Spike Protein Dynamics and Host Receptor Binding: Computational Simulations of SARS-CoV-2 Variants and Zoonotic Potential

Introduction

The emergence and sustained circulation of SARS-CoV-2 in diverse host species have underscored the need for computational frameworks capable of predicting spike protein behavior, receptor engagement, and zoonotic spillover risk. The spike glycoprotein, a class I fusion protein, mediates viral entry by binding to host receptors and is the primary target of neutralizing antibodies. Understanding the biophysical principles governing spike protein dynamics is essential for assessing cross-species transmission potential and for rational design of intervention strategies in veterinary medicine [1, 2]. Computational simulations, including molecular dynamics (MD), free energy calculations, and machine learning (ML)-guided mutational scanning, now offer high-resolution views of spike-receptor interactions at atomic and near-atomic scales [3, 4].

This article reviews computational approaches used to examine spike protein structural dynamics, receptor binding affinities, and immune evasion mechanisms across SARS-CoV-2 variants, with a focus on implications for zoonotic potential. The discussion emphasizes veterinary species and comparative host-range analysis, drawing parallels to other zoonotic coronaviruses where appropriate.

Structural Basis of Spike Protein Dynamics

The spike trimer undergoes large-scale conformational rearrangements to facilitate receptor binding and membrane fusion. Cryo-electron microscopy (cryo-EM) studies have revealed cooperative conformational changes in the spike trimer, including transitions between closed (prefusion) and open (receptor-accessible) states [2]. The receptor-binding domain (RBD) within each spike protomer adopts either a "down" (closed) or "up" (open) conformation; opening is required for high-affinity binding to angiotensin-converting enzyme 2 (ACE2) [2, 5]. The D614G substitution, which became dominant early in the pandemic, reshapes allosteric networks and shifts the equilibrium toward the open state, enhancing receptor binding and infectivity [5, 6].

Furin cleavage at the S1/S2 boundary primes the spike for subsequent activation by transmembrane protease serine 2 (TMPRSS2). Allosteric effects of furin cleavage and the D614G mutation on membrane-embedded spike conformational dynamics have been characterized using coarse-grained MD simulations [6]. These studies demonstrate that cleavage and D614G act synergistically to stabilize the open conformation, thereby increasing the probability of ACE2 engagement [5, 6].

N-glycosylation plays a critical role in modulating spike conformation and shielding immunogenic epitopes. Comprehensive MD simulations combined with glycoproteomics have shown that glycan moieties at specific asparagine residues (e.g., N165, N234) influence RBD opening and antibody recognition [7]. Glycan shielding also contributes to immune evasion in Omicron variants, as demonstrated for sotrovimab resistance [8]. In silico removal or modification of glycans alters epitope accessibility and binding free energies, highlighting the importance of including glycosylation in computational models [8, 7].

Computational Methods for Spike-Receptor Interaction Analysis

Molecular Dynamics Simulations

All-atom and coarse-grained MD simulations provide atomistic detail on the timescales of conformational changes relevant to receptor binding and antibody escape. MD simulations have been instrumental in mapping the energy landscapes of RBD-ACE2 interactions and in evaluating the impact of mutations on binding affinity [9, 10, 11, 12, 13]. For example, ligand-induced modulations of the HSPA8-spike interaction were elucidated using in-depth MD simulations, revealing potential chaperone-mediated entry pathways [10]. Similarly, MD simulations of the N481K mutation demonstrated altered hydrogen-bonding networks at the RBD-ACE2 interface, leading to increased binding affinity [14].

Free energy perturbation (FEP) and molecular mechanics/generalized Born surface area (MM/GBSA) methods are routinely applied to compute binding free energy differences between variant RBDs and ACE2 or alternative receptors [15, 16, 12, 17]. Solvated interaction energy (SIE) calculations prioritized cannabinoid compounds as variant-spanning RBD-ACE2 interface blockers, with binding free energies correlating well with experimental neutralization data [12].

Docking and Virtual Screening

Structure-based virtual screening using docking algorithms (e.g., AutoDock Vina, Glide) identifies small molecules or peptides that compete with receptor binding. A ligand-based pharmacophore model and structure-based virtual screening campaign identified potential inhibitors targeting the spike-ACE2 interface [15]. Synergistic in vitro and MD approaches validated isatin-hydrazide hybrids as selective spike binders [11]. Computational discovery of entry-inhibitory peptides from scorpion venom (Androctonus mauretanicus) relied on molecular docking and subsequent MD refinement [13]. These approaches are directly transferable to veterinary species for identifying host-specific entry blockers.

