence described above, which C-terminal was followed by a 3C protease cleavage site (LEVLFQGP), a spacer (His10 and the C-terminus of the b2 adrenergic receptor), and a C-terminal rho1D4-tag (ETSQVAPA) (Figures S1A and 2A). The signaling potency of PAR1 constructs related the one characterized here were tested for their ability to mediate thrombin signaling (data available upon request). For purification, the PAR1 construct was expressed in Sf9 cells (ExpressionSystems) by the pFastBac baculovirus system (Invitrogen). Infected Sf9 cells were cultured in ESF 921 insect cell culture medium (ExpressionSystems). The cells were lysed by osmotic shock in low-salt buffer (10 mM Tris-HCl, pH 7.5, 1 mM ethylenediaminetetraacetic acid (EDTA, Sigma)) containing 100 nM vorapaxar derivative (Alsteens et al., 2015) and 100 mM tris(2
carboxyethyl)phosphine (TCEP) (Sigma), and PAR1 extracted from cell membranes using extraction buffer (20 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES, Sigma), pH 7.5, 500 mM NaCl, 1 wt% dodecyl maltoside (DDM, Anatrace), 0.03 wt% cholesterol hemisuccinate (CHS, Steraloids), 0.2 wt% sodium cholate (Sigma), 15 vol% glycerol (ACROS ORGANICS), 100 nM vorapaxar derivative (provided by S. Coughlin, UCSF) and 100 mM Tris(2-carboxyethyl) phosphine hydrochloride (TCEP). Cell debris was removed by high-speed centrifugation, the supernatant passed through an anti-1D4 antibody affinity column and washed with washing buffer (20 mM HEPES, pH 7.5, 500 mM NaCl, 0.1 wt% DDM, 0.02 wt% CHS and 1 mM vorapaxar derivative). The bound receptor was eluted by adding rho1D4 peptide and loaded onto an anti-FLAG M1 affinity column (M1 antibodies were produced from hybridoma cells and covalently attached to CNBr-Activated Sepharose 4 resin (GE healthcare)). After extensive washing with washing buffer with 2 mM CaCl2, the receptor was eluted from the M1 resin using washing buffer supplemented with 200 mg ml
1 FLAG peptide (facility-made) and 5 mM EDTA. Size-exclusion chromatography (Superdex 200 10/300 column, GE healthcare) was used to obtain vorapaxar-free PAR1 in running buffer (20 mM HEPES, pH 7.5, 100 mM NaCl, 0.1 wt% DDM and 0.02 wt% CHS). 10 mM purified vorapaxar-free PAR1 was reconstituted into liposomes made from 0.5 mg ml
1 phospholipids (1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC, Avanti Polar Lipids)) and 0.05 mg ml
1 CHS as described (Alsteens et al., 2015). Briefly, a DOPC/CHS mixture was prepared by mixing DOPC with CHS in chloroform at a 10:1 (w/w) ratio, dried under argon, and resuspended in buffer (20 mM HEPES, pH 7.5, 100 mM NaCl, 1 wt% octylglucoside (Anatrace)) to a final concentration of 10 mg ml
1 DOPC and 1 mg ml
1 CHS. For reconstitution, purified PAR1 was mixed with the DOPC/CHS solution and buffer (20 mM HEPES, pH 7.5, 100 mM NaCl) at final concentrations of 10 mM PAR1, 1 mg ml
1 DOPC, and 0.1 mg ml
1 CHS. The mixture was incubated on ice for 2 h and then passed over a Sephadex G-50 fine (Sigma) desalting column (disposable 2 ml polystyrene columns, Thermo Fisher Scientific) to remove the detergent and form proteoliposomes (Figure S1B). Single-Molecule Force Spectroscopy (SMFS) PAR1 was unfolded at 300, 600, 900, 1200, 2500, and 5000 nm s–1 using an automated AFM (ForceRobot 300, JPK Instruments, Germany). To record force-distance (FD) curves at sufficient resolution at pulling speeds of 1200, 2500, and 5000 nm s-1, an external 16-bit data acquisition hardware (NI PCI-6251, National Instruments, Germany) was used. We used 60 mm long silicon nitride cantilevers (A-BioLever, BL-RC150VB, Olympus, Japan), having a nominal resonance frequency of z 8 kHz in water and a spring constant of z 30 pN nm–1. Prior to the experiments the spring constant was determined in SMFS buffer (300 mM NaCl, 25 mM MgCl2, 25 mM Tris, pH 7.0) for each cantilever using the equipartition theorem. PAR1 was unfolded at room temperature (27 C) using at least five different cantilevers per pulling speed in order to average out the uncertainties in calibrating the cantilever spring constant (z 10%). Characterizing PAR1 in each of the unliganded or liganded states required over 50 preparations of fresh samples on mica supports and more than 100 different cantilevers. For AFM imaging and SMFS, 2 ml of PAR1 proteoliposomes were adsorbed for 1 h to freshly cleaved muscovite mica in 20 ml SMFS buffer at room temperature (Spoerri et al., 2018). After rinsing the sample with SMFS buffer, the AFM stylus was approached to and retracted from proteoliposome membranes to unfold PAR1. To characterize BMS-bound PAR1, 2 ml of proteoliposomes were adsorbed on mica for 2 h in 20 ml SMFS buffer supplemented with 20 mM BMS-200261 (Sigma, B4188, discontinued) at room temperature. To characterize SFLLRN-bound PAR1, the proteoliposomes were adsorbed and SMFS performed in SMFS buffer supplemented with 100 mM activating peptide SFLLRN (TRAP-6, Sigma, T1573). SMFS Data Selection and Analysis FD curves showing force peak patterns approaching the length of the fully unfolded and stretched polypeptide of PAR1 (R 65 nm) were selected for analysis (Figures 1, 2A and S2). Only z 2 – 4.5% of all FD curves recorded (n z 2.5 x 106) passed this length filter. After further filtering and sorting (Spoerri et al., 2018), we observed that 0.07% (n = 840) of the FD curves recorded from unliganded PAR1 and 0.1% of the FD curves recorded from BMS-bound (n = 681) and SFLLRN-bound (n = 778) PAR1 showed a distinct pattern of force peaks. Every single force peak of every FD curve was automatically fitted using the worm-like chain (WLC) model (Bustamante et al., 1994; Spoerri et al., 2018),
 kB T 1 x 
2 1 x FðxÞ =
+ 1
(Equation 1) P 4 L 4 L where P is the average persistence length of a polypeptide (assuming 1 aa to be 0.36 nm), kB the Boltzmann constant, T the absolute temperature in Kelvin, x the extension (nm). The polypeptide contour length L was obtained from fitting a force peak with the WLC e2 Structure 27, 1517–1526.e1–e3, October 1, 2019

