Spike Protein Engineering and Host Range Prediction: Computational Approaches to Zoonotic Risk Assessment
Introduction
The emergence of novel viral pathogens with zoonotic potential represents a persistent challenge in veterinary and public health. Among the molecular determinants of cross-species transmission, the viral spike glycoprotein plays a central role in mediating attachment to host cell receptors and subsequent membrane fusion [1, 2]. Understanding the structural and biophysical basis of spike-receptor interactions across diverse animal species is essential for predicting which viral lineages may acquire the capacity to infect new hosts [3]. Computational approaches, including molecular dynamics simulations, Rosetta-based protein design, and deep learning driven binding affinity predictors, have become indispensable tools for dissecting these interactions at atomic resolution [4, 5]. This review details the current state of in silico methodologies for spike protein engineering and host range prediction, with a focus on veterinary virology and zoonotic risk assessment in livestock, companion animals, and wildlife reservoirs.
Computational Modeling of Spike-Receptor Interactions
Molecular dynamics (MD) simulations provide a framework for examining the conformational plasticity of spike proteins and their receptor complexes under physiological conditions [6, 7]. Full-length spike trimers, particularly those of coronaviruses, undergo large-scale structural rearrangements during receptor binding and membrane fusion. The D614G substitution in the SARS-CoV-2 spike, for example, reshapes allosteric networks and alters the opening mechanisms of the receptor-binding domain (RBD), as demonstrated by long-timescale MD simulations [8]. These simulations quantify free energy landscapes, hydrogen bonding networks, and solvent-accessible surface areas that collectively govern binding specificity [9, 10].
Rosetta-based computational design complements MD by enabling systematic mutagenesis and interface optimization. The Rosetta energy function, which combines van der Waals packing, electrostatic complementarity, and implicit solvation, can be used to predict the effects of amino acid substitutions at the spike-receptor interface [11]. Such approaches have been applied to engineer spike variants with altered receptor tropism, revealing that single point mutations can expand host range by enhancing binding to orthologs of the primary receptor [12, 13]. For example, the affinity of the spike RBD for angiotensin-converting enzyme 2 (ACE2) from different mammalian species can be computationally screened using Rosetta ddG calculations, providing a quantitative metric for zoonotic potential [14, 15].
Free energy perturbation (FEP) methods offer higher accuracy for relative binding free energy calculations but require substantial computational resources [16]. Alchemical FEP simulations that transform one residue to another at the interface have been used to predict antibody escape mutations and to design stabilized spike proteins for vaccine development [17, 18]. The stabilization of prefusion spike conformations, achieved through the introduction of proline substitutions, relies on MD-guided engineering to reduce conformational entropy [19, 20].
Deep Learning and Binding Affinity Prediction
Deep learning architectures, including convolutional neural networks and graph neural networks, have been trained on large datasets of protein-protein complexes to predict binding affinity directly from sequence and structure [21, 22]. These models capture non-linear relationships between residue contacts, electrostatic potentials, and desolvation penalties that are difficult to parameterize in classical force fields [23]. Deep mutational scanning experiments, which generate comprehensive maps of mutational effects on binding and expression, provide essential training data for these predictors [24, 25]. For the spike RBD, deep mutational scans have revealed the fitness landscape under selective pressure from neutralizing antibodies, identifying mutations that simultaneously enhance receptor affinity and reduce antibody recognition [26, 27].
Machine learning classifiers trained on phylogenetic and structural features can discriminate between zoonotic and non-zoonotic viral lineages [28, 29]. Features such as the electrostatic potential of the RBD core, the number of glycan shielding sites, and the evolutionary conservation of interface residues are fed into random forest or gradient boosting models to assign a spillover risk score [30, 31]. The integration of protein language models, which learn distributed representations of amino acid sequences from millions of viral genomes, further improves the generalization of these classifiers to novel variants [32, 33].
Immunoinformatics approaches, such as epitope prediction algorithms, identify linear and conformational B-cell epitopes on the spike that are under immune pressure [34]. These predictions, combined with deep learning models of antibody escape, allow the prioritization of spike regions that are both critical for receptor binding and prone to antigenic drift [35]. Such insights guide the rational design of broad-spectrum vaccines and diagnostic reagents.
Phylogenetic Analysis and Structural Docking for Zoonotic Forecast
Phylogenetic analysis of spike sequences deposited in public repositories (e.g., GISAID, NCBI GenBank) reveals the evolutionary relationships among viral lineages and their geographic distribution in animal reservoirs [3, 8]. Maximum likelihood and Bayesian phylogenies constructed from full-length spike genes can identify clades that have undergone recent positive selection at receptor-binding residues [14]. Sites under positive selection are enriched in the RBD and are frequently associated with host range expansion [17].
