key: cord-0758021-vlwzosj8
authors: Cheng, Mary Hongying; Krieger, James M.; Banerjee, Anupam; Xiang, Yufei; Kaynak, Burak; Shi, Yi; Arditi, Moshe; Bahar, Ivet
title: Impact of New Variants on SARS-CoV-2 Infectivity and Neutralization: A Molecular Assessment of the Alterations in the Spike-Host Protein Interactions
date: 2022-02-17
journal: iScience
DOI: 10.1016/j.isci.2022.103939
sha: 706080d460be093ec3bfa7bd7258ce66235fd9ff
doc_id: 758021
cord_uid: vlwzosj8

The emergence of SARS-CoV-2 variants necessitates rational assessment of their impact on the recognition and neutralization of the virus by the host cell. We present a comparative analysis of the interactions of Alpha, Beta, Gamma, and Delta variants with cognate molecules (ACE2 and/or furin), neutralizing nanobodies (Nbs) and monoclonal antibodies (mAbs) using in silico methods, in addition to Nb-binding assays. Our study elucidates the molecular origin of the ability of Beta and Delta variants to evade selected antibodies, such as REGN10933, LY-CoV555, B38, C105 or H11-H4, while being insensitive to others including REGN10987. Experiments confirm that nanobody Nb20 retains neutralizing activity against the Delta variant. The substitutions T478K and L452R in Delta variant enhance the associations with ACE2, while P681R promotes recognition by proteases thus facilitating viral entry. The Ab-specific responses of variants highlight how full-atomic structure and dynamics analyses are required for assessing the response to newly emerging variants.

The high transmissibility and mutation rates of SARS-CoV-2, together with the lack of a robust pre-existing immunity by hosts (Petersen et al., 2020; Tortorici et al., 2020) and slow rate of immunization through vaccinations, have caused COVID-19 cases to surge to more than 240 million worldwide by the end of October 2021. While several antibodies (Abs) proved to neutralize the wildtype (WT) virus, their efficacies against emerging variants ought to be carefully monitored, especially against those noted to possibly evade the host immune response (Andreano et al., 2021; Kemp et al., 2021; McCarthy et al., 2021; Meng et al., 2021; Planas et al., 2021) .

The receptor binding domain (RBD; residues 331-524) of SARS-CoV-2 spike (S) glycoprotein has been a focal point in many studies due to its role in binding to the human receptor ACE2 as well as recognition by the immune system (Belizario, 2021) . Many mutations in the emerging variants are located in the RBD, and not surprisingly, the RBD is targeted by the majority of mAbs from COVID-19 patients and mAb therapies under investigation. Several RBD residues underwent mutations arisen independently in multiple lineages (Plante et al., 2021) . These include the N501Y in the Alpha, Beta, Gamma, and Omicron variants; K417 mutated to N in the Beta, Delta and Omicron variants and to T in the Gamma variant; and E484 mutated to K in the Beta and Gamma variants and to A in the Omicron variant.

SARS-CoV-2 variants have been studied in deep scanning mutagenesis Greaney et al., 2021a; Greaney et al., 2021c; Starr et al., 2021a; Starr et al., 2021b; Starr et al., 2020) , in vitro evolution (Liu et al., 2021d; Muecksch et al., 2021; Wang et al., 2021c; Weisblum et al., 2020; Zahradnik et al., 2021) , cryo-EM (Cai et al., 2021; Dejnirattisai et al., 2021b; Du et al., 2021; Gobeil et al., 2021; Han et al., 2021; Liu et al., 2021a; McCallum et al., 2021a; McCallum et al., 2021b; Pymm et al., 2021; Supasa et al., 2021; Wang et al., 2021a; Zhang et al., 2021a; Zhu et al., 2021) , computational studies Cheng et al., 2021a; Fratev, 2021; Khan et al., 2021; Villoutreix et al., 2021) , and Ab-binding experiments (see below). There have been comprehensive attempts to map out different classes of Abs and relate them to mutations (Barnes et al., 2020a; Barnes et al., 2020b; Cerutti et al., 2021; Dejnirattisai et al., 2021a; Greaney et al., 2021b; Liu et al., 2020; Rogers et al., 2020; Wang et al., 2021b; Wheatley et al., 2021; Xiang et al., 2020; Yuan et al., 2021) , but a systematic comparative analysis of the behaviour of selected variants using structure-based simulations and analytical methods is lacking.

In addition to the RBD, another site of critical importance is the S1/S2 cleavage site P681RRAR↑S of the spike , which is implicated in multiple roles: The cleavage of the spike monomers at this site is an essential step priming the subunit S2 to its fusogenic activity for viral entry. Of interest is to understand how mutations at those sites affect the interaction with host cell proteins such as furins involved in the proteolytic cleavage. Furthermore, this region belongs to a superantigenic site shown in earlier work to trigger an adaptive immune response that could escalate into hyperinflammation and cytokine storm typical of the multi-system inflammatory syndrome (MIS) observed in children and severely infected adults (Cheng et al., 2021b; Cheng et al., 2020) . Of interest is mutations at P681, to R in the Delta variant (Sheikh et al., 2021) and

to H in Alpha and Omicron, which lies within this superantigen-like motif and has been proposed to be a key site in SARS-CoV-2 infection and pathogenesis .

In the present study, we present results from structure-based in silico modelling and full-atomic simulations to assess the interactions of the variants listed in Table 1 with cognate proteins essential to viral entry, and their ability to escape neutralizing monoclonal antibodies (mAbs) and nanobodies (Nbs) listed in Table 2 . We focus on the Alpha (Kent, UK), UK2 (Bristol, UK), Beta (South Africa), and Delta (India) variants. Our study indicates higher affinity of all these variants to bind to ACE2, compared to WT spike. The study further provides a molecular rationale for experimentally observed iterations in the neutralizing potential of specific mAbs and Nbs.

