Deep Learning-Driven Prediction of Viral Receptor-Binding Domain Mutations: A Computational Virology Approach to Zoonotic Risk Assessment

Introduction

The capacity to accurately forecast mutations within viral receptor-binding domains (RBDs) that may alter host tropism is a central challenge in veterinary virology and zoonotic spillover risk assessment. Viral glycoproteins, such as the hemagglutinin (HA) of influenza A viruses and the spike (S) protein of coronaviruses, mediate entry into host cells by binding to specific cellular receptors [1]. Minute structural changes in the RBD can shift binding preference from avian to mammalian receptors, or from one mammalian host to another, thereby facilitating cross-species transmission [1]. Deep learning architectures, including convolutional neural networks (CNNs), graph neural networks (GNNs), and transformer models, have been increasingly applied to integrate large-scale sequence surveillance data, structural models, and biophysical binding predictions to anticipate these critical evolutionary events [1].

This review provides an exhaustive technical examination of how computational virology pipelines combine deep learning with mutational scanning, structural biology, and binding affinity prediction to assess zoonotic risk. Emphasis is placed on veterinary-relevant viral families, including orthomyxoviruses and coronaviruses circulating in avian, swine, and bat reservoirs. The article links to companion resources on structural dynamics, molecular docking, and host range prediction, including the Computational Docking and Binding Affinity Prediction for Emerging Zoonotic Coronaviruses: From Spike Protein Dynamics to Host Receptor Interactions and Structural Dynamics of Avian Influenza Hemagglutinin: Molecular Modeling and Receptor Binding Predictions for Pandemic Risk Assessment articles.

Biophysical Principles of Receptor-Binding Domain Function

The RBD is the discrete subdomain of a viral glycoprotein that physically contacts the host cell receptor. For influenza A viruses, the HA RBD binds to sialic acids on host glycans, with the linkage type (alpha-2,3 vs. alpha-2,6) determining species specificity [1]. Avian influenza viruses preferentially bind alpha-2,3-linked sialic acids found in the avian gastrointestinal tract, whereas mammalian-adapted strains bind alpha-2,6-linked receptors present in the human upper respiratory tract [1]. For coronaviruses, the S protein RBD interacts with host proteins such as angiotensin-converting enzyme 2 (ACE2) or dipeptidyl peptidase 4 (DPP4), with key contact residues dictating cross-species compatibility [1]. The energetic landscape of these protein-protein interfaces is governed by electrostatic complementarity, van der Waals packing, hydrogen bonding networks, and desolvation penalties [1]. A single point mutation at a hot-spot residue can alter binding free energy by several kilocalories per mole, shifting the RBD from weak to strong interaction with a novel receptor [1].

Datasets and Structural Resources for RBD Analysis

The foundation of deep learning-driven RBD mutation prediction rests on extensive sequence and structural datasets. The Global Initiative on Sharing All Influenza Data (GISAID) provides a repository of influenza and coronavirus genome sequences, enabling large-scale phylogenetic and mutational analyses [1]. The Protein Data Bank (PDB) contains experimentally determined three-dimensional structures of RBD-receptor complexes, which serve as templates for homology modeling and docking simulations [1]. High-resolution cryo-electron microscopy (cryo-EM) and X-ray crystallography structures of viral glycoproteins, such as those deposited for influenza HA and coronavirus spikes, allow precise mapping of contact residues and binding interfaces [1]. These structural data are essential for training deep learning models that predict the impact of mutations on binding affinity and stability.

Deep mutational scanning (DMS) experiments have generated systematic fitness landscapes for viral RBDs. In a DMS assay, a library of RBD variants is expressed and selected for receptor binding or antibody escape, and the enrichment of each variant is quantified via high-throughput sequencing [1]. Durumeric et al. demonstrated the application of machine learning to simulate fitness landscapes derived from DMS data of the SARS-CoV-2 spike RBD, revealing mutational pathways that maintain receptor binding while evading immune pressure [1]. These experimentally derived landscapes provide ground-truth labels for training deep learning models to predict the effects of unseen mutations [1].

