The SARS-CoV-2 virus, like other β-coronaviruses, uses the spike protein to mediate virus entry into host cells. The spike is a trimeric glycoprotein embedded in the virus envelope. During the initial stage of the SARS-CoV-2 infection process, the spike binds to host target cells, primarily via the receptor angiotensin-converting enzyme 2 (ACE2) (Yan et al., 2020; Wang et al., 2020), but it can also bind to other targets, such as neuropilin-1 (Daly et al., 2020; Cantuti-Castelvetri et al., 2020), estrogen receptor α (Solis et al., 2022), and potentially to nicotinic acetylcholine receptors (Hone et al., 2024; O’Brien et al., 2023; Chrestia et al., 2022; Oliveira et al., 2021b) and sugar receptors (Behloul et al., 2020). Given its crucial role in the infection process and the fact that it is one of the main targets for antibody neutralisation, the spike is one of the most important targets for developing COVID-19 therapies and vaccines (e.g. Wang et al., 2023; Focosi et al., 2022; Wang et al., 2022; Bojadzic et al., 2021; Pomplun, 2020).

Each spike monomer comprises three regions: a large ectodomain, a transmembrane domain, and a short cytoplasmic tail (Walls et al., 2020; Wrapp et al., 2020; Cai et al., 2020). The ectodomain, the main focus of this work, is composed of two subunits (S1 and S2) and contains the structural motifs that directly bind to the host receptors as well as those needed for the membrane fusion process (Walls et al., 2020; Wrapp et al., 2020; Cai et al., 2020). S1 is responsible for binding to the human ACE2 receptors, while S2 for the fusion of the viral and host membranes (Walls et al., 2020; Wrapp et al., 2020; Cai et al., 2020). The spike also contains three equivalent fatty acid (FA) binding sites at the interfaces between neighbouring receptor-binding domains (RBDs) (Toelzer et al., 2020; Figure 1). Each FA binding site is a hydrophobic pocket formed by two RBDs, with one RBD providing the aromatic and hydrophobic residues to accommodate the FA hydrocarbon tail and the other providing the polar and positively charged residues that bind the FA carboxylate headgroup (Figure 1). The essential FA linoleic acid binds (as linoleate [LA]) with high affinity to the FA pocket, stabilising the spike in a non-infectious locked conformation, in which the RBDs are all ‘down’ with the receptor-binding motifs (RBMs) occluded inside the trimer, and thus inaccessible for binding to ACE2 (Toelzer et al., 2020). The discovery of this site inspired the development of new spike-based potential therapies based on FAs or other natural, repurposed, or specifically designed small molecules able to bind to the FA site (Wang et al., 2023; Tong et al., 2022; Ma et al., 2021; Shoemark et al., 2021; Queirós-Reis et al., 2023; Creutznacher et al., 2022). Several cryo-EM structures of the SARS-CoV-2 spike in complex with small molecules, such as linoleic, oleic, and all-trans retinoic acid and SPC-14, bound to the FA site, are now available (Wang et al., 2023; Toelzer et al., 2020; Tong et al., 2022; Ma et al., 2021). Following this discovery, equivalent FA sites have been identified in several closely related coronavirus spikes (Toelzer et al., 2020; Zhang et al., 2021; Bangaru et al., 2020). Surface plasmon resonance experiments and cryo-EM structures show that the FA site is conserved in the spike proteins of highly several pathogenic β-coronaviruses, such as SARS-CoV, MERS-CoV, SARS-CoV-2, but not in the spikes of common, mild disease-causing β-coronaviruses (Toelzer et al., 2022).

Figure 1

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Simulations using the dynamical nonequilibrium molecular dynamics (D-NEMD) approach showed that the FA site allosterically modulates the behaviour of functional motifs both in the ancestral (also known as wild type, ‘early 2020’, or original) spike and in several variants (Oliveira et al., 2023; Gupta et al., 2022; Oliveira et al., 2022), and that these effects differ between variants. These D-NEMD simulations (which tested the effects of removing LA) showed that the FA site is allosterically connected to the RBM, N-terminal domain (NTD), furin cleavage site, and the region surrounding the fusion peptide (FP) (Oliveira et al., 2023; Gupta et al., 2022; Oliveira et al., 2022). These regions have significantly different allosteric behaviours between the ancestral, Alpha, Delta, Delta plus, and Omicron BA.1 variants (Oliveira et al., 2023; Gupta et al., 2022; Oliveira et al., 2022). They differ not only in the amplitude of the structural responses of these regions but also in the rates at which the structural changes propagate (Oliveira et al., 2023; Gupta et al., 2022; Oliveira et al., 2022). However, these previous D-NEMD simulations did not incorporate the spike’s many N- and O-linked glycans, which are critical to its function, not only in protecting it from immune recognition, but also in modulating its dynamics (Harbison et al., 2022; Sztain et al., 2021; Casalino et al., 2020; Turoňová et al., 2020). A crucial unresolved question, therefore, is whether and how glycosylation affects allosteric communication with the FA site.

The ancestral spike is heavily glycosylated with 22 predicted N-linked glycosylation sites per monomer (Walls et al., 2020; Watanabe et al., 2020), of which 17 have been found to be occupied (Watanabe et al., 2020; Watanabe et al., 2021; Shajahan et al., 2020; Yao et al., 2020). The ancestral spike also contains at least two O-glycosylation sites per monomer with low occupancy (Shajahan et al., 2020; Bagdonaite et al., 2021; Sanda et al., 2021). The occupancy and composition profile of the glycosylation sites differ between variants, expression systems, and experimental methods (Xie and Butler, 2023). This glycan coating plays a crucial role in shielding the virus from the immune system (Casalino et al., 2020; Turoňová et al., 2020), and in infection (Harbison et al., 2022; Sztain et al., 2021; Casalino et al., 2020; Turoňová et al., 2020; Blazhynska et al., 2024). Glycans also affect the dynamics and stability of essential regions of the protein, including the RBDs, and modulate binding to ACE2 (Harbison et al., 2022; Sztain et al., 2021; Casalino et al., 2020). Here, we use D-NEMD simulations (Oliveira et al., 2021a; Ciccotti and Ferrario, 2016; Balega et al., 2024) to characterise the response of the fully glycosylated SARS-CoV-2 ancestral spike to LA removal, and investigate allosteric modulation by the FA site and the effects of glycans on the protein’s allosteric behaviour. In recent years, D-NEMD simulations have emerged as a powerful computational approach to investigate a diversity of biological problems from the transmission of structural changes (Oliveira et al., 2019b; Oliveira et al., 2019a) to the identification of allosteric effects (Oliveira et al., 2023; Gupta et al., 2022; Oliveira et al., 2022; Beer et al., 2024; Kamsri et al., 2024; Castelli et al., 2024; Pan et al., 2024; Castelli et al., 2023; Chan et al., 2023; Galdadas et al., 2021) and the impact of pH changes (Dommer et al., 2023) in fundamentally different biomolecular systems, including SARS-CoV-2 targets (Oliveira et al., 2023; Gupta et al., 2022; Oliveira et al., 2022; Chan et al., 2023; Dommer et al., 2023). For example, in the SARS-CoV-2 main protease, dynamical responses from D-NEMD pinpointed positions associated with drug resistance (Chan et al., 2023), and for the spike, D-NEMD indicated the regions of the protein affected by pH changes (Dommer et al., 2023).

We performed extensive equilibrium MD simulations, followed by hundreds of D-NEMD simulations, to analyse the response of the fully glycosylated, cleaved (at the furin recognition site) spike to LA removal. Principal component analysis was performed to check the equilibration and sampling of the equilibrium replicates (Figure 2—figure supplement 1). In the equilibrium simulations, the locked state of the spike (with all RBDs down) with LA bound remained stable, showing structural convergence after ~50 ns and minimal secondary structure loss after 750 ns (Figure 2—figure supplement 1). The comparison between the average C_α fluctuations calculated from the equilibrium simulations for the locked (this work) and closed (from Casalino et al., 2020) glycosylated spikes shows that the dynamics of the protein is generally similar (Figure 2—figure supplement 1). The largest difference is observed in RBM_B, which exhibits decreased dynamics in the locked state (i.e. when LA is present in the FA sites) (Figure 2—figure supplement 1). In the locked spike equilibrium simulations, all LA molecules remained stably bound to the protein, with the carboxylate headgroup of LA making consistent salt-bridge interactions with K417 and occasional interactions with R408 (Figure 2—figure supplement 2).

Analysis of the dynamics of the glycans in the equilibrium trajectories showed (as previously observed for the spike without LA; Casalino et al., 2020) that the glycans are very mobile, exhibiting diverse levels of motion depending on their composition, branching, and solvent exposure (Figure 2, Figure 2—figure supplement 3). Generally, N-glycans in the NTD show higher fluctuations than those of the RBD (Figure 2—figure supplement 3). The glycan linked to N331 from chain C is an exception, with one of the largest root mean square fluctuation (RMSF) values. The O-glycans connected to T323 and S325, close to the RBD, are less flexible than the N-glycans (Figure 2—figure supplement 3).