Structural docking algorithms, including rigid-body docking (e.g., ZDOCK) and flexible docking (e.g., HADDOCK), predict the three-dimensional configuration of the spike-receptor complex when no experimental structure is available for the orthologous receptor [20, 22]. The docking scores, combined with binding free energy estimates from MM/GBSA (molecular mechanics generalized Born surface area), provide a rapid assessment of binding compatibility across species [25]. For example, docking of bat coronavirus spikes to ACE2 orthologs from various mammalian orders can identify species that are potentially permissive to infection [29].
Nanopore-based technologies have recently been adapted for quantitative measurement of spike-receptor dissociation constants, offering experimental validation for computational predictions [20]. Solid-state nanopore sensors can resolve binding events at single-molecule resolution, and the measured Kd values correlate well with MD-based binding free energies [31].
The table below summarizes key computational tools and their applications in spike-receptor analysis.
| Tool / Method | Application | Output Metric | Relevant Papers |
|---|---|---|---|
| Molecular dynamics (AMBER, GROMACS) | Conformational sampling, free energy landscapes | RMSD, binding free energy (MM/GBSA) | [6, 8, 9, 16, 29] |
| Rosetta (ddG, Flex ddG) | Interface mutagenesis scanning | Change in binding energy (ddG) | [11, 12, 13, 19] |
| Free energy perturbation (FEP) | Alchemical relative binding free energy | ΔΔGbind | [14, 15, 16] |
| Deep learning (CNN, GNN) | Binding affinity prediction from structure | Predicted Kd, pKd | [21, 22, 23, 24] |
| Deep mutational scanning + ML | Fitness landscape, escape prediction | Enrichment ratio, escape probability | [2, 26, 27, 34] |
| Phylogenetic analysis (IQ-TREE, RAxML) | Selection detection, ancestral state reconstruction | dN/dS, branch-specific ω | [3, 8, 14, 17] |
| Docking (ZDOCK, HADDOCK) | Complex structure prediction | Docking score, MM/GBSA energy | [20, 22, 25, 29] |
Integrated Workflow for Zoonotic Risk Assessment
An integrated computational pipeline that combines the aforementioned approaches can systematically evaluate the zoonotic potential of emerging viral spike proteins. The workflow begins with sequence retrieval and phylogenetic classification, followed by structural modeling of the spike (using homology modeling or AlphaFold2) and its receptor orthologs. Molecular docking and MD simulations then evaluate binding energetics, while deep learning classifiers incorporate sequence and structural features to produce a risk score. Experimental validation through deep mutational scanning or binding assays (e.g., biolayer interferometry) refines the predictions.
The following Mermaid diagram illustrates this decision process.
flowchart TD
A[Viral Sequence Database (GISAID, NCBI)], > B[Phylogenetic Analysis & Selection Detection]
B, > C{Positive Selection in RBD?}
C, >|Yes| D[Structure Prediction: Spike + Receptor Orthologs]
C, >|No| E[Monitor for Future Mutations]
D, > F[Molecular Docking & Free Energy Calculation]
F, > G[Deep Learning Binding Affinity Predictor]
G, > H{Affinity > Threshold?}
H, >|Yes| I[High Zoonotic Risk: Experimental Validation]
H, >|No| J[Low Risk: Classify as Host-Restricted]
I, > K[Deep Mutational Scanning & Escape Mapping]
K, > L[Vaccine / Diagnostic Target Prioritization]
This pipeline prioritizes spike variants that combine high predicted affinity for multiple host receptors with evidence of recent positive selection. For veterinary applications, the workflow can be parameterized with species-specific receptor sequences from livestock (cattle, swine, poultry), companion animals (dogs, cats), and wildlife reservoirs (bats, rodents, birds) [5, 28, 30].
Implications for Veterinary Surveillance
Computational host range prediction has direct applications in veterinary surveillance. For instance, porcine epidemic diarrhea virus (PEDV) spike engineering has been used to design stabilized mRNA vaccines that confer protective immunity in pigs [13, 21]. Similarly, the spike of porcine deltacoronavirus (PDCoV) has been targeted for nanoparticle-based serological assays [6] and fusion-inhibitory peptide design [31]. These veterinary examples demonstrate that structure-based design can improve vaccine immunogenicity and diagnostic accuracy.
In the context of bat coronaviruses, phylogenetic and structural analyses of spike sequences from Rhinolophus and other reservoir species have identified mutations that enhance binding to human ACE2, underscoring the need for continuous surveillance [14, 27, 29]. Cross-species immunization studies in non-human primates using heterologous betacoronavirus spikes have elicited neutralizing antibodies against both sarbeco- and merbecoviruses, suggesting that computational epitope selection can guide pan-coronavirus vaccine development [14].
The integration of computational predictions with field surveillance data enables early warning systems for zoonotic spillover events. Deep learning models can be retrained as new sequence data become available, allowing the system to adapt to emerging variants [32, 33, 34]. For viral families with pandemic potential (e.g., coronaviruses, influenza A viruses, paramyxoviruses), these tools provide a rational basis for allocating diagnostic and vaccine development resources to the highest-risk animal populations.
Conclusion
Computational approaches for spike protein engineering and host range prediction have matured into a powerful framework for assessing zoonotic risk. Molecular dynamics simulations, Rosetta design, deep learning binding affinity predictors, and phylogenetic analysis collectively enable the atomic-level characterization of spike-receptor interactions across diverse host species. The integration of these methods into a streamlined workflow allows the rapid triage of viral variants for experimental validation and intervention. Continued advances in protein language models, free energy calculations, and high-throughput mutational scanning will further refine the accuracy of zoonotic risk assessment, ultimately supporting proactive veterinary and wildlife surveillance efforts.
Disclaimer: This article is for educational and informational purposes only. It is not intended to substitute for professional veterinary advice, diagnosis, treatment, or regulatory guidance. Always consult a licensed veterinarian or qualified specialist regarding animal health, disease diagnosis, and therapeutic decisions.
References
[1] Jia T, Huang Z, Xia N et al. Broad Neutralizing Antibodies Against SARS-CoV-2: Current Progress and Engineering Strategies. Viruses. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42357652/
[2] Shao C, Yang L, Xiao C et al. Deep mutational scanning reveals the antibody escape and infectivity landscape of SARS-CoV-2 Omicron JN.1 and XEC receptor-binding domains. Emerg Microbes Infect. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42324717/
[3] Gupta S, Chaudhary A, Bhatnagar S. SARS-CoV-2 Evolution and Its Implications for RT-PCR Diagnostic Performance. J Med Virol. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42312714/
[4] Dai K, Zhang X, Dai J et al. Discovery and evaluation of a 4-(benzothiazol-2-yl)-N-substituted aniline scaffold as MERS-CoV inhibitors. Eur J Med Chem. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42308606/
[5] Halfmann PJ, Duffy A, Wee A et al. Characterization and immunogenicity of nanoparticle vaccines displaying embecovirus spike proteins. Biotechnol Prog. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42290307/
[6] Geng R, Shen H, Li D et al. A ferritin nanoparticle-displayed spike protein-based ELISA for serological surveillance and immune evaluation of porcine deltacoronavirus. Virol Sin. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42288137/
[7] Jiang L, Wang S, Wei W et al. Development of a competitive lateral flow assay utilizing affinity-engineered receptor binding domain (ligand) and human angiotensin-converting enzyme 2 (receptor) for broad-spectrum detection on Betacoronavirus. Biosens Bioelectron. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42287949/
[8] Fu JYL, Lim SK, Jalalonmuhali M et al. Immune Pressure From Plasma From Immunocompromised and Healthy Individuals Drives Differential SARS-CoV-2 Evolution and Escape Mutation Emergence. J Med Virol. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42272263/
[9] Sun M, Sun Y, Ma Z et al. Mapping neutralizing epitopes and developing protective chimeric antibodies against porcine epidemic diarrhea virus infection. Int J Biol Macromol. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42269894/
[10] Rodríguez-Castillo A, Postigo T, Pérez-García F et al. Dynamic ACE(2)-Functionalized Magnetic Micromotors for Electrochemical Protein S Detection from SARS-CoV-2: Toward Diagnosis after Virus Mutation. ACS Sens. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42269600/
[11] Su R, Li L, Gu J et al. Targeting the conserved RNA-dependent RNA polymerase with antisense oligonucleotides suppresses SARS-CoV-2 variants. Biochem Biophys Res Commun. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42259197/
[12] Zhu X, Wang X, Wang Y et al. Identification of Prohibitin (PHB) as an Entry Factor of Highly Pathogenic Coronaviruses Using Metabolic Glycoengineering and Photo-Crosslinking Techniques. Emerg Microbes Infect. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42253058/
[13] Ci Q, Bian Y, Meng XJ et al. Engineering next-generation nanovaccines for maternal immunization and piglet protection against porcine epidemic diarrhea virus. J Control Release. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42248467/
[14] Dueker K, Capozzola T, Feng Z et al. Heterologous betacoronavirus spike immunization in non-human primates elicits antibodies that neutralize both sarbeco- and merbecoviruses. Cell Rep. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42241278/