Examination of the effect L452R shared by the Delta and Epsilon (Californian) variants shows that this mutation weakens interaction with, and thereby reduces the efficacy of, selected mAbs (e.g. Eli Lilly LY-CoV555) ; whereas an ultrapotent inhalable nanobody Nb20 (Xiang et al., 2020) shows a comparable if not slightly increased affinity to the Delta variant.

Finally, the Delta variant exhibits high affinity to ACE2 and furin, thus facilitating viral entry.

N501Y was the hallmark of the Alpha variant, shared with UK2, Beta, and Gamma variants. To examine the effect of N501Y in these contexts, we generated structural models for three mutant RBDs complexed with ACE2: N501Y (Alpha); double mutant E484K+N501Y (UK2; previous variant of concern VOC-202102-02); and K417N, E484K, and N501Y (Beta) using the approach described in STAR*Methods; and performed a series of molecular dynamics (MD) simulations of the WT RBD-ACE2 complex (run 1 in Table S1 ) and these mutants (runs 2, 3, and 5).

We first verified that the Alpha RBD-ACE2 model (Cheng et al., 2021a) we constructed by in silico mutagenesis shows a root-mean-square-deviation (RMSD) of only 0.9 Å from the more recently resolved cryo-EM structure (PDB: 7MJN) (Figure 1A ) and our in silico Beta RBD-ACE2 model (Cheng et al., 2021a) aligned against the cryo-EM structure of Gamma RBD-ACE2 (PDB: 7NXC) (Dejnirattisai et al., 2021b) shows an RMSD of 1.0 Å only ( Figure 1B) , in support of the models used in simulations.

Simulations of the WT RBD-ACE2 complex (PDB: 6LZG (Wang et al., 2020a) ; run 1) showed that N501 forms hydrogen bonds with ACE2 residues Y41 and K353 (Figure 1C ; non-spike residues written in italic in the text and figures to distinguish them from spike residues). During the entire course of the three mutant sets of simulations, N501Y formed stable cation-π interactions with ACE2 K353 and engaged in aromatic-aromatic interactions with Y41 ( Figure 1D ). These associations are also captured by cryo-EM structural studies (Dejnirattisai et al., 2021b; . The same ACE2 residues have also been reported to regulate binding to SARS-CoV S with K353H, K353A, K353D and Y41A disrupting interactions (Li et al., 2005) .

Binding free energy histograms for the complexes formed between ACE2 and the RBD (mutants and WT) evaluated using the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) method (Genheden and Ryde, 2015) (Figure 2A ) applied to MD snapshots showed that van der Waals interactions played a dominant role in defining the significantly higher stability of the Alpha variant RBD-ACE2 complex compared to WT RBD-ACE2. This effect persists in UK2 , being further strengthened by electrostatic and solvent-accessible surface area (SASA) effects ( Figure   2B ; see also Figure S1A'-C'). Independent computations performed using PRODIGY (Xue et al., 2016) for evaluating the binding free energy of the MD snapshots (see STAR* Methods) confirmed the higher affinity of all Alpha, Beta, and UK2 variants for binding ACE2 compared to that of the WT RBD (Figure S1A-C), UK2 yielding the strongest binding affinity. Therefore, shared substitution N501Y promotes ACE2 binding to potentially increase infectivity. This is also J o u r n a l P r e -p r o o f indicated by binding assays (Cai et al., 2021; Supasa et al., 2021; Zhu et al., 2021) , deep mutational scanning (Starr et al., 2020) , and free energy perturbations (Fratev, 2021) . K417N mutation abolishes a salt bridge otherwise formed between ACE2 and the RBD, while E484K mutation favours a new interfacial salt bridge K417, mutated to N and T in the Beta and Gamma variants, is specific to SARS-CoV-2 S, compared with SARS-CoV. The salt bridge between K417 (RBD) and D30 (ACE2) (Figure 1C) assists in RBD-ACE2 binding (Wang et al., 2020b) . Simulations showed that this salt-bridge is highly stable ( Figure 1E ) and its occurrence probability (also called salt-bridge (SB) occupancy) of 0.59 ± 0.2 (averaged over three runs) is substantially higher than other (intermittently formed) interfacial salt bridges ( Figure 1F) . Interestingly, the local conformational changes induced by the mutation N501Y further stabilize the K417-D30 salt bridge (to increase SB occupancy to 0.81 ± 0.1; Figure S2A -B). The loss of this salt bridge by the mutations K417N and K417T would therefore decrease the binding affinity of the respective Beta and Gamma variants to ACE2 (compare Figure S2 panels C and D). This has been observed for the Delta+ variants AY.1 and AY.2 where the K417N mutation reduced ACE2 binding affinity by at least 3-fold relative to the Delta variant (McCallum et al., 2021b) .

The effect of E484K (shared by UK2, Beta, and Gamma; Table 1 ) is more complex. An interfacial salt-bridge between E484 and ACE2 K31 was observed to form intermittently in the complex with the WT spike, albeit with low occupancy (Figure 1E -F). MD simulations with the variants (runs 3, 5 and 6 in Table S1 ) showed that a new interfacial salt bridge, K484-E75, forms instead for relatively extended durations (up to 35% of the trajectory in the Beta variant; Figure 1G -H). This salt bridge was also noted in a recent MD simulation study of E484K-associated mutants . Simulations also revealed that the UK2 variant could form up to three interfacial salt bridges (K417-D30, K484-E75 and K458-E23; Figure S2A -C).

Notably, E484 is in a loop region that undergoes significant conformational fluctuations ( Figure   S2C -D), as also noted earlier (Bhattarai et al., 2021) . This loop contains a potential disulphide bridge, C480-C488. Simulations performed with this disulphide (Table S1 ) showed a decrease in RMSD from the crystal structure (PDB: 6LZG) from 2.8 ± 1.0 Å in the absence of Cys-Cys link (runs 1 to 6) to 2.0 ± 0.5 Å in its presence (runs 7-11) due to suppression of loop mobility.