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The highly dynamic profile of the glycans (Figure 2) helps the spike to evade the host immune response by masking immunogenic epitopes, thus preventing them from being targeted by the host’s neutralising antibodies. To quantify the shielding effect, the spike accessible surface area covered by the glycans was determined for probe radii ranging from 0.14 nm (approximate radius of a water molecule) to 1.1 nm (approximate radius of a small antibody molecule). As can be seen in Figure 2B and C, consistent with previous findings reported for the closed state (with all RBDs in the ‘down’ conformation, without LA bound) (Casalino et al., 2020), in the locked state, the spike head has a thick glycan shield, which covers ~60% of the protein accessible area for a 1.0-nm-radius probe and restricts the binding of medium size molecules to the protein. However, small molecules (probes with a radius 0.14–0.3 nm) can penetrate the shield more easily as it only covers ~26% of the area of the protein accessible to smaller probes .

An ensemble of 210 conformations (70 configurations per replicate) was extracted from the equilibrium MD simulations and used as starting points for the D-NEMD simulations, which investigated the effect of LA removal (Figures 3—6, Figure 7, Figure 7—figure supplement 1). The D-NEMD method, originally proposed by Ciccotti et al., 1979; Ciccotti and Jacucci, 1975, combines simulations in equilibrium and nonequilibrium conditions (Figure 7—figure supplement 1). It allows for the direct computing of the evolution of the dynamical response of a system to an external perturbation. The rationale for the D-NEMD can be described as follows: if an external perturbation is applied to an equilibrium simulation and, by doing so, a parallel nonequilibrium simulation is started, then the response of the protein to the perturbation can be straightforwardly extracted using the Kubo-Onsager relation (for more details, see Oliveira et al., 2021a; Balega et al., 2024).

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The perturbation used here, the instantaneous removal of LA from all three FA sites, is the same as in previous D-NEMD simulations of non-glycosylated spikes (Oliveira et al., 2023; Gupta et al., 2022; Oliveira et al., 2022). This perturbation takes the system out of equilibrium, and creates a driving force for changes to occur as the protein responds to the perturbation and relaxes back towards equilibrium. LA removal from the FA sites triggers the structural response of the protein as it adapts to an empty FA site. Analysis of the evolution of the structural changes reveals the mechanical and dynamical coupling between the structural elements involved in response to LA removal and identifies the allosteric pathways connecting the FA site to the rest of the protein. The evolution of the structural response of the protein is extracted using the Kubo-Onsager relation (Oliveira et al., 2021a; Ciccotti and Ferrario, 2016; Ciccotti et al., 1979; Ciccotti, 1991) from the difference between the equilibrium and nonequilibrium trajectories at equivalent points in time (Figure 7). The response obtained for each pair of equilibrium and nonequilibrium simulations is averaged over all 210 trajectories here, hence reducing noise (Oliveira et al., 2021a; Ciccotti and Ferrario, 2016), and allowing the statistical significance of the responses to be assessed from the standard error of the mean (Figure 3—figure supplement 1, Figure 3—figure supplement 2, Figure 3—figure supplement 3; Oliveira et al., 2021a).

LA removal initiates a complex chain of structural changes that are, over time, propagated within the protein. The deletion of the LA molecules immediately triggers a structural change in the FA site, which contracts as the sidechains of the residues lining it move closer to each other, filling the space once occupied by the LA molecule (Figure 3—figure supplement 4). The changes in the FA site are then swiftly transmitted to well-defined regions of the protein, notably the NTD, RBM, and FP-surrounding regions (Figure 3, Figure 3—figure supplement 5, Figure 3—figure supplement 6).

The cascade of events observed here for the fully glycosylated ancestral spike mirrors that of the non-glycosylated protein (Oliveira et al., 2023; Gupta et al., 2022; Oliveira et al., 2022), with LA removal triggering immediate structural changes in the FA site, which are then transmitted to key regions of the protein, including the FP-surrounding region which is more than >40 Å away from the FA site. Figure 3—figure supplement 7 shows a strong positive correlation between the responses obtained for the non-glycosylated and glycosylated spikes, underscoring the overall similarity between our current and previous D-NEMD simulations. The largest differences in the responses between the glycosylated and non-glycosylated spikes are found around the furin recognition site (Figure 3—figure supplement 7). This is unsurprising as the furin site is cleaved in the current (glycosylated) simulations but remains uncleaved in the previous non-glycosylated simulations. The furin cleavage/recognition site is a polybasic four-residue insertion located on a solvent-exposed loop at the S1/S2 junction (Walls et al., 2020; Wrapp et al., 2020). This site is important for the activation of the spike (Davidson et al., 2020), and its presence affects viral infectivity (e.g. Gupta et al., 2022; Davidson et al., 2020; Peacock et al., 2021; Hoffmann et al., 2020a; Hoffmann et al., 2020b).

The evolution of the response of the spike to LA removal reveals the pathways through which structural changes propagate from the FA site to functional motifs (e.g. motifs involved in membrane fusion and antigenic epitopes; Figure 4, Figure 4—figure supplement 1, Figure 4—figure supplement 2). These pathways lie mainly within the protein and are generally similar to those previously found for the non-glycosylated spike (Oliveira et al., 2023; Gupta et al., 2022; Oliveira et al., 2022). The structural changes induced by LA removal, which start in the FA site (mainly in the P337-A348 and S366-A372 regions of one monomer and T415-K417 of the other one), are rapidly transmitted to the rest of the RBD. The R454-K458 region is particularly important as it mediates the transmission of the structural changes to the A475-C488 segment in the RBM (Figure 4).

In addition to the amplitude of the structural changes induced by LA removal, the average directions of the motion can also be computed by determining the average displacement vector of C_α atoms (Chan et al., 2023) between the equilibrium and nonequilibrium trajectories at equivalent time points (Figure 5, Figure 5—figure supplement 1, Figure 5—figure supplement 2, Figure 5—figure supplement 3). Indeed, the amplitude of the structural changes in Figures 3 and 4, Figure 3—figure supplement 5, Figure 3—figure supplement 6, Figure 4—figure supplement 1, Figure 4—figure supplement 2, corresponds to the norm of the average C_α displacement vector. Upon LA removal, the spike regions that form the FA site show responses with well-defined directions. These segments include P337-A348 and S366-A372, which are regions with residues whose sidechains form the FA pocket (Figure 5, Figure 5—figure supplement 1, Figure 5—figure supplement 2, Figure 5—figure supplement 3, Figure 5—figure supplement 4). Soon after LA deletion, P337-A348 and S366-A372 (P337_A-A348_A and S366_A-A372_A in FA site 1, P337_B-A348_B and S366_B-A372_B in FA site 2, and P337_C-A348_C and S366_C-A372_C in FA site 3) move inwards towards the FA site. These motions collectively reflect the contraction of the FA site upon LA removal. The directions of RBM motions are more diverse, with two of the RBMs, namely RBM_C (Figure 5—figure supplement 2) and RBM_B (Figure 5—figure supplement 3), displaying an upward movement and the third showing an opposite downward one (RBM_A) at t=10 ns (Figure 5, Figure 5—figure supplement 4).

The ancestral SARS-CoV-2 spike has two glycans located on the RBD: N-glycans at positions N331 and N343 (Watanabe et al., 2020). The N343 glycan is particularly interesting because it is situated immediately after one of the regions that transmits structural changes from the FA site, namely P337-F342 (Figure 4). As well as being close to the FA site, this glycan can also directly bridge the two neighbouring RBDs (Figure 4—figure supplement 3), which may strengthen the connection between the FA site and these regions. N343 has been shown to play a role in the RBD opening mechanism by acting as a gate for the change from the ‘down’ to the ‘up’ conformation (Sztain et al., 2021).

The structural changes induced by LA removal are also swiftly propagated to the NTD via P337-F342, W353-I358, and C161-P172 (Figure 4, Figure 4—figure supplement 1, Figure 4—figure supplement 2). Despite variations in amplitude, the structural response to LA removal is consistent across the three FA sites, exhibiting the same motifs and sequence of events for signal propagation to the NTDs. The response of the protein, which starts in the P337-A348 segment in the FA site, is transmitted to W353-I358, C161-P172, and then to several antigenic epitopes located in the periphery of the NTDs (Chi et al., 2020). The external regions showing high displacements, namely S71-R78, H146-E156, Q173-Q183, and L249-G257, are all part of an antigenic supersite in the distal-loop region of the NTD (Cerutti et al., 2021). In particular, the GTNGTKR motif in S71-R78, besides being an antigenic epitope, has also been suggested to be involved in binding to other receptors, such as sugar receptors (Behloul et al., 2020).

The conformational response of the Q173-Q183 is of particular interest as this region forms a pocket that binds heme and the tetrapyrrole products of its metabolism (Freeman et al., 2023; Rosa et al., 2021). The structural response initiated in the FA site propagates through the NTD, reaching the Q173-Q183 segment (Figures 4 and 6, Figure 4—figure supplement 1, Figure 4—figure supplement 2). This region, which is located in the distal face of the NTD, forms the entrance of an allosteric site (Figure 6) that has been shown to bind heme (Freeman et al., 2023) and its metabolites biliverdin and bilirubin (Rosa et al., 2021). X-ray crystallography, cryo-EM, and mutagenesis experiments together with modelling show that heme and biliverdin bind to a deep cleft in the NTD (Freeman et al., 2023; Rosa et al., 2021) gated by the Q173-Q183 flexible loop (Figure 6). Physiological concentrations of biliverdin suppress binding of some neutralising antibodies to the spike (Rosa et al., 2021). Such data suggested a new mode of immune evasion of the spike via the allosteric effect of biliverdin/heme binding (Rosa et al., 2021). In our D-NEMD simulations, two of the three Q173-Q183 regions (in chains A and B) show well-defined outward motions in response to LA removal (Figure 6, Figure 6—figure supplement 1). Our results show a clear connection between the FA and biliverdin/heme allosteric sites via internal conformational motions despite the two sites being >30 Å apart. This connection, captured by the D-NEMD approach, is remarkable and illustrates the complexity of the potential allosteric modulation of the spike. These results suggest that the presence of heme or its metabolite in the NTD site affects the internal networks and how dynamic and structural changes are transmitted to and from the FA site. The presence of heme/biliverdin may modulate the response of the spike to FAs and vice versa, potentially affecting the rate and/or affinity of binding of the molecules to their respective allosteric sites. This apparent connection between allosteric sites is worthy of further investigation.

