key: cord-0817887-djg5ynbl
authors: Verkhivker, Gennady M.; Agajanian, Steve; Oztas, Deniz Yazar; Gupta, Grace
title: Comparative Perturbation-Based Modeling of the SARS-CoV-2 Spike Protein Binding with Host Receptor and Neutralizing Antibodies : Structurally Adaptable Allosteric Communication Hotspots Define Spike Sites Targeted by Global Circulating Mutations
date: 2021-02-22
journal: bioRxiv
DOI: 10.1101/2021.02.21.432165
sha: 916e38652043a997175ef9075d22813378f818bf
doc_id: 817887
cord_uid: djg5ynbl

In this study, we used an integrative computational approach focused on comparative perturbation-based modeling to examine molecular mechanisms and determine functional signatures underlying role of functional residues in the SARS-CoV-2 spike protein that are targeted by novel mutational variants and antibody-escaping mutations. Atomistic simulations and functional dynamics analysis are combined with alanine scanning and mutational sensitivity profiling for the SARS-CoV-2 spike protein complexes with the ACE2 host receptor are REGN-COV2 antibody cocktail (REG10987+REG10933). Using alanine scanning and mutational sensitivity analysis, we have shown that K417, E484 and N501 residues correspond to key interacting centers with a significant degree of structural and energetic plasticity that allow mutants in these positions to afford the improved binding affinity with ACE2. Through perturbation-based network modeling and community analysis of the SARS-CoV-2 spike protein complexes with ACE2 we demonstrate that E406, N439, K417 and N501 residues serve as effector centers of allosteric interactions and anchor major inter-molecular communities that mediate long-range communication in the complexes. The results provide support to a model according to which mutational variants and antibody-escaping mutations constrained by the requirements for host receptor binding and preservation of stability may preferentially select structurally plastic and energetically adaptable allosteric centers to differentially modulate collective motions and allosteric interactions in the complexes with the ACE2 enzyme and REGN-COV2 antibody combination. This study suggests that SARS-CoV-2 spike protein may function as a versatile and functionally adaptable allosteric machine that exploits plasticity of allosteric regulatory centers to fine-tune response to antibody binding without compromising activity of the spike protein.

December in Brazil and contains a constellation of lineage defining mutations, including several mutations of known biological importance such as E484K, K417T, and N501Y mutations. 64, 65 Functional mapping of mutations in the SARS-CoV-2 S-RBD that escape antibody binding using deep mutational scanning showed that the escape mutations cluster on several surfaces of the RBD and have large effects on antibody escape while a negligible negative impact on ACE2 binding and RBD folding. 66 This illuminating study demonstrated that escape sites from antibodies can be constrained with respect to their effects on expression of properly folded RBD and ACE2 binding, suggesting that escape-resistant antibody cocktails can compete for binding to the same RBD region but have different escape mutations which limits the virus ability to acquire novel sites of immune escape in the RBD without compromising its binding to ACE2. 66 Comprehensive mapping of mutations in the SARS-CoV-2 RBD that affect recognition by polyclonal human serum antibodies revealed that mutations in E484 site tend to have the largest effect on antibody binding to the RBD, 67 and various functional neutralization assay experiments indicated that E484 modifications can reduce the neutralization potency by some antibodies by >10 fold. [67] [68] [69] These studies also indicated that K417N and N501Y mutants can escape neutralization by some monoclonal antibodies but typically only modestly affected binding. 67, 70 At the same time, mutations in the epitope centered around E484 position (G485, F486, F490) or in the 443-455 loop (K444, V445, L455, F456 sites) strongly affected neutralization for several Abs. [67] [68] [69] [70] [71] Functional mapping of the SARS-CoV-2 RBD residues that affect binding of the REGN-COV2 cocktail showed that REGN10933 and REGN10987 are escaped by different mutations as mutation at F486 escaped neutralization only by REGN10933, whereas mutations at K444 escaped neutralization only by REGN10987, while E406W escaped both individual REGN-COV2 antibodies. 70 This study confirmed that escape mutations at 8 Q493, Q498 and N501 sites may enhance binding affinity with ACE2 and that escape mutations can also emerge in positions distant from the immediate proximity of the binding epitope, highlighting structural and energetic plasticity of the RBD regions and potential allosteric-based mechanism of immune escape. 70 The REGN-COV2 cocktail (REG10987+REG10933) demonstrated significant potential in preventing mutational escape, 72 several other antibody cocktails such as COV2-2130+COV2-2196, 73 BD-368-2+BD-629 74 and B38+H4 52 displayed promising neutralization activities. Analysis of the molecular determinants and mechanisms of mutational escape showed that SARS-CoV-2 virus rapidly escapes from individual antibodies but doesn't easily escape from the cocktail due to stronger evolutionary constraints on RBD-ACE2 interaction and RBD protein folding. 75 According to this study, the key RBD positions critical for escape of antibody combinations include K444 which is important epitope residue for CoV2-06, P2B-2F6, S309 and REG10987 Abs as well as E484/F486 sites that are central for binding of CoV2-14 and REG10933. Functional analysis validated that mutations of these residues are responsible for viral escape from the individual Abs and in combination with other currently circulating variants (N501Y, K417N, E484K) may induce the reduced neutralization by the antibody cocktails. 75 The SARS-CoV-2 501Y.V2 lineage that includes one cluster in NTD with four substitutions and a deletion (L18F, D80A, D215G, Δ242-244, and R246I), and another cluster of three substitutions in RBD (K417N, E484K, and N501Y) can confer neutralization escape from SARS-CoV-2 directed monoclonal antibodies and significantly increased neutralization resistance from individuals previously infected with SARS-CoV-2 virus. 76 Moreover, three class 1 antibodies (CA1, CB6 and CC12.1) that target the ACE2-binding RBM region showed a complete lack of binding for 501Y.V2 variant, suggesting that mutations in the RBD and NTD clusters may to amplify the mutational escape from RBD-targeted Abs.

