Predicting Viral Escape Mutations through Computational Structural Analysis of Antibody-Binding Interfaces

Introduction

The continuous evolution of viral surface proteins under selective pressure from host humoral immunity drives the emergence of escape mutations that reduce or abolish antibody neutralization. Understanding the molecular determinants of immune evasion is critical for designing effective vaccines and therapeutic monoclonal antibodies in both human and veterinary medicine. Computational structural analysis of antibody-binding interfaces has emerged as a powerful approach to predict escape mutations before they arise in circulating viral populations [1, 2]. By integrating biophysical simulations, energetic calculations, and evolutionary data, these methods can identify residues that are both critical for antibody binding and permissive to mutation without compromising viral fitness [3, 4].

This review focuses on the computational frameworks used to predict viral escape mutations, with emphasis on their application to veterinary pathogens such as feline infectious peritonitis virus (FIPV), avian influenza virus, and other coronaviruses. Parallels are drawn from extensive studies on human pathogens, including SARS-CoV-2 and HIV, to illustrate transferable principles. The article covers molecular dynamics (MD) simulations, Rosetta-based design, free energy perturbation (FEP), and frustration analysis, and discusses how these tools can be integrated into a predictive pipeline for veterinary virology.

Computational Methods for Interface Analysis

Molecular Dynamics Simulations of Antibody-Antigen Complexes

Molecular dynamics simulations provide atomistic resolution of the conformational ensembles sampled by antibody-antigen complexes. By simulating the complex over microsecond timescales, researchers can identify transient binding modes, cryptic epitopes, and allosteric networks that influence antibody recognition [1, 2]. For the SARS-CoV-2 spike protein, MD simulations have revealed that conformational dynamics of the receptor-binding domain (RBD) modulate accessibility of antibody epitopes and that mutations can shift the equilibrium toward states that evade neutralization [3, 4]. These principles apply directly to veterinary coronaviruses such as FIPV, where the spike protein undergoes similar conformational rearrangements during host cell entry.

MD simulations also enable the calculation of binding free energies using methods such as molecular mechanics generalized Born surface area (MM-GBSA). This approach decomposes the binding energy into van der Waals, electrostatic, and solvation contributions, allowing identification of hotspot residues that contribute disproportionately to affinity [5, 6]. For example, MM-GBSA analysis of ultrapotent neutralizing antibodies against SARS-CoV-2 showed that hydrophobic packing dominates binding, with primary hotspots such as Y489 and Y501 contributing the largest energy terms [5, 6]. Such analyses can be directly applied to veterinary antibody-antigen complexes to predict which residues, if mutated, would most severely disrupt neutralization.

Rosetta-Based Computational Protein Design

The Rosetta software suite offers a flexible platform for modeling sequence-structure relationships at protein-protein interfaces. Interface-guided computational protein design (CPD) systematically mutates interface residues and evaluates the energetic consequences using a combination of side-chain packing, rotamer optimization, and scoring functions [7, 8]. This approach has been used to predict resistance mutations for monoclonal antibodies targeting SARS-CoV-2 RBD. In one study, Rosetta-based CPD identified bebtelovimab-resistance mutations with 69-100% correlation to clinically observed escape variants [7]. The method prioritizes substitutions that maintain or improve binding to the antibody while preserving RBD stability, thereby mimicking the evolutionary constraints faced by the virus [7, 8].

For veterinary applications, Rosetta design can be applied to predict escape mutations in the hemagglutinin (HA) of avian influenza virus or the spike protein of FIPV. By constructing homology models of these proteins bound to relevant antibodies, researchers can perform saturation mutagenesis in silico and rank mutations by their predicted effect on binding affinity and protein stability.

Free Energy Perturbation and Alchemical Methods

Free energy perturbation (FEP) is the most rigorous computational approach for calculating the change in binding free energy upon mutation. Alchemical FEP simulations gradually transform one residue into another while computing the free energy difference along the perturbation pathway [3, 4]. Although computationally expensive, FEP provides highly accurate predictions of how single-point mutations affect antibody-antigen binding. Studies on SARS-CoV-2 spike-antibody complexes have demonstrated that FEP can quantitatively reproduce experimentally measured escape profiles for mutations such as E484K and N501Y [3, 4]. The method is particularly valuable for prioritizing mutations that are likely to emerge under antibody pressure.

Frustration Analysis and Allosteric Networks

Beyond direct binding energetics, the concept of conformational and mutational frustration has emerged as a key predictor of escape mutations. Frustration analysis quantifies the degree to which local interactions in a protein structure are energetically optimized (minimally frustrated) or suboptimal (highly frustrated) [5, 6]. Regions of neutral frustration, where interactions are neither strongly stabilizing nor destabilizing, are permissive to mutational exploration without compromising protein folding or function. For SARS-CoV-2 spike-antibody interfaces, immune escape hotspots were found to reside in neutral frustration "playgrounds" that allow convergent mutations to arise repeatedly across lineages [5, 6]. This framework explains why certain residues, such as L455 and F456, are frequently mutated in emerging variants while others remain conserved.