There are eight N-glycans on the NTD, linked to N17, N61, N74, N122, N149, N165, N234, and N282 (Watanabe et al., 2020). Of these, four (N74, N122, N149, and N165) are located in or close to the segments that respond to FA site occupancy (Figure 4—figure supplement 3). N74, N122, and N149 are involved in shielding the spike protein from the host immune system (Casalino et al., 2020), whereas the role of N165 and N234 goes beyond shielding: they are involved in the stabilisation of the RBD ‘up’ conformation (Casalino et al., 2020).

The structural changes induced by LA removal are not restricted to the RBD and NTD: they are also transmitted to several regions far from the FA site, notably the furin site at the S1/S2 boundary, the S2’ protease recognition and cleavage site, R815, located at the S2 subunit (immediately upstream the FP), V620-L629 loop, and FP proximal region, fusion-peptide proximal region (FPPR) (Figure 4, Figure 4—figure supplement 1, Figure 4—figure supplement 2). The structural responses starting in the FA site quickly propagate downwards to the furin cleavage sites and V620-L629 loop via the F318-I326 and C525-K537 segments (Figure 4, Figure 4—figure supplement 1, Figure 4—figure supplement 2). The furin cleavage site, which harbours a polybasic motif containing multiple arginine residues, is located at the boundary between the S1 and S2 subunits and more than 40 Å from the FA site. The furin cleavage site is important for protein activation, and its removal reduces viral infectivity (Gupta et al., 2022; Davidson et al., 2020; Hoffmann et al., 2020a; Matsuyama et al., 2020). The addition of extra positively charged residues near the furin cleavage site, as observed in several variants, has been suggested to increase proteolytic processing (Whittaker, 2021), and has been shown to increase the rate of binding and the affinity of glycosaminoglycans such as heparin and heparan sulfate for this area (Kim et al., 2023). In our previous work, the comparison of the D-NEMD responses to LA removal between variants of concern has also suggested that the addition of extra positive flanking charges, which is observed in some variants (such as P681R in Delta and N679K in Omicron), strengthens the allosteric connection between the FA and furin cleavage site (Oliveira et al., 2023). Overall, the allosteric effects observed here for the glycosylated ancestral spike are qualitatively similar to those in the non-glycosylated protein: the same regions are connected to the FA site and are affected significantly by ligand removal from this site (Oliveira et al., 2023; Gupta et al., 2022; Oliveira et al., 2022). This indicates that the glycans on the exterior of the protein do not substantially affect the internal allosteric communication pathways within the spike.

The structural changes starting in the FA site are transmitted to the furin cleavage site and V620-L629, and from there, over time, propagated to the FPPR and S2’ cleavage site (Figure 4, Figure 4—figure supplement 1, Figure 4—figure supplement 2). In addition to being an epitope for neutralising antibodies (Farrera-Soler et al., 2020; Poh et al., 2020), the S2’ cleavage site is also crucial for infection (Hoffmann et al., 2020b; Takeda, 2022). This proteolytic site is located immediately before the hydrophobic FP in the S2 subunit, and its cleavage is mediated by the transmembrane protease serine 2 (TMPRSS2) after binding to the host receptor (Takeda, 2022). The FPPR is located after the FP, and it is thought to have a functional role in membrane fusion by mediating the transitions between pre- and post-fusion structures of the protein (Cai et al., 2020). Upon removal of LA, the residues of the FPPR in direct contact with the FP show a well-defined response upwards in two of the chains, namely chains B and C, and an outwards motion in chain A (Figure 5, Figure 5—figure supplement 2, Figure 5—figure supplement 3, Figure 5—figure supplement 4). FPPR_B and FPPR_C, located closer to FA sites 1 and 2 respectively, show an upward movement towards the C-terminal domain 1 (CTD1), whereas FPPR_A, which is nearer to FA site 3, exhibits a lateral outward motion. The CTD1 has been suggested (based on cryo-EM structures) to be a structural relay between RBD and FP, sensing displacement on either side (Cai et al., 2020). Interestingly, the chains displaying FPPR motion towards CTD1, notably chains B and C, also exhibit an RBM upward movement away from the body of the spike. The direction of the S2’ motion observed in the D-NEMD simulations is diverse, with two of the sites (S2’_A and S2’_B) displaying a motion towards the FP and one showing an opposite movement away from the FP (S2’_C) after t=10 ns (Figure 5, Figure 5—figure supplement 1, Figure 5—figure supplement 2, Figure 5—figure supplement 3, Figure 5—figure supplement 4).

The spike contains several complex N- and O-glycans in or close to the furin and S2’ cleavage sites, FPPR, F318-I326, C525-K537, and V620-L629 (Watanabe et al., 2020). All three monomers contain one O- and two N-glycans (at positions T323, N616, and N657) close to the pathway that connects that FA site to the furin cleavage site and FP regions (Figure 4—figure supplement 3). Monomer A also contains an additional O-glycan linked to S323 (Figure 4—figure supplement 3). Notably, interactions between the O-glycans S323 and T325 and the N-glycan at N234 create a direct connection between the NTD and the F318-I326 region of the same monomer (Figure 4—figure supplement 3). This glycan ‘link’ may facilitate and enhance the transmission of structural changes within an individual subunit. The glycan at position N234 has also been suggested to play a mechanical role in the spike infection mechanism by helping to stabilise the RBD in the ‘up’ conformation (Casalino et al., 2020).

Cross-correlation analysis was performed for the equilibrium and D-NEMD simulations (similarly to references; Oliveira et al., 2023; Galdadas et al., 2021) to identify the coupled regions in the protein, including those with motions connected to the FA site. In Figure 4—figure supplement 4, the dark and light blue regions represent high and moderate negative correlations between the C_α atoms in the protein, and red and orange regions correspond to high and moderate positive correlations. Negative correlation values indicate that the atoms are moving in opposite directions, whereas atoms systematically moving along the same direction show strong positive correlations. Overall, the cross-correlation maps computed from the equilibrium and D-NEMD trajectories show similar patterns, with the former exhibiting a subtle increase in the correlations between the FA sites and RBDs (Figure 4—figure supplement 4). This increase indicates that binding an FA molecule, such as LA, to the FA site reinforces the connection between this site and other parts of the protein.

The cross-correlation maps also show positive correlations between each FA site and two of the three RBDs in the protein. This is because each FA site sits at the interfaces between every two neighbouring RBDs (e.g. FA site 1 is formed by residues from subunits A and C). Low to moderate negative and small positive correlated motions are observed between the FA site and the NTDs and FP regions, respectively (Figure 4—figure supplement 4). To visualise these motions, the statistical correlations for R408 and K417 (two FA site residues able to form salt-bridge interactions with the carboxylate headgroup of LA) were mapped on the protein structure. Figure 4—figure supplement 5 shows the patterns of movement described above and the regions whose motions are connected to the FA site. Interestingly, some segments forming the signal propagation pathways, such as R454-K458 in all three monomers and C525-K537 in monomers B and C, can also be identified from the cross-correlation analysis, showing moderate to high correlations with the FA site (Figure 4—figure supplement 4, Figure 4—figure supplement 5).

To assess whether the substitutions, deletions, and insertions seen in various SARS-CoV-2 variants lie on the allosteric communication pathways identified, we overlapped, in the same 3D structure, the sequence variations (using spheres to highlight the position of the changes) with the D-NEMD responses reported above (Figure 4—figure supplement 6). In Figure 4—figure supplement 6, changes within the allosteric pathways are indicated by red spheres, while those within 0.6 nm of any atom (i.e. both main and sidechain atoms) forming the paths are highlighted in dark blue. The results are interesting: 22 of the 77 amino acid positions per chain that differ in the Alpha, Beta, Gamma, Delta, and Omicron (BA.1, BA.2, BA.4, BA.5, BQ.1.1, and XBB.1.5) variants directly map onto the allosteric communication pathway identified using D-NEMD. A further 28 out of the 77 variations are in direct contact with, or very close proximity to, these networks. H655Y (present in Gamma and all Omicron sub-variants), T547K (in Omicron BA.1), D614G (in Alpha, Beta, Gamma, Delta, and all Omicron sub-variants), W856K (in Omicron BA.1), and S982A (in Alpha) are all examples of mutations close to the communication pathways, which may influence the connection to the FA site. These mutations are responsible for the previously observed differences in allosteric behaviour between SARS-CoV-2 variants (Oliveira et al., 2023).