Concomitantly, the occurrence of the salt bridge K484-E75 was prevented, drawing attention to J o u r n a l P r e -p r o o f the potential role of this Cys-Cys crosslink in the binding of ACE2. Yet, cumulative results from all runs confirmed that the ACE2 binding enthalpy HMM/GBSA of the Beta variant (as well as the Alpha variant) remained significantly higher than that of the WT RBD (Figure S1E-F), consistent with deep mutational scanning (Starr et al., 2020) and in vitro evolution experiments (Zahradnik et al., 2021) .

The residues L452 and T478 (mutated in the Delta variant) do not make direct contacts with ACE2 in the resolved structures (see Table 2 ). Yet, given that simulations may induce conformational changes and expose newly formed interactions in the mutants not evident from examination of static structures or homology models (as observed in Figure 1C -H), we carried out multiple MD runs and evaluated the energetics of conformers sampled in those simulations. MM/GBSA computations indicated the significantly higher affinity of the Delta RBD to bind ACE2 compared to other variants (Figure S1G), the major contribution again originating from van der Waals interactions ( Figure 2C) . Examination of the Delta RBD further revealed a change in the surface distribution of charges in comparison to the WT RBD, the two mutations T478K and L452R rendering it more basic (positively charged), which would overall increase its attraction to the predominantly acidic ACE2 interfacial surface ( Figure 3A) . A significant increase in binding affinity may partly explain the high virulence of the Delta variant.

In summary, out of the three mutations that define the Beta or Gamma variants, N501Y promotes the association with ACE2 through amino-aromatic and aromatic-aromatic interactions; E484K further contributes by electrostatic attractions that are otherwise 'mute', such that the double mutant E484K+N501Y (UK2) exhibits a higher affinity. Notably, the third mutation K417N or K417T in the Beta or Gamma variant reduces the ACE2-binding affinity and may help moderate the infectivity of these variants. In contrast, the mutations T478K and L452K significantly increase the affinity of the Delta variant RBD to bind ACE2. 

Notably, all variants exhibit higher affinity to bind ACE2 compared to WT RBD. The first three rows represent the computational results obtained here with two different methods (Methods I and II both use PRODIGY, the former applied to MD snapshots and the latter applied to HADDOCK-refined conformers (Cheng et al., 2021a) ; see STAR*Methods), and their average. Method I yielded the histograms presented in Figure S1A -D. The experimentally measured dissociation constants show broad variations between different studies, e.g. the dissociation constant for WT RBD-ACE2 varies from 3.1 nM (Yang et al., 2021) to 218.29 nM (Gobeil et al., 2021) . Therefore, it is hard to obtain a precise absolute value, but the relative values reported by the same group for different variants (listed in the same row) as well as the averages over the data compiled for the same variant, indicate some consistent patterns. Overall, combining the data from independent computations and experiments, the rank order WT < Beta ≈ Gamma < Alpha ≈ Delta < UK2 emerges in increasing ACE2-binding affinity. The Delta and Alpha variants' affinities, as well as that of UK2, are stronger than that of the WT by a factor of approximately two based on our computations.

P681R renders the polybasic S1/S2 cleavage site more attractive to furin, thus promoting proteolytic cleavage and fusogenicity Proteolytic cleavage of the spike into S1 and S2 subunits at the site R685↑S686 is an essential step for the acquisition of fusogenic properties by the S2 trimer. Recognition by host cell proteases such as TMPRSS2 and furin is enabled by a polybasic stretch, R682RAR685, that serves as furinrecognition motif. The third mutation characterizing the Delta variant, P681R, is adjacent to this motif. The same residue is also mutated in the Alpha, UK2 and Omicron variants as P681H.

These substitutions render the segment P681RRAR685↑S686 even more basic than the WT cleavage site, and might be expected to be more attractive to the acidic epitope on furin or other proteases, thus increasing the probability (or population) of cleaved spikes. To test this hypothesis, we examined the propensity of the Delta variant to bind to furin, compared to WT SARS-CoV-2 S using a previously generated complex of the WT spike with furin (Cheng et al., 2021b) . The majority of models generated by ClusPro for the furin-spike complex showed that R681 associates with the acidic residues of furin, complementing other basic residues in the spike polybasic motif. Figure 3B illustrates such a highly stable binding pose between furin and the Delta variant. Three arginines, R681, R682 and R683, form a network of salt bridges with acidic residues from furin to J o u r n a l P r e -p r o o f enable the insertion of the catalytic serine S368 (forming the catalytic triad of furin with H194 and D153) near the cleavage site. In particular, the spatial and sequential juxtaposition of furin acidic residues E257 and E258 to R681 is an additional attractor to the segment RRRAR685↑S686, which implies an increased probability of interaction with furin in favor of the ensuing transition of the S2 trimer into its fusogenic form and shedding of the S1 subunits to the extracellular region. Notably, a recent experimental study indicated that P681R in the Delta variant enhanced replication via increased S1/S2 cleavage (Liu et al., 2021c) .

It is worth noting the location of the mutation P681R overlaps with a superantigenic region Noval Rivas et al., 2020; Porritt et al., 2021) that has been shown to elicit multiinflammatory response. The increased S1 shedding and exposure in the Delta variant may enhance such superantigenic effects. Notably, a recent study has shown that increased S1 levels in the extracellular medium correlate with more severe disease (Ogata et al., 2020) .

Apart from interfacial interactions, it is of interest to examine the role of mutated residues in the structural mechanics of the spike and the complexes it forms. Gaussian Network Model (GNM) analysis (Bahar et al., 1997) of the global dynamics of the RBD-ACE2 complex revealed that N501 plays a central mechanical role as well as T500 and Y505, participating in a hinge centre (minimum in the global mobility profile) as illustrated in Figure 4A and D. Further analysis of essential sites based on ESSA (Kaynak et al., 2020) yielded the profiles in Figure 4B and C for the RBD-ACE2 complex and isolated RBD, respectively. Here the peaks correspond to key sites/residues that play a dominant role in altering the essential/global dynamics of the structure (see STAR*Methods). We present in panels F and G color-coded ribbon diagrams (peaks are coloured red, and minima, blue), which show that the residues at the interface between ACE2 and RBD, as well as those lying in the ACE2 catalytic cleft are essential residues in the global dynamics of the complex.