Allosteric network analysis, often performed using correlation-based methods from MD trajectories, identifies residues that communicate dynamically across the protein. Mutations at allosteric sites can indirectly affect antibody binding by altering the conformational ensemble of the epitope [1, 2]. Integrating frustration and allostery provides a more complete picture of the molecular mechanisms underlying immune escape.

Case Studies in Viral Escape Prediction

SARS-CoV-2 Spike Protein and Antibody Escape

The most extensively studied system for computational escape prediction is the SARS-CoV-2 spike protein. Multiple studies have employed integrative computational modeling combining MD, Rosetta, MM-GBSA, and frustration analysis to dissect the binding mechanisms of broadly neutralizing antibodies [1, 2, 3, 4, 5, 6]. These investigations revealed that ultrapotent antibodies such as BD55-1205 and S309 bind via rigid, pre-configured interfaces that distribute binding energy across extensive epitopes [5, 6]. Primary hotspots (e.g., H505, Y501, Y489, Y421) overlap with ACE2-contact residues and incur high fitness costs upon mutation, while secondary hotspots (e.g., F456, L455) are more permissive to variation [5, 6]. This hierarchical organization explains why some escape mutations (e.g., E484K) emerge rapidly while others are rarely observed.

Interface-guided CPD specifically predicted bebtelovimab-resistance mutations with high accuracy, validated against clinical viral genome sequences [7, 8]. The predicted mutations included single and multipoint changes in the RBD that reduced antibody binding while maintaining spike function. These studies underscore the translational value of computational predictions for guiding antibody development.

Influenza Hemagglutinin and Antigenic Drift

Influenza A virus undergoes continuous antigenic drift driven by mutations in the hemagglutinin (HA) glycoprotein. Computational structural analysis of HA-antibody interfaces has been used to predict drift variants before they become epidemiologically dominant. By combining MD simulations with phylogenetic data, researchers can identify epitope regions that are both immunodominant and structurally tolerant to mutation. For avian influenza viruses, such predictions are critical for updating vaccine strains in poultry. The same frustration-based framework applied to SARS-CoV-2 can be extended to HA, where neutral frustration landscapes in the globular head domain permit accumulation of escape mutations without compromising receptor binding or membrane fusion.

HIV Envelope Glycoprotein and Broadly Neutralizing Antibodies

HIV-1 envelope (Env) is a heavily glycosylated trimer that presents a formidable challenge for antibody neutralization. Computational studies have mapped the binding interfaces of broadly neutralizing antibodies (bNAbs) to conserved epitopes on Env, such as the CD4-binding site and the V3-glycan supersite. MD simulations and FEP calculations have been used to predict escape mutations that arise under bNAb pressure in both human and simian immunodeficiency virus (SIV) models. For veterinary applications, similar approaches can be applied to feline immunodeficiency virus (FIV) Env to predict mutations that enable immune evasion in cats.

Veterinary Coronaviruses: FIPV and Other Animal Coronaviruses

Feline infectious peritonitis virus (FIPV) is a highly pathogenic coronavirus of domestic cats. The spike protein of FIPV mediates host cell entry and is the primary target of neutralizing antibodies. Computational structural analysis of FIPV spike-antibody interfaces can leverage homology models built from SARS-CoV-2 structures, given the conserved architecture of coronavirus spike proteins. By applying Rosetta design and frustration analysis, researchers can predict which residues in the FIPV RBD are likely to mutate under antibody pressure, informing the design of next-generation vaccines and therapeutic antibodies. Similar approaches are applicable to other veterinary coronaviruses, including porcine epidemic diarrhea virus (PEDV) and bovine coronavirus (BCoV).

Integrated Workflow for Predicting Escape Mutations

The following Mermaid diagram illustrates a typical computational pipeline for predicting viral escape mutations from antibody-binding interfaces.

flowchart TD
    A[Experimental or Predicted Structure of Antibody-Antigen Complex], > B[MD Simulations of Complex]
    B, > C[Conformational Ensemble Analysis]
    C, > D[Identification of Epitope and Paratope Residues]
    D, > E[Saturation Mutagenesis in Silico]
    E, > F[Binding Energy Calculation: MM-GBSA, Rosetta, FEP]
    F, > G[Frustration and Allostery Analysis]
    G, > H[Ranking of Mutations by Predicted Escape Potential]
    H, > I[Validation with Viral Genome Surveillance Data]
    I, > J[Prediction of Emerging Escape Variants]

The pipeline begins with a high-resolution structure of the antibody-antigen complex, obtained from X-ray crystallography, cryo-electron microscopy, or computational modeling using tools such as AlphaFold2 [9]. MD simulations generate conformational ensembles that capture the flexibility of the interface. Saturation mutagenesis is then performed computationally, and the effect of each mutation on binding energy is calculated using MM-GBSA, Rosetta, or FEP. Frustration analysis identifies residues that are energetically permissive to mutation. Finally, predictions are validated against global viral genome sequences to confirm that predicted escape mutations are observed in circulating strains [7, 8].