9

Structural and functional studies showed that the activity of mRNA vaccine-elicited antibodies to SARS-CoV-2 and circulating variant encoding E484K or N501Y or the K417N/E484K/N501Y combination can be reduced by a small but significant margin, suggesting that these mutations in individuals with compromised immunity may erode the effectiveness of vaccine elicited immunity. 77 Importantly, it was also shown that neutralization by 14 of the 17 most potent tested mAbs can be partly reduced or even abolished by either K417N, or E484K, or N501Y mutations. Another latest study reported the preserved neutralization of N501Y, Δ69/70 + N501Y + D614G and E484K + N501Y + D614G viruses by BNT162b2 vaccine-elicited human sera. 77 Consistent with other studies, it was shown that the neutralization against the virus with three mutations from the SA variant (E484K + N501Y + D614G) was slightly lower than the neutralization against the N501Y virus and the virus with three UK mutations (Δ69/70 + N501Y + D614G), but these differences were relatively small. 78 SARS-CoV-2-S pseudo-viruses bearing either the reference strain or the B.1.1.7 lineage spike protein with sera of 40 participants who were vaccinated with the mRNA-based vaccine BNT162b2 showed largely preserved neutralization, indicating that the B.1.1.7 lineage will not escape BNT162b2-mediated protection. 79 The recent data demonstrate reduced but still significant neutralization against the full B.1.351 variant following mRNA-1273 vaccination. 80 New SARS-CoV-2 variants that resist neutralizing antibodies are now emerging in low frequencies in circulating SARS-CoV-2 populations. In particular, recent reports presented evidence of circulating SARS-CoV-2 spike N439K variants evading antibody-mediated immunity, particularly N439K mutation that confers resistance against several neutralizing monoclonal antibodies and reduces the activity of mRNA vaccine-elicited antibodies. 81 Computational modeling and molecular dynamics (MD)

simulations have been instrumental in predicting conformational and energetic determinants 10 of SARS-CoV-2 mechanisms and the binding affinity and selectivity with the host receptor ACE2. [82] [83] [84] [85] [86] [87] [88] [89] [90] [91] [92] [93] Molecular mechanisms of the SARS-CoV-2 binding with ACE2 enzyme were analyzed in our recent study using coevolution and conformational dynamics. 94 Using protein contact networks and perturbation response scanning based on elastic network models, we recently discovered existence of allosteric sites on the SARS-CoV-2 spike protein. 95 By using molecular simulations and network modeling we recently presented the first evidence that the SARS-CoV-2 spike protein can function as an allosteric regulatory engine that fluctuates between dynamically distinct functional states. 96 Coarse-grained normal mode analyses combined with Markov model and computation of transition probabilities characterized the dynamics of the S protein and the effects of mutational variants D614G and N501Y on protein dynamics and energetics. 97 Using time-independent component analysis (tICA) and protein networks, another computational study identified the hotspot residues that may exhibit long-distance coupling with the RBD opening, showing that some mutations may allosterically affect the stability of the RBD regions. 98 Molecular simulations reveal that N501Y mutation increases ACE2 binding affinity, and may impact the collective dynamics of the ACE2-RBD complex while mutations K417N and E484K reduce the ACE2-binding affinity by abolishing the interfacial salt bridges. 99 The growing body of computational modeling studies investigating dynamics and molecular mechanisms of SARS-CoV-2 mutational variants produced inconsistent results that propose different mechanisms.

The development of a more unified view and a working theoretical model which can explain the diverse experimental observations is an important area of current efforts in the field.

In this study, we used an integrative computational approach to examine molecular mechanisms underlying the functional role of K417, N439, E484 and N501 positions targeted by novel mutational variants in the SARS-CoV-2 S protein.

We combined coarse-grained (CG) simulations and atomistic reconstruction of dynamics trajectories with dynamic fluctuation communication analysis, mutational sensitivity analysis and network community modeling to examine complexes of the SARS-CoV-2 S-RBD and dissociated S1 domain of the S protein formed with the ACE2 host receptor. Using distance fluctuations communication analysis and functional dynamics analysis, we determine and compare the distribution of regulatory centers in the RBD complexes with ACE2 and REGN-COV2 antibody cocktail (REG10987+REG10933).

Using alanine scanning and mutational sensitivity analysis, we show that K417, E484 and N501 residues correspond to key interacting centers with a significant degree of structural and energetic plasticity that allow mutants in these positions to afford the improved binding affinity with ACE2. Through perturbation-based network modeling and community analysis of the SARS-CoV-2 RBD complexes with ACE2 we demonstrate that E406, N439, K417 and N501 residues serve as effector centers of allosteric interactions and anchor major inter-molecular communities that mediate long-range communication in the complexes. The results of the comparative network analysis with antibody complexes, we show that mutations in these positions can alter structural arrangements with antibodies and compromise their neutralization effects. These results suggest that antibody-escaping mutations target allosteric mediating hotspots with sufficient plasticity and adaptability to modulate and improve binding and allosteric signaling functions with the host receptor activity while reducing efficiency of antibody recognition and long-range communications. This analysis suggests that SARS-CoV-2 S protein may function as a versatile and functionally adaptable allosteric machine that exploits plasticity of allosteric regulatory centers to generate escape mutants that fine-tune response to antibody binding without compromising activity of the spike protein.

Coarse-grained (CG) models are computationally effective approaches for simulations of large systems over long timescales. In this study, CG-CABS model [100] [101] [102] [103] [104] was used for simulations of the crystal structures of the SARS-CoV-2 RBD complex with ACE2 (pdb id 6M0J) 15 and complexes formed by the dissociated S1 domain of SARS-CoV-2 Spike bound to ACE2 (pdb id 7A91, 7A92) 34 ( Figure 1A -C). We also simulated the cryo-EM structure of the SARS-CoV-2 RBD complex with the Fab fragments of two neutralizing antibodies, REGN10933 and REGN10987 (pdb id 6XDG) 105 ( Figure 1D -F). In this high-resolution model, the amino acid residues are represented by Cα, Cβ, the center of mass of side chains and another pseudoatom placed in the center of the Cα-Cα pseudo-bond. In this model, the amino acid residues are represented by Cα, Cβ, the center of mass of side chains and the center of the Cα-Cα pseudo-bond. The CABS-flex approach implemented as a Python 2.7 object-oriented standalone package was used in this study to integrate a high-resolution coarse-grained model with robust and efficient conformational sampling proven to accurately recapitulate all-atom MD simulation trajectories of proteins on a long time scale. 104 Conformational sampling in the CABS-flex approach is conducted with the aid of Monte Carlo replica-exchange dynamics and involves local moves of individual amino acids in the protein structure and global moves of small fragments. [100] [101] [102] The default settings were applied in which soft native-like restraints are imposed only on pairs of residues fulfilling the following conditions : the distance between their C α atoms was smaller than 8 Å, and both residues belong to the same secondary structure elements. A total of 1,000 independent CG-CABS simulations were performed for each of the studied systems.

In each simulation, the total number of cycles was set to 10,000 and the number of cycles between trajectory frames was 100. MODELLER-based reconstruction of simulation trajectories to all-atom representation provided by the CABS-flex package was employed to produce atomistic models of the equilibrium ensembles for studied systems. The crystal structure of the dissociated S1 domain form in the complex with ACE2 (pdb id 7A91). S1-RBD is in cyan ribbons and ACE2 is in green ribbons. (C) The crystal structure of the fully dissociated S1 domain in the complex with ACE2 (pdb id 7A92). S1 domain of the SARS-CoV-2 S protein is in cyan ribbons and ACE2 is in green ribbons. 

