Machine Learning-Driven Prediction of Viral Receptor-Binding Domain Mutations for Zoonotic Spillover Risk Assessment

Introduction

Zoonotic spillover events, in which a pathogen circulating in an animal reservoir acquires the capacity to infect a new host species, are governed by a series of molecular barriers that must be overcome [1, 2]. Among the most critical determinants of host range is the interaction between the viral receptor-binding domain (RBD) and the cognate host cell receptor [3]. For enveloped viruses such as influenza A virus (IAV) and coronaviruses, the RBD resides on the hemagglutinin (HA) or spike (S) glycoprotein, respectively [4, 5]. Mutations within the RBD can enhance binding affinity for receptors of a new host, alter receptor specificity, or evade pre-existing immune responses in the reservoir [6, 7]. The ability to predict which RBD mutations are most likely to facilitate cross-species transmission would substantially improve pandemic preparedness efforts [8].

Machine learning (ML) methods have emerged as powerful tools for integrating large-scale sequence surveillance data with structural and biophysical information to forecast high-risk variants [9, 10]. These approaches can prioritize mutations for experimental characterization, reducing the search space from astronomical combinatorial possibilities to tractable candidates [11]. This article reviews the biophysical basis of RBD-receptor interactions, the computational workflows used to predict mutational effects, and the integration of ML into zoonotic risk assessment pipelines for veterinary and comparative virology.

Biophysical Basis of Receptor-Binding Domain Mutations

Structural Architecture of Viral RBDs

The RBD of influenza HA is a globular domain located at the membrane-distal tip of each HA monomer [12]. It contains a receptor-binding site (RBS) that accommodates sialic acid terminated glycans [13]. In avian IAV, the RBS preferentially binds alpha-2,3 linked sialic acids, whereas mammalian-adapted IAVs bind alpha-2,6 linked sialic acids [14]. The switch in specificity is largely mediated by amino acid substitutions at positions 226 and 228 (H3 numbering) in the RBS [15]. For coronaviruses, the RBD lies within the S1 subunit of the spike protein and can adopt either a standing-up (receptor-accessible) or lying-down (receptor-inaccessible) conformation [16]. Bat-derived coronaviruses use angiotensin-converting enzyme 2 (ACE2) from various species, and key residues at the RBD-ACE2 interface determine binding compatibility [17].

Binding Free Energy and Specificity

The strength of the RBD-receptor interaction is quantified by the binding free energy (Delta G). A more negative Delta G indicates higher affinity and thus a lower free energy barrier for cell entry [18]. Mutations that alter the electrostatic complementarity, van der Waals contacts, or hydrogen-bonding network at the interface can shift Delta G by several kcal/mol [19]. For example, the N501Y mutation in the spike RBD of certain coronaviruses increases affinity for human ACE2 by enhancing pi-pi stacking interactions with tyrosine 41 [20]. Similarly, the Q226L and G228S mutations in HA enable human receptor recognition by deepening the RBS and repositioning a key loop [21].

Computational alchemical free energy perturbation (FEP) and end-point methods such as molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) can estimate the Delta G change upon mutation with moderate accuracy (root mean square error approximately 1-2 kcal/mol) [22]. These calculations rely on high-resolution structures from X-ray crystallography or cryo-electron microscopy, or on computational models generated by deep learning-based structure prediction tools like AlphaFold2 [23].

Conformational Dynamics and Allostery

RBD mutations can exert effects beyond the local binding interface. Conformational changes in the RBD, such as the opening-closing motion of the coronavirus spike, can modulate receptor accessibility [24]. Mutations remote from the RBS can allosterically alter the dynamics of the binding site [25]. Molecular dynamics (MD) simulations are essential for capturing these long-range effects, as they provide atomistic trajectories that reveal transient conformational states [26]. Features extracted from MD simulations, such as residue contact maps, backbone dihedral angles, and root mean square fluctuations, can serve as inputs to ML models to predict mutational impacts on binding [27].

Role of Sequence Surveillance and Structural Bioinformatics

Global Sequence Databases

Comprehensive viral sequence surveillance is the foundation of computational spillover risk assessment. Repositories such as GISAID (for influenza and coronaviruses) and GenBank house millions of sequences from animal and environmental samples [28]. These data enable the construction of phylogenies, identification of positively selected sites, and tracking of variants over time [29]. For zoonotic risk, sampling from wild birds, bats, swine, and poultry is particularly informative [30]. The frequency of a mutation in reservoir populations can be combined with predicted functional effects to compute a spillover risk score [31].

Structural Coverage and Homology Modeling

While many animal virus strains lack experimental structures, homology modeling or AlphaFold2 predictions can fill the gap [32]. AlphaFold2 produces per-residue confidence metrics (pLDDT scores) that guide the selection of reliable structural models for downstream analyses [33]. For RBDs of bat coronaviruses that share 70-90% sequence identity with known human pathogens, AlphaFold2 models have been used to dock against various host ACE2 orthologs and predict binding affinity [34]. Rosetta-based protocols, including RosettaDock and FlexPepDock, further refine the interfaces and calculate interface scores [35].