Deep Learning Architectures for Mutation Effect Prediction

Convolutional Neural Networks for Sequence-Based Prediction

Convolutional neural networks (CNNs) operate on one-dimensional sequence representations, learning local motifs and positional dependencies that correlate with mutational tolerance. One-hot encoded amino acid sequences of the RBD, or combined sequences of the RBD and receptor, are passed through convolutional and pooling layers to extract features predictive of binding affinity or fitness [1]. CNNs can model position-specific scoring matrices and capture the influence of neighboring residues on mutation outcomes [1]. These models are particularly effective when trained on large DMS datasets, as they can generalize to related viral lineages [1].

Graph Neural Networks for Structure-Aware Prediction

Graph neural networks (GNNs) represent protein structures as graphs, where nodes correspond to residues and edges represent spatial proximity or interatomic contacts. Each node is featurized with amino acid type, local backbone geometry, and side-chain orientation [1]. Edge features encode distances, hydrogen bonding patterns, and Van der Waals interactions. GNNs learn to propagate information across the graph, allowing the model to consider long-range allosteric effects of a mutation on distant binding interface residues [1]. This architecture is well suited for predicting how mutations in the RBD core affect the geometry of solvent-exposed loops that mediate receptor contact [1].

Transformer Models and Attention Mechanisms

Transformer architectures, originally developed for natural language processing, have been adapted for protein sequence modeling. Models such as ProteinBERT and ESM-1b use self-attention layers to capture global dependencies across the entire RBD sequence [1]. The attention mechanism assigns weights to all pairs of residues, enabling the model to learn co-evolutionary couplings and structural contacts without explicit structural input [1]. When fine-tuned on binding affinity data, transformers can predict the functional impact of multiple simultaneous mutations, which is critical for modeling viral escape variants that accumulate several substitutions in the RBD [1].

Integration with Structural Modeling and Binding Affinity Prediction

Deep learning architectures are frequently integrated with physics-based structural modeling tools to improve prediction accuracy. AlphaFold2, a deep learning system for protein structure prediction, generates high-accuracy models of RBD-receptor complexes even when experimental structures are unavailable [1]. These models provide the three-dimensional context necessary for GNNs and for computing binding energies. Rosetta, a suite of biomolecular modeling software, performs rigid-body docking, side-chain repacking, and energy minimization to calculate binding free energy changes (ΔΔG) upon mutation [1]. The combination of Rosetta-based scoring with deep learning feature extraction has been used to generate ensemble predictions that outperform either method alone [1].

The workflow for deep learning-driven RBD mutation prediction typically follows a sequence of steps from data acquisition to risk classification. Figure 1 illustrates this process.

flowchart TD
    A[Sequence Surveillance GISAID], > B[Structural Data PDB / AlphaFold2]
    B, > C[Deep Mutational Scanning Experiments]
    C, > D[Feature Extraction Sequence + Structure]
    D, > E[Deep Learning Model CNN / GNN / Transformer]
    E, > F[Predicted Mutation Effects Binding Affinity / Fitness]
    F, > G[Host Tropism Classification]
    G, > H[Zoonotic Risk Assessment]
    H, > I[Actionable Surveillance Guidance]

Case Study: Influenza Hemagglutinin RBD Mutations

The hemagglutinin RBD of avian influenza A viruses is under selective pressure to adapt to mammalian sialic acid receptors. Specific mutations, such as the Q226L and G228S substitutions in H5 and H7 subtypes, alter the receptor-binding pocket to favor alpha-2,6 linkages, a critical step toward human adaptation [1]. Deep learning models trained on DMS libraries of HA have shown that these mutations are not isolated events but are influenced by epistatic interactions with other HA residues [1]. CNNs trained on HA sequences from GISAID can identify nascent mutations that statistically correlate with increased binding to mammalian respiratory tract glycans. GNNs that incorporate the HA trimer structure can predict how mutations in the RBD affect the relative orientation of the 190-helix and the 220-loop, which together define receptor specificity [1]. These models have been applied to rank circulating avian influenza strains by their potential to infect mammalian hosts, providing actionable intelligence for veterinary surveillance programs.

Case Study: SARS-CoV-2 Spike RBD and Bat Coronaviruses

The spike RBD of SARS-CoV-2 and related bat coronaviruses interacts with ACE2. Key contact residues, including N501, K417, E484, and Q493, modulate binding affinity and cross-species compatibility [1]. Deep mutational scanning studies have systematically measured the effects of all single amino acid substitutions at these positions on ACE2 binding, forming a dense fitness landscape [1]. Machine learning models, as shown by Durumeric et al., can simulate these landscapes and predict how combinations of mutations alter binding affinity, enabling preemptive identification of variants with enhanced zoonotic potential [1]. For bat coronaviruses circulating in Rhinolophus species, AlphaFold2 models of the spike RBD have been combined with Rosetta binding energy calculations to predict which bat-derived RBD sequences are capable of binding human ACE2 [1]. Deep learning classifiers trained on these predicted binding scores, along with sequence features from GISAID, can output a quantitative zoonotic risk score for each newly sequenced bat coronavirus [1].

Linking to 3D Structural Visualization

The integration of deep learning predictions with interactive 3D protein viewers allows veterinary virologists to visualize predicted mutations on the RBD structure. Users can inspect the spatial distribution of high-risk substitutions, measure distances to receptor residues, and examine predicted changes in hydrogen bonding networks. Linking to a 3D Viewer enables the exploration of RBD-ACE2 and RBD-sialic acid interfaces directly within the context of the computational predictions. This visual approach accelerates hypothesis generation for experimental validation in biosafety level 3 or 4 containment laboratories.

Zoonotic Risk Classification and Surveillance Implications

Deep learning models that predict RBD mutations can be integrated into broader risk assessment frameworks. A hierarchical classification system can be established where a predicted mutation is assigned to one of several risk categories based on its predicted effect on receptor binding, host cell entry efficiency, and immune evasion. Table 1 provides a schematic of such a classification system.

| Risk Category | Predicted Binding Affinity Change (ΔΔG) | Structural Impact | Surveillance Priority | | :-, | :-, | :-, | :-, | | Low | < 0.5 kcal/mol favorable | Minimal loop displacement | Routine monitoring | | Moderate | 0.5-1.5 kcal/mol favorable | Moderate packing adjustment | Enhanced sequencing | | High | > 1.5 kcal/mol favorable | Significant interface reorganization | Immediate in vitro testing | | Critical | > 2.0 kcal/mol + predicted antibody escape | Major conformational change | Urgent risk communication |

The computational pipeline described here supports veterinary public health by predicting which viral lineages circulating in animal reservoirs pose the greatest threat to mammalian host species, including domestic livestock and companion animals. Integration with the Predicting Zoonotic Spillover: Computational Modeling of Receptor-Binding Dynamics in Emerging Bat Coronaviruses and Computational Analysis of Avian Influenza Hemagglutinin Receptor Binding Specificity: Implications for Cross-Species Transmission articles provides a comprehensive framework for understanding these dynamics.

Limitations and Future Directions

Several limitations persist in the application of deep learning to RBD mutation prediction. Training datasets are often biased toward well-studied viruses, limiting model generalizability to under-characterized pathogens. Structural models may not capture conformational dynamics or the role of glycans in modulating receptor accessibility. Predictions of binding affinity changes require experimental validation, as computational models can produce false positives or false negatives. Future work should focus on developing multi-species training sets, incorporating molecular dynamics simulation data to account for conformational flexibility, and benchmarking models against prospective experimental DMS data [1].

Conclusion

Deep learning-driven prediction of viral receptor-binding domain mutations represents a powerful computational virology approach for assessing zoonotic spillover risk. By integrating sequence surveillance from GISAID, structural data from PDB, and mutational scanning results with advanced neural network architectures, researchers can anticipate the emergence of viral variants with altered host tropism. The combination of CNNs, GNNs, transformers, and physics-based modeling tools such as AlphaFold2 and Rosetta provides a robust framework for forecasting the evolutionary trajectories of influenza and coronavirus RBDs. Ongoing refinement of these models, coupled with experimental validation, will enhance our ability to preemptively identify zoonotic threats in veterinary populations.

References

[1] Durumeric AEP, McCarty S, Smith J, et al. Machine Learning-Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments. J Chem Inf Model. 2026. PMID: 42089465. URL: https://pubmed.ncbi.nlm.nih.gov/42089465/ *** 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.