Sequence alignment of the original spike and the Alpha, Beta, Gamma, Delta, and Omicron (BA.1, BA.2, BA.4, BA.5, BQ.1.1, and XBB.1.5) variants shows that several of the mutations, deletions, and insertions present in the variants are located either in or near the pathways identified here (Figure 4—figure supplement 7). Furthermore, some variants, such as Beta, Gamma, and Omicron, contain residue substitutions at the FA site. For example, the lysine in position 417 in the original spike is mutated to asparagine in Beta and Omicron and threonine in the Gamma variant. Another example is arginine 408 in the ancestral protein, which has been replaced by asparagine in several Omicron sub-variants. As future variants emerge, it will be of interest to establish whether mutations lie in the FA and heme/biliverdin (Freeman et al., 2023; Rosa et al., 2021) sites or along the allosteric pathways described here. Differences in allosteric behaviour and regulation in the spike are likely to be of functional relevance and may be useful in understanding differences between SARS-CoV-2 variants. Our previous work using D-NEMD revealed significant differences in the allosteric responses of spike variants to LA removal (Oliveira et al., 2023). The substitutions, insertions, and deletions in the variants affected both the amplitude of the structural responses and the rates at which these rearrangements propagate within the protein (Oliveira et al., 2023). The allosteric connections identified in Alpha were generally similar to the ancestral protein, whereas Delta exhibited increased connections between the FA site and the furin cleavage site but diminished links to V622-L629 (Oliveira et al., 2023). Omicron displayed significant changes in the NTD, RBM, and furin cleavage site, with stronger couplings observed between the FA site and these regions (Oliveira et al., 2023).

All equilibrium and D-NEMD simulation data (including input and trajectories files) are at the University of Bristol data repository, data.bris, at https://doi.org/10.5523/bris.1gr9arko2apr126bsty2e3b41a.

1. 1. Oliveira ASF
   2. Kearns FL
   3. Rosenfeld MA
   4. Casalino L
   5. Tulli L
   6. Berger I
   7. Schaffitzel C
   8. Davidson AD
   9. Amaro RE
   10. Mulholland AJ

   (2025) University of Bristol Research Data Repository

   Glycosylated_spike.

   https://doi.org/10.5523/bris.1gr9arko2apr126bsty2e3b41a

1. 1. Abraham MJ
   2. Murtola T
   3. Schulz R
   4. Páll S
   5. Smith JC
   6. Hess B
   7. Lindahl E

   (2015) GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

   SoftwareX 1–2:19–25.

   https://doi.org/10.1016/j.softx.2015.06.001

   • Google Scholar
2. 1. Bagdonaite I
   2. Thompson AJ
   3. Wang X
   4. Søgaard M
   5. Fougeroux C
   6. Frank M
   7. Yates JR
   8. Diedrich JK
   9. Salanti A
   10. Vakhrushev SY
   11. Paulson JC
   12. Wandall HH

   (2021) Site-specific O-glycosylation analysis of SARS-CoV-2 spike protein produced in insect and human cells

   Viruses 13:551.

   https://doi.org/10.3390/v13040551

   • PubMed
   • Google Scholar
3. 1. Balega B
   2. Beer M
   3. Spencer J
   4. Colombo G
   5. Serapian SA
   6. Oliveira ASF
   7. Mulholland AJ

   (2024) Dynamical nonequilibrium molecular dynamics simulations reveal allosteric networks, signal transduction mechanisms, and sites associated with drug resistance in biomolecular systems

   Molecular Physics 1:e2428350.

   https://doi.org/10.1080/00268976.2024.2428350

   • Google Scholar
4. 1. Bangaru S
   2. Ozorowski G
   3. Turner HL
   4. Antanasijevic A
   5. Huang D
   6. Wang X
   7. Torres JL
   8. Diedrich JK
   9. Tian JH
   10. Portnoff AD
   11. Patel N
   12. Massare MJ
   13. Yates JR III
   14. Nemazee D
   15. Paulson JC
   16. Glenn G
   17. Smith G
   18. Ward AB

   (2020) Structural analysis of full-length SARS-CoV-2 spike protein from an advanced vaccine candidate

   Science 370:1089–1094.

   https://doi.org/10.1126/science.abe1502

   • PubMed
   • Google Scholar
5. 1. Beer M
   2. Oliveira ASF
   3. Tooke CL
   4. Hinchliffe P
   5. Tsz Yan Li A
   6. Balega B
   7. Spencer J
   8. Mulholland AJ

   (2024) Dynamical responses predict a distal site that modulates activity in an antibiotic resistance enzyme

   Chemical Science 15:17232–17244.

   https://doi.org/10.1039/d4sc03295k

   • PubMed
   • Google Scholar
6. 1. Behloul N
   2. Baha S
   3. Shi R
   4. Meng J

   (2020)

   Role of the GTNGTKR motif in the N-terminal receptor-binding domain of the SARS-CoV-2 spike protein

   Virus Research 286:198058.

   • PubMed
   • Google Scholar
7. 1. Blazhynska M
   2. Lagardère L
   3. Liu C
   4. Adjoua O
   5. Ren P
   6. Piquemal J-P

   (2024) Water-glycan interactions drive the SARS-CoV-2 spike dynamics: insights into glycan-gate control and camouflage mechanisms

   Chemical Science 15:14177–14187.

   https://doi.org/10.1039/d4sc04364b

   • PubMed
   • Google Scholar
8. 1. Bojadzic D
   2. Alcazar O
   3. Chen J
   4. Chuang S-T
   5. Condor Capcha JM
   6. Shehadeh LA
   7. Buchwald P

   (2021) Small-molecule inhibitors of the coronavirus spike: ACE2 protein-protein interaction as blockers of viral attachment and entry for SARS-CoV-2

   ACS Infectious Diseases 7:1519–1534.

   https://doi.org/10.1021/acsinfecdis.1c00070

   • PubMed
   • Google Scholar
9. 1. Brooks BR
   2. Brooks CL
   3. Mackerell AD Jr
   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
10. 1. Cai Y
    2. Zhang J
    3. Xiao T
    4. Peng H
    5. Sterling SM
    6. Walsh RM
    7. Rawson S
    8. Rits-Volloch S
    9. Chen B

    (2020) Distinct conformational states of SARS-CoV-2 spike protein

    Science 369:1586–1592.

    https://doi.org/10.1126/science.abd4251

    • PubMed
    • Google Scholar
11. 1. Cantuti-Castelvetri L
    2. Ojha R
    3. Pedro LD
    4. Djannatian M
    5. Franz J
    6. Kuivanen S
    7. van der Meer F
    8. Kallio K
    9. Kaya T
    10. Anastasina M
    11. Smura T
    12. Levanov L
    13. Szirovicza L
    14. Tobi A
    15. Kallio-Kokko H
    16. Österlund P
    17. Joensuu M
    18. Meunier FA
    19. Butcher SJ
    20. Winkler MS
    21. Mollenhauer B
    22. Helenius A
    23. Gokce O
    24. Teesalu T
    25. Hepojoki J
    26. Vapalahti O
    27. Stadelmann C
    28. Balistreri G
    29. Simons M

    (2020) Neuropilin-1 facilitates SARS-CoV-2 cell entry and infectivity

    Science 370:856–860.

    https://doi.org/10.1126/science.abd2985

    • PubMed
    • Google Scholar
12. 1. Casalino L
    2. Gaieb Z
    3. Goldsmith JA
    4. Hjorth CK
    5. Dommer AC
    6. Harbison AM
    7. Fogarty CA
    8. Barros EP
    9. Taylor BC
    10. McLellan JS
    11. Fadda E
    12. Amaro RE

    (2020) Beyond shielding: the roles of glycans in the SARS-CoV-2 spike protein

    ACS Central Science 6:1722–1734.

    https://doi.org/10.1021/acscentsci.0c01056

    • Google Scholar
13. 1. Casalino L
    2. Dommer AC
    3. Gaieb Z
    4. Barros EP
    5. Sztain T
    6. Ahn SH
    7. Trifan A
    8. Brace A
    9. Bogetti AT
    10. Clyde A
    11. Ma H
    12. Lee H
    13. Turilli M
    14. Khalid S
    15. Chong LT
    16. Simmerling C
    17. Hardy DJ
    18. Maia JD
    19. Phillips JC
    20. Kurth T
    21. Stern AC
    22. Huang L
    23. McCalpin JD
    24. Tatineni M
    25. Gibbs T
    26. Stone JE
    27. Jha S
    28. Ramanathan A
    29. Amaro RE

    (2021) AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

    The International Journal of High Performance Computing Applications 35:432–451.

    https://doi.org/10.1177/10943420211006452

    • PubMed
    • Google Scholar
14. 1. Castelli M
    2. Magni A
    3. Bonollo G
    4. Pavoni S
    5. Frigerio F
    6. Oliveira ASF
    7. Cinquini F
    8. Serapian SA
    9. Colombo G

    (2023) Molecular mechanisms of chaperone-directed protein folding: Insights from atomistic simulations

    Protein Science 33:e4880.

    https://doi.org/10.1002/pro.4880

    • PubMed
    • Google Scholar
15. 1. Castelli M
    2. Marchetti F
    3. Osuna S
    4. F Oliveira AS
    5. Mulholland AJ
    6. Serapian SA
    7. Colombo G

    (2024) Decrypting allostery in membrane-bound K-Ras4B using complementary In Silico approaches based on unbiased molecular dynamics simulations

    Journal of the American Chemical Society 146:901–919.

    https://doi.org/10.1021/jacs.3c11396

    • PubMed
    • Google Scholar
16. 1. Cerutti G
    2. Guo Y
    3. Zhou T
    4. Gorman J
    5. Lee M
    6. Rapp M
    7. Reddem ER
    8. Yu J
    9. Bahna F
    10. Bimela J
    11. Huang Y
    12. Katsamba PS
    13. Liu L
    14. Nair MS
    15. Rawi R
    16. Olia AS
    17. Wang P
    18. Zhang B
    19. Chuang G-Y
    20. Ho DD
    21. Sheng Z
    22. Kwong PD
    23. Shapiro L

