key: cord-1032499-jqb1fvb8
authors: Verkhivker, Gennady M.; Agajanian, Steve; Oztas, Denis; Gupta, Grace
title: Computational Analysis of Protein Stability and Allosteric Interaction Networks in Distinct Conformational Forms of the SARS-CoV-2 Spike D614G Mutant: Reconciling Functional Mechanisms through Allosteric Model of Spike Regulation
date: 2021-01-27
journal: bioRxiv
DOI: 10.1101/2021.01.26.428331
sha: dfc24591332aaa9f5f31583c0046a2e799deb7de
doc_id: 1032499
cord_uid: jqb1fvb8

Structural and biochemical studies SARS-CoV-2 spike mutants with the enhanced infectivity have attracted significant attention and offered several mechanisms to explain the experimental data. The development of a unified view and a working model which is consistent with the diverse experimental data is an important focal point of the current work. In this study, we used an integrative computational approach to examine molecular mechanisms underlying functional effects of the D614G mutation by exploring atomistic modeling of the SARS-CoV-2 spike proteins as allosteric regulatory machines. We combined coarse-grained simulations, protein stability and dynamic fluctuation communication analysis along with network-based community analysis to simulate structures of the native and mutant SARS-CoV-2 spike proteins in different functional states. The results demonstrated that the D614 position anchors a key regulatory cluster that dictates functional transitions between open and closed states. Using molecular simulations and mutational sensitivity analysis of the SARS-CoV-2 spike proteins we showed that the D614G mutation can improve stability of the spike protein in both closed and open forms, but shifting thermodynamic preferences towards the open mutant form. The results offer support to the reduced shedding mechanism of S1 domain as a driver of the increased infectivity triggered by the D614G mutation. Through distance fluctuations communication analysis, we probed stability and allosteric communication propensities of protein residues in the native and mutant SARS-CoV-2 spike proteins, providing evidence that the D614G mutation can enhance long-range signaling of the allosteric spike engine. By employing network community analysis of the SARS-CoV-2 spike proteins, our results revealed that the D614G mutation can promote the increased number of stable communities and allosteric hub centers in the open form by reorganizing and enhancing the stability of the S1-S2 inter-domain interactions and restricting mobility of the S1 regions. This study provides atomistic-based view of the allosteric interactions and communications in the SARS-CoV-2 spike proteins, suggesting that the D614G mutation can exert its primary effect through allosterically induced changes on stability and communications in the residue interaction networks.

Understanding of the molecular principles driving the coronavirus disease 2019 associated with the severe acute respiratory syndrome (SARS) [1] [2] [3] [4] [5] has been at the focal point of biomedical research since the start of the pandemic a year ago. SARS-CoV-2 infection is transmitted when the viral spike (S) glycoprotein binds to the host cell receptor, leading to the entry of S protein into host cells and membrane fusion. [6] [7] [8] The full-length SARS-CoV-2 S protein consists of two main domains, amino (N)-terminal S1 subunit and carboxyl (C)-terminal S2 subunit. The subunit S1 is involved in the interactions with the host receptor and includes an N-terminal domain (NTD), the receptor-binding domain (RBD), and two structurally conserved subdomains (SD1 and SD2). Structural and biochemical studies have shown that the mechanism of virus infection may involve spontaneous conformational transformations of the SARS-CoV-2 S protein between a spectrum of closed and receptor-accessible open forms, where RBD continuously switches between "down" and "up" positions where the latter can promote binding with the host receptor ACE2. [9] [10] [11] The S1 subunit is characterized by variant regions, particularly in the receptor binding motif (RBM) interacting with the host receptor ACE2 enzyme. The S1 regions also include C-terminal domain 1/CTD1 (or SD1) and Cterminal domain 2/CTD2 (or SD2) domains. Genomic studies established that the S2 subunit is an evolutionary conserved fusion machinery that contains upstream helix (UH), an N-terminal hydrophobic fusion peptide (FP), fusion peptide proximal region (FPPR), heptad repeat 1 (HR1), central helix region (CH), connector domain (CD), heptad repeat 2 (HR2), transmembrane domain (TM) and cytoplasmic tail (CT). 12 The S1 regions are structurally situated above the S2 subunit [13] [14] [15] [16] [17] and serve as dynamic protective shield of the fusion machine.