In Figure 4B , our analysis highlights two of the residues mutated in the Beta variant (K417 and N501; in red) and two ACE2 residues (Y41 and K353; in blue) reported (Li et al., 2005) to regulate ACE2-spike binding, as essential residues. It is interesting to note that several residues gain functional importance (emerge as peaks) upon complexation ( Figure 4C) , including K417 and J o u r n a l P r e -p r o o f N501 as well as T500 and Y505 in RBD, and D30, K31, Y41 and K353 in ACE2. Moreover, the mutation site L452 shared by Delta and Epsilon variants stands out as an essential site in the RBD. In contrast, the Delta mutation site T478 and Beta mutation site E484 do not exert a substantial effect on the global dynamics.

We also note that in an insightful study of the spike interactions with ACE2 and Regeneron mAbs, the authors predicted N501 and K417 (as well as E406 and N439) as effectors of allosteric communication (Verkhivker et al., 2021) . Our ESSA-based allosteric pocket prediction corroborates that this interfacial region (comprising RBD K417, N501, and Y505, and ACE2 D30 and K31; Figure 4E ) has an allosteric potential to interfere with the overall conformational dynamics of RBD-containing complexes.

We analysed a set of 12 Abs, consisting of ten mAbs and two Nbs H11-H4 and Nb20, whose complexes with SARS-CoV-2 S or RBD have been structurally resolved ( Table 2 see below) , columns 1 and 3). Figure S3A displays their binding poses, generated by structurally aligning the RBD regions of the corresponding S/RBD-Ab complex structures. Table 2 column 2 lists the spike residues that make interfacial contacts with the listed Abs, which reveals five epitope classes, termed here classes I to V (last column). Examination of these residues shows that none of the Abs makes contacts with the residues T478, D614 and P681 mutated in one or more of the examined variants (Table 1) . Likewise, the binding epitopes of four mAbs (REGN10987, EY6A, HO14, and CR3022) do not include the mutation sites K417, E484, N501, L452, and T478 ( Figure   S3B ). These four mAbs belong to class 3 (REGN10987) or 4 in the structural nomenclature (Barnes et al., 2020a ) (corresponding to classes H and G in the competition epitope mapping) , which are designated as IV and V here. We focused therefore on the remaining eight Abs, and quantitatively examined the effects on the binding to Beta and Delta variants. The computed changes in binding free energies (see STAR*Methods) and corresponding data from the literature (experiments and/or prior computations) are listed in columns 4-8, with the up/down arrows in green/red indicating an increase/decrease in binding affinity, compared to the WT RBD. Table S2 lists the computed binding affinities to WT RBD.

The mutations K417N and E484K reduce the binding affinity of REGN10933, C105, B38,

Two mAbs adversely impacted by the mutations in the variants are C105 (Figure 5A -C) and REGN10933 ( Figure 5D -I), which both belong to Class 1 or E and depend heavily on K417. In C105, K417 coordinates a polar network that supports its high affinity to bind the SARS-CoV-2 RBD. K417 forms a salt bridge with C105 residues E96/E99, which also engage RBD Y453 (blue circle in Figure 5A ; zoomed view in Figure 5B ). This network is completely abolished by the substitution K417N ( Figure 5C ) in the Beta variant. This significant loss in the attractive interaction is partially alleviated by a new favourable interaction between N417 and mAb Y52, and a cationπ interaction that Y501 makes with mAb K31, but the net effect is a reduction in binding affinity by 0.42 kcal/mol relative to the WT RBD ( Table 2, (Figure 5D and G). This is accompanied by rearrangement of the loop containing E484 (and T478) as noted in previous simulations (Bhattarai et al., 2021) . Even though R403-D31 partly compensates for loss of K417-D31 in the Beta variant ( Figure 5H) , the additional loss of E484-R100 salt bridge and accompanying changes in interactions overall decrease the binding efficacy of REGN10933 to the Beta variant by 1.93 kcal/mol. For Eli Lilly LY-CoV555, E484 makes two critical salt bridges with mAb residues R50 and R96

( Figure 6A , red circle). E484K severely disrupts these interactions via charge repulsion. Indeed, during our MD simulations, we observed a dissociation of mAb LY-CoV555 from its initial contact pose with the Beta variant RBD within 10 ns, succeeded by occasional return to bound forms, and eventual drift away after ~150 ns ( Figure 6B ). The significant reduction in MM/GBSA interaction energy ( Figure 6C ) is also consistent with the escape of Beta RBD from mAb LY-CoV555. Independent computations using anisotropic network model (ANM) (Eyal et al., 2006) J o u r n a l P r e -p r o o f conformers (see STAR*Methods) also confirmed a reduction in binding affinity of 0.29 kcal/mol ( Table 2) . Figure S4 panels A-B present the results for the Nb H11-H4. The strong attractive interaction between WT RBD E484 and H11-H4 R52 turns into a strong repulsion between R52 and K484, and the free energy change Gbinding of the Beta variant with respect to WT confirms a weakening in interaction by 0.32 kcal/mol. While a similar effect is observed in BD23 (panels C-D) where the original favourable interaction between E484 and BD23 R107 is disrupted in the Beta variant, compensating interactions take place near K484, plus Y501 of the Beta RBD engages in close interactions with BD23, such that this mAb exhibits an increase in binding affinity to Beta variant (Gbinding = -0.94 kcal/mol relative to WT RBD; Table 2 ), in accord with recent computations (Ray et al., 2021) . Wang et al., 2021b ) or no effect .

The Abs REGN10933, B38, BD23, LY-CoV555, and H11-H4 undergo a decrease in binding affinity to Delta RBD compared to the WT RBD Similar analysis conducted for the Delta variant led to a reduction in binding affinity of many Abs (Table 2, column 5) consistent with a recent study where 16 out of 26 mAbs showed a marked decrease or complete loss in neutralizing activity to the Delta variant .