All structures were obtained from the Protein Data Bank. 106, 107 During structure preparation stage, protein residues in the crystal structures were inspected for missing residues and protons. Hydrogen atoms and missing residues were initially added and assigned according to the WHATIF program web interface. 108, 109 The structures were further pre-processed through the Protein Preparation Wizard (Schrödinger, LLC, New York, NY) and included the check of bond order, assignment and adjustment of ionization states, formation of disulphide bonds, removal of crystallographic water molecules and co-factors, capping of the termini, assignment of partial charges, and addition of possible missing atoms and side chains that were not assigned in the initial processing with the WHATIF program. The missing loops in the studied crystal structures of the dissociated S1 domain complexes with ACE2 (residues 556-573, 618-632) were reconstructed and optimized using template-based loop prediction approaches ModLoop, 110 ArchPRED server 111 and further confirmed by FALC (Fragment Assembly and Loop Closure) program. 112 The side chain rotamers were refined and optimized by SCWRL4 tool. 113 The shielding of the receptor binding sites by glycans is an important common feature of viral glycoproteins, and glycosylation on SARS-CoV proteins can camouflage immunogenic protein epitopes. 114, 115 The atomistic structures from simulation trajectories of the dissociated S1 domain complex with ACE2 (pdb id 7A92) were elaborated by adding N-acetyl glycosamine (NAG) glycan residues and optimized. The 

We performed principal component analysis (PCA) of reconstructed trajectories derived from CABS-CG simulations using the CARMA package 116 and also determined the essential slow mode profiles using elastic network models (ENM) analysis. 117 Two elastic network models:

Gaussian network model (GNM) 117, 118 and Anisotropic network model (ANM) approaches 119 were used to compute the amplitudes of isotropic thermal motions and directionality of anisotropic motions. The functional dynamics analysis was conducted using the GNM in which protein structure is reduced to a network of N residue nodes identified by C α atoms and the fluctuations of each node are assumed to be isotropic and Gaussian. Conformational mobility profiles in the essential space of low frequency modes were obtained using ANM server 119 and DynOmics server. 120

We have computed the relative solvent accessibility parameter (RSA) that is defined as the ratio of the absolute solvent accessible surface area (SASA) of that residue observed in a given structure and the maximum attainable value of the solvent-exposed surface area for this residue. 121 According to this model, residues are considered to be solvent exposed if the ratio value exceeds 50% and to be buried if the ratio is less than 20%. Analytical SASA is estimated computationally using analytical equations and their first and second derivatives and was computed using web server GetArea. 121

To compute protein stability and binding free energy changes in the SARS-CoV-2 RBD structures upon complex formation with ACE2 receptor and REGN-COV2 antibody cocktail, we conducted a systematic alanine scanning of protein residues in the SARS-CoV-2 RBD and S1

domain. In addition, a complete mutational sensitivity analysis was done for binding free energy hotspots and residues E406, K417, N439, K444, E484, F486, and N501 targeted by widely circulating and antibody-escaping mutations. Alanine scanning and mutational sensitivity profiling of protein residues was performed using BeAtMuSiC approach. 122 

Perturbation Response Scanning (PRS) approach 124,125 was used to estimate residue response to external forces applied systematically to each residue in the protein system. This approach has successfully identified hotspot residues driving allosteric mechanisms in single protein domains and large multi-domain assemblies. [126] [127] [128] [129] [130] [131] The implementation of this approach follows the protocol originally proposed by Bahar and colleagues 126, 127 and was described in details in our previous studies. 96 In brief, through monitoring the response to forces on the protein residues, the PRS approach can quantify allosteric couplings and determine the protein response in functional movements. In this approach, it 3N × 3N Hessian matrix whose elements represent second derivatives of the potential at the local minimum connect the perturbation forces to the residue displacements. The 3N-dimensional vector of node displacements in response to 3Ndimensional perturbation force follows Hooke's law = * . A perturbation force is applied to one residue at a time, and the response of the protein system is measured by the displacement vector ∆ ( ) = − ( ) that is then translated into N×N PRS matrix. The second derivatives matrix is obtained from simulation trajectories for each protein structure, with residues represented by atoms and the deviation of each residue from an average structure was calculated by ∆ ( ) = ( ) − 〈 ( )〉, and corresponding covariance matrix C was then calculated by ∆ ∆ . We sequentially perturbed each residue in the SARS-CoV-2 spike structures by applying a total of 250 random forces to each residue to mimic a sphere of randomly selected directions. The displacement changes, ∆ is a 3N-dimensional vector describing the linear response of the protein and deformation of all the residues.

Using the residue displacements upon multiple external force perturbations, we compute the magnitude of the response of residue k as

averaged over multiple perturbation forces F (i) , yielding the ik th element of the N×N PRS matrix. 126, 127 The average effect of the perturbed effector site on all other residues is computed by averaging over all sensors (receivers) residues and can be expressed as〈(∆ ) 2 〉 . The effector profile determines the global influence of a given residue node on the perturbations in other protein residues and can be used as proxy for detecting allosteric regulatory hotspots in the interaction networks. In turn, the j th column of the PRS matrix describes the sensitivity profile of sensor residue j in response to perturbations of all residues and its average is denoted as 〈(∆ ) 2 〉 . The sensor profile measures the ability of residue j to serve as a receiver (or transmitter) of dynamic changes in the system.

A graph-based representation of protein structures 132,133 is used to represent residues as network nodes and the inter-residue edges to describe residue interactions. The details of graph construction using residue interaction cut-off strength ( min I ) were outlined in our previous studies. 96 We constructed the residue interaction networks using both dynamic correlations 134 and coevolutionary residue couplings 135 that yield robust network signatures of long-range couplings and communications. The details of this model were described in our previous studies. [135] [136] [137] The ensemble of shortest paths is determined from matrix of communication distances by the Floyd-Warshall algorithm. 138 Network graph calculations were performed using the python package NetworkX. 139 The Girvan-Newman algorithm 140-142 is used to identify local communities. An improvement of Girvan-Newman method was implemented where all highest betweenness edges are removed at each step of the protocol. The algorithmic details of this modified scheme were presented in our recent study. 143, 144 The network parameters were computed using the python package NetworkX 139 and Cytoscape package for network analysis. 145, 146 Results and Discussion