Challenges and Future Directions

Despite significant progress, several challenges remain in computational escape prediction. First, the accuracy of binding energy calculations depends on the quality of the input structure and the force field parameters. For veterinary viruses with limited structural data, homology modeling introduces uncertainty that can propagate through the pipeline. Second, the computational cost of FEP simulations limits their application to a small number of mutations, whereas Rosetta-based methods offer higher throughput but lower accuracy. Third, the role of glycan shielding in antibody evasion is difficult to model computationally, as glycans are highly flexible and heterogeneous. Recent advances in integrative modeling that combine MD with glycan dynamics are beginning to address this gap [9].

Future directions include the integration of machine learning models trained on large datasets of experimentally characterized escape mutations. Deep learning approaches can learn sequence-structure relationships directly from data and may outperform physics-based methods for certain predictions. Additionally, the development of coarse-grained models will enable simulation of larger systems, such as full viral spikes, over longer timescales. For veterinary applications, expanding structural databases for animal viruses through cryo-EM and computational prediction will be essential.

Conclusion

Computational structural analysis of antibody-binding interfaces provides a powerful framework for predicting viral escape mutations. By combining molecular dynamics, Rosetta design, free energy perturbation, and frustration analysis, researchers can identify residues that are both critical for antibody binding and permissive to mutation. These methods have been validated extensively for SARS-CoV-2 and are directly transferable to veterinary pathogens such as FIPV, avian influenza virus, and other coronaviruses. The integration of computational predictions with genomic surveillance enables proactive identification of emerging escape variants, guiding vaccine and therapeutic development. As structural data for animal viruses continue to grow, computational escape prediction will become an indispensable tool in veterinary virology.

References

[1] Alshahrani M, Parikh V, Foley B, et al. Dynamic mutational profiling of binding interactions and allosteric networks in conformational ensembles of the SARS-CoV-2 spike protein complexes with classes of antibodies targeting cryptic binding sites: confluence of binding and allostery determines molecular mechanisms and hotspots of immune escape. Phys Chem Chem Phys. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40842437/

[2] Alshahrani M, Parikh V, Foley B, et al. Exploring Dynamic Modulation of Binding, Allostery and Immune Resistance in the SARS-CoV-2 Spike Complexes with Classes of Antibodies Targeting Cryptic Binding Sites: Antibody-Specific Augmentations of Conserved Allosteric Architecture Can Influence Evolution of Viral Escape. bioRxiv. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40631092/

[3] Alshahrani M, Parikh V, Foley B, et al. Integrative Computational Modeling of Distinct Binding Mechanisms for Broadly Neutralizing Antibodies Targeting SARS-CoV-2 Spike Omicron Variants: Balance of Evolutionary and Dynamic Adaptability in Shaping Molecular Determinants of Immune Escape. Viruses. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40573332/

[4] Alshahrani M, Parikh V, Foley B, et al. Exploring Diverse Binding Mechanisms of Broadly Neutralizing Antibodies S309, S304, CYFN-1006 and VIR-7229 Targeting SARS-CoV-2 Spike Omicron Variants: Integrative Computational Modeling Reveals Balance of Evolutionary and Dynamic Adaptability in Shaping Molecular Determinants of Immune Escape. bioRxiv. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40376091/

[5] Alshahrani M, Parikh V, Foley B, et al. Neutral Frustration Landscape Architecture of SARS-CoV-2 Spike-Antibody Interfaces Shapes Immune Evasion Mechanisms for Ultrapotent Neutralizing Antibodies and Determines Pathways of Viral Adaptation: Insights from Integrative Computational Approach. bioRxiv. 2025. URL: https://www.semanticscholar.org/paper/eead78fdd0da584c224d4e3d07eda9651724d091

[6] Alshahrani M, Parikh V, Foley B, et al. Dissecting binding and immune evasion mechanisms for ultrapotent Class I and Class 4/1 neutralizing antibodies of SARS-CoV-2 spike protein using a multi-pronged computational approach: neutral frustration architecture of binding interfaces and immune escape hotspots drives adaptive evolution. Phys Chem Chem Phys. 2026. URL: https://www.semanticscholar.org/paper/f60b020c9e8b982925eb742158cd578a44081d6c

[7] Maurya S, Kumar S, Padhi A. Interface-Guided Computational Protein Design Reveals Bebtelovimab-Resistance Mutations in SARS-CoV-2 RBD: Correlation with Global Viral Genomes and Bebtelovimab-Escape Mutations. ChemistrySelect. 2023. URL: https://www.semanticscholar.org/paper/216ddfd49ee256520f769245e4520dc9aeec0efa

[8] Bhagat K, Maurya S, Yadav AJ, et al. Bebtelovimab-bound SARS-CoV-2 RBD mutants: resistance profiling and validation with escape mutations, clinical results, and viral genome sequences. FEBS Letters. 2024. URL: https://www.semanticscholar.org/paper/815ab1e34117e5aea86aa74b8faff7319489e9d2

[9] Raisinghani N, Alshahrani M, Gupta G, et al. Accurate Characterization of Conformational Ensembles and Binding Mechanisms of the SARS-CoV-2 Omicron BA.2 and BA.2.86 Spike Protein with the Host Receptor and Distinct Classes of Antibodies Using AlphaFