key: cord-0710472-b9wxssgj
authors: Cao, Wenpeng; Dong, Chuqiao; Kim, Seonghan; Hou, Decheng; Tai, Wanbo; Du, Lanying; Im, Wonpil; Zhang, X. Frank
title: Biomechanical Characterization of SARS-CoV-2 Spike RBD and Human ACE2 Protein-Protein Interaction
date: 2021-02-17
journal: Biophys J
DOI: 10.1016/j.bpj.2021.02.007
sha: df97f0c9404581641b887c5824fb0f7b2a9a437a
doc_id: 710472
cord_uid: b9wxssgj

The current COVID-19 pandemic has led to a devastating impact across the world. SARS-CoV-2 (the virus causing COVID-19) is known to use the receptor-binding domain (RBD) at viral surface spike (S) protein to interact with the angiotensin-converting enzyme 2 (ACE2) receptor expressed on many human cell types. The RBD−ACE2 interaction is a crucial step to mediate the host cell entry of SARS-CoV-2. Recent studies indicate that the ACE2 interaction with the SARS-CoV-2 S protein has a higher affinity than its binding with the structurally identical S protein of SARS-CoV-1, the virus causing the 2002-2004 SARS outbreak. However, the biophysical mechanism behind such binding affinity difference is unclear. This study utilizes combined single-molecule force spectroscopy and steered molecular dynamics (SMD) simulation approaches to quantify the specific interactions between CoV-2 or CoV-1 RBD and ACE2. Depending on the loading rates, the unbinding forces between CoV-2 RBD and ACE2 range from 70 to 105 pN and are 30-40% higher than those of CoV-1 RBD and ACE2 under similar loading rates. SMD results indicate that CoV-2 RBD interacts with the N-linked glycan on Asn90 of ACE2. This interaction is mostly absent in the CoV-1 RBD−ACE2 complex. During the SMD simulations, the extra RBD-N-glycan interaction contributes to a greater force and prolonged interaction lifetime. The observation is confirmed by our experimental force spectroscopy study. After removing N-linked glycans on ACE2, its mechanical binding strength with CoV-2 RBD decreases to a similar level of the CoV-1 RBD−ACE2 interaction. Together, the study uncovers the mechanism behind the difference in ACE2 binding between SARS-CoV-2 and SARS-CoV-1 and could help develop new strategies to block SARS-CoV-2 entry.

The M protein maintains the viral lipid membrane integrity. The E protein facilitates the virus's assembly and release, and the N protein encapsulates and protects the viral genome (4).

The S protein (∼150 kDa) is a heavily N-linked glycosylated homo-trimer projecting 20 nm from the CoV's surface (2) . The trimeric S glycoprotein is a class I fusion protein and mediates attachment to the host receptor. The S1 portion contains the large receptor-binding domain (RBD), and the S2 portion forms the spike molecule's stalk. The atomic structures of the SARS-CoV-2 S protein in a trimeric form (5) , as well as the RBD-receptor complex, have been determined (6) . These structures are similar to the previously reported structures of SARS-CoV-1 S protein (7) (8) (9) , indicating that the two proteins might function similarly.

Angiotensin-converting enzyme 2 (ACE2) is a known receptor for SARS-CoV-1 and SARS-CoV-2 S proteins. The primary physiological function of ACE2 is to hydrolyze angiotensin II (a vasoconstrictor) into angiotensin-(1-7) (a vasodilator), and thereby to lower blood pressure (10, 11) . ACE2 is a type I transmembrane protein expressed in various organs, J o u r n a l P r e -p r o o f including the lungs, heart, kidneys, and intestine (12, 13) . Recent structural studies show that ACE2 is a homodimer with each monomer consisting of an N-terminal peptidase domain, a Cterminal Collectrin-like domain, a single-pass transmembrane region, and a short cytoplasmic region (9) . The RBD binding region on ACE2 is located in its N-terminal peptidase domain with major contact regions located in the α1 and α2 helixes, as well as the linker between β3 and β4 strands (9) .

The binding interactions between ACE2 and CoV S proteins have been widely studied recently. Although there are variations among different binding assays reported, most reports show a higher binding affinity between ACE2 and SARS-CoV-2 S than the binding between ACE2 and SARS-CoV-1 S (14, 15) . However, the mechanism behind such a difference is still unclear. Little is known about the biomechanical strength of ACE2-S interaction that drives viral adhesion and helps withstand the force exerted during viral entry.