Upon proteolytic activation at the S1/S2 and dissociation of S1 from S2, a cascade of tectonic 5 structural rearrangements in the S2 subunit is initiated to mediate the fusion of the viral and cellular membranes. 18, 19 The rapidly evolving body of biophysical studies and cryo-EM structures of the SARS-CoV-2 S proteins characterized distinct conformational arrangements of the S protein trimers in the prefusion form that are manifested by a dynamic equilibrium between the closed ("RBD-down") and the receptor-accessible open ("RBD-up") form required for the S protein fusion to the viral membrane. [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] The recent cryo-EM structure of the SARS-CoV-2 S trimer demonstrated a population-shift between a spectrum of closed states that included a structurally rigid closed form and more dynamic closed states preceding a transition to the fully open S conformation. 30 Protein engineering and structural studies also showed how disulfide bonds and proline mutations can modulate stability of the SARS-CoV-2 S trimer 31 and lead to the thermodynamic shifts between the closed-down conformation and the open form exposed to binding with the ACE2 host receptor. [32] [33] [34] The cryo-EM structures and biophysical tomography tools characterized the structures of the SARS-CoV-2 S trimers in situ on the virion surface and confirmed a population shift between different functional states,

showing that conformational transitions can proceed through an obligatory intermediate in which all three RBD domains are in the closed conformations and are oriented towards the viral particle membrane. 35, 36 Cryo-EM structural studies also mapped a mechanism of conformational events associated with ACE2 binding, showing that the compact closed form of the SARS-CoV-2 S protein becomes weakened after furin cleavage between the S1 and S2 domains, leading to the increased population of partially open states and followed by ACE2 recognition that accelerates conformational transformations to a fully open and ACE2-bound form priming the protein for fusion activation. 37 Cryo-EM structures of SARS-CoV-2 S protein in the presence and absence of ACE2 receptor suggested a pH-dependent switch that mediates conformational 6 switching of RBD regions. 38 This study demonstrated that pH-dependent refolding region (residues 824-858) at the interdomain interface displayed dramatic structural rearrangements and mediated coordinated movements of the entire trimer, giving rise to a single 1 RBD-up conformation at pH 5.5 while all-down closed conformation was favorable at lower pH. 38 SARS-CoV-2 S mutants with the enhanced infectivity profile including D614G mutational variant have attracted an enormous attention in the scientific community following the evidence of the mutation enrichment via epidemiological surveillance, resulting in proliferation of experimental data and a considerable variety of the proposed mechanisms explaining functional observations. [39] [40] [41] The latest biochemical studies provided a compelling evidence of a phenotypic advantage and the enhanced infectivity conferred by the D614G mutation. 42 The This study also demonstrated that the S-D614G mutant could modulate conformational population of the S protein and result in the increased furin cleavage efficiency of the S ectodomain. In addition, these experiments suggested that the D614G mutation in the SD2 domain can induce allosteric effect leading to coordinated movements and structural shifts between the up and down RBD conformations. 44 Negative stain electron microscopy revealed 7 the higher 84% percentage of the 1-up RBD conformation in the S-G614 protein, suggesting the increased epitope exposure as a mechanism of enhanced vulnerability to neutralization. 45 The reported retroviruses pseudotyped with S-G614 showed a markedly greater infectivity than the S-D614 protein that was correlated with a reduced S1 shedding, greater stability of the S-G614 mutant and more significant incorporation of the S protein into the pseudovirion. 46 In addition, it was confirmed that the D614G mutation does not produce greater binding affinity of S protein for ACE2 neither makes it more resistant to neutralization. 46 This evidence offered an alternative mechanism to the previously proposed functional scenario in which D614G mutation would promote rather than limit shedding of the S1 domain. 47 Consistent with the reduced shedding mechanism induced by the D614G mutation, the reported cryo-EM structures of a full-length S-G614 trimer featuring three distinct prefusion conformations provided a mechanistic explanation for the increased stability of the highly infective mutant. 48 According to this study, D614G may promote ordering of the partly disordered loop located near the furin cleavage site that strengthens the inter-domain interactions between the NTD and CTD1 regions and enhances the inter-protomer contacts and stability of the mutated S protein, thereby inhibiting a premature dissociation of the S1 subunit which eventually leads to the increased number of functional spikes and stronger infectivity. 