Balancing Functional Tradeoffs between Protein Stability and ACE2 Binding in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB Lineages: Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic and Energetic Changes

Abstract

Evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity for ACE2, conformational plasticity, protein stability and allosteric modulation. In this study, we systematically characterize conformational dynamics, structural stability and binding affinities of the SARS-CoV-2 Spike Omicron complexes with the host receptor ACE2 for BA.2, BA.2.75, XBB.1 and XBB.1.5 variants. We combined multiscale molecular simulations and dynamic analysis of allosteric interactions together with the ensemble-based mutational scanning of the protein residues and network modeling of epistatic interactions. This multifaceted computational study characterized molecular mechanisms and identified energetic hotspots that can mediate the predicted increased stability and the enhanced binding affinity of the BA.2.75 and XBB.1.5 complexes. The results suggested a mechanism driven by the stability hotspots and a spatially localized group of the Omicron binding affinity centers, while allowing for functionally beneficial neutral Omicron mutations in other binding interface positions. A network-based community model for the analysis of epistatic contributions in the Omicron complexes is proposed revealing the key role of the binding hotspots R498 and Y501 in mediating community-based epistatic couplings with other Omicron sites and allowing for compensatory dynamics and binding energetic changes. The results also showed that mutations in the convergent evolutionary hotspot F486 can modulate not only local interactions but also rewire the global network of local communities in this region allowing the F486P mutation to restore both the stability and binding affinity of the XBB.1.5 variant which may explain the growth advantages over the XBB.1 variant. The results of this study are consistent with a broad range of functional studies rationalizing functional roles of the Omicron mutation sites that form a coordinated network of hotspots enabling a balance of multiple fitness tradeoffs and shaping up a complex functional landscape of virus transmissibility.

Markov State Models and Perturbation-Based Approaches Reveal Distinct Dynamic Signatures and Hidden Allosteric Pockets in the Emerging SARS-Cov-2 Spike Omicron Variant Complexes with the Host Receptor: The Interplay of Dynamics and Convergent Evolution Modulates Allostery and Functional Mechanisms

“…133,134 The adaptation of this approach for the analysis of rigid and flexible residues in the SARS-CoV-2 S proteins was detailed in our previous studies. 83 In this approach, the high values of distance fluctuation stability indexes point to structurally rigid residues as they display small fluctuations in their distances to all other residues, while small values of this index would point to more flexible sites that display larger deviations of their inter-residue distances. A comparative analysis of the residuebased distance fluctuation profiles revealed several dominant and common peaks reflecting the similarity of the topological and dynamical features of the RBD-ACE2 complexes (Supporting Materials, Figure S4).…”

“…82 By combining atomistic simulations and a community-based network model of epistatic couplings, we found that convergent Omicron mutations such as G446S (BA.2.75, BA.2.75.2, XBB), F486V (BA.4, BA.5, BQ.1, BQ.1.1), F486S, F490S (XBB.1), F486P (XBB.1.5) can display epistatic relationships with the major stability and binding affinity hotspots which may allow for the observed broad antibody resistance. 83 In the current study, we perform multiple microsecond MD simulations and Markov state model (MSM) analysis to characterize conformational landscapes and identify specific dynamic signatures of the SARS-CoV-2 S RBD-ACE2 complexes for the recently emerged XBB.1, XBB.1.5, BQ.1, and BQ.1.1 Omicron variants. The results of simulations and MSM analysis provide a detailed characterization of the conformational states in the Omicron complexes, showing the increased thermodynamic stabilization of the XBB.1.5 complex, which is contrasted to more dynamic BQ.1 and BQ.1.1 variants.…”

Probing conformational landscapes of binding and allostery in the SARS-CoV-2 omicron variant complexes using microsecond atomistic simulations and perturbation-based profiling approaches: hidden role of omicron mutations as modulators of allosteric signaling and epistatic relationships

“…By applying mutations of protein residues, we compute dynamic couplings of residues and changes in the short path betweenness centrality (SPC), and the average short path length (ASPL) averaged over all modifications in a given position. The details of this approach were described in our recent studies 72,77,78 Here, we briefly outlined the key elements of this approach. The change of SPC or ASPL upon mutational changes of each node is done by systematically removing nodes from the network.Δ L i = 〈‖Δ L node i ( j )‖ 2 〉where i is a given site, j is a mutation and 〈⋯〉 denotes averaging over mutations.…”

“…We complemented the PRS results with the network-based mutational profiling of allosteric residue propensities 72,77,78 that are computed using topological network parameters SPC and ASPL (see Materials and Methods) and can characterize the effect of mutations on long-range interactions and global network of allosteric communications in the RBD-ACE2 complexes. Through ensemble-based averaging over mutation-induced changes in these network metrics, the proposed model can identify positions in which mutations on average cause network changes.…”

“…77 A network-based community model for the analysis of epistatic contributions in the Omicron complexes was recently developed revealing the key role of the binding energy hotspots R498 and Y501 in mediating long-range cooperativity with other Omicron sites that creates a complex functional landscape of virus transmissibility. 78 These network-based computational studies suggested allosteric cooperativity between the Omicron mutational sites where the effects of mutations are propagated and can be amplified through a coordinated interaction network that enables modulation of host receptor binding and immune evasion.…”