In this work, using atomic force microscopy (AFM)-based single-molecule force spectroscopy, a method where a single bond rupture (i.e., interaction) between two molecules can be measured directly, we have quantified the mechanical strengths between ACE2 and SARS-CoV-1 RBD (shortly RBD CoV1 ) or SARS-CoV-2 RBD (shortly RBD CoV2 ). As AFM can measure forces in the pico-Newton (pN) range, it is possible to detect inter-molecular forces and allow for weak interactions between tip-bound ligands and surface-bound receptor molecules to be quantified in terms of their affinities and rate constants (16) . Furthermore, AFM has been recently adopted by us and others to study the interactions between viruses and host cells (17) (18) (19) (20) . We also used all-atom steered molecular dynamics (SMD) simulations to pull the RBD CoV1 -ACE2 or RBD CoV2 -ACE2 complexes with or without N-glycans. Both AFM and SMD confirmed a stronger force/energy associated with the dissociation of the RBD CoV2 -ACE2 complex. This enhanced mechanical strength stems from an additional interaction of RBD CoV2 with an N-linked glycan of ACE2 Asn90.

J o u r n a l P r e -p r o o f

Immortalized human embryonic kidney (HEK) 293T cells purchased from American Type Culture Collection (ATCC) were cultured in DMEM medium (ATCC) and supplemented with 4 mM L-glutamine, 4500 mg/L glucose, 1 mM sodium pyruvate, 1500 mg/L sodium bicarbonate, 1% penicillin-streptomycin, and 10% fetal bovine serum. RBD proteins were expressed as previously described with some modifications (21) . Briefly, genes encoding SARS-CoV-1 RBD (residues 318-510) and SARS-CoV-2 RBD (residues 331-524) proteins containing a C-terminal Fc tag were amplified by PCR using codon-optimized SARS-CoV-1 (GenBank accession number: AY278488.2) or SARS-CoV-2 S (GenBank accession number: QHD43416.1) plasmid and inserted into pFUSE-hIgG1-Fc expression vector (Invitrogen). The recombinant RBD plasmids were transiently transfected into HEK293T cells through the calcium phosphate transfection method. The RBD proteins were expressed in the culture supernatants and purified by protein A affinity chromatography following the manuscript's instructions (GE Healthcare).

The purity of the protein was >90 %, as determined by SDS-PAGE.

The ACE2 protein was purchased from Acrobiosystems (Newark, DE, catalog #AC2-H52H8). It is a recombinant N-terminal His-tagged protein consisting of Gln18-Ser740 of human ACE2 (GenBank accession number: AF291820.1), expressed from HEK293 cells. According to the manufacturer, the protein had a purity of ~95%, determined by SDS-PAGE. It has been shown to bind the SARS-CoV-2 spike protein with high affinity (22) . The MERS-CoV RBD was from Sinobiological (Wayne, PA, catalog # 40071-V08B1). It is a recombinant C-terminal Histagged protein consisting of Glu367-Tyr606 of MERS-CoV spike protein (GenBank accession number: AFS88936.1) (23) . The purity of the protein was >90 % as determined by SDS-PAGE. (19, 24) . A 5,000-MW Acetal-PEG-NHS (Creative PEGworks) was also used as a control study. Soluble recombinant ACE2 (1 µM) was attached to the APTES-silanized glass coverslips (Novascan, Chicago, IL) using the same crosslinking approach. Functionalized cantilevers and glass surfaces were stored in PBS (3 × 5 min) and used for the AFM experiment within 8 hours.

All single-molecule force measurements were conducted using a custom-designed AFM apparatus (25, 26) used previously to quantify viral protein-receptor interactions (27, 28) . AFM measurements were collected at cantilever retraction speeds ranging from 0.19 to 7.5 µm/s to achieve the desired loading rate (5,000-20,000 pN/s). All measurements were conducted at 25°C

in phosphate-buffered saline (PBS, 137 mM NaCl, 2.7 mM KCl, 10 mM Na 2 HPO 4 , and 1.8 mM

Calibration of the cantilevers (lever C of MLCT) was done by first measuring the inverse optical lever sensitivity (InvOLS) via recording force curves on a hard glass surface, followed by obtaining the spring constant of the cantilever via thermally-induced fluctuations (29) . The spring constants (9.6 ± 2.8 pN/nm, mean ± SD) of the calibrated cantilevers agreed with the values specified by the manufacturer (10 pN/nm). All the unbinding rupture forces have been corrected for viscous drag force (30, 31) , which was obtained by multiplying the tip movement velocity by the viscous drag coefficient. The viscous drag coefficient was measured by moving the cantilever at varying velocities near the substrate (30, 32) and is 4 pN⋅s/µm for the lever C of MLCT.