Deep Mutational Scanning and Machine Learning

Deep mutational scanning (DMS) experimentally evaluates the functional impact of thousands of single amino acid substitutions in the RBD. Combining DMS with machine learning enables prediction of mutation effects on receptor binding and antibody escape across variant backgrounds [3, 4]. Taylor and Starr [3] highlighted changing amino acid preferences within epistatic hotspot residues in recent variants, emphasizing that mutational effects depend on the genetic background. ML iterative experiments, as demonstrated by Sheffield et al. [4], accelerate the characterization of emerging variants of concern by training models on sparse experimental data.

ML-guided rational engineering of ACE2-derived peptides achieved broad-spectrum neutralization across variants, tuned by mutational scanning and binding affinity predictions [18]. Additionally, ML models trained on structural features and evolutionary conservation predict the impact of RBD mutations on host tropism, as applied to bat coronavirus spike proteins [4, 18].

Homology Modeling and Rosetta Protein Design

Homology modeling using templates from the [Protein Data Bank](/knowledge/bioinformatics/protein-data-bank-formats-archival-validation 2) (PDB) generates three-dimensional structures for variants lacking experimental coordinates. Rosetta-based flexible backbone design and binding energy calculations optimize protein-protein interfaces and engineer decoy receptors or antibodies [1, 16, 19, 20, 21]. The Rosetta energy function, combined with mutational profiling and frustration landscape analysis, identifies escape-prone versus escape-proof antibody epitopes [16, 19]. Comparative nanomechanical studies of antibody and nanobody binding to variant spikes employed Rosetta-based interface analysis alongside atomic force microscopy [20].

Receptor Binding Beyond ACE2: Implications for Zoonotic Potential

While ACE2 is the primary receptor for SARS-CoV-2, several alternative receptors and co-receptors expand the possible host range. Single-particle imaging revealed receptor-mediated endocytic dynamics involving alternative receptors such as transferrin receptor (TfR) and neuropilin-1 (NRP1) [22, 23, 24]. Wu et al. [23] provided mechanistic insights into the interaction of emerging spike variants with TfR, demonstrating that mutations in the RBD can modulate TfR binding independently of ACE2. NRP1 binds to the CendR motif (682RRAR685) present in the furin-cleaved spike, facilitating viral entry into cells expressing low ACE2 levels [24].

Polyphenols identified as NRP1 CendR pocket inhibitors blocked SARS-CoV-2 entry and enhanced variant resistance, suggesting a druggable target for cross-species intervention [24]. Hosts expressing NRP1 homologs with conserved CendR binding pockets may be susceptible to infection via this pathway. Additionally, HSPA8, a chaperone protein, was shown via MD simulations to interact with the spike and potentially mediate endocytosis in a receptor-independent manner [10].

The ability to engage multiple receptors broadens the potential host range and complicates risk assessment. Computational prediction of host tropism now incorporates panels of receptor candidates (ACE2, TfR, NRP1, CD147, etc.) and evaluates binding compatibility using docking and MD [22, 23, 24]. This multi-receptor framework is essential for assessing spillover risk from reservoir species such as bats, pangolins, and mink into domesticated animals.

Case Studies: Computational Characterization of Specific Variants

D614G

The D614G substitution in the spike S1 domain was one of the earliest globally dominant mutations. MD simulations by Kearns et al. [5] and Shoemaker et al. [6] demonstrated that D614G allosterically reshapes the spike trimer, increasing the population of open RBD conformations. This mutation does not directly alter ACE2 binding affinity but enhances infectivity by facilitating receptor engagement. D614G also modulates furin cleavage efficiency, further stabilizing the open state [6].

N481K

Discovered in several variants, N481K increases ACE2 binding affinity by introducing a positive charge that strengthens electrostatic interactions with negatively charged ACE2 residues [14]. MD simulations confirmed a more stable RBD-ACE2 complex with reduced RMSD and increased hydrogen bond occupancy [14].

Omicron Subvariants (e.g., KP.3.1.1, LP.8.1, NB.1.8.1)

Functional characterization of spike RBD mutations in Omicron-derived subvariants revealed enhanced ACE2 affinity accompanied by immune evasion [25]. Computational simulations predicted that mutations such as F456L and R346T alter antibody epitope geometry while preserving receptor binding [25, 26]. The XBB.1.5 subvariant harbored mutations that altered four conserved antigenic determinants, as shown by structural modeling and epitope mapping [26].

Recombination-Driven Epistatic Effects

Intra-host recombination between co-infecting SARS-CoV-2 strains generates chimeric spike proteins with epistatic interactions. Altaf et al. [27] demonstrated that recombinant spikes exhibit temperature-dependent adaptation, with some combinations showing enhanced stability at lower temperatures, which may be relevant for transmission in animal hosts with different body temperatures.

Workflow: Integrating Computational Simulations for Zoonotic Risk Assessment

The following Mermaid diagram illustrates a typical computational pipeline for evaluating spike protein dynamics and host receptor binding for zoonotic risk assessment.

flowchart TD
    A[Spike Sequence Data from Surveillance], > B[Homology Modeling / AlphaFold Structure Prediction]
    B, > C[Glycosylation Site Prediction and Glycan Attachment]
    C, > D[Molecular Dynamics Simulations of Spike Trimer]
    D, > E[Conformational Sampling: Open/Closed States]
    E, > F[Receptor Docking (ACE2, TfR, NRP1, etc.)]
    F, > G[Binding Free Energy Calculation (MM/GBSA, FEP)]
    G, > H[Deep Mutational Scanning and ML Prediction of Mutation Effects]
    H, > I[Identification of Key RBD Mutations and Escape Hotspots]
    I, > J[Cross-Species Binding Affinity Comparison]
    J, > K[Zoonotic Spillover Risk Score]
    K, > L[Veterinary Surveillance and Intervention Prioritization]

Summary of Key Computational Methods and Their Applications

Veterinary and Zoonotic Implications

Computational simulations of spike protein dynamics directly inform veterinary virology by predicting which animal species may serve as vulnerable hosts or bridging reservoirs. Structural constraints acting on the spike limit the sequence space available for viral adaptation, but certain mutations can expand host tropism [30]. For instance, the ability of SARS-CoV-2 to infect farmed mink, deer, cats, and dogs has been linked to specific RBD mutations that enhance binding to each species' ACE2 ortholog [23, 25, 28]. ML-driven models trained on ACE2 sequence diversity across mammals can rank species by predicted susceptibility [18, 20].

Furthermore, the development of variant-spanning antibody-like binders and receptor decoys for veterinary use benefits from computational design pipelines [1, 16, 20, 21]. For example, nanobodies designed against conserved spike epitopes show promise as therapeutic or prophylactic agents in animal models [20]. The same computational tools used to predict human immune escape (e.g., by bebtelovimab [9] or sotrovimab [8]) are adapted to evaluate cross-reactivity with antibodies elicited in vaccinated or infected animals.

Future Directions

Advances in coarse-grained and all-atom simulations, combined with enhanced sampling techniques (e.g., metadynamics, replica exchange), will enable routine simulation of full-length spike trimers embedded in viral membranes [6]. Integration of glycan shield dynamics with MD is becoming standard to capture epitope masking [8, 7]. ML models that incorporate epistatic interactions are needed to predict combinatorial mutation effects accurately [3, 27]. Additionally, the development of high-throughput aptamer selection platforms [31] and immunoinformatics tools for vaccine design [32] can be repurposed for animal health applications.

The veterinary computational virology community should adopt standardized protocols for evaluating spike-receptor binding across diverse hosts. Collaborative databases linking structural, computational, and serological data will accelerate risk assessment. Finally, biological foundation models [4] that predict host tropism from sequence alone represent a paradigm shift, enabling real-time evaluation of emerging variants from any animal-origin sample.

Conclusion

Computational simulations have become indispensable for dissecting spike protein dynamics and host receptor binding in SARS-CoV-2 and related coronaviruses. Molecular dynamics, free energy calculations, deep mutational scanning, and machine learning provide mechanistic insights into how mutations alter receptor affinity, immune evasion, and zoonotic potential. These methods, when integrated into structured workflows, empower veterinary virologists to predict cross-species transmission risks and design targeted countermeasures. Continued investment in computational infrastructure and interdisciplinary training will be essential to keep pace with viral evolution.

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