model. Loading rates were determined from the slope of a linear function fitting the last data points of a force peak of the force-time curve (Alsteens et al., 2015). Histograms were generated by R scripts (Kawamura et al., 2013) to show at which contour lengths force peaks occurred reproducibly. The force peak probabilities of the histograms were calculated by dividing all counts of force peaks falling into bins of 1 aa (0.36 nm) contour length by the total number of FD curves analyzed. Three force peak regions were identified on these histograms and fitted with a Gaussian mixture model (Kawamura et al., 2013). Briefly, all force peaks falling into one of these three regions were modeled as a mixture of M different force peak classes, each described by a Gaussian distribution with mean contour length ms and variance ss2, and of uniform background noise. As it is not known from which force peak class an observed peak li originates, the probability density f of li is a mixture of the above-mentioned Gaussians with probability densities F(li, ms, ss2) and weights ps, and uniform background noise g with weight p0: XM   fðli Þ = ps F li ; ms ; s2s + p0 gðli Þ (Equation 2) s=1 The model parameters ps, ms and ss2 for each force peak class, and p0 for the background noise were then obtained by expectation maximization. After assigning every observed force peak to the force peak class s it most likely belonged to (or to background noise), the mean unfolding force Fsv to approximate the most probable unfolding force and the mean logarithm of the loading rate log(rsv) were determined for each class s and pulling speed v. Energy Landscape and Mechanical Properties Estimations of the distance xu separating the free-energy valley of the folded state from the transition barrier and of the transition rate k0 were obtained by iteratively fitting the Bell-Evans model to the DFS plots (Figure 3). Fitting was done using Igor Pro 6 (WaveMetrics, USA). The Bell-Evans model (Bell, 1978; Evans, 2001; Evans and Ritchie, 1997) states that the most probable unfolding force F* is a function of the loading rate r.  kB T xu r ln (Equation 3) F = xu kB Tk0 As the force distribution for each force peak class and speed appeared to be Gaussian, Fsv was used to approximate the most probable unfolding force F* and rsv as the corresponding loading rate r. The height difference of the free-energy barrier DGz was then calculated using: DGz =
kB TlnðtA k0 Þ

(Equation 4)

where tA is the Arrhenius frequency (Grater et al., 2005). For analysis we chose a tA of 10–8 s–1 as published (Zocher et al., 2012a). The transition of a structural segment toward unfolding was assumed to be a simple harmonic motion. In this case, the mechanical spring constant k of the structural segment can be estimated by: k=

2DGz xu2

(Equation 5)

Protein Representations Representations of the PAR1 structure (PDB ID 3VW7) (Zhang et al., 2012) were generated with VMD software support. VMD is developed with NIH support by the Theoretical and Computational Biophysics group at the Beckman Institute, University of Illinois at Urbana-Champaign. DATA AND CODE AVAILABILITY The raw data of this study and the software codes written for data analysis are available upon request.

Structure 27, 1517–1526.e1–e3, October 1, 2019 e3