To enable measurement of a single-molecular interaction, the contact between the cantilever tip and the substrate was minimized by reducing both the contact duration (<100 ms) and the contact force (100-200 pN). The brief contact duration was chosen to ensure that, for the majority of contacts (70% or greater), no adhesion (rupture force) was observed between the AFM tip and surface. Assuming the adhesion bond formation obeyed Poisson statistics, an adhesion frequency of ~30% in the force measurements implies that among the observed unbinding events, the probabilities of forming a single, double, and triple adhesion bonds between AFM tip and surface were 83%, 16%, and 1%, respectively (33) . Therefore, our J o u r n a l P r e -p r o o f experimental condition ensured a >83% probability that the adhesion event was mediated by a single bond (34) . Loading rates of the rupture forces were determined from each unbinding force curve by conducting a linear fit to the force-time curve shortly (the last 50 data points) before rupture (35) .

300 to 500 force curves were typically recorded for each pulling speed, which yielded 60 to 100 unbinding forces with an adhesion frequency of 25-30%. At least four independent cantilevers were used to conduct the measurement of each ligand-receptor pair. Curve fitting was performed using IGOR Pro or Origin software by minimizing the chi-square statistic for the optimal fit. Unless otherwise stated, the data is reported as the mean and the standard error of the estimate. Statistical analyses between groups were performed using an unpaired t-test or ANOVA using the Prism software, with a p-value less than 0.05 considered statistically significant.

All SMD simulations were performed using NAMD (36) The CHARMM36(m) (37, 38) force field was used for protein and carbohydrates. PDB ID 2AJF (7) from Protein Data Bank was used for an RBD CoV1 -ACE2 complex structure and PDB ID 6VW1 (6) for an RBD CoV2 -ACE2 complex structure. We used a TIP3P water model (39) , and K + and Clions with a concentration of 0.15 M were added to neutralize the system. All simulation systems and parameters were set up through CHARMM-GUI Solution Builder (40, 41) . The analysis was done with CHARMM (42) and visualization through VMD (43) .

PDB:6VW1 has five N-linked glycans in ACE2 (Asn53, Asn90, Asn103, Asn322, and Asn549) and one N-glycan in RBD CoV2 (Asn343), and PDB:2AJF has four N-linked glycans in ACE2 (Asn53, Asn90, Asn322, and Asn549) and one N-glycan in RBD CoV1 (Asn330). Similar to other crystal or cryo-EM structures, all N-glycan structures in both PDB structures are incomplete, as they are truncated in the experiment or not observable due to low resolution and high structural flexibility. Since we did not know the glycoforms of the ACE2 glycosylation sites at the time of this study, we used the N-glycan core pentasaccharide (a minimum structure of all N-glycans: Fig. S1 ) in all N-glycosylation sites, including Asn103 of ACE2 in PDB:2AJF.

Glycan Reader & Modeler (44) (45) (46) in CHARMM-GUI was used to model N-glycan core pentasaccharide in all glycosylation sites using the templates from GFDB (47) (Glycan Fragment Database). To compare the receptor-binding affinity between RBD CoV1 and RBD CoV2 and to explore the influences of N-glycans on binding affinity, we made four systems: S CoV1+G

(RBD CoV2 -ACE2 with N-glycans), and S CoV2-G (RBD CoV2 -ACE2 without N-glycans).

For the SMD simulations, the protein complex structures were initially aligned along the X-axis in a cubic water box with an initial size of 171 Å for S CoV1±G and 172 Å for S CoV2±G ; a total number of atoms is about 470,000. The pulling forces were applied to the center of mass (COM) of each protein (i.e., RBD and ACE2). In the pulling process, the spring constant was set to 5 kcal/mol/Å 2 and its moving speed to 0.5 Å/ns in the opposite directions along the X-axis.

Gentle restrains with a force constant of 5 kcal/mol/Å 2 were applied to each protein's COM to restrict their movement along with the Y/Z directions during the pulling process. The SMD simulations stopped at 30 ns when two proteins were detached from each other. Nine independent simulations for each system were performed for better statistics.

The van der Waals interactions were smoothly switched off over 10-12 Å by a forcebased switching function (48) . The electrostatic interactions were calculated by the particle-mesh

Ewald method with a mesh size of 1 Å for fast Fourier transformation and sixth-order B-spline interpolation. SHAKE algorithm was used to constrain bond lengths involving hydrogen atom (49) , and the simulation time-step was set to 2 fs. We first relaxed the system in an NVT (constant particle number, volume, and temperature) ensemble at 303.15 K with harmonic restraints to all solute atoms. The constant temperature was controlled by Langevin dynamics with a damping frequency of 50 fs -1 . 100-120 ps NPT (constant particle number, pressure, and temperature) simulation was then applied to adjust the solvent density. The Langevin piston method was used to control the pressure. A dihedral restraint with a force constant of 1 kcal//mol/rad 2 was applied to carbohydrates to keep the carbohydrate chair conformation during these equilibration steps. To perform the SMD simulation, a COLVARS method was used (50) , and the COMs of two proteins were calculated first and used as the external forces' initial positions. The effective spring potential (whose negative derivative is used to represent external forces) acting on the COM of each protein was calculated using the following equation:

, , , … , = − R • n , where k is the spring constant, v is the moving speed of J o u r n a l P r e -p r o o f the spring potentials, R(t) is the current position of the selected protein COM, and n is the unit vector along the protein COMs. As a result of this spring potential, the spring-connected protein would move following the energy well, so that two proteins are pulled apart.

First, we characterized the mechanical interaction between the RBD proteins and ACE2

using AFM. We have attached RBD CoV1 , RBD CoV2 , or RBD MERS-CoV (negative control) to a micro-cantilever, the force probe, via an established protocol using polyethylene glycol coupling chemistry (19, 20) . The cantilever-bound RBD was brought to interact with surface-immobilized soluble ACE2 via AFM force scans (Fig. 1A) .

All single-molecule force measurements were conducted using a custom-built AFM designed for operation in the force spectroscopy mode (25-27, 52, 53) . Using a piezoelectric translator, the functionalized cantilever was lowered onto an ACE2-functionalized surface to allow possible binding between RBD and ACE2 to occur. After a brief contact, the cantilever was retracted from the surface. Any binding interaction between tip and substrate would lead to an adhesive pull-off force determined from the cantilever's deflection via a position-sensitive two-segment photodiode. To validate the PEG linker used in this study, we analyzed the force-extension property of the unbinding curves using the worm-like chain model (55, 56) (Fig. S3) . In this work, the molecular tether being stretch consists of the RBD-ACE2 complex in the middle, flanked by two pieces of PEG linkers of 2000 MW (PEG2000) (Fig. 1B, lower panel) . Fitting the forceextension curve of RBD CoV2 −ACE2 interaction to the WLC model yielded a contour length of 45 ± 11 nm (average ± SD), consistent with the predicted 37 nm contour length of the molecular tether (Fig. S3) . The WLC fit also yielded a persistence length of 0.52 ± 0.15 nm (average ± SD), which is consistent with the persistence length of PEG linker or polypeptide (~0.4 nm) (57) .

Moreover, we conducted a control study using a longer PEG linker of 5000 MW, PEG5000.

Using the RBD CoV2 −ACE2 system, we found that the contour length for the PEG5000 experimental group increased by 2.2 fold to 99 ± 14 nm, while the persistence length remained at a similar range at 0.48 ± 0.09 nm. Together, the data shown in Fig. S3 validates the PEG linker used in the study and suggests that the unbinding events indeed occur between the proteins coupled to the two PEG linkers' termini.

The biophysical properties of RBD-ACE2 interactions were studied through a dynamic force spectrum (DFS), the plot of the most probable unbinding force as a function of the loading rate. The unbinding forces of each RBD-ACE2 interactions were first grouped into five groups by their loading rates. The distribution of forces within the same group was analyzed by histograms (see Fig. S4 ). The most probable unbinding forces were then recorded as the center of the tallest bin of each histogram. Fig. 1D shows that the unbinding force of both RBD-ACE2 complexes increased linearly with the logarithm of the loading rate. However, the unbinding forces of RBD CoV2 −ACE2 are stronger, ranging from 70 to 105 pN over a loading rate of 500 to 20,000 pN/s, whereas the RBD CoV1 −ACE2 unbinding forces are 30-40% lower under similar loading rates.

A more detailed analysis of the biophysical properties of RBD-ACE2 interactions was conducted by fitting the acquired DFS data to the Bell-Evans model (58) . According to this model, a pulling force (F) distorts the intermolecular potential of a ligand-receptor complex, leading to a lowering of the activation energy and an increase in the dissociation rate k(f) as follows:

[1]

where k 0 is the dissociation rate constant in the absence of a pulling force, γ is the position of the transition state, T is the absolute temperature, and k B is the Boltzmann constant. If the applied force increases linearly with a loading rate (" ) (such as in the case of our AFM unbinding experiment), the probability for protein-protein unbinding as a function of the force f is given by:

The most probable unbinding force F* at a given loading rate R f can then be written as: * = / ln ) / ! + ln " [3] Hence, as predicted by the model, the most probable unbinding force F* is a linear function of the logarithm of the loading rate. Experimentally, F* was determined from the unbinding force histograms. Fitting the DFS of RBD CoV2 −ACE2 interaction to the Bell-Evans model (Eq. 3)

yielded a k 0 of 0.047 s -1 , and a γ (i.e., activation barrier width) of 0.39 nm. The best-fit parameters for RBD CoV2 −ACE2 and RBD CoV1 −ACE2 interactions are summarized in Table 1 .

Clearly, compared to RBD CoV1 , RBD CoV2 binds ACE2 with a 12-fold smaller k 0 and a similar γ,

indicating that the RBD CoV2 −ACE2 interaction is stronger.

To gain molecular insight into the receptor-binding affinity between RBD CoV1 and RBD CoV2 and to explore influences of N-glycans on binding affinity, we performed SMD simulations on the following four systems: S CoV1+G (RBD CoV1 -ACE2 with N-glycans), S CoV1-G (RBD CoV1 -ACE2 without N-glycans), S CoV2+G (RBD CoV2 -ACE2 with N-glycans), and S CoV2-G (RBD CoV2 -ACE2 without N-glycans). To compare RBD CoV2 -ACE2 interactions with RBD CoV1 -ACE2 interactions, pulling force analysis was performed as a function of distance (D RBD-ACE2 ) between the COMs of RBD and ACE2. Also, to investigate how many residues between RBD and ACE2 interact as a function of D RBD-ACE2 , the number of contacts analysis was performed.

Contact was counted if any heavy atom of RBD was within 4.5 Å from any heavy atom of ACE2.

As shown in Fig. 2A , the overall force profile of S CoV2+G shows higher forces than S CoV1+G due to greater numbers of RBD CoV2 -ACE2 contacts compared to RBD CoV1 -ACE2 (Fig. 2B) ; see

for the force profiles of individual replicas. Initially, S CoV2+G has more contacts than S CoV1+G, and the difference in the number of contacts between S CoV2+G and S CoV1+G is about 20

at D RBD-ACE2 of 52 Å (Fig. 2B) . The difference decreases to about 17 starting from 55 Å and to 9 at 65 Å, where ACE2 Asn90-glycan maintains its interactions with RBD CoV2 , whereas such interactions are lost in S CoV1+G (Fig. 2C) . Note that the force profile in S CoV2+G has a plateau around 60 Å and a small peak around 66 Å, which are attributed to the interactions between ACE2 Asn90-glycan and RBD CoV2 from 55 Å to 65 Å (Fig. 2C) . Because of relatively negligible interactions between ACE2 Asn90-glycan and RBD CoV1 , the plateau is not observed around 60 Å in S CoV1+G . Fig. S6 shows representative snapshots of S CoV1+G during the pulling simulation at the initial state (Fig. S6A) and D RBD-ACE2 of 57 Å (Fig. S6B) . It shows that Asn90-glycan of S CoV1+G barely interacts with RBD CoV1 , while Asn90-glycan of S CoV2+G still contacts with RBD CoV2 at the same distance (Fig. S6B, Fig. 2E ). This indicates that the interaction between ACE2 Asn90-glycan and RBD CoV2 somewhat blocks the direct contact between RBD CoV2 and ACE2 at 55 Å < D RBD-ACE2 < 65 Å, suggesting that ACE2

Asn90-glycan can hinder the association of RBD CoV2 to ACE2 more than RBD CoV1 , but makes RBD CoV2 -ACE2 dissociation harder than RBD CoV1 -ACE2.