Multiple CG-CABS simulations of the SARS-CoV-2 RBD and S1 domain complexes with the ACE2 host receptor were performed to analyze similarities and differences in th conformational dynamics profiles of the RBD regions and specifically binding interface residues the dissociated S1 domain ( Figure 2B ). Of special interest were the ACE2 induced changes in the RBM region (residues 437-508) and particularly in a stretch of residues 471-503 involved in multiple contacts with the ACE2 receptor. The overall similar RBM profiles in all systems were seen but the greater stabilization of the interfacial residues in the ACE2 complex was observed for the bound S1 domain structure (Figure 2A Tables S1-S3) . Notably, this analysis showed that the interacting RBD motif 495-YGFQPTNG-502 is involved in the most persistent interaction contacts with ACE2 that is exemplified by stabilization of Y489, F490, Q493, Y495, G496, Q498, T500, and Y505 residues ( Figure 2 ). These RBD residues form the largest number of contacts with ACE2 (Supporting Information, Tables S1-S3) and experienced the most significant stabilization in the complex ( Figure 2A ,B). Importantly, not only these residues showed markedly reduced fluctuations, but the large interfacial stretch of residues across entire binding interface (residues 486-FNCYFPLQSYGFQ-498 ) including key Q493 and Q498 interacting sites exhibited even a stronger stabilization in the complex formed by the dissociated S1 domain ( Figure 2B ). ACE2 binding can induce disassembly of the SARS-CoV-2 S trimer and promote formation of a stable dissociated monomeric S1 complex with ACE2 receptor. 147 At the same time, a modest mobility of the RBM positions N439, L452, T470, E484, Q498 and N501 was seen in the S1-ACE2 complex ( Figure 2B ). Importantly, several of these positions N49, E484, and N501

correspond to sites that confer mutational variants with the increased binding to ACE2 and elevated level of transmission and infectivity. The observed partial flexibility of this SARS-CoV-2 RBM motif in the complex formed by the dissociated S1 domain may allow for tolerance and adaptability of these sites to specific modifications resulting in the improved binding affinity with ACE2.

We also report the relative solvent accessibility (RSA) ratio in the SARS-CoV-2 RBD and S1

domain complexes with ACE2 that were obtained by averaging the SASA computations over the simulation trajectories (Supporting Information, Figure S1 ) that are central focus of our investigation. We found that E406, K417 and N439 showed moderate RSA values (~20-30%) indicating that these positions could retain a certain degree of plasticity in the RBD and S1 domain complexes with ACE2 (Supporting Information, Figure   S1 ). The more extreme cases were exemplified by E484 that maintains significant solvent exposure (RSA ~ 65%) in the ACE2 complexes, while N501 is largely buried with very small RSA values (Supporting Information, Figure S1 ). However, conformational dynamics profiles indicated that N501 may still maintain some level of plasticity in the ACE2 complex.

To compare the differences in the local flexibility with experimental functional data, we specifically analyzed a group of SARS-CoV-2 RBD residues L455, F456, S459, Q474, A475, F486, F490, Q493 and P499 whose mutations to their SARS-CoV RBD counterpart positions resulted in the abolished binding affinity. 37 It could be noticed that in the S1 domain complex with ACE2 these residues become appreciably more stable than residues from another group ACE2. 37 Our analysis allowed to capture these subtle differences showing that this group of RBD residues may experience larger fluctuations (Figure 2A,B) . Interestingly, these differences become more evident only in the ACE2 complex with the dissociated S1 domain, suggesting that the partial redistribution of mobility in the S1-ACE2 complex could provide more room for structural adaptation of N439, E484, and N501 positions ( Figure 2B ). Hence, a moderate level of residual fluctuations can be preserved even when RBM residues are involved in strong stabilizing contacts with ACE2. Although E484 interacts with the K31 interaction hotspot residue of hACE2, this residue retains a more significant degree of mobility and plasticity in the RBM region which may be associated with the mutational variability and emergence of E484K variant that can improve binding affinity with the host receptor. Interestingly, we found that escape mutations and variants improving binding affinity with the ACE receptor may emerge in sites that are moderately flexible in the S1-ACE2 complex. This suggested that although some of these positions such as K417 and N501 are involved in multiple contacts with ACE2 there should be a substantial energetic plasticity in the interaction network . According to our findings, there may be more room for tolerant modifications of N439 and E484 positions, while potential for favorable mutations at K417 and N501 sites could be more limited.

To summarize, the central finding of this analysis is that conformational dynamics profile for the SARS-CoV-2 S-RBD residues remained largely conserved among ACE2 complexes of the S1-RBD and fully dissociated S1 domain. Another important observation is a consistent trend for moderate residual mobility of RBD residues whose mutations may often lead to the enhanced binding with ACE2. Conformational dynamics analysis also indicated that RBM residues targeted by novel mutational variants may be adaptable and display a range of flexibilityfrom more dynamic positions at N439 and E486 to more constrained K417 and N501 residues.

To characterize collective motions and determine the distribution of hinge regions in the SARS-CoV-2 S-RBD and SARS-CoV-2 S1 domain complexes with ACE2 ( Figure 3 The slow mode profiles obtained for complexes of the dissociated S1 domain with ACE2 further highlighted the role of these functional residues in collective motions ( Figure 3E Importantly, antibody binding altered dynamic role of residues E484 and N501 that become aligned with moving regions in the collective dynamics of the complex ( Figure 4A ). Structural mapping of the essential profiles further illustrated this point, showing that functionally immobilized in collective motions hinge centers are localized near E406, N439 and K444 sites, while E484 and N501 positions could undergo some movements in the complex ( Figure 4B ).

In this context it is particularly interesting to compare our observations with functional studies

showing that K417 and F486 are sites of escape from RERGN10933, while mutations in K444 and G446 escape neutralization by REGN10987 and E406 is a unique site susceptible to mutations escaping both antibodies. 70 In line with these experiments, we found that E406 and K444 positions may correspond to the antibody-specific unique hinge centers of collective motions that control relative orientation and rigid body movements of REGN10933 and REGN10987 molecules ( Figure 4B ). This is in some contrast to SARS-CoV-2 RBD complexes with ACE2 in which N439 and N501 form the major hinge center of functional dynamics. As a result, it is possible that mutations in K444 and E406 positions may perturb not only local interactions with antibody molecules but alter the global collective movements and long-range communication which may be sufficient to trigger mutational escape from antibody binding. At the same time, these mutations could only moderately change the SARS-CoV-2 RBD local interactions with ACE2 without affecting the collective movements in the complex.

To summarize, this analysis suggested that mutational variants and escape mutations may preferentially target specific positions involved in regulation and coordination of functional dynamics motions and allosteric changes in the SARS-CoV-2 complexes with ACE2 and REGN-COV2 antibody cocktail.

We first performed a systematic alanine scanning of the SARS-CoV RBD S protein residues ( Figure 5A ) and residues from the dissociated S1 domain in the complexes with the ACE2 host receptor ( Figure 5B ,C). Using the equilibrium ensembles obtained from simulation trajectories, we evaluated the average cumulative mutational effect of alanine substitutions on protein stability and binding affinity with the host receptor. The alanine scanning of the SARS-CoV-2 RBD residues highlighted a significant destabilization effect caused by mutations of G446, Y453, L455, F456, F486, Y489, Y495, T500, and Y505 residues ( Figure 5A ). These residues also corresponded to the binding free energy hotspots in the complexes formed by the dissociated S1 domain with ACE2 ( Figure 5B ,C). In particular, large destabilization effects were observed upon mutations of Y453, L455, F456, Y489, and F490 residues in the S1-ACE2 complexes.