    (2021) Potent SARS-CoV-2 neutralizing antibodies directed against spike N-terminal domain target a single supersite

    Cell Host & Microbe 29:819–833.

    https://doi.org/10.1016/j.chom.2021.03.005

    • PubMed
    • Google Scholar
17. 1. Chan HTH
    2. Oliveira ASF
    3. Schofield CJ
    4. Mulholland AJ
    5. Duarte F

    (2023) Dynamical nonequilibrium molecular dynamics simulations identify allosteric sites and positions associated with drug resistance in the SARS-CoV-2 main protease

    JACS Au 3:1767–1774.

    https://doi.org/10.1021/jacsau.3c00185

    • Google Scholar
18. 1. Chi X
    2. Yan R
    3. Zhang J
    4. Zhang G
    5. Zhang Y
    6. Hao M
    7. Zhang Z
    8. Fan P
    9. Dong Y
    10. Yang Y
    11. Chen Z
    12. Guo Y
    13. Zhang J
    14. Li Y
    15. Song X
    16. Chen Y
    17. Xia L
    18. Fu L
    19. Hou L
    20. Xu J
    21. Yu C
    22. Li J
    23. Zhou Q
    24. Chen W

    (2020) A neutralizing human antibody binds to the N-terminal domain of the Spike protein of SARS-CoV-2

    Science 369:650–655.

    https://doi.org/10.1126/science.abc6952

    • PubMed
    • Google Scholar
19. 1. Chrestia JF
    2. Oliveira AS
    3. Mulholland AJ
    4. Gallagher T
    5. Bermúdez I
    6. Bouzat C

    (2022) A functional interaction between Y674-R685 region of the SARS-CoV-2 spike protein and the human α7 nicotinic receptor

    Molecular Neurobiology 59:6076–6090.

    https://doi.org/10.1007/s12035-022-02947-8

    • PubMed
    • Google Scholar
20. 1. Ciccotti G
    2. Jacucci G

    (1975) Direct computation of dynamical response by molecular dynamics: the mobility of a charged lennard-jones particle

    Physical Review Letters 35:789–792.

    https://doi.org/10.1103/PhysRevLett.35.789

    • Google Scholar
21. 1. Ciccotti G
    2. Jacucci G
    3. McDonald IR

    (1979) Thought-experiments? by molecular dynamics

    Journal of Statistical Physics 21:1–22.

    https://doi.org/10.1007/BF01011477

    • Google Scholar
22. Book

    1. Ciccotti G

    (1991) Molecular dynamics simulation of non equilibrium phenomena and rare dynamical events

    In: Meyer PV, editors. Computer Simulation in Material Science. Kluwer Academic Publishers. pp. 119–137.

    https://doi.org/10.1007/978-94-011-3546-7_6

    • Google Scholar
23. 1. Ciccotti G
    2. Ferrario M

    (2016) Non-equilibrium by molecular dynamics: a dynamical approach

    Molecular Simulation 42:1385–1400.

    https://doi.org/10.1080/08927022.2015.1121543

    • Google Scholar
24. 1. Creutznacher R
    2. Maass T
    3. Veselkova B
    4. Ssebyatika G
    5. Krey T
    6. Empting M
    7. Tautz N
    8. Frank M
    9. Kölbel K
    10. Uetrecht C
    11. Peters T

    (2022) NMR experiments provide insights into ligand-binding to the SARS-CoV-2 spike protein receptor-binding domain

    Journal of the American Chemical Society 144:13060–13065.

    https://doi.org/10.1021/jacs.2c05603

    • PubMed
    • Google Scholar
25. 1. Daly JL
    2. Simonetti B
    3. Klein K
    4. Chen K-E
    5. Williamson MK
    6. Antón-Plágaro C
    7. Shoemark DK
    8. Simón-Gracia L
    9. Bauer M
    10. Hollandi R
    11. Greber UF
    12. Horvath P
    13. Sessions RB
    14. Helenius A
    15. Hiscox JA
    16. Teesalu T
    17. Matthews DA
    18. Davidson AD
    19. Collins BM
    20. Cullen PJ
    21. Yamauchi Y

    (2020) Neuropilin-1 is a host factor for SARS-CoV-2 infection

    Science 370:861–865.

    https://doi.org/10.1126/science.abd3072

    • PubMed
    • Google Scholar
26. 1. Davidson AD
    2. Williamson MK
    3. Lewis S
    4. Shoemark D
    5. Carroll MW
    6. Heesom KJ
    7. Zambon M
    8. Ellis J
    9. Lewis PA
    10. Hiscox JA
    11. Matthews DA

    (2020) Characterisation of the transcriptome and proteome of SARS-CoV-2 reveals a cell passage induced in-frame deletion of the furin-like cleavage site from the spike glycoprotein

    Genome Medicine 12:68.

    https://doi.org/10.1186/s13073-020-00763-0

    • PubMed
    • Google Scholar
27. 1. Dommer A
    2. Casalino L
    3. Kearns F
    4. Rosenfeld M
    5. Wauer N
    6. Ahn SH
    7. Russo J
    8. Oliveira S
    9. Morris C
    10. Bogetti A
    11. Trifan A
    12. Brace A
    13. Sztain T
    14. Clyde A
    15. Ma H
    16. Chennubhotla C
    17. Lee H
    18. Turilli M
    19. Khalid S
    20. Tamayo-Mendoza T
    21. Welborn M
    22. Christensen A
    23. Smith DG
    24. Qiao Z
    25. Sirumalla SK
    26. O’Connor M
    27. Manby F
    28. Anandkumar A
    29. Hardy D
    30. Phillips J
    31. Stern A
    32. Romero J
    33. Clark D
    34. Dorrell M
    35. Maiden T
    36. Huang L
    37. McCalpin J
    38. Woods C
    39. Gray A
    40. Williams M
    41. Barker B
    42. Rajapaksha H
    43. Pitts R
    44. Gibbs T
    45. Stone J
    46. Zuckerman DM
    47. Mulholland AJ
    48. Miller T
    49. Jha S
    50. Ramanathan A
    51. Chong L
    52. Amaro RE

    (2023) #COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol

    The International Journal of High Performance Computing Applications 37:28–44.

    https://doi.org/10.1177/10943420221128233

    • PubMed
    • Google Scholar
28. 1. Farrera-Soler L
    2. Daguer J-P
    3. Barluenga S
    4. Vadas O
    5. Cohen P
    6. Pagano S
    7. Yerly S
    8. Kaiser L
    9. Vuilleumier N
    10. Winssinger N

    (2020) Identification of immunodominant linear epitopes from SARS-CoV-2 patient plasma

    PLOS ONE 15:e0238089.

    https://doi.org/10.1371/journal.pone.0238089

    • PubMed
    • Google Scholar
29. 1. Focosi D
    2. McConnell S
    3. Casadevall A
    4. Cappello E
    5. Valdiserra G
    6. Tuccori M

    (2022) Monoclonal antibody therapies against SARS-CoV-2

    The Lancet. Infectious Diseases 22:e311–e326.

    https://doi.org/10.1016/S1473-3099(22)00311-5

    • PubMed
    • Google Scholar
30. 1. Freeman SL
    2. Oliveira ASF
    3. Gallio AE
    4. Rosa A
    5. Simitakou MK
    6. Arthur CJ
    7. Mulholland AJ
    8. Cherepanov P
    9. Raven EL

    (2023) Heme binding to the SARS-CoV-2 spike glycoprotein

    The Journal of Biological Chemistry 299:105014.

    https://doi.org/10.1016/j.jbc.2023.105014

    • PubMed
    • Google Scholar
31. 1. Galdadas I
    2. Qu S
    3. Oliveira ASF
    4. Olehnovics E
    5. Mack AR
    6. Mojica MF
    7. Agarwal PK
    8. Tooke CL
    9. Gervasio FL
    10. Spencer J
    11. Bonomo RA
    12. Mulholland AJ
    13. Haider S

    (2021) Allosteric communication in class A β-lactamases occurs via cooperative coupling of loop dynamics

    eLife 10:e66567.

    https://doi.org/10.7554/eLife.66567

    • PubMed
    • Google Scholar
32. 1. Gangavarapu K
    2. Latif AA
    3. Mullen JL
    4. Alkuzweny M
    5. Hufbauer E
    6. Tsueng G
    7. Haag E
    8. Zeller M
    9. Aceves CM
    10. Zaiets K
    11. Cano M
    12. Zhou X
    13. Qian Z
    14. Sattler R
    15. Matteson NL
    16. Levy JI
    17. Lee RTC
    18. Freitas L
    19. Maurer-Stroh S
    20. GISAID Core and Curation Team
    21. Suchard MA
    22. Wu C
    23. Su AI
    24. Andersen KG
    25. Hughes LD

    (2023) Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations

    Nature Methods 20:512–522.

    https://doi.org/10.1038/s41592-023-01769-3

    • PubMed
    • Google Scholar
33. 1. Gupta K
    2. Toelzer C
    3. Williamson MK
    4. Shoemark DK
    5. Oliveira ASF
    6. Matthews DA
    7. Almuqrin A
    8. Staufer O
    9. Yadav SKN
    10. Borucu U
    11. Garzoni F
    12. Fitzgerald D
    13. Spatz J
    14. Mulholland AJ
    15. Davidson AD
    16. Schaffitzel C
    17. Berger I

    (2022) Structural insights in cell-type specific evolution of intra-host diversity by SARS-CoV-2

    Nature Communications 13:222.

    https://doi.org/10.1038/s41467-021-27881-6

    • PubMed
    • Google Scholar
34. 1. Guvench O
    2. Hatcher ER
    3. Venable RM
    4. Pastor RW
    5. Mackerell AD

    (2009) CHARMM additive all-atom force field for glycosidic linkages between hexopyranoses

    Journal of Chemical Theory and Computation 5:2353–2370.

    https://doi.org/10.1021/ct900242e

    • PubMed
    • Google Scholar
35. 1. Harbison AM
    2. Fogarty CA
    3. Phung TK
    4. Satheesan A
    5. Schulz BL
    6. Fadda E

    (2022) Fine-tuning the spike: role of the nature and topology of the glycan shield in the structure and dynamics of the SARS-CoV-2 S

    Chemical Science 13:386–395.

    https://doi.org/10.1039/d1sc04832e

    • PubMed
    • Google Scholar
36. 1. Hoffmann M
    2. Kleine-Weber H
    3. Pöhlmann S

    (2020a) A multibasic cleavage site in the spike protein of SARS-CoV-2 is essential for infection of human lung cells

    Molecular Cell 78:779–784.

    https://doi.org/10.1016/j.molcel.2020.04.022

    • PubMed
    • Google Scholar
37. 1. Hoffmann M
    2. Kleine-Weber H
    3. Schroeder S
    4. Krüger N
    5. Herrler T
    6. Erichsen S
    7. Schiergens TS
    8. Herrler G
    9. Wu NH
    10. Nitsche A
    11. Müller MA
    12. Drosten C
    13. Pöhlmann S

    (2020b) SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and Is blocked by a clinically proven protease inhibitor

    Cell 181:271–280.

    https://doi.org/10.1016/j.cell.2020.02.052

    • PubMed
    • Google Scholar
38. 1. Hone AJ
    2. Santiago U
    3. Harvey PJ
    4. Tekarli B
    5. Gajewiak J
    6. Craik DJ
    7. Camacho CJ
    8. McIntosh JM

    (2024) Design, synthesis, and structure-activity relationships of novel peptide derivatives of the severe acute respiratory syndrome-coronavirus-2 spike-protein that potently inhibit nicotinic acetylcholine receptors

    Journal of Medicinal Chemistry 67:9587–9598.

    https://doi.org/10.1021/acs.jmedchem.4c00735

    • PubMed
    • Google Scholar
39. 1. Huang J
    2. MacKerell AD Jr

    (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

    • Google Scholar
40. 1. Jo S
    2. Kim T
    3. Iyer VG
    4. Im W

    (2008) CHARMM-GUI: a web-based graphical user interface for CHARMM

    Journal of Computational Chemistry 29:1859–1865.

    https://doi.org/10.1002/jcc.20945

    • PubMed
    • Google Scholar
41. 1. Kabsch W
    2. Sander C

    (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features

    Biopolymers 22:2577–2637.

    https://doi.org/10.1002/bip.360221211

    • PubMed
    • Google Scholar
42. 1. Kamsri B
    2. Kamsri P
    3. Punkvang A
    4. Chimprasit A
    5. Saparpakorn P
    6. Hannongbua S
    7. Spencer J
    8. Oliveira ASF
    9. Mulholland AJ
    10. Pungpo P

    (2024) Signal Propagation in the ATPase Domain of Mycobacterium tuberculosis DNA Gyrase from dynamical-nonequilibrium molecular dynamics simulations

    Biochemistry 63:1493–1504.

    https://doi.org/10.1021/acs.biochem.4c00161

    • PubMed
    • Google Scholar
43. 1. Kim SH
    2. Kearns FL
    3. Rosenfeld MA
    4. Votapka L
    5. Casalino L
    6. Papanikolas M
    7. Amaro RE
    8. Freeman R

    (2023) SARS-CoV-2 evolved variants optimize binding to cellular glycocalyx

    Cell Reports. Physical Science 4:101346.

    https://doi.org/10.1016/j.xcrp.2023.101346

    • PubMed
    • Google Scholar
44. 1. Lee J
    2. Cheng X
    3. Swails JM
    4. Yeom MS
    5. Eastman PK
    6. Lemkul JA
    7. Wei S
    8. Buckner J
    9. Jeong JC
    10. Qi Y
    11. Jo S
    12. Pande VS
    13. Case DA
    14. Brooks CL
    15. MacKerell AD Jr
    16. Klauda JB
    17. Im W

    (2016) CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field

    Journal of Chemical Theory and Computation 12:405–413.

    https://doi.org/10.1021/acs.jctc.5b00935

    • PubMed
    • Google Scholar
45. 1. Ma J
    2. Su D
    3. Sun Y
    4. Huang X
    5. Liang Y
    6. Fang L
    7. Ma Y
    8. Li W
    9. Liang P
    10. Zheng S

    (2021) Cryo-EM structure of S-Trimer, a subunit vaccine candidate for COVID-19

    Journal of Virology 95:e00194-21.

    https://doi.org/10.1128/JVI.00194-21

    • PubMed
    • Google Scholar
46. 1. Matsuyama S
    2. Nao N
    3. Shirato K
    4. Kawase M
    5. Saito S
    6. Takayama I
    7. Nagata N
    8. Sekizuka T
    9. Katoh H
    10. Kato F
    11. Sakata M
    12. Tahara M
    13. Kutsuna S
    14. Ohmagari N
    15. Kuroda M
    16. Suzuki T
    17. Kageyama T
    18. Takeda M

    (2020) Enhanced isolation of SARS-CoV-2 by TMPRSS2-expressing cells

    PNAS 117:7001–7003.

    https://doi.org/10.1073/pnas.2002589117

    • PubMed
    • Google Scholar
47. 1. O’Brien BCV
    2. Weber L
    3. Hueffer K
    4. Weltzin MM

    (2023) SARS-CoV-2 spike ectodomain targets α7 nicotinic acetylcholine receptors

    Journal of Biological Chemistry 299:104707.

    https://doi.org/10.1016/j.jbc.2023.104707

    • Google Scholar
48. 1. Oliveira ASF
    2. Edsall CJ
    3. Woods CJ
    4. Bates P
    5. Nunez GV
    6. Wonnacott S
    7. Bermudez I
    8. Ciccotti G
    9. Gallagher T
    10. Sessions RB
    11. Mulholland AJ

    (2019a) A general mechanism for signal propagation in the nicotinic acetylcholine receptor family

    Journal of the American Chemical Society 141:19953–19958.

    https://doi.org/10.1021/jacs.9b09055

    • PubMed
    • Google Scholar
49. 1. Oliveira ASF
    2. Shoemark DK
    3. Campello HR
    4. Wonnacott S
    5. Gallagher T
    6. Sessions RB
    7. Mulholland AJ

    (2019b) Identification of the Initial Steps in Signal Transduction in the α4β2 nicotinic receptor: insights from equilibrium and nonequilibrium simulations

    Structure 27:1171–1183.

    https://doi.org/10.1016/j.str.2019.04.008

    • PubMed
    • Google Scholar
50. 1. Oliveira ASF
    2. Ciccotti G
    3. Haider S
    4. Mulholland AJ

    (2021a) Dynamical nonequilibrium molecular dynamics reveals the structural basis for allostery and signal propagation in biomolecular systems

    The European Physical Journal. B 94:144.

    https://doi.org/10.1140/epjb/s10051-021-00157-0

    • PubMed
    • Google Scholar
51. 1. Oliveira ASF
    2. Ibarra AA
    3. Bermudez I
    4. Casalino L
    5. Gaieb Z
    6. Shoemark DK
    7. Gallagher T
    8. Sessions RB
    9. Amaro RE
    10. Mulholland AJ

    (2021b) A potential interaction between the SARS-CoV-2 spike protein and nicotinic acetylcholine receptors

    Biophysical Journal 120:983–993.

    https://doi.org/10.1016/j.bpj.2021.01.037

    • PubMed
    • Google Scholar
52. 1. Oliveira ASF
    2. Shoemark DK
    3. Avila Ibarra A
    4. Davidson AD
    5. Berger I
    6. Schaffitzel C
    7. Mulholland AJ

    (2022) The fatty acid site is coupled to functional motifs in the SARS-CoV-2 spike protein and modulates spike allosteric behaviour

    Computational and Structural Biotechnology Journal 20:139–147.

    https://doi.org/10.1016/j.csbj.2021.12.011

    • PubMed
    • Google Scholar
53. 1. Oliveira ASF
    2. Shoemark DK
    3. Davidson AD
    4. Berger I
    5. Schaffitzel C
    6. Mulholland AJ

    (2023) SARS-CoV-2 spike variants differ in their allosteric responses to linoleic acid

    Journal of Molecular Cell Biology 15:mjad021.

    https://doi.org/10.1093/jmcb/mjad021

    • PubMed
    • Google Scholar
54. 1. Pan J
    2. Chen K
    3. Lin L
    4. Xu L-Y
    5. Li E-M
    6. Dong G

    (2024) Exploring the allosteric response of fascin to its inhibitor

    The Journal of Physical Chemistry B 128:12050–12058.

    https://doi.org/10.1021/acs.jpcb.4c04813

    • Google Scholar
55. 1. Peacock TP
    2. Goldhill DH
    3. Zhou J
    4. Baillon L
    5. Frise R
    6. Swann OC
    7. Kugathasan R
    8. Penn R
    9. Brown JC
    10. Sanchez-David RY
    11. Braga L
    12. Williamson MK
    13. Hassard JA
    14. Staller E
    15. Hanley B
    16. Osborn M
    17. Giacca M
    18. Davidson AD
    19. Matthews DA
    20. Barclay WS