In the case of the complex formed with REGN10933, the L452R mutation is observed to induce a conformational rearrangement leading to the disruption of the K417-D31 and R403-D31 salt J o u r n a l P r e -p r o o f bridges (Figure 5E and I). The T478K mutation generates a new salt bridge K478-D92 ( Figure   5F ), which may help in retaining the binding and neutralizing activity of mAb REGN10933 to the Delta variant observed in an extensive study . On the other hand, a decrease in neutralization activity has been reported in another study (Tada et al., 2021) . Our current evaluation indicates a 6-fold decrease in the binding affinity of REGN10933 to Delta variant (Gbinding = 1.06 kcal/mol; Table 2 ).

The RBD residue L452 is also engaged in a network of hydrophobic contacts with LY-CoV0555 I54 and L55 in WT S (complemented by L492) (Figure 6A ; blue circle). The L452R mutation breaks these favourable hydrophobic interactions ( Figure 6D) . Even though the Delta variant RBD remained bound to LY-CoV555 due to the salt bridge between E484 and R50/R96 in our 200 ns MD simulations, energy calculations showed a 3-fold reduction in binding affinity of LY-CoV555 due to L452R mutation shared by the Epsilon and Delta variants in line with recent experiments showing reduced neutralizing activity by this mAb . Notably, a previous experimental study also found that L452R enabled escape from LY-CoV0555 . The Kappa variant (B.1.617.1) containing E484Q and L452R (Cherian et al., 2021; Verghese et al., 2021) is less able to escape from Abs McCallum et al., 2021b) , which is likely due to Q484 still being able to make favourable interfacial contacts.

Computations repeated for the second Eli Lilly mAb, LY-CoV016, on the other hand, indicated no clear effect on binding of this mAb to Delta variant ( Table 2) , in agreement with its experimentally observed retained neutralization activity .

Overall, the results compiled in Table 2 columns 4-5 are in agreement with experiments or prior simulations whenever there are available data. A few cases stand out where the predicted changes in binding affinities differ from previous observations. For example, in the case of BD23, we predict a decrease in binding affinity (by 0.33 kcal/mol) to the Delta variant compared to the WT RBD. This contrasts the favourable interaction observed in a recent work (Ray et al., 2021) . Finally, as a proof of concept, we explored the validity of our computational predictions with the help of enzyme-linked immunosorbent assays (ELISA) performed for Nb20, an ultrapotent and inhalable nanobody to block WT SARS-CoV-2 (Xiang et al., 2020) , for the WT RBD and a mutant RBD with critical substitutions L452R and T478K of the Delta variant.

In the case of the WT RBD, Nb20 forms a salt bridge E484-R31/R97 and hydrophobic interaction L452-M55 (Figure 7A) , both of which remained stable during MD simulations, the E484-R31/R97 salt bridge with an occupancy of 96 ± 4 %, suggesting these may play a substantial role in stabilizing the Nb20 complex.

For the Delta variant, our modelling suggests that the hydrophobic interaction (L452-M55 in the WT) would not significantly contribute to the overall RBD binding energy as a result of the L452R substitution. However, L452R promoted a new interaction with S52 via hydrogen bonding ( Figure   7B ). Interestingly, the T478K mutation favoured a new salt bridge formation with E42/E44 ( Figure   7C ). The E484-associated salt bridges remained stable (>95% occupancy), indicating that E484 was essential to binding Nb20. Mutations breaking this salt bridge, e.g. E484K of Beta variant, reduce, if not abolish, its ultrahigh-affinity to the RBD . The predicted change in interaction energy of -0.15 kcal/mol ( Table 2 ) indeed suggests that the Delta variant would not evade neutralization by Nb20. In agreement with this prediction, Nb20 is observed in binding assays to retain (if not increase) its potency for the Delta RBD ( Figure 7D ).

Recently observed variants continue to adapt to binding the host receptor ACE2, which together with the high mutation rate of SARS-CoV-2, points to a risk of still-undetected additional members of the coronaviridae family making the jump from their traditional hosts to humans in the future.

Our analysis shows an enhancement in the binding affinity to all variants compared to the WT RBD (Table 3) . Computed dissociation constants fall within the broad range observed in experiments. The relative ACE2-binding affinities of variants is indicated by both computations J o u r n a l P r e -p r o o f and experiments to yield a rank order of WT < Beta ~ Gamma < Alpha < Delta < UK2 with increasing affinity.

To assess the impact of the variants on interaction with and hence neutralization by antibodies, we examined 10 mAbs and 2 Nbs ( Table 2) . We found five Ab binding epitopes (I to V) on the SARS-CoV-2 RBD similar to those identified in other studies (Barnes et al., 2020a; Liu et al., 2020; Xiang et al., 2020) , including major classes (I to III) where residues E484 and K417 contribute to the binding and their mutations could be responsible for evading selected Abs. The interactions between either the Beta or Delta spike and many Abs such as C105, B38, REGN10933, LY-CoV555 and H11-H4 were shown to be weakened if not abolished. MD simulations of hundreds of nanoseconds revealed associations and dissociations that stabilized or destabilized the specific complexes, leading to the final assessments. Notably, many structurebased arguments we recently made with regard to the infectivity and neutralization of the Alpha and Beta variants in our recent report (Cheng et al., 2021a) are corroborated by structural data in a recent study (Cai et al., 2021) , and simulations may further help provide efficient assessments of the effect of future mutations.

A recent mapping of RBD mutations that escape REGN-COV2 cocktail and Eli Lilly's LY-CoV016 antibodies ) using a deep mutational scanning method (Greaney et al., 2021c) revealed several sites whose point mutations mediate escape. Interestingly the mutation (E406W) escaped the cocktail of both REGN antibodies even though it does not make direct contacts with either mAb in the resolved structure (Table 2) , inviting attention to the necessity of considering structural changes induced by substitutions, hence the need to conduct MD simulations as well as network-based analysis of allosteric dynamics to identify critical substitutions. Our analysis indicates the critical mechanical roles of N501, Y505 and E406 (Figure 4) .