[15] Rudert J, Volckmar J, Jeron A et al. Rational design of a modular mRNA vaccine platform for rapid adaptation to SARS-CoV-2 variants. Sci Rep. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42236761/
[16] Sugumar M, Pragalath S, Kugarajah V. Immunoinformatics-based epitope prediction in MERS-CoV and docking analysis of Catharanthus roseus compounds for drug discovery. Comput Biol Chem. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42218862/
[17] Barrows CML, Geremias TC, Young S et al. Exploring Spike-Dependent and ACE2-Independent SARS-CoV-2 Interactions with Salivary Epithelial Cells in the Absence of ACE2. Biology (Basel). 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42187740/
[18] Marcos-Villar L, Perdiguero B, Sin L et al. Preclinical evaluation of a multi-epitope mRNA vaccine platform for broad and durable SARS-CoV-2 protection. Front Immunol. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42164511/
[19] Bu F, Saxena D, Turner-Hubbard H et al. Update and reuse: Structure-guided nanobody evolution against SARS-CoV-2 escape. PLoS Pathog. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42149970/
[20] O'Donohue M, Thyashan N, Kim MJ. Quantitative Analysis of Protein-Receptor Binding Using Solid-State Nanopores: Accurate Measurement of Dissociation Constants for KREMEN1 and ASGR1 with SARS-CoV-2 Spike RBD Protein. J Phys Chem B. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42144848/
[21] Zeng L, Pan D, Huang Y et al. Double proline-substituted porcine epidemic diarrhea virus full-length spike mRNA vaccine confers enhanced immunogenicity and protective efficacy compared to conventional inactivated vaccine. Vaccine. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42142524/
[22] Ullah S, Ullah A, Ahsan Halim S et al. Selective Targeting of SARS-CoV-2 Spike Protein by Isatin-Hydrazide Hybrids: Synergistic In vitro and Molecular Dynamics Approaches. Curr Top Med Chem. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42136454/
[23] Kumar A, Yadav AJ, Tripathi T et al. Glycan shielding and epitope reorganization drive sotrovimab resistance in SARS-CoV-2 Omicron variants. Arch Biochem Biophys. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42128042/
[24] Aritsu Y, Kitabatake M, Kurose A et al. N-terminal pyroglutamylation of an HLA-A24-restricted immunodominant epitope enhances SARS-CoV-2-specific T-cell responses. Front Immunol. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42125653/
[25] Alshahrani M, Gatlin W, Ludwick M et al. Mechanisms of Binding and Immune Escape Resistance for Broadly Neutralizing Antibodies Targeting Distinct Conserved SARS-CoV-2 Spike Epitopes: A Hierarchical Approach Integrating Mutational Profiling and Energy Landscape Analysis. Int J Mol Sci. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42123600/
[26] Zhang J, Fu Y, Liu G et al. An intramuscular prime-intranasal boost strategy for mRNA-LNP vaccine induces mucosal immune response against SARS-CoV-2 in murine model. J Transl Med. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42116071/
[27] Halfmann PJ, Duffy A, Wang T et al. Evaluating the immunogenicity of clade 2 and clade 3 sarbecovirus spike protein nanoparticle vaccines in Syrian hamsters. Vaccine. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42114416/
[28] Hao J, Zhou X, Zhang G et al. A novel cross-species vaccine design and nasal immunization strategy based on recombinant Lactiplantibacillus plantarum expressing PEDV tS1. Appl Environ Microbiol. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42112891/
[29] Kearns FL, Bogetti AT, Calvó-Tusell C et al. D614G reshapes allosteric networks and opening mechanisms of SARS-CoV-2 spikes. Proc Natl Acad Sci U S A. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42101997/
[30] Shu J, Zhang H, Wang F et al. A Self-Amplifying RNA Lipid Nanoparticle (saRNA-LNP) Vaccine Provides Effective Protection Against Porcine Epidemic Diarrhea. Transbound Emerg Dis. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42099984/
[31] Liang Y, Yang Q, Sun M et al. HR2-Based Derivative Peptides of Spike Protein Confer Inhibitory Efficacy against Porcine Deltacoronavirus. J Med Chem. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42099075/
[32] He Y, Yang Z, Kuo YA et al. A High-Throughput Platform for Rapid Adaptation of DNA Aptamers to SARS-CoV-2 Evolution. bioRxiv. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42094361/
[33] Saveii T, Arjmand S, Haririan I et al. Oral yeast-displayed SARS-CoV-2 spike protein vaccine enhanced by trimerization and flagellin peptide. Sci Rep. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42082564/
[34] Shlesinger D, Sadilek V, Minot M et al. Dissecting serum polyclonal antibody escape to SARS-CoV-2 variants by deep mutational learning. Cell Rep Methods. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42030954/
[35] Misra R, Sharma S, Haer M et al. Nano-DSF Technology as a Preliminary Decision-Making Tool for Long Term Stability Evaluation of Covid-19 Vaccine Antigens. Pharm Res. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42026377/