    (2021) The furin cleavage site in the SARS-CoV-2 spike protein is required for transmission in ferrets

    Nature Microbiology 6:899–909.

    https://doi.org/10.1038/s41564-021-00908-w

    • PubMed
    • Google Scholar
56. 1. Phillips JC
    2. Hardy DJ
    3. Maia JDC
    4. Stone JE
    5. Ribeiro JV
    6. Bernardi RC
    7. Buch R
    8. Fiorin G
    9. Hénin J
    10. Jiang W
    11. McGreevy R
    12. Melo MCR
    13. Radak BK
    14. Skeel RD
    15. Singharoy A
    16. Wang Y
    17. Roux B
    18. Aksimentiev A
    19. Luthey-Schulten Z
    20. Kalé LV
    21. Schulten K
    22. Chipot C
    23. Tajkhorshid E

    (2020) Scalable molecular dynamics on CPU and GPU architectures with NAMD

    The Journal of Chemical Physics 153:044130.

    https://doi.org/10.1063/5.0014475

    • PubMed
    • Google Scholar
57. 1. Poh CM
    2. Carissimo G
    3. Wang B
    4. Amrun SN
    5. Lee CYP
    6. Chee RSL
    7. Fong SW
    8. Yeo NKW
    9. Lee WH
    10. Torres-Ruesta A
    11. Leo YS
    12. Chen MIC
    13. Tan SY
    14. Chai LYA
    15. Kalimuddin S
    16. Kheng SSG
    17. Thien SY
    18. Young BE
    19. Lye DC
    20. Hanson BJ
    21. Wang CI
    22. Renia L
    23. Ng LFP

    (2020) Two linear epitopes on the SARS-CoV-2 spike protein that elicit neutralising antibodies in COVID-19 patients

    Nature Communications 11:2806.

    https://doi.org/10.1038/s41467-020-16638-2

    • PubMed
    • Google Scholar
58. 1. Pomplun S

    (2020) Targeting the SARS-CoV-2-spike protein: from antibodies to miniproteins and peptides

    RSC Medicinal Chemistry 12:197–202.

    https://doi.org/10.1039/d0md00385a

    • PubMed
    • Google Scholar
59. 1. Queirós-Reis L
    2. Mesquita JR
    3. Brancale A
    4. Bassetto M

    (2023) Exploring the fatty acid binding pocket in the SARS-CoV-2 spike protein - confirmed and potential ligands

    Journal of Chemical Information and Modeling 63:7282–7298.

    https://doi.org/10.1021/acs.jcim.3c00803

    • PubMed
    • Google Scholar
60. 1. Rosa A
    2. Pye VE
    3. Graham C
    4. Muir L
    5. Seow J
    6. Ng KW
    7. Cook NJ
    8. Rees-Spear C
    9. Parker E
    10. Dos Santos MS
    11. Rosadas C
    12. Susana A
    13. Rhys H
    14. Nans A
    15. Masino L
    16. Roustan C
    17. Christodoulou E
    18. Ulferts R
    19. Wrobel AG
    20. Short C-E
    21. Fertleman M
    22. Sanders RW
    23. Heaney J
    24. Spyer M
    25. Kjær S
    26. Riddell A
    27. Malim MH
    28. Beale R
    29. MacRae JI
    30. Taylor GP
    31. Nastouli E
    32. van Gils MJ
    33. Rosenthal PB
    34. Pizzato M
    35. McClure MO
    36. Tedder RS
    37. Kassiotis G
    38. McCoy LE
    39. Doores KJ
    40. Cherepanov P

    (2021) SARS-CoV-2 can recruit a heme metabolite to evade antibody immunity

    Science Advances 7:7607.

    https://doi.org/10.1126/sciadv.abg7607

    • PubMed
    • Google Scholar
61. 1. Samsudin F
    2. Zuzic L
    3. Marzinek JK
    4. Bond PJ

    (2024) Mechanisms of allostery at the viral surface through the eyes of molecular simulation

    Current Opinion in Structural Biology 84:102761.

    https://doi.org/10.1016/j.sbi.2023.102761

    • PubMed
    • Google Scholar
62. 1. Sanda M
    2. Morrison L
    3. Goldman R

    (2021) N- and O-Glycosylation of the SARS-CoV-2 spike protein

    Analytical Chemistry 93:2003–2009.

    https://doi.org/10.1021/acs.analchem.0c03173

    • Google Scholar
63. 1. Shajahan A
    2. Supekar NT
    3. Gleinich AS
    4. Azadi P

    (2020) Deducing the N- and O-glycosylation profile of the spike protein of novel coronavirus SARS-CoV-2

    Glycobiology 30:981–988.

    https://doi.org/10.1093/glycob/cwaa042

    • PubMed
    • Google Scholar
64. 1. Shoemark DK
    2. Colenso CK
    3. Toelzer C
    4. Gupta K
    5. Sessions RB
    6. Davidson AD
    7. Berger I
    8. Schaffitzel C
    9. Spencer J
    10. Mulholland AJ

    (2021) Molecular simulations suggest vitamins, retinoids and steroids as ligands of the free fatty acid pocket of the SARS-CoV-2 Spike Protein*

    Angewandte Chemie 60:7098–7110.

    https://doi.org/10.1002/anie.202015639

    • PubMed
    • Google Scholar
65. 1. Solis O
    2. Beccari AR
    3. Iaconis D
    4. Talarico C
    5. Ruiz-Bedoya CA
    6. Nwachukwu JC
    7. Cimini A
    8. Castelli V
    9. Bertini R
    10. Montopoli M
    11. Cocetta V
    12. Borocci S
    13. Prandi IG
    14. Flavahan K
    15. Bahr M
    16. Napiorkowski A
    17. Chillemi G
    18. Ooka M
    19. Yang X
    20. Zhang S
    21. Xia M
    22. Zheng W
    23. Bonaventura J
    24. Pomper MG
    25. Hooper JE
    26. Morales M
    27. Rosenberg AZ
    28. Nettles KW
    29. Jain SK
    30. Allegretti M
    31. Michaelides M

    (2022) The SARS-CoV-2 spike protein binds and modulates estrogen receptors

    Science Advances 8:eadd4150.

    https://doi.org/10.1126/sciadv.add4150

    • PubMed
    • Google Scholar
66. 1. Sztain T
    2. Ahn S-H
    3. Bogetti AT
    4. Casalino L
    5. Goldsmith JA
    6. Seitz E
    7. McCool RS
    8. Kearns FL
    9. Acosta-Reyes F
    10. Maji S
    11. Mashayekhi G
    12. McCammon JA
    13. Ourmazd A
    14. Frank J
    15. McLellan JS
    16. Chong LT
    17. Amaro RE

    (2021) A glycan gate controls opening of the SARS-CoV-2 spike protein

    Nature Chemistry 13:963–968.

    https://doi.org/10.1038/s41557-021-00758-3

    • PubMed
    • Google Scholar
67. 1. Takeda M

    (2022) Proteolytic activation of SARS-CoV-2 spike protein

    Microbiology and Immunology 66:15–23.

    https://doi.org/10.1111/1348-0421.12945

    • PubMed
    • Google Scholar
68. 1. Toelzer C
    2. Gupta K
    3. Yadav SKN
    4. Borucu U
    5. Davidson AD
    6. Kavanagh Williamson M
    7. Shoemark DK
    8. Garzoni F
    9. Staufer O
    10. Milligan R
    11. Capin J
    12. Mulholland AJ
    13. Spatz J
    14. Fitzgerald D
    15. Berger I
    16. Schaffitzel C

    (2020) Free fatty acid binding pocket in the locked structure of SARS-CoV-2 spike protein

    Science 370:725–730.

    https://doi.org/10.1126/science.abd3255

    • PubMed
    • Google Scholar
69. 1. Toelzer C
    2. Gupta K
    3. Yadav SKN
    4. Hodgson L
    5. Williamson MK
    6. Buzas D
    7. Borucu U
    8. Powers K
    9. Stenner R
    10. Vasileiou K
    11. Garzoni F
    12. Fitzgerald D
    13. Payré C
    14. Gautam G
    15. Lambeau G
    16. Davidson AD
    17. Verkade P
    18. Frank M
    19. Berger I
    20. Schaffitzel C

    (2022) The free fatty acid-binding pocket is a conserved hallmark in pathogenic β-coronavirus spike proteins from SARS-CoV to Omicron

    Science Advances 8:eadc9179.

    https://doi.org/10.1126/sciadv.adc9179

    • PubMed
    • Google Scholar
70. 1. Tong L
    2. Wang L
    3. Liao S
    4. Xiao X
    5. Qu J
    6. Wu C
    7. Zhu Y
    8. Tai W
    9. Huang Y
    10. Wang P
    11. Li L
    12. Zhang R
    13. Xiang Y
    14. Cheng G

    (2022) A retinol derivative inhibits SARS-CoV-2 infection by interrupting spike-mediated cellular Entry

    mBio 13:e0148522.

    https://doi.org/10.1128/mbio.01485-22

    • PubMed
    • Google Scholar
71. 1. Turoňová B
    2. Sikora M
    3. Schürmann C
    4. Hagen WJH
    5. Welsch S
    6. Blanc FEC
    7. von Bülow S
    8. Gecht M
    9. Bagola K
    10. Hörner C
    11. van Zandbergen G
    12. Landry J
    13. de Azevedo NTD
    14. Mosalaganti S
    15. Schwarz A
    16. Covino R
    17. Mühlebach MD
    18. Hummer G
    19. Krijnse Locker J
    20. Beck M