The location of the Delta variant spike mutation P681R is worth special attention. Our simulations demonstrated that the mutation rendered this site more attractive (than the WT) to furin ( Figure   3B ), which could thus enhance proteolytic cleavage and thereby viral entry. Another important aspect of this specific site P681RRAR685 is that it belongs to a putative superantigenic segment having sequence and structure features highly similar to those of a bacterial toxin, Staphyloccocal J o u r n a l P r e -p r o o f enterotoxin B (SEB), potentially causing a cytokine storm or MIS-C .

Enhanced cleavage of S and shedding of the S1 subunits would further expose the highly reactive C-terminal end R681RRAR685 of the superantigenic segment in the Delta variant, which might further escalate hyperinflammatory responses. Indeed, recent studies show that higher serum levels of circulating free S1 spike protein are associated with more severe disease among adult COVID-19 patients (Ogata et al., 2020) , as well as among children with severe MIS-C disease (Porritt et al., 2021) . The importance of the furin cleavage site in SARS-CoV-2 infection and pathogenesis is confirmed in several studies Lau et al., 2020) .

Given the importance of the furin cleavage site and the shed S1 in infectivity and disease pathology, it is of great interest that two dominant SARS-CoV-2 variants, Alpha and Delta, both have altered furin cleavage sites. In the Alpha variant, which was 50% more contagious than the original Wuhan strain, P681 is substituted by a histidine (P681H); in the Delta variant, which is about 50% more contagious than the Alpha variant, it is substituted by an arginine (P681R) (SPI-M-O, 2021). Both changes make the sequence more basic potentially leading to a more effective recognition by the polyacidic furin motif. Our simulations have shown that the P681R form binds more efficiently to furin. Notably, a recent study reported that Delta variant (P681R) S proteins were highly cleaved (>50 (Peacock et al., 2021) . A more recent preprint has also shown that P681R mutation enhances the fitness of Delta over the Alpha variant and that increased cleavage leads to increased infection via cell surface entry (Liu et al., 2021c) . Finally, since increased S1/S2 cleavage would result in increased cleaved products, and since circulating levels of S1 correlate with disease severity, in line with reports that the Delta variant may lead to more severe disease (Fisman and Tuite, 2021; Sheikh et al., 2021) . Our data, together with other recent studies, suggest that spike mutations that affect the furin cleavage site should be carefully monitored, as they may increase viral fitness, infectivity, and virulence.

Overall, our analysis indicates the ability of variants to evade mAb or Nb treatment depends on the mutations and on the specific Abs and may be alleviated by combination treatments consisting of mAbs that bind non-overlapping epitopes. Notably, the mutation sites on the Delta variant make no contacts with most of the investigated mAbs, except the mAb LY-CoV555 and the Nbs H11-H4 and Nb20. So, this variant would not be expected to escape all the mAbs, except for these J o u r n a l P r e -p r o o f three, which we examined more carefully. Our analysis demonstrated that among them Nb20 would not lose its potency, which was also confirmed by ELISA experiments (Figure 7) ; whereas LY-CoV555 would exhibit a significant weakening in its neutralization activity.

A potential approach to minimize the impact of mAb escape mutations is to develop additional mAbs with epitopes that are outside of the RBD and/or cross-reactive across coronaviridae, with low tolerance for mutation Wec et al., 2020) . We have recently described such a mAb (Cheng et al., 2021b ) that binds the putative superantigenic-like motif of SARS-CoV-2 S . That particular mAb, 6D3, presents the dual advantage of potentially blocking the S1/S2 cleavage site thus interfering with proteolytic cleavage that is essential for viral entry, in addition to targeting the superantigenic region that may contribute to MIS in adults, children and adolescents with severe COVID-19 (Cheng et al., 2021b) . Given the potential emergence of immune evasion mutations that maintain virulence or those that confer resistance to immunizations or therapies (Weisblum et al., 2020) , in silico assisted genomic/molecular surveillance may provide valuable feedback for accelerating the design of experimental studies in response to the pandemic.

In the present study, we have benefited from the wealth of structural data resolved for several complexes of the SARS-CoV-2 spike (or RBD) with ACE2 or various Abs. While the in silico structural models for mutants often yield good agreement with resolved structures (as illustrated here in Figure 1A -B), model selection and refinement often require extensive simulations (docking, energy minimization and/or MD simulations), which lead to multiple models and conformers, and accurate sampling of the complete space of conformations is often a challenge.

Even though MD simulations are considered as the ground-truth in molecular computations, they are also known to suffer from sampling inaccuracies, in addition to approximations in the force fields and parameters. PRODIGY, optimized to correlate experimental binding affinities with the number of interfacial contacts and non-interacting surface properties (Kastritis et al., 2014) derived from resolved structures (Vangone and Bonvin, 2015) , appears to be one of the bestperforming empirical predictors reported to date (Vangone and Bonvin, 2015) , which we adopted here, together with methods of different complexities, from HADDOCK refinement with short energy minimization to detailed MM/GBSA evaluation of thousands of conformers sampled in MD trajectories. Current simulations starting from equilibrium conformations stabilized under crystallization conditions may remain in the close vicinity of this highly stable state, and not sample J o u r n a l P r e -p r o o f the whole conformational space accessible under physiological conditions. Finally, MM/GBSA energies evaluated here represent the enthalpic contribution to binding free energies.

Examination of the differences in entropic effects between WT and variant complexes would be valuable. Despite all these limitations, the combination of empirical machine-learning type approach (PRODIGY energies) with full-atomic free energy calculations (MM/GBSA) helped consolidate the data from simulations and make robust inferences on the relative affinity of different variants to bind ACE2 (Table 3) , or on the impact of mutations on the neutralizing activity of various Abs ( Table 2 ). The method of analysis adopted here may be applied to other variants to estimate their ACE2-binding affinity relative to that of the WT spike, as well as their propensity to escape specific Abs. Furthermore, the comparative analysis gives insights into the mechanistic bases of the observed behaviour, while providing a framework for predictions on systems not experimentally characterized to date.