Notably, the largest destabilization changes were produced by alanine mutations of F456 and Y489 residues, displaying clear and pronounced peaks of the profile and pointing to these positions as key binding affinity hotspots in the S1-ACE2 complexes. (Figure 5B ,C). Several key binding energy hotspot sites (Y453, Y489, and Y505) are conserved between SARS-CoV and SARS-CoV-2 proteins and are located in the central segment of the interface. A detailed analysis of the intermolecular contacts in the SARS-CoV-2 RBD and S1 complexes with ACE2 aided in understanding of the binding energy preferences of RBD residues (Supporting Information, Tables S1-S3). This analysis is particularly instructive by considering contact distributions with two virus-binding hotspots on ACE2 formed by interacting residues K31 and E35 as well as K353 and D38. Information, Table S4 , Figure 5D ). The largest free energy changes exceeding 3 kcal/mol were observed for alanine modifications of F456 and F486 residues which is consistent with the prominent role these residues play in eliciting antibody-escaping mutations. 70 We also specifically highlighted binding free energy changes 38 Consistent with the deep mutational scanning experiments 35, 66 we found that E406, N439, and E484 sites are energetically adaptable and can effectively tolerate different mutations without incurring significant changes in protein stability and binding affinity (Figure 7 A,C,D).

Somewhat larger but still relatively tolerable were binding free energy changes induced by mutations in K417 and N501 positions ( Figure 7B,E) . K417 is a unique ACE2-interacting residue that forms favorable contacts with central residues of the ACE2 interface H34 and D30

(Supporting Information, Table S1-S3). However, an appreciable energetic plasticity could be seen in mutational sensitivity profiling of K417 residue ( Figure 7B ). Although K417 mutations to alanine or glycine produced fairly significant destabilization changes, K417N and K417D mutations led to only small perturbations (~0.4-0.5 kcal/mol). Indeed, deep mutational scanning suggests that the K417N mutation has minimal impact on binding affinity with ACE2. 35 The results also predicted the marginal improvement in the binding free energy mediated by E484K mutation and only very modest increase in the binding affinity upon K417N modification ( Figure 7D ). The experimental studies indicated that the E484K mutation may induce a moderate improvement in binding affinity and showed that other single mutations of E484 may only slightly compromise spike folding stability and binding affinity for ACE2. 35, 70 According to our analysis, several hydrophobic substitutions in this position (E484I, E484V, E484F, E484W, and E484P) may in fact lead to the moderately improved affinity, while other mutations appeared to produce only marginal destabilization ( Figure 7D) . These results indicated a significant plasticity of this important RBD position that is relatively exposed and may favor hydrophobic residues in this position to improve both stability and binding. The mutational sensitivity profiling at N501 position is consistent with deep mutational scanning experiments 35, 66 reproducing the improvements in binding mediated by N501F and N501Y 39 mutations ( Figure 7E ). Indeed, deep mutation scanning showed that N501F, N501T and N501Y mutations may lead to moderate enhancement of binding with ACE2, while N501D is an affinity-decreasing mutation. 35 We observed only a small destabilization effect for N501T and more significant destabilization upon N501D and N501A/G mutations ( Figure 7E ). Importantly, these results supported the notion that N501Y mutational variant could be beneficial for ACE2

binding, while escaping neutralizing antibodies targeting the same region.

We 

Consistent with the functional analysis of the immune-selected mutational landscape in the S protein, we found that a wide spectrum of K444 modifications induced a significant loss of binding free energy ( Figure 8E ) including K444E and K444N mutations that showed a broadrange resistance against multiple antibodies. 69 The large destabilization changes caused by F486 mutations can be contrasted to fairly small changes incurred by E484 mutations, indicating that E484 site is characterized by sufficient level of structural plasticity and energetic adaptability to readily accommodate mutations in complexes with ACE2 and REGN-COV2 cocktail. These findings may explain why single-site mutations of these residues can only slightly change binding affinity for ACE2 and folding stability, while double-site mutations of proximal E484

and F486 can significantly weaken the fitness of the SARS-CoV-2 RBD region and binding. 66 To summarize, our results pointed to several interesting trends. First, mutational sensitivity profiling of the conserved hydrophobic binding energy hotspots Y453, L455, F456, F486, and Y505 consistently yielded large destabilization changes affecting folding stability and binding to ACE2 receptor, making these positions unlikely candidates for antibody escaping mutations as even small modifications in these positions could have a severe detrimental effect on the spike activity. Second, we found that SARS-CoV-2 binding affinity could be strongly influenced by the virus-binding hotspot K31 and H34 in the middle of the interface through an extensive interaction network with K417, Y453, L455, F456, and Q493 residues. Finally, mutational analysis of K417, E484 and N501 positions implicated in new mutational strains and antibodyescaping changes showed that these residues correspond to important interacting centers with a significant degree of structural and energetic plasticity. Indeed, N501Y, E484K and K417N mutations can result in the improved or only slightly decreased affinity with ACE2. These results suggest a hypothesis that antibody-escaping mutations target residues with sufficient plasticity 42 and adaptability to preserve a sufficient spike activity while having a more detrimental effect on antibody recognition. These findings are particularly interesting in light of recent functional studies 66 showing that escape mutations target a subset of sites in the antibody-RBD interfaces corresponding to binding energy hotspots. Importantly, these experiments suggested that escape mutations are consistently those that have significant deleterious effects on antibody binding but little negative impact on ACE2 binding and RBD folding. Based on our findings, we argue that escape mutations constrained by the requirements for ACE2 binding and preservation of RBD stability may preferentially select structurally plastic and energetically adaptable allosteric centers at the key interfacial regions to compromise antibody recognition through modulation of global motions and allosteric interactions in the complex.

Using the PRS method [124] [125] [126] [127] we quantified the allosteric effect of each residue in the SARS-CoV-2 complexes in response to external perturbations. The effector profiles estimate the propensities of a given residue to influence dynamic changes in other residues and can be applied to identify regulatory hotspots of allosteric interactions as the local maxima along the profile. First, we computed the residue-based effector response profiles for the SARS-CoV-2 RBD complex with ACE2 ( Figure 9A ) and the complexes formed by the dissociated S1 domain with ACE2 ( Figure 9B,C) . By comparing the PRS profiles in the ACE2 complexes with SARS-CoV-2 S1/RBD and REGN-COV2 antibody cocktail, we determined the distribution of regulatory allosteric centers and highlighted a potential role of sites targeted by global circulating mutations.