Evolutionary and Structural Features for ML

Machine learning models require a set of informative features. These can be categorized as:

• Sequence-based features: position-specific scoring matrices (PSSMs), conservation scores, amino acid physicochemical properties, and co-evolutionary couplings from multiple sequence alignments [36].
• Structure-based features: solvent accessibility, residue depth, B-factors, inter-residue distances, and interface area [37].
• Dynamic features: principal components of MD trajectories, flexibility indices, and correlation matrices [38].
• Evolutionary features: branch lengths, dN/dS ratios, and substitution rates at the site [39].

A curated set of such features can be extracted for each possible mutation in the RBD and used to train supervised models on experimental datasets of binding affinity changes (e.g., from deep mutational scanning) [40].

Machine Learning Approaches for Predicting Mutations

Supervised Learning for Binding Affinity Prediction

Deep mutational scanning (DMS) experiments have generated large-scale fitness landscapes for several viral RBDs, quantifying the effect of every single amino acid substitution on receptor binding or antibody escape [41]. These datasets provide ground truth labels for training ML models. Random forest, gradient boosting, and deep neural networks have been applied to predict Delta Delta G (the change in binding free energy) given the features described above [42]. For example, a model trained on DMS data for the avian IAV HA RBS achieved a Pearson correlation of 0.75 between predicted and measured binding scores for human receptor analogs [43]. Graph neural networks that operate on the protein contact graph can capture non-local interactions and improve accuracy for mutations at the interface [44].

Unsupervised and Semi-Supervised Approaches

When experimental data are scarce, unsupervised models based on protein language models (PLMs) can be employed. PLMs such as ESM-1b and ProtBERT learn evolutionary constraints from unlabeled sequences and can assign likelihood scores to single mutations based on masked language modeling [45]. A low likelihood indicates a variant that is evolutionarily disfavored but may still arise under selective pressure. Combining PLM scores with structural energetic calculations in a semi-supervised framework has shown promise for predicting immune escape mutations in both influenza and coronaviruses [46].

Generative Models for Mutant Exploration

To proactively identify mutations that could enhance zoonotic potential, generative adversarial networks (GANs) or variational autoencoders (VAEs) can propose novel RBD sequences optimized for binding to a target host receptor [47]. These models sample the latent space of protein sequences and are guided by a discriminator or decoder that predicts binding affinity. The generated variants can then be ranked by structural modeling and MD simulations [48]. Such de novo design strategies complement the surveillance-based approach by exploring sequences not yet observed in nature.

Workflow Integration and Risk Assessment

A computational pipeline for zoonotic spillover risk assessment integrates multiple modules. Figure 1 illustrates the overall workflow.

flowchart TD
    A[Sequence Surveillance Data\n(GISAID, GenBank)], > B[Multiple Sequence Alignment\n& Phylogenetic Analysis]
    B, > C[Feature Extraction\n(Sequence, Structure, Dynamics)]
    C, > D[Machine Learning Model\n(e.g., Random Forest, GNN, PLM)]
    D, > E[Predicted ΔΔG & Binding Scores\nfor Each Variant]
    E, > F[Ranking of High-Risk Mutations]
    F, > G[Experimental Validation\n(Surface Plasmon Resonance, Pseudovirus Entry)]
    G, > H[Updated Risk Score\n& Public Health Alert]
    H, > A

Figure 1. Schematic of a machine learning-driven pipeline for predicting RBD mutations that enhance zoonotic spillover risk. Dashed lines represent feedback loops that incorporate experimental validation to refine model predictions.

Development of a Risk Score

The final output of the pipeline is a quantitative risk score for each viral variant. This score can combine:

• Predicted binding affinity gain to the new host receptor (Delta Delta G) [49].
• The frequency of the mutation in the reservoir population (surveillance data) [50].
• The degree of antigenic novelty relative to existing vaccines or immunity in the target host [51].
• Structural compatibility with the new host receptor, as assessed by docking scores [52].

Variants that score above a threshold are flagged for experimental testing. For example, a bat coronavirus spike variant predicted to have a Delta Delta G of less than -2.5 kcal/mol for human ACE2 and observed in at least 1% of bat sequences would be considered high priority [53].

Experimental Validation

Priority variants are validated using in vitro binding assays such as surface plasmon resonance (SPR) or biolayer interferometry (BLI) with purified RBDs and receptor ectodomains [54]. Pseudovirus entry assays using lentiviral or vesicular stomatitis virus backbones carrying the variant spike or HA then quantify infectious entry into cell lines expressing the target receptor [55]. Confirmed high-affinity variants can be further assessed in animal models to evaluate airborne transmissibility and pathogenesis [56]. The resulting data are fed back into the ML model to improve future predictions.

Conclusion

Machine learning-driven prediction of viral RBD mutations represents a powerful strategy for preemptive zoonotic risk assessment. By integrating large-scale sequence surveillance, structural bioinformatics, and biophysical simulations, these computational pipelines can identify which mutations in influenza HA and coronavirus spike proteins are most likely to enable cross-species transmission. The continuous evolution of viral glycoproteins in animal reservoirs demands that these predictive tools be updated regularly with new sequence data and experimental feedback. Future advances in deep learning architectures, particularly those incorporating protein conformational dynamics and multi-species receptor repertoires, will further enhance the accuracy and timeliness of spillover risk forecasts for veterinary and public health applications.

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.

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