48 Structure-based protein design and cryo-EM structure determination established that both D614G and D614N mutations can result in the increased fusogenicity and stability which can be explained by a decrease in a premature shedding of the S1 domain. 49 The cryo-EM structure revealed a stable closed mutant conformation, suggesting that D614G/N mutations can attenuate the repulsive charge interactions at the interface between S1 and S2 providing tighter packing of the head domains against S2. 49 8 Nonetheless, a consensus view on the exact mechanism underlying the functional effects and increased infectivity of S-D614G spike mutant is yet to be established, owing to often conflicting experimental evidence and the proposed mutually exclusive scenarios underpinning molecular consequences of the D614G mutation on structure, dynamics and energetics of virus entry. 42 The recent illumining review highlighted several prevalent mechanisms actively debated in the field offered to explain diverse experimental data, including D614G-induced modulation of cleavage efficiency of S protein; "openness" scenario advocating mutationinduced shift to the open states favorable for RBD-ACE2 interaction; "density" hypothesis suggesting a more efficient S incorporation into the virion; and "stability" mechanism that implicates mutation-induced enhancement in the association and stability of prefusion spike trimers as a driving force of greater infectivity. 42 The growing body of computational modeling studies investigating dynamics and molecular mechanisms of S-D614G mutational variant produced interesting but often inconsistent data that fit different mechanisms. strengthening of the inter-protomer association between S1 and S2 regions. 53 Using timeindependent component analysis (tICA) and protein graph connectivity network, another computational study identified the hotspot residues that may exhibit long-distance coupling with the RBD opening, showing that the D614G could exert allosteric effect on the flexibility of the RBD regions. 54 Structure-based physical model showed that the D614G mutation may induce a packing defect in S1 that promotes closer association and stronger interactions with S2 subunit, thereby supporting the reduced shedding hypothesis. 55 Computational modeling and MD simulations have been instrumental in predicting dynamics and function of SARS-CoV-2 glycoproteins. [56] [57] [58] [59] [60] [61] [62] [63] [64] Our recent study examined molecular basis of the SARS-CoV-2 binding with ACE2 enzyme showing that coevolution and conformational dynamics conspire to drive cooperative binding interactions and signal transmission. 62 Using protein contact networks and perturbation response scanning allosteric sites on the SARS-CoV-2 spike protein were proposed. 63 Molecular simulations and network modeling approaches were used on our most recent investigation to present evidence that the SARS-CoV-2 spike protein can function as an allosteric regulatory engine that fluctuates between dynamically distinct functional states. 64 In this study, we used an integrative computational approach to examine molecular mechanisms underlying the functional effects of the D614G mutation in different states of the SARS-CoV-2 S protein.

We 

We employed coarse-grained (CG) CABS model [65] [66] [67] [68] [69] [70] [71] 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.

All structures were obtained from the Protein Data Bank. 72, 73 Protein residues in the cryo-EM structures were inspected for missing residues and protons that were initially added and assigned according to the WHATIF program web interface. 74, 75 The structures were further parsed through the Protein Preparation Wizard (Schrödinger, LLC, New York, NY) and were subjected to the check of bond order, assignment and adjustment of ionization states, 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 RBD regions (residues 445-446,469-488) were reconstructed by template-based modeling using the cryo-EM structure of human ACE2 in the presence of the neutral amino acid transporter B 0 AT1 complexed with SARS-CoV-2 RBD protein (pdb id 6M17) that includes the complete RBD region as a template. The missing loops in the studied cryo-EM structures of the SARS-CoV-2 S protein were reconstructed and optimized using template-based loop prediction approaches ModLoop, 76 ArchPRED server 77 and further confirmed by FALC (Fragment Assembly and Loop Closure) program. 78 The side chain rotamers were refined and optimized by SCWRL4 tool. 79 The conformational ensembles were also subjected to all-atom reconstruction using PULCHRA method 80 and CG2AA tool 81 to produce atomistic models of simulation trajectories. The protein structures were then optimized using atomic-level energy minimization with a composite physics and knowledgebased force fields as implemented in the 3Drefine method. 82 Principal component analysis (PCA) of simulation trajectories was done using elastic network models (ENM) analysis. 83 Two elastic network models: Gaussian network model (GNM) [83] [84] [85] and Anisotropic network model (ANM) approaches 86 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 Cα 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 DynOmics server 85 and ANM server. 86