Lead contact: All requests should be directed to and will be fulfilled by the Lead Contact, Ivet Bahar (bahar@pitt.edu) .

This study did not generate new unique reagents.

 The source code for all computations and input/output data are available upon request.  Additional data are available to other researchers for use in independent scientific research upon request (bahar@pitt.edu).

In silico mutagenesis. We performed in silico mutagenesis and assessment of structural models for SARS-CoV-2 S RBD variants complexed with ACE2 and Abs. For ACE2, we used the resolved structure of the complex between WT S RBD and ACE2 (PDB: 6LZG) (Wang et al., 2020a) . We generated structural models for five mutant RBDs complexed with ACE2 -(1) N501Y (Alpha); (2) E484K; (3) N501Y and E484K; (4) K417N, E484K, and N501Y (Beta); and (5) L452R and T478K (Delta) -using CHARMM-GUI and MD energy minimization with the refinement protocol and default parameters implemented in the webserver HADDOCK 2.4 (van Zundert et al., 2016) . Our in silico models yielded excellent agreement with cryo-EM data (Figure 1A-B) . Interactions were visualized with PyMOL (DeLano, 2002) using mutagenesis and sculpting wizards for rotamer optimization. Unless otherwise specified, the RBD residues 333-527 and ACE2 residues 19-614 were included in the complexes. Similar modelling and computational protocols were adopted for J o u r n a l P r e -p r o o f the complexes with Abs (both heavy and light chains), using as template the PDB structures listed in Table S1. MD simulation systems and protocols. All initial MD simulation systems were generated using CHARMM-GUI Solution Builder module (Jo et al., 2008) . Simulations were performed in triplicates for the lowest binding affinity HADDOCK-refined WT or mutant RBD-ACE2 as described above (runs 1-6 in Table S1 ). We also performed additional runs (runs 7-11, in triplicate) to examine the effect of N-linked glycans and disulphides. Both groups had explicit water molecules to a distance of 10 Å away from protein edges, and sodium and chloride ions corresponding to 0.15 M NaCl, which resulted in a simulation box of 133×133×133 Å 3 for the RBD-ACE2 complex. The boxes for RBD-Ab complexes (runs 12-23, each in duplicate) varied from 105×105×105 Å 3 (RBD-Nb20) to 147×147×147 Å 3 (RBD-LY CoV-555).

All MD simulations were performed using the NAMD (version 2.13) (Phillips et al., 2005) , with a well-established protocol (Lee et al., 2016) . The CHARMM36 force field with CMAP corrections was used for protein, water, and glycan molecules (Guvench et al., 2011; Huang et al., 2017) .

Briefly, prior to productive runs, each system was energy-minimized for 20,000 steps, followed by a modified Nosé-Hoover (Hoover, 1985; Nosé, 1984) constant pressure (1bar) equilibration with temperature varied from 1 K to 310 K within 62,000 steps, during which the constraint on the protein backbone was set to 10 kcal/mol. A subsequent 4 ns Nosé-Hoover (Hoover, 1985; Nosé, 1984) constant pressure and temperature (1 bar, 310 K; NPT) simulation was performed, during which the constraints on the protein backbone were reduced from 10 to 0 kcal/mol. Finally, the unconstrained protein was subjected to NPT simulations. Periodic boundary conditions were employed for all simulations, and the particle mesh Ewald (PME) method (Essmann et al., 1995) was used for long-range electrostatic interactions with the pair list distance (pairlistdist) 13.5 Å. The simulation time step was set to 2 fs with the covalent bonds to hydrogen atoms constrained with the SHAKE algorithm (Ryckaert et al., 1977) . The force-based switching function was used for the LJ interactions with switching distance (switchdist) set to 10 Å. We applied Langevin dynamics conditions with a piston period (langevinPistonPeriod) of 200 fs and a piston decay (langevinPistonDecay) of 100 fs as well as Langevin temperature coupling with a friction coefficient (langevinDamping) of 1ps -1 . Supplemental Table S1 lists all simulations, along with the corresponding systems, durations and PDB files and chains used in simulations. VMD (Humphrey et al., 1996) with in-house scripts was used for visualization and trajectory analysis.

J o u r n a l P r e -p r o o f MM/GBSA computational protocol and parameters. We adopted the steps described in earlier work (Tanner et al., 2011) . First, we took our MD trajectories generated for the protein−protein complex, i.e. RBD-ACE2, in explicit solvent. Second, all solvent molecules, ions, and glycan ligands if any, were removed from each MD snapshot, yielding trajectories for the RBD, ACE2, and RBD-ACE2 complex. Third, for each of these trajectores, the MM/GBSA free energy is calculated using GBIS module (Tanner et al., 2011) implemented in NAMD. The binding free energy change ∆ / is estimated as

where GRBD-ACE2, GRBD, GACE2 are the MM/GBSA energies for the RBD-ACE2 complex, RBD and ACE2, respectively, calculated using (Genheden and Ryde, 2015) :

The first three terms are the standard molecular mechanics (MM) energy terms from bonded (bond length, bond angle, and dihedral angle), electrostatic, and van der Waals (vdW) interactions. Gpol and Gnp are the polar and non-polar contributions to the solvation free energies. Gpol is obtained using the Generalized Born (GB) model, the non-polar term Gnp is estimated from a linear relation to the solvent accessible surface area (SASA) (Genheden and Ryde, 2015) . Note that the MM/GBSA energy (POTENTIAL term from the output) released by NAMD does not contain the entropic contribution to free energy. Therefore, in a strict sense, the enthalpy change is evaluated, designated as ∆ / in the manuscript. Binding is usually accompanied by an entropy loss, and the associated free energy increase is comparable in magnitude to the free energy decrease induced by enthalpic effects.