Using S CoV2+G as an example, the overall RBD and ACE2 dissociation during the pulling simulation can be divided into three states: state I (<55 Å, Fig. 2D ), state II (56~70Å, Fig. 2E , F), and state III (>70 Å, Fig. 2G ). In state I, RBD CoV2 -ACE2 has a number of interactions. As D RBD-ACE2 increases to 56 Å (state II), RBD CoV2 and ACE2 start to lose some of its polar interactions (RBD CoV2 -ACE2: Gln493-Glu35 and Tyr449-Asp38), but the interaction between Gln498 and Glu42 is intact. Note that ACE2 Asn90-glycan has polar interactions with Gln409 and Thr415 (Fig. 2E) . At 65 Å (Fig. 2F) , Asn487 and Try489 of the RBD CoV2 loop can still interact with ACE2 Tyr83 and Gln24 due to the flexibility of the loop, and Asn487 can also contact Gln24 from time to time. At this period, Asn90-glycan loses its contacts with RBD CoV2 .

In state III, RBD CoV2 and ACE2 are fully detached with no close interactions (Fig. 2G) . While the average forces show a subtle difference in between S CoV1+G and S CoV1-G when RBD CoV1 and ACE2 start to detach at D RBD-ACE2 = 56 Å (Fig. S7A) , S CoV2+G has higher forces over 56 Å to 70 J o u r n a l P r e -p r o o f Å than S CoV2-G (Fig. S7B) . And, RBD CoV2 shows slightly higher forces than RBD CoV1, even with no glycans (Fig. S7C) .

In light of the SMD results, we tested the effect of ACE2 N-linked glycan on the mechanical strength of RBD-ACE2 interactions. To remove the ACE2 N-linked glycans, surface-immobilized ACE2 was incubated with PNGase F (New England Biolabs) for one hour at 37 °C. The effect of PNGase F treatment was analyzed by SDS-PAGE (Fig. 3A) . After one hour of treatment, the molecular weight of ACE2 was visibly reduced from approximately 115 to 95 kDa. Assuming each N-linked glycosylation adds 2.5 kDa of molecular mass, the result is consistent with seven N-glycosylation sites on ACE2.

Next, AFM unbinding experiments were performed between tip-immobilized RBD and surface-immobilized, PNGase F-treated ACE2. As shown in Fig. 3B , N-linked glycan removal resulted in a significant decrease of the unbinding forces between RBD CoV2 and ACE2, from 70-105 pN to 50-70 pN. The unbinding forces between RBD CoV1 and ACE2 also decreased, but to a lesser extent. The DFS of RBD CoV2 and RBD CoV1 are almost overlapped with each other. This trend is also similar to the SMD results, showing that the force profiles of S CoV1-G and S CoV2-G are within the error bars t (Fig. S7C) . The Bell-Evans model fit confirmed that after N-glycan removal, the k 0 of RBD CoV2 −ACE2 interaction increases by 17 fold (from 0.047 s -1 to 0.82 s -1 ), and the k 0 of RBD CoV1 −ACE2 interaction increase by only 6 fold.

J o u r n a l P r e -p r o o f

Interactions between the viral protein and host receptors require direct physical contact between viral and host cell membranes. Unlike interactions in solution (3D), which have at least one interacting molecular species in the fluid phase, the interactions between receptors and ligands anchored on two opposing membranes (2D) are constrained in molecular movement or transport and are under common tensile force. Hence, the 2D reaction kinetics may be different from 3D kinetics (59, 60) . To study the mechanism underlying virus-cell interaction, it is necessary to probe the interaction between anchored molecules using 2D binding assays such as the single-molecule AFM used in this study. Using this method, we found that the dissociate rate J o u r n a l P r e -p r o o f ∆G 12 =∆G * 2 −∆G * 1 = −k B T ln(k 1 /k 2 ), where k 1 and k 2 are the k 0 values of two RBD-ACE2 interactions used for comparison, respectively. Using this equation, the activation energy for RBD CoV1 −ACE2 dissociation is estimated to be 2.5 k B T lower than that of the RBD CoV2 −ACE2 interaction. After deglycosylation of ACE2, the activation energy are lower by 2.9 k B T (RBD CoV2 ) and 1.8 k B T (RBD CoV1 ), compared to the unbinding of glycosylated ACE2, suggesting that ACE2 deglycosylation has a greater effect on RBD CoV2 binding than on the RBD CoV1 . SMD simulations provide molecular-level insight into RBD CoV -ACE2 interactions and help us to interpret the AFM data. The SMD simulations manifest that RBD CoV2 interacts stronger with ACE2 than RBD CoV1 because the former has more direct contacts with ACE2 than the latter. In particular, ACE2 Asn90-glycan appears to have an important role in having stronger interactions with RBD CoV2 than RBD CoV1 by retaining contacts with residues of RBD CoV2 , Gln409, and Thr415, even when the original contacts of RBD CoV2 -ACE2 start to lose (Fig. 2E) .