To compute protein stability changes in the SARS-CoV-2 S structures, we conducted a systematic alanine scanning of protein residues in the SARS-CoV-2 trimer mutants as well as mutational sensitivity analysis at the mutational site for both SARS-CoV-2 S-D614 and SARS-CoV-2 S-G614 structures. Two different approaches were used. Alanine scanning of protein residues was performed using FoldX approach. [87] [88] [89] [90] and BeAtMuSiC approach. [91] [92] [93] If a free energy change between a mutant and the wild type (WT) proteins ΔΔG= ΔG (MT)-ΔG (WT) > 0, the mutation is destabilizing, while when ΔΔG <0 the respective mutation is stabilizing.

BeAtMuSiC approach is based on statistical potentials describing the pairwise inter-residue distances, backbone torsion angles and solvent accessibilities, and considers the effect of the mutation on the strength of the interactions at the interface and on the overall stability of the complex. [91] [92] [93] We leveraged rapid calculations based on statistical potentials to compute the ensemble-averaged alanine scanning computations and mutational sensitivity analysis at D614 and G614 positions using equilibrium samples from reconstructed simulation trajectories.

Using a protein mechanics-based approach 94 We computed the fluctuations of the mean distance between each atom within a given residue and the atoms that belong to the remaining residues of the protein.

The high values of distance fluctuation indexes are associated with residues that display small fluctuations in their distances to all other residues, while small values of this stability parameter would point to more flexible sites that experience large deviations of their inter-residue distances. In our model, the distance fluctuation stability index for each residue is calculated by averaging the distances between the residues over the simulation trajectory using the following expression: 

A graph-based representation of protein structures 102,103 is used to represent residues as network nodes and the inter-residue edges to describe residue interactions. The details of network construction were described in our previous studies. 104 , 105 We constructed the residue interaction networks using both dynamic correlations 103 Network graph calculations were performed using the python package NetworkX. 107 Using the constructed protein structure networks, we computed the residue-based betweenness parameter. The short path betweenness centrality of residue i is defined to be the sum of the fraction of shortest paths between all pairs of residues that pass through residue i :

where jk g denotes the number of shortest geodesics paths connecting j and k , and ( ) jk g i is the number of shortest paths between residues j and k passing through the node i n .

The Girvan-Newman algorithm 108-110 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. 111, 112 The network parameters were computed using the python package 

We employed multiple CG-CABS simulations followed by atomistic reconstruction and In the closed state, the RBD (residues 331-528) and CTD1 (residues 528-591) corresponded to the most stable regions in the S1 subunit, while UH (residues 736-781) and CH (residues 986-1035) were the most stable regions in the S2 subunit ( Figure 2A ). Only marginally larger fluctuations were seen in the CTD2 region (residues 592-686) that connects S1 and S2 subunits.