Strikingly, the effector profile of the SARS-CoV-2 RBD complex with ACE2 featured two major peaks corresponding to residues E606 and T500/N501, indicating that E406 and N501

positions are aligned with the regulatory centers that may control allosteric communications in the complex ( Figure 9A ). Several other notable peaks corresponded to W353, K417, N439 and L452 residues. Hence, all known positions targeted by novel circulating mutational variants with the exception of E484 corresponded to the effector peaks and are involved in coordination of allosteric communications in the SARS-CoV-2 RBD complex with ACE2. Moreover, the effector profiles indicated that these regulatory sites may function in a coordinated manner and maintain an allosteric cross-talk to control signal transmission "traffic" and long-range interactions in the RBD-ACE2 complex. Interestingly, several of these effector centers L452, N439 and N501 were among SARS-CoV-2 RBD residues whose mutations to the SARS-CoV RBD counterparts N439/R426, L452/K439, and N501/T487 enhanced the binding affinity. 37 The prominent role of these residues as regulatory effector centers becomes even more apparent in the S1 domain complexes with ACE2 ( Figure 9B,C) . It is evident that E406, K417, N439 and especially N501 positions corresponded to sharp peaks of the effector profile. This implies that these sites may be collectively responsible for coordination of long-range communication in the system. The central result of this analysis is that circulating and escape mutations appeared to target residues corresponding to structurally and energetically adaptable regulatory control points that can tolerate individual mutations and often enhance ACE2 binding, while at the same time allowing for coordinated modulation of allosteric communications. We suggest that allosteric signaling in the SARS-CoV-2 RBD complex with ACE2 is adaptable where a mutation of a regulatory control point can be functionally compensated through energetic rebalancing of structurally plastic allosteric hotspots. 

Using a protein mechanics-based approach [149] [150] [151] [152] we also employed distance fluctuations analysis of the conformational ensembles to further probe allosteric communication preferences of the RBD residues in the SARS-CoV-2 RBD and S1 complexes with ACE2.

The residue-based distance fluctuation communication indexes measure the energy cost of the dynamic residue deformations and could serve as a robust metric for assessment of allosteric propensities of protein residues. [153] [154] [155] In this model, dynamically correlated residues whose effective distances fluctuate with low or moderate intensity are expected to communicate with the higher efficiency than the residues that experience large fluctuations. Notably, structurally stable and densely interconnected residues as well as moderately flexible residues that serve as a source or sink of allosteric signals could feature high value of these indexes.

The distance fluctuation profile of the SARS-CoV-2 RBD and S1 domain complexes with ACE2

showed a small but important redistriubution of major peaks, pointing to sites E406, W436, N439, N501 and Y505 ( Figure 9D ). Notably, this group of residues is featured prominently among peaks of the profile when the entire S1 domain monomer forms complex with ACE2

( Figure 9E ,F). We also noticed that the overall shape and distribution of the peaks are similar between the PRS effector profiles and distance fluctuation communication indexes profiles.

Notably, E406, N439 and N501 sites were featured as recurring peaks in both distributions, strengening the proposed notion that positions targted by the emerging mutational variants can cooperate and play a central role in regulation of long-range couplings and allosteric communications in the complexes with the ACE2 host receptor. Hence, the distance fluctuation profiling and analysis of communication indexes provide important supporting evidence to the PRS modeling, suggesting that structurally stable positions and potential allosteric hotspot residues only partially overlap, and allosteric hubs may exhibit a certain degree of structural plasticity and energetic adaptability to enable balance between binding and signaling function.

To understand a potential role of the E484 residue, it is instructive to analyze the PRS sensor profile (Supporting Information, Figure S2 ). A comparison between sensor profiles obtained for the unbound and bound forms of the SARS-CoV-2 RBD showed that E484 position is aligned with the dominant peak of the sensor profile in the unbound form (Supporting Information, Figure S2A ). Interestingly, in the complex with ACE2, this site also corresponded to a major sensor peak at the binding interface (Supporting Information, Figure S2B ). Structural mapping of sensor profiles in the unbound and bound RBD forms illustrated these observations, pointing to role of E484 residue as a major receiver site of allosteric signaling in the RBD-ACE2 complex (Supporting Information, Figure S2C ,and D). Hence, the PRS analysis of the RBD-ACE2 and S1-ACE2 complexes demonstrated that while E406, K417, N439 an N501 are aligned with dominant effector positions representing source and regulatory points of allosteric signaling, E484 corresponded to a major sensor/receiver site that may absorb signal information.

Collectively, these sites may represent key nodes of the allosteric interaction network in the functional ACE2-bound complexes and determine the robustness and efficiency of signal transmission.

We also computed the PRS effector profiles for the SARS-RBD complex with REGN-COV2 antibody combination ( Figure 10 ). The effector profile revealed some redistribution of peaks, featuring V401/E406, N439, K444/G446 and G496 positions as major effector centers ( Figure   10A ). At the same time, residues E484/F486 and N501 were aligned with the local sensor peaks ( Figure 10B ). These results could provide a feasible rationale for a critical role of K444 and F486 positions in escaping antibody combinations. Indeed, K444 is a central epitope residue for REG10987 while F486 residue is fundamental for recognition of REG10933 antibody. 70 Our findings also indicated that E406 and K444 are the dominant effector centers in the RBD complex with REGN-COV2 ( Figure 10A ) and may be functionally important not only for binding affinity but also for mediating signaling and longrange communications in the complex. In the context of perturbation-based PRS model, this implies that single mutations at these positions could affect collective movements and allosteric couplings between many residues in the system and potentially compromise functional activity of the REGN-COV2 cocktail. Interestingly, other positions targeted by antibodyescaping mutants E484 and F486 are the major sensor sites ( Figure 10B ). Based on these observations, we suggest that allosteric control of the RBD-REGN COV2 complex is provided through a cross-talk between major effector sites (E406, K444) and receiver sites (E484 and F486).

To summarize, perturbation-based modeling of the SARS-CoV-2 RBD complexes suggested that functional residues targeted by global circulating variants and antibody-escaping mutants could form a network of structurally adaptable allosteric hotspots that collectively coordinate allosteric interactions in the system. These results bear some significance and support the latest illuminating study suggesting a model functional plasticity and evolutionary adaptation of allosteric regulation. 156 This function-centric model of allostery revealed a remarkable functional plasticity of allosteric switches allowing modulate and restore regulatory activity through mutational combinations or ligand interactions. Our results similarly suggested that functional plasticity and cross-talk of allosteric control points in the SARS-CoV-2 RBD region can allow for differential modulation of recognition and long-range communication with ACE2 and antibodies.