We used the default parameters set in the GBIS module (Tanner et al., 2011) . Briefly, CHARMM36 force field with CMAP corrections (Huang et al., 2017) was used to calculate MM energy. For GBSA calculations, the dielectric constant was set 78.5 and ion concentration to 0.3 M. The surface tension was set to be 0.00542 kcal/(mol·Å 2 ). The switching distance (switchdist)

for the LJ interactions was 15 Å; the long-range electrostatic cutoff distance (pairlistdist) was 18 Å; and the cutoff (cutoff) for the non-bonded interactions was 16 Å.

Evaluation of binding energetics. We evaluated the binding energetics ACE2-RBD (WT or variant) complexes using three methods: (I) PRODIGY (Xue et al., 2016) , for evaluating the binding energy (Gbinding) based on a series of snapshot from MD trajectories; (II) PRODIGY, applied to an ensemble of structural models refined by HADDOCK and energy-minimized by short MD, as described earlier (Cheng et al., 2021a) ; and (III) the MM/GBSA method described in the J o u r n a l P r e -p r o o f previous section, for evaluating the enthalpic contribution HMM/GBSA to the binding free energies for the same series of MD snapshots as in (i). In methods I and III, Gbinding or HMM/GBSA histograms for binding to ACE2 were generated based on 800 snapshots evenly collected during the MD simulation time interval 20 < t ≤ 100 ns for each run. Dissociation constants were calculated using Kd = exp(Gbinding/RT) x 10 9 with RT = 0.6 kcal/mol at T = 300 K, yielding nM units.

The binding energies of the complexes formed by the Abs were evaluated by generating an ensemble of conformers along ANM modes. We considered the collective motions along the first three lowest frequency modes. A total of 21 conformations generated for each mode resulted in a total of 63 conformers for each complex. The softest (first) mode was allowed a maximum deformation (RMSD) of 5.0 Å and the next two modes were rescaled proportionately to their eigenvalues. Each of the 63 conformations were stringently optimized using OpenMM (Eastman et al., 2017) for energy minimization with the Amber14 force field (Wang et al., 2004) . Binding energies of resulting conformers were evaluated using PRODIGY.

We evaluated the global dynamics of the RBD-ACE2 complex using the Gaussian network model (GNM) (Bahar et al., 1997) and essential site scanning analysis (ESSA) (Kaynak et al., 2020) . Both tools are accessible and described in detail in our open source ProDy API (Bakan et al., 2014; Bakan et al., 2011; Zhang et al., 2021b) .

GNM analysis provides information on the role of individual residues in the global dynamics (Bahar et al., 2010) , i.e. collective motions at the low frequency end of the mode spectrum accessible under equilibrium conditions. Briefly, we evaluated the GNM-predicted square fluctuations of residues as driven by three slowest modes of motions and identified the most constrained residues (i.e., minima, referred to as hinge sites). These sites cannot usually tolerate mutations (Haliloglu and Bahar, 2015) . ESSA, on the other hand, scans all residues for identifying the so-called essential sites whose perturbation (by local crowding or ligand binding) would elicit a strong change in the frequency distribution of global motions. Integration of ESSA with a pocket detection algorithm predicts allosteric pockets (Kaynak et al., 2020) .

The three mutations L452R, T478K, and P681R were generated using CHARMM-GUI (Jo et al., 2008) using the WT Spike model from our previous studies (Cheng et al., 2021b; Cheng et al., 2020) . A crystal structure of human furin (PDB: 5JMO) (Dahms et al., 2016) was used for docking simulations by ClusPro (Kozakov et al., J o u r n a l P r e -p r o o f 2017) with the same protocol as for WT S (Cheng et al., 2021b) . Complexes were refined using HADDOCK 2.4. 

For each system, we performed multiple MD runs and verified the convergence and reproducibility of the trajectories. We evaluated the binding energetics using three methods, as described above.

Free energy histograms were generated by using 800 x n snapshots generated for n = 2 or 3 runs performed for each system, and corresponding mean values and standard deviations are reported for each system. Statistical analyses were performed using Matlab. We repeated ELISA experiments three times and we show data points a as mean +/-standard deviation. Table S1 ). Table S1 ). Plante et al., 2021; Tegally et al., 2020; Zhang et al., 2021c) . (-) no effect). f Three classifications are provided based on our observations first (excluding the NTD), and structural studies (Barnes et al., 2020a) and competition epitope mapping (Liu et al., 2020) underneath J o u r n a l P r e -p r o o f

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Escape from neutralizing antibodies by SARS-CoV-2 spike protein variants

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Cryo-electron microscopy structures of the N501Y SARS-CoV-2 spike protein in complex with ACE2 and 2 potent neutralizing antibodies

This work was supported by National Institutes of Health awards (P41GM103712 and 1R01GM139297 to I.B; 3R01AI072726-10S10 to M.A.; R35GM137905 and R21CA261737 to Y.S.) and a MolSSI COVID-19 Seed Software Fellowship (to J.M.K.).

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

 Atomic models and simulations explain how SARS-CoV-2 variants evade some antibodies  All variants bind ACE2 more strongly with Delta variant optimizing electrostatics  P681H and P681R enhance recognition of Alpha and Delta variants by Furin  Some antibodies retain binding to variant RBDs including Nb20 to Delta variant J o u r n a l P r e -p r o o f REAGENT or RESOURCE  SOURCE  IDENTIFIER  Antibodies  Nb20 PMID: 33154108 N/A Anti-T7 tag HRP-conjugated secondary antibodies Thermo Fisher Cat# PA1-31449 Chemicals, Peptides, and Recombinant Proteins ELISA kit R&D system DY990-DY999 SARS-CoV-2 RBD wild-type (his-tag) Acro Biosystem Cat# SPD-C52H3 SARS-CoV-2 RBD Delta (his-tag) Acro Biosystem Cat# SPD-C52Hh Deposited Data Antibody-Spike complexes see Table 2 Protein Data Bank (PDB) J o u r n a l P r e -p r o o f