    (2020) In situ structural analysis of SARS-CoV-2 spike reveals flexibility mediated by three hinges

    Science 370:203–208.

    https://doi.org/10.1126/science.abd5223

    • PubMed
    • Google Scholar
72. 1. Vanommeslaeghe K
    2. Hatcher E
    3. Acharya C
    4. Kundu S
    5. Zhong S
    6. Shim J
    7. Darian E
    8. Guvench O
    9. Lopes P
    10. Vorobyov I
    11. Mackerell AD Jr

    (2010) CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields

    Journal of Computational Chemistry 31:671–690.

    https://doi.org/10.1002/jcc.21367

    • PubMed
    • Google Scholar
73. 1. Vanommeslaeghe K
    2. MacKerell AD Jr

    (2012) Automation of the CHARMM General Force Field (CGenFF) I: bond perception and atom typing

    Journal of Chemical Information and Modeling 52:3144–3154.

    https://doi.org/10.1021/ci300363c

    • PubMed
    • Google Scholar
74. 1. Vanommeslaeghe K
    2. Raman EP
    3. MacKerell AD Jr

    (2012) Automation of the CHARMM general force field (CGenFF) II: assignment of bonded parameters and partial atomic charges

    Journal of Chemical Information and Modeling 52:3155–3168.

    https://doi.org/10.1021/ci3003649

    • PubMed
    • Google Scholar
75. 1. Walls AC
    2. Park YJ
    3. Tortorici MA
    4. Wall A
    5. McGuire AT
    6. Veesler D

    (2020) Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein

    Cell 181:281–292.

    https://doi.org/10.1016/j.cell.2020.02.058

    • PubMed
    • Google Scholar
76. 1. Wang Q
    2. Zhang Y
    3. Wu L
    4. Niu S
    5. Song C
    6. Zhang Z
    7. Lu G
    8. Qiao C
    9. Hu Y
    10. Yuen KY
    11. Wang Q
    12. Zhou H
    13. Yan J
    14. Qi J

    (2020) Structural and functional basis of SARS-CoV-2 entry by using human ACE2

    Cell 181:894–904.

    https://doi.org/10.1016/j.cell.2020.03.045

    • PubMed
    • Google Scholar
77. 1. Wang L
    2. Wu Y
    3. Yao S
    4. Ge H
    5. Zhu Y
    6. Chen K
    7. Chen W-Z
    8. Zhang Y
    9. Zhu W
    10. Wang H-Y
    11. Guo Y
    12. Ma P-X
    13. Ren P-X
    14. Zhang X-L
    15. Li H-Q
    16. Ali MA
    17. Xu W-Q
    18. Jiang H-L
    19. Zhang L-K
    20. Zhu L-L
    21. Ye Y
    22. Shang W-J
    23. Bai F

    (2022) Discovery of potential small molecular SARS-CoV-2 entry blockers targeting the spike protein

    Acta Pharmacologica Sinica 43:788–796.

    https://doi.org/10.1038/s41401-021-00735-z

    • PubMed
    • Google Scholar
78. 1. Wang Q
    2. Meng F
    3. Xie Y
    4. Wang W
    5. Meng Y
    6. Li L
    7. Liu T
    8. Qi J
    9. Ni X
    10. Zheng S
    11. Huang J
    12. Huang N

    (2023) In Silico discovery of small molecule modulators targeting the achilles’ Heel of SARS-CoV-2 spike protein

    ACS Central Science 9:252–265.

    https://doi.org/10.1021/acscentsci.2c01190

    • Google Scholar
79. 1. Watanabe Y
    2. Allen JD
    3. Wrapp D
    4. McLellan JS
    5. Crispin M

    (2020) Site-specific glycan analysis of the SARS-CoV-2 spike

    Science 369:330–333.

    https://doi.org/10.1126/science.abb9983

    • PubMed
    • Google Scholar
80. 1. Watanabe Y
    2. Mendonça L
    3. Allen ER
    4. Howe A
    5. Lee M
    6. Allen JD
    7. Chawla H
    8. Pulido D
    9. Donnellan F
    10. Davies H
    11. Ulaszewska M
    12. Belij-Rammerstorfer S
    13. Morris S
    14. Krebs A-S
    15. Dejnirattisai W
    16. Mongkolsapaya J
    17. Supasa P
    18. Screaton GR
    19. Green CM
    20. Lambe T
    21. Zhang P
    22. Gilbert SC
    23. Crispin M

    (2021) Native-like SARS-CoV-2 spike glycoprotein expressed by ChAdOx1 nCoV-19/AZD1222 Vaccine

    ACS Central Science 7:594–602.

    https://doi.org/10.1021/acscentsci.1c00080

    • PubMed
    • Google Scholar
81. 1. Whittaker GR

    (2021) SARS-CoV-2 spike and its adaptable furin cleavage site

    The Lancet. Microbe 2:e488–e489.

    https://doi.org/10.1016/S2666-5247(21)00174-9

    • PubMed
    • Google Scholar
82. 1. Wrapp D
    2. Wang N
    3. Corbett KS
    4. Goldsmith JA
    5. Hsieh CL
    6. Abiona O
    7. Graham BS
    8. McLellan JS

    (2020) Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation

    Science 367:1260–1263.

    https://doi.org/10.1126/science.abb2507

    • PubMed
    • Google Scholar
83. 1. Xie Y
    2. Butler M

    (2023) Quantitative profiling of N-glycosylation of SARS-CoV-2 spike protein variants

    Glycobiology 33:188–202.

    https://doi.org/10.1093/glycob/cwad007

    • PubMed
    • Google Scholar
84. 1. Yan R
    2. Zhang Y
    3. Li Y
    4. Xia L
    5. Guo Y
    6. Zhou Q

    (2020) Structural basis for the recognition of SARS-CoV-2 by full-length human ACE2

    Science 367:1444–1448.

    https://doi.org/10.1126/science.abb2762

    • PubMed
    • Google Scholar
85. 1. Yao H
    2. Song Y
    3. Chen Y
    4. Wu N
    5. Xu J
    6. Sun C
    7. Zhang J
    8. Weng T
    9. Zhang Z
    10. Wu Z
    11. Cheng L
    12. Shi D
    13. Lu X
    14. Lei J
    15. Crispin M
    16. Shi Y
    17. Li L
    18. Li S

    (2020) Molecular architecture of the SARS-CoV-2 virus

    Cell 183:730–738.

    https://doi.org/10.1016/j.cell.2020.09.018

    • Google Scholar
86. 1. Yu W
    2. He X
    3. Vanommeslaeghe K
    4. MacKerell AD Jr

    (2012) Extension of the CHARMM general force field to sulfonyl-containing compounds and its utility in biomolecular simulations

    Journal of Computational Chemistry 33:2451–2468.

    https://doi.org/10.1002/jcc.23067

    • PubMed
    • Google Scholar
87. 1. Zhang S
    2. Qiao S
    3. Yu J
    4. Zeng J
    5. Shan S
    6. Tian L
    7. Lan J
    8. Zhang L
    9. Wang X

    (2021) Bat and pangolin coronavirus spike glycoprotein structures provide insights into SARS-CoV-2 evolution

    Nature Communications 12:1607.

    https://doi.org/10.1038/s41467-021-21767-3

    • PubMed
    • Google Scholar

Author details

1. A Sofia F Oliveira

   1. Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, United Kingdom
   2. School of Chemistry, University of Bristol, Bristol, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   sofia.oliveira@bristol.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8753-4950

2. Fiona L Kearns

   Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, United States

   Contribution

   Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

3. Mia A Rosenfeld

   Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, United States

   Contribution

   Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

4. Lorenzo Casalino

   Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, United States

   Contribution

   Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

5. Lorenzo Tulli

   1. Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, United Kingdom
   2. School of Chemistry, University of Bristol, Bristol, United Kingdom

   Contribution

   Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0008-1268-1217

6. Imre Berger

   1. School of Chemistry, University of Bristol, Bristol, United Kingdom
   2. School of Biochemistry, University of Bristol, Bristol, United Kingdom
   3. Max Planck Bristol Centre for Minimal Biology, School of Chemistry, Bristol, United Kingdom

   Contribution

   Writing – original draft, Writing – review and editing

   Competing interests

   holds shares in Halo Therapeutics Ltd

   "This ORCID iD identifies the author of this article:" 0000-0001-7518-9045

7. Christiane Schaffitzel

   School of Biochemistry, University of Bristol, Bristol, United Kingdom

   Contribution

   Writing – original draft, Writing – review and editing

   Competing interests

   holds shares in Halo Therapeutics Ltd

   "This ORCID iD identifies the author of this article:" 0000-0002-1516-9760

8. Andrew D Davidson

   School of Cellular and Molecular Medicine, University of Bristol, University Walk, Bristol, United Kingdom

   Contribution

   Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1136-4008

9. Rommie E Amaro

   Department of Molecular Biology, University of California San Diego, La Jolla, United States

   Contribution

   Supervision, Investigation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

10. Adrian J Mulholland

    1. Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, United Kingdom
    2. School of Chemistry, University of Bristol, Bristol, United Kingdom

    Contribution

    Conceptualization, Supervision, Funding acquisition, Writing – original draft, Writing – review and editing

    For correspondence

    adrian.mulholland@bristol.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1015-4567

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