Mechanistic network-based models allow for a quantitative analysis of allosteric molecular events in which conformational landscapes of protein systems can be remodeled by various perturbations such as mutations, ligand binding, or interactions with other proteins. The residue interaction networks in the SARS-CoV-2 RBD complexes with ACE2 and REGN-COV2

antibodies were built using a graph-based representation of protein structures in which residue nodes are interconnected through both dynamic 134 and coevolutionary correlations. 135 Using community decomposition, the residue interaction networks were further divided into local stable interaction modules in which residues are densely interconnected and highly correlated during simulations, while different communities are connected through long-range couplings.

Using this network-centric description of residue interactions, we compared the organization of stable local communities in the SARS-CoV-2 RBD complexes with ACE2 ( Figure 11 ) and REGN-COV2 cocktail ( Figure 12 ). In the RBD-ACE2 and S1-ACE2 complexes we found a deeply interconnected community organization ( Figure 11 ) where stable modules in the RBD core are tightly linked with the interfacial clusters. A number of stable intramolecular communities in the RBD core contribute to stability of the RBD regions. Some of these communities are formed by hydrophobic core residues including F342-V511-F374-W436-F347-R509 , W353-F400-Y423 as well as E406-Q409-I418 and N439-443-P499 centered around functional residues E406 and N439 ( Figure 11 ). Of particular interest an importance was a more detailed comparative analysis of the inter-molecular communities. In the RBD-ACE2 and S1-ACE2 complexes, these modules are integrated around key anchor residues K417, F456, Y489, N501, and Y505. The largest and most stable community in which each node is strongly 50 linked with each other is centered on N501 (K353-D38-Y41-Q498-N501-Y505) and engaged ACE2 hotspots K353, D38, and Y41 ( Figure 11A ). This interfacial module anchored by N501 allows for persistent interactions by N501, Y505 and Q498 RBD residues. Moreover, this community may be instrumental for signal transmission between RBD and ACE2 molecules, highlighting the important role of N501 position. By introducing N501Y mutation, we rebuilt the residue interaction network and performed community decomposition which revealed preservation of this major community. This observation supported our energetic analysis indicating structural plasticity and stability of the key intermolecular communities in the RBD-ACE2 complex.

Another group of interfacial communities is anchored by K417 and F456 positions that couple modules Y489-F456-K31 and D30-K417-F456 ( Figure 11B ). Interestingly, these intermolecular communities are directly coupled through K417 with the intramolecular module I402-I418-E406-Y495-Q409 centered on the E406 residue. Hence, the community organization revealed strong interconnectivity between key functional sites E406, K417, F456, and N501 that integrate the residue interaction network and enable allosteric couplings between RBD and ACE2 molecules. The important revelation of this analysis is that only a fraction of the RBD residues anchor the intermolecular community organization and mediate long-range interactions in the RBD-ACE2 complex. Furthermore, the binding free energy hotspots are not necessarily involved in community-mediating function. Instead, a group of structurally plastic allosteric centers such as E406, K417, F456, and N501 play key roles in integrating local communities into a robust and adaptable global network that can mediate signal transmission and communication between SARS-CoV-2 RBD and ACE2. The community includes K417, F456, Y489 and E484 residues of RBD and K31/D30 hotspot 52 residues of ACE2. The community residues are shown in spheres and annotated. Notably, these two major intermolecular communities that mediate communications and stability of the interface include several key functional sites K417, F456, E484, and N501 targeted by mutational variants and antibody-escaping mutations.

The REGN-COV2 antibody binding induced a partial and yet significant reorganization of the interface communities ( Figure 12A ). We found that key RBD residues F486, Y489 and K417

anchor major intermolecular communities with REGN10933 including R100-Y50-F486-W47-L94, Y32-T102-K417 and Y33-T52-Y489 ( Figure 12B ). Interestingly, K417 and another site of escape mutations E406 are inter-connected in the local RBD community E406-Q409-K417-I418. E484 is involved in formation of the inter-molecular contacts with T52, Y53, T57, Y59

residues of the heavy chain of REGN10933 and could bridge several interfacial communities anchored by F486 and Y489 residues. Among major interfacial communities formed by the RBD with REGN10987 is W99-W47-V445-K444-Y59 module in which V445 and K444 play a key role in mediating intermolecular communication. Another notable community is anchored by T500 which engages N439, P499, K444 residues of the RBD and W99 of the heavy chain of REGN10987 ( Figure 12B ). 

E406 site is at the center of the largest intramolecular community in the SARS-CoV-2 RBD (R403-Y453-V350-I418-N422-Y423-Y495-F497-E406-Q409) that connects the inter-molecular interfaces with the RBD core (Supporting Information, Figure S3 ). This site could anchor large communities in the core with the interfacial regions facing both antibodies. Based on these observations, we suggested that unique escape mutations in E406 position may be largely determined by its allosteric mediating role in interconnecting functional regions of the RBD.

Given the fact that E406 is only involved in several contacts with T28 of REGN10933, the strong mutational escape effect may be mainly driven by long-range allosteric effects and attributed to the strategic position of this residue in the global network. Noticeably, E406 is closely connected with Y453 residue where another REGN10933 specific escape-mutant Y453F was detected. 70 Another key member of this community is F497 that effectively bridges local intermolecular modules interacting with REGN10933 and REGN10987 antibodies (Supporting Information, Figure S3 ). Recent studies indicated that SARS-CoV-2 neighbor residues G496 and F497 are critical for the RBD-ACE2 interaction and F497 may play important role for enhancing the RBD-ACE2 interaction for SARS-Cov-2 RBD. Our network analysis quantifies the structural hypothesis offered in the experimental study according to which E406 escape mutation may affect recognition by REGN10987 through cascading effect onto adjacent structural elements across the RBD and propagating changes through aromatic residues Y453, Y495, and F497. 70 Importantly, network analysis revealed that these hydrophobic residues belong to the tightly packed stable community anchored by the E406 residue. Owing to the modular interconnectivity where each of these residues is connected with every community neighbor, it is likely that E406W mutation may simultaneously perturb multiple contacts and alter couplings between these residues, thus adversely affecting the fidelity of allosteric 55 communication with REGN10987. Hence, although this residue makes no persistent contacts with either of the interacting antibodies, its position in the largest community anchored by E406 could be important for integrity of the network organization in the complex with antibodies.

The network analysis showed that not only these residues are involved in favorable contacts with these antibodies but they also define key regulatory nodes that mediate stability and connectivity of the inter-molecular communities and may be responsible for control of signal transmission between SARS-CoV-2 RBD and REGN10933/REGN10987 antibodies. In agreement with functional data, the network community analysis singled out N439, K444, F486

and Y489 sites as allosteric network hubs where mutations would result in weakening of the entire interface and compromise the efficiency of allosteric interactions.