This additional interaction implies that ACE2 Asn90-glycan can have effects on the association and dissociation of RBD CoV2 -ACE2. In other words, ACE2 Asn90-glycan could hinder the association of RBD CoV2 with ACE2 more than RBD CoV1 , but make RBD CoV2 -ACE2 dissociation harder than RBD CoV1 -ACE2. In addition, based on the SMD simulations, we propose a threestep dissociation mechanism of RBD CoV2 -ACE2 complex.

It should be noted that the current models utilize only RBD out of trimeric SARS-CoV-2 S protein and the N-glycan core structure for all N-glycans. Having a fully-glycosylated SARS-CoV-2 S protein and ACE2 models would provide further insight into the RBD-ACE2

interactions. With a recently modeled fully glycosylated SARS-CoV-2 S protein model (62) and recently-determined glycosylation patterns of ACE2 (63), we plan to study the RBD-ACE2 interactions in a more realistic model.

In conclusion, the study shows the biomechanical parameters important for RBD CoV1 and RBD CoV2 to attach to host cells. Our results suggest important viral−host cell interaction through ACE2 Asn90-glycan. Nonetheless, the potential effect of ACE2 Asn90-glycan on the transmission of COVID-19 remains to be further investigated.

J o u r n a l P r e -p r o o f The dynamic force spectra (i.e., the plot of most probable unbinding force (F u * ) as a function of loading rate ( & ) of the RBD-ACE2 interactions. The data is fitted to the single-barrier Bell-Evans model (Eq. 3) to extract the off-rate k 0 (51). The bars denote half bin widths of the unbinding force histograms (shown in Fig. S4) , representing the force determination error.

Individual data points of RBD CoV2 -ACE2 (N=305) and RBD CoV1 -ACE2 (N=245) unbinding forces were plotted as scatter plots, using smaller symbols and the same color scheme. 

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CHARMM-GUI: a web-based graphical user interface for CHARMM

CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field

CHARMM: A program for macromolecular energy, minimization, and dynamics calculations

VMD: visual molecular dynamics

Glycan Reader: automated sugar identification and simulation preparation for carbohydrates and glycoproteins

Glycan Reader is improved to recognize most sugar types and chemical modifications in the Protein Data Bank

CHARMM-GUI Glycan Modeler for modeling and simulation of carbohydrates and glycoconjugates

Glycan fragment database: a database of PDB-based glycan 3D structures

Van der Waals Density Functional for General Geometries

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

NAMD User's Guide. Version 2

Atomic force microscopy of protein-protein interactions. Handbook of Single-Molecule

Molecular basis for the dynamic strength of the integrin alpha(4)beta(1)/VCAM-1 interaction

Integrin alpha4beta7 switches its ligand specificity via distinct conformer-specific activation

Dipeptidyl peptidase 4 is a functional receptor for the emerging human coronavirus-EMC

Worm-Like Chain (WLC) Model. Encyclopedia of Biophysics

Novel polymer linkers for single molecule AFM force spectroscopy

Static and Dynamical Properties of Single Poly(Ethylene Glycol) Molecules Investigated by Force Spectroscopy

Dynamic strength of molecular adhesion bonds

Identification of self through twodimensional chemistry and synapses

T cells like a firm molecular handshake

Molecular interaction and inhibition of SARS-CoV-2 binding to the ACE2 receptor

Developing a Fully Glycosylated Full-Length SARS-CoV-2 Spike Protein Model in a Viral Membrane

Comprehensive characterization of N-and O-glycosylation of SARS-CoV-2 human receptor angiotensin converting enzyme 2. Glycobiology

The authors declare no competing interests.J o u r n a l P r e -p r o o f