Our analysis showed that the conformational dynamics profiles were generally similar for the (Tables S1-S6) using a contacts-based Prodigy approach for prediction of binding affinity in protein-protein complexes and distance threshold of 5.5 Å to define an inter-molecular contact . 116, 117 This analysis showed that the number of inter-protomer contacts is consistently greater in the closed state of the S-D614 and S-G614 proteins as compared to the partially open state. According to this assessment, D614 and Q613 residues can anchor the intra-protomer clusters with V597, S596

and T315 of the same protomer, while establishing contacts with T859, L861 and S735 residues of the adjacent protomers ( Figure 1 , Tables S1-S6). It is worth noting that in agreement with several computational studies advocating for the "openness" mechanism 50 we also found a The experimental studies indicated that the D614G mutational site is located in the immobilized structural region of the SD2 domain where local environment of D614 combined with β strand formed by residues 311-319 may correspond to a hinge center governing motions of NTD and RBD, as well as isolating the motions in S1 from the S2 subunit. 44 To identify hinge sites and characterize collective motions in the SARS-CoV-2 S-D614 and SARS-CoV-2 S-G614 structures, we performed PCA of trajectories derived from CABS-CG simulations and also determined the essential slow modes using ENM analysis. 85, 86 The reported ENM-based functional dynamics profiles were averaged over the first three major low frequency modes ( Figure 3A,B) . 

Previous mutagenesis analysis suggested that the D614G mutant may improve the stability of the S protein by strengthening the inter-protomer association between S1 and S2 regions. 53 We employed the equilibrium ensembles generated by CABS-CG simulations of the SARS-CoVstructures to perform alanine scanning of the protein residues and mutational sensitivity analysis of the S-D614 and S-G614 proteins at the mutational site ( Figure 5 ). The primary objective of this energetic analysis was to further test the shedding hypothesis suggesting that D614G may lead to stabilization of the S protein and block a premature dissociation of the S1 subunit, leading to the increased number of functional spikes and stronger infectivity. 48 Hence, our data suggested that the local energetic gains caused by D614G can cause stabilization of the S trimer and stronger interactions between S1 and S2 domains that could also manifest in the improved communications in the residue interaction networks in the mutant variant. These results also offer support to the experimental observations that the enhanced stability of the S-D614G mutant may be linked with the mechanism of the reduced S1 shedding. 49 and "S1-shedding" mechanism underpinning the D614G effects.

We By using this analysis, we identified allosteric hotspots that are responsible for modulation of stability and allosteric changes. The distance fluctuation profile of the SARS-CoV-2 S-D614 closed form featured a number of high peaks that were distributed in the S1 and S2 subunits ( Figure 7A ). In particular, a strong peak was seen in the S1-RBD region (residues 312-320) that corresponds to the key component of the regulatory hinge site and located in the close structural proximity of the D614 position. Notably, the largest peak was aligned with cluster of residues anchored by the Q613/D614 sites that also included S596, V597 and I598 positions ( Figure 7A ). Other notable peaks corresponded to residues in the S2 regions including cluster 948-LQDVV-952 in the HR1 region (residues 910-985). According to our latest study of the SARS-CoV-2 S mutant trimers, this hydrophobic center is coupled with the Y855/I856 conformational switch that mediate couplings between the S2 subunit and the RBD regions. 118 Interestingly, the HR1 region (residues 934-940) is also known to be targeted by naturally occurring mutations in SARS-CoV-2 protein. 119, 120 In the open state of the S-D614 spike protein, we observed notable changes in the distance fluctuation profile, particularly indicating the loss of appreciable peaks near the D614 cluster ( Figure 7B ). Structural mapping of the distance fluctuation profiles further highlighted these changes ( Figure 8A,B) . Indeed, the density of communication hotspots markedly changed in the open form of S-D614, featuring clusters in the S1 regions but revealing a conspicuous lack of the communication hotspots near the S1-S2 inter-domain regions ( Figure 8B ). Accordingly, based on this model, the allosteric interaction network in the native S-D614 protein may be stronger in the closed form than in the partially open state. Consistent with the energetic analysis, we observed that the distribution seen in the SARS-CoV-2 S-D614 closed form ( Figure 7A ) can be partially altered in the S-G614 closed states ( Figure 7C,E) . Indeed, in the closed form of the D614G mutant strong peaks remained in the S1-RBD hinge region (residues 312-320) and near the D614G local cluster, while the density of hotspots in the S2 regions was weakened. Structural mapping illustrated these subtle adjustments ( Figure   8A ,C), pointing to the increased consolidation of communication hotspots near S1-S2 interface in the S-G614 mutant. These observations suggested that allosteric couplings between S1 and S2 subunits may be partly reorganized in the S-G614 mutant to strengthen stability and communication at the S1-S2 interface. Of special importance are the distribution profiles of the SARS-CoV-2 S-G614 mutant in the open state featuring multiple pronounced peaks in the S1