This integrative computational investigation combined molecular simulations and functional dynamics analysis with mutational energetic profiling of the SARS-CoV-2 S protein binding and network-based community modeling to delineate specific allosteric signatures of functionally important residues that are subjected to novel circulating variants. A comparative perturbation-based modeling of the SARS-CoV-2 S complexes with ACE2 and REGN-COV2 antibody combination revealed several important trends and characterized the unique allostericcentric signatures of functional RBD residues. First, we found that the RBM residues targeted by novel mutational variants may be fairly structurally adaptable and display a range of flexibilityfrom more dynamic positions at N439 and E486 to more constrained K417 and N501 residues. Functional dynamics analysis of global motions and identification of major hinge centers in the SARS-CoV-2 S complexes demonstrated that these sites play a central role in regulation of collective movements and long-range couplings, explaining why mutations in 56 these positions could escape from antibody binding while maintaining and enhancing binding with the host receptor. Through comprehensive mutational scanning and sensitivity analysis of the SARS-CoV-2 RBD residues in the studied complexes, we accurately reproduced the binding affinity changes for N501Y, E484K and K417N mutations. Moreover, this analysis strongly indicated that these residues correspond to important interacting centers with a significant degree of structural and energetic plasticity. As a result, we proposed a model according to which circulating variants and antibody-escaping mutations tend to target residues with sufficient plasticity and adaptability to preserve a sufficient spike activity while having a more detrimental effect on antibody recognition. Our results also suggested that these functionally important for spike activity residues correspond to structurally plastic and energetically Figure S1 presents the computations of the solvent accessible surface area (SASA) and RSA ratio for the SARS-CoV-2 S complexes with ACE2 and REGN-COV2 antibody cocktail. Figure   S2 reports the PRS sensor profiles for the unbound and bound forms of the SARS-CoV-2 RBD with ACE2 host receptor. Figure S3 describes the structural maps of local communities in the SARS-CoV-2 S-RBD complex with REGN-COV2 cocktail of antibodies REGN10933 and REGN10987. Supporting information contains Tables S1, S2 and S3 that list the intermolecular contacts formed by the S-RBD residues in the SARS-CoV-2 S-RBD complex with ACE2 (pdb id 6M0J) and S1 domain complexes with the host receptor (pdb id 7A91 and 7A92) respectively. Table S4 lists the intermolecular contacts formed by the S-RBD residues in the SARS-CoV-2 S-RBD complex with REGN-COV2 antibody cocktail (pdb id 6XDG).

This material is available free of charge via the Internet at http://pubs.acs.org.

Phone: 714-516-4586; Fax: 714-532-6048; E-mail: verkhivk@chapman.edu

The authors declare no competing financial interest.

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Characterization of the receptor-binding domain (RBD) of 2019 novel coronavirus: implication for development of RBD protein as a viral attachment inhibitor and vaccine

SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor

Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding

The SARS-CoV-2 spike glycoprotein biosynthesis, structure, function, and antigenicity: Implications for the design of spike-based vaccine immunogens

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

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Structural and Functional Basis of SARS-CoV-2 Entry by Using Human ACE2

Receptor Recognition by the Novel Coronavirus from Wuhan: an Analysis Based on Decade-Long Structural Studies of SARS Coronavirus

Structure of SARS coronavirus spike receptorbinding domain complexed with receptor

Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor

Structural basis of receptor recognition by SARS-CoV-2

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Cryo-electron microscopy structure of a coronavirus spike glycoprotein trimer

Cryo-electron microscopy structures of the SARS-CoV spike glycoprotein reveal a prerequisite conformational state for receptor binding

Unexpected receptor functional mimicry elucidates activation of coronavirus fusion

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Cryo-EM structure of the SARS coronavirus spike glycoprotein in complex with its host cell receptor ACE2

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Structurebased design of prefusion-stabilized SARS-CoV-2 spikes

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Structural basis for neutralization of SARS-CoV-2 and SARS-CoV by a potent therapeutic antibody

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Structural Basis of SARS-CoV-2 and SARS-CoV Antibody Interactions

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Human neutralizing antibodies elicited by SARS-CoV-2 infection

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Structures of Human Antibodies Bound to SARS-CoV-2 Spike Reveal Common Epitopes and Recurrent Features of Antibodies

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Protein modeling and structure prediction with a reduced representation

Coarsegrained protein models and their applications

Modeling of protein structural flexibility and large-scale dynamics: Coarse-grained simulations and elastic network models

Modeling of disordered protein structures using monte carlo simulations and knowledge-based statistical force fields

CABSflex standalone: A simulation environment for fast modeling of protein flexibility

The Protein Data Bank

The RCSB protein data bank: integrative view of protein, gene and 3D structural information

Positioning hydrogen atoms by optimizing hydrogenbond networks in protein structures

WIWS: A protein structure bioinformatics web service collection

Automated modeling of loops in protein structures

ArchPRED: A template based loop structure prediction server

The FALC-Loop web server for protein loop modeling

Improved prediction of protein side-chain conformations with SCWRL4

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Site-specific glycan analysis of the SARS-CoV-2 spike

A fully automated task-oriented interface for the analysis of molecular dynamics trajectories

Global dynamics of proteins: bridging between structure and function

oGNM: online computation of structural dynamics using the Gaussian Network Model

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DynOmics: dynamics of structural proteome and beyond

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A network representation of protein structures: Implications for protein stability

Interaction energy based protein structure networks

Dynamical networks in tRNA:protein complexes

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Atomistic simulations and network-based modeling of the Hsp90-Cdc37 chaperone binding with Cdk4 client protein: A mechanism of chaperoning kinase clients by exploiting weak spots of intrinsically dynamic kinase domains

Dancing through life: Molecular dynamics simulations and network-centric modeling of allosteric mechanisms in Hsp70 and Hsp110 chaperone proteins

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Exploring network structure, dynamics, and function using NetworkX

Finding and Evaluating Community Structure in Networks

Modularity and Community Structure in Networks

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Cytoscape: A software environment for integrated models of biomolecular interaction networks

Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics

Structural investigation of ACE2 dependent disassembly of the trimeric SARS-CoV-2 Spike glycoprotein

Dynamic Network Modeling of Allosteric Interactions and Communication Pathways in the SARS-CoV-2 Spike Trimer Mutants: Differential Modulation of Conformational Landscapes and Signal Transmission via Cascades of Regulatory Switches

Probing protein mechanics: residue-level properties and their use in defining domains

Investigating the local flexibility of functional residues in hemoproteins

Locating the active sites of enzymes using mechanical properties

Protein mechanics: a route from structure to function

Computational modeling of allosteric regulation in the hsp90 chaperones: a statistical ensemble analysis of protein structure networks and allosteric

Correlating allostery with rigidity

A rigid network of long-range contacts increases thermostability in a mutant endoglucanase

Functional plasticity and evolutionary adaptation of allosteric regulation

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