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

Abstract

The new generation of SARS-CoV-2 Omicron variants displayed a significant growth advantage and increased viral fitness by acquiring convergent mutations, suggesting that the immune pressure can promote convergent evolution leading to the sudden acceleration of SARS-CoV-2 evolution. In the current study, we combined structural modeling, microsecond molecular dynamics simulations, and Markov state models to characterize conformational landscapes and identify specific dynamic signatures of the SARS-CoV-2 spike complexes with the host receptor ACE2 for the recently emerged highly transmissible XBB.1, XBB.1.5, BQ.1, and BQ.1.1 Omicron variants. Microsecond simulations and Markovian modeling provided a detailed characterization of the functional conformational states and revealed the increased thermodynamic stabilization of the XBB.1.5 subvariant, which can be contrasted to more dynamic BQ.1 and BQ.1.1 subvariants. Despite considerable structural similarities, Omicron mutations can induce unique dynamic signatures and specific distributions of the conformational states. The results suggested that variant-specific changes of the conformational mobility in the functional interfacial loops of the receptor-binding domain in the SARS-CoV-2 spike protein can be fine-tuned through crosstalk between convergent mutations which could provide an evolutionary path for modulation of immune escape. By combining atomistic simulations and Markovian modeling analysis with perturbation-based approaches, we determined important complementary roles of convergent mutation sites as effectors and receivers of allosteric signaling involved in modulation of conformational plasticity and regulation of allosteric communications. This study also revealed hidden allosteric pockets and suggested that convergent mutation sites could control evolution and distribution of allosteric pockets through modulation of conformational plasticity in the flexible adaptable regions.

AlphaFold2-Enabled Atomistic Modeling of Structure, Conformational Ensembles, and Binding Energetics of the SARS-CoV-2 Omicron BA.2.86 Spike Protein with ACE2 Host Receptor and Antibodies: Compensatory Functional Effects of Binding Hotspots in Modulating Mechanisms of Receptor Binding and Immune Escape

“…Our previous studies showed that well-ordered and stable "hook-like" conformation of the RBM tip is maintained in BA.2 and XBB.1.5 variants due to hydrophobic interactions provided by F486 (BA.2) and F486P (XBB.1.5). 70,71 The RMSF analysis of the ACE2 residues showed similar profiles across all the examined variants (Supporting Information, Figure S7). The The conformational ensembles of the S-RBD complexes with ACE2 were subjected to distance fluctuation stability analysis based on the dynamic residue correlations (Figure 6B).…”

“…The advantages of this approach are fast and accurate predictions of the effect of mutations on both the strength of the binding interactions and on the stability of the complex using statistical potentials and neural networks. The benchmark studies and our analysis of different approaches for assessment of mutational effects on the S-RBD binding − showed that the BeatMusic approach is comparable to other knowledge-based structural methods such as D-complex, physics-based FoldX potentials, , and BindProfX approach . The recent analysis of computational tools for estimating protein–protein binding showed that physics-based FoldX methods often display a similar predictive power to comparable but much faster knowledge-based methods such as BeatMusic which is indispensable for massive mutational scanning experiments .…”

“…This analysis is consistent with previous studies, suggesting that conserved hydrophobic RBD residues may be universally important for binding across all Omicron variants and function as stabilizing sites of the RBD stability and binding affinity. 70,71 The mutational heatmap for the BA.2.86 RBD-ACE2 was quite similar showing the major hotspots for Y453, L455, F456, Y489, Y501, and H505 positions that are shared between BA.2 and BA.2.86 (Figure 7B). The direct binding interface is broader for BA.2.86 revealing small variations and tolerance to mutational changes in positions Y473, A475, G476, N477, K484, G485, and P486 (Figure 7B).…”

“…61−69 We recently combined MD simulations and Markov state models to systematically characterize conformational landscapes of the Omicron BA.1, BA.2, BA.3 and BA.4/BA.5 variants 70 and highly transmissible XBB.1, XBB.1.5, BQ.1, and BQ.1.1 variant complexes. 71 Here, we combined AF2-based structural predictions of the top models for BA.2.86 RBD complexes with subsequent microsecond atomistic MD simulations performed to study the stability and dynamics of the AF2-predicted conformational states. Complementary to AF2-guided structural predictions, MD simulations reveal the details of the conformational landscape and the dynamic effect of BA.2.86 RBD mutations on binding to ACE2.…”

“…In the present work, we also leveraged complementary purposes and synergies between AF2 and molecular dynamics (MD) simulations to perform a comprehensive atomistic prediction and analysis of both structure, dynamics, and binding of the Omicron BA.2 and BA.2.86 RBD complexes with ACE2 receptor. MD simulation studies provided important atomistic insights into understanding the dynamics of the SARS-CoV-2 S protein and the effects of Omicron mutations on the conformational adaptability of the S protein and its complexes. − We recently combined MD simulations and Markov state models to systematically characterize conformational landscapes of the Omicron BA.1, BA.2, BA.3 and BA.4/BA.5 variants and highly transmissible XBB.1, XBB.1.5, BQ.1, and BQ.1.1 variant complexes …”