and S2 regions as well as at the S1-S2 interface ( Figure 7D the S-G614 mutant may promote the greater stability and efficient allosteric couplings between S1 and S2 regions. We argue that D614G mutation-induced modulation of stability and allosteric propensities could therefore limit S1 shedding and favor acquisition of the open state to restore and further optimize allosteric signaling of the SARS-CoV-2 S machinery.

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 Figure 9 ).

In this model, we explored the community analysis as a network proxy for stability assessment.

The number of communities for the S-D614 protein was greater in the closed form ( Figure 9A ).

In some contrast, the network analysis of the S-G614 mutant showed a subtle redistribution in the number and allocation of communities as the open state of the mutant harbored more communities and this difference was more pronounced in the S-RRAR/G614 mutant ( Figure   9A ). Interestingly, the total number of local communities is moderately increased in both closed and open states of the S-G614 mutant. In network terms, this indicated the increased stability of the residue interaction networks in the mutant structures ( Figure 9 ). These results are consistent with the latest experimental data that demonstrated the improved stability of the D614G mutant as compared to the S-D614 protein allowing for reduction in a premature shedding of S1 domain. 49 Table S7 ). Structural mapping of local communities in the S-D614 states illustrated subtle differences in the distribution and density of stable modules ( Figure 9B ,C).

Characteristically, the key community in the closed form of the S-D614 protein is anchored by Q613, which is the immediate neighbor of D614, forming a tight stable cluster with Q314 in the NTD and S596 in CTD2 ( Figure 9B ,C). A general comparison of structural maps indicated the better connectivity of local communities in the closed form of S-D614 forming a broad network linking the S2 regions with the NTD and RBD regions ( Figure 9B ). In addition, a number of unique communities are localized in the CTD2 region (I693-V656-Y660 and I666-L650-I670-T645), suggesting the stronger S1-S2 interfacial interactions and tighter packing between S1 and S2 domains in the closed form of the S-D614 protein. Notably, we also detected the larger number of communities in the NTD and RBD S1 regions in the closed form, indicating that the allosteric interaction network in the S1 and S1-S2 regions is stronger in the closed form, ensuring efficient signaling between S1 and S2 regions in protecting These findings provide an interesting explanation supporting the reduced shedding hypothesis.

Indeed, it has been previously speculated that the loss of hydrogen bonding interactions between D614 in S1 and T859 in S2 caused by D614G mutation may promote rather than limit shedding of the S1 domain. 47 It was also indicated that D614-T859 protomer-protomer hydrogen bonding may be of critical importance for the integrity and stability of the trimer, and that the D614G mutation could weaken the interaction between the S1 and S2 units, facilitating the shedding of S1 from membrane-bound S2. Our results are more consistent with an alternative explanation and latest experimental data 46 To conclude, dynamic network modeling and community analysis of the S-D614 and S-G614

proteins revealed that D614G mutation can induce a partial rearrangement of the residue interaction networks and promote the larger number of stable communities in both the closed and open forms by enhancing the S1-S2 inter-domain interactions. Furthermore, the network analysis suggested a differential stabilization of the S-G614 mutant, favoring the open form in which strengthened allosteric couplings between mutational sites, S1-S2 regions and NTD/RBD regions in S1 could contribute to a decrease in premature shedding of S1 domain. One of the overarching objectives of our computational investigation is to examine functional mechanisms of D614G mutation from the point of view of allosteric regulation and reconcile multiple conflicting scenarios underlying D614G effects. By comparing our results with the growing body of experimental data, we suggested that an allosteric regulatory model may help to explain how the D614G mutation exerts its effect. The proposed allosteric model of SARS-CoV-2 S regulation and functions may be also useful to address questions concerning the effect of mutations on evading antibody binding and immune resistance. Although recent studies indicated that D614G variant did not itself drive escape from antibody binding, it was found that D614G can remarkably potentiate escape mutations at some positions in certain patients, supporting an allosteric mechanism of action triggered by this mutation on dynamics and function in remote regions exposed to interactions with antibodies. 121 

We combined several simulation-based approaches with dynamic network modeling and community analysis to quantify the effect of D614G mutation on dynamics, stability and network organization of the SARS-CoV-2 S proteins. The results of this study provide a novel insight into the molecular mechanisms underlying the effect of D614G mutation by examining SARS-CoV-2 S protein as an allosteric regulatory machine. By examining the dynamic and network properties of the SARS-CoV-2 S trimer proteins, we characterized the distribution of allosteric hotspots in the S-D614 and S-G614 mutant structures revealing consolidation of the communication hotspots in the CTD1-CTD2 regions of the S-G614 mutant that may promote the greater stability and efficient allosteric couplings between S1 and S2 regions. Using mutational sensitivity analysis of the SARS-CoV-2 S-D614 and S-G614 proteins we showed that This study provides support to the reduced shedding hypothesis suggesting that D614G mutation can exert its primary effect through allosterically induced changes on stability and long-range communications in the residue interaction networks.

We argue that considering functions of the SARS-CoV-2 pike proteins through the prism of an allosterically regulated machine may prove to be useful in uncovering functional mechanisms and rationalizing the growing body of diverse experimental data via allosteric models underpinning signaling events. These models can ultimately link the allosteric mechanisms of proteins to their functional role in regulatory processes and signaling events. [122] [123] [124] 

Supporting information contains Tables S1-S6 that characterizes the inter-protomer contacts in the SARS-CoV-2 S-D614 and SARS-CoV-2 S-G614 structures in the closed and open states. The authors declare no competing financial interest.

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The protein data nank

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

Fast procedure for reconstruction of full-atom protein models from reduced representations

CG2AA: backmapping protein coarse-grained structures

3Drefine: an interactive web server for efficient protein structure refinement

Global dynamics of proteins: bridging between structure and function

oGNM: online computation of structural dynamics using the gaussian network model

DynOmics: dynamics of structural proteome and beyond

Bahar, I. The anisotropic network model web server at 2015 (ANM 2.0)

Predicting Changes in the Stability of Proteins and Protein Complexes: A Study of More than 1000 Mutations

The Stability Effects of Protein Mutations Appear to be Universally Distributed

The FoldX Web Server: An Online Force Field

Graphical Interface for the FoldX Force Field

BeAtMuSiC: Prediction of changes in protein-protein binding affinity on mutations

A new generation of statistical potentials for proteins

Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0

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 communications

Computational analysis of residue interaction networks and coevolutionary relationships in the Hsp70 chaperones: A community-hopping model of allosteric regulation and communication

Correlating allostery with rigidity

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

Interaction energy based protein structure networks

Dynamical networks in tRNA:protein complexes

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

Algorithm 97: Shortest path

Exploring network structure, dynamics, and function using NetworkX

Finding and Evaluating Community Structure in Networks

Modularity and Community Structure in Networks

Atomistic modeling of the ABL kinase regulation by allosteric modulators using structural perturbation analysis and community-based network reconstruction of allosteric communications

Dynamic view of allosteric regulation in the Hsp70 chaperones by J-Domain cochaperone and post-translational modifications: Computational analysis of Hsp70 mechanisms by exploring conformational landscapes and residue interaction networks

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

Surveying the Side-Chain Network Approach to Protein Structure and Dynamics: The SARS-CoV-2 Spike Protein as an Illustrative Case

Contacts-based prediction of binding affinity in proteinprotein complexes

PRODIGY: a web server for predicting the binding affinity of protein-protein complexes

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

The impact of mutations in SARS-CoV-2 spike on viral infectivity and antigenicity

Characterizations of SARS-CoV-2 mutational profile, spike protein stability and viral transmission

High resolution profiling of pathways of escape for SARS-CoV-2 spike-binding antibodies

Controlling allosteric networks in proteins

A unified view of "how allostery works

Allostery in its many disguises: From theory to applications

Allostery in its many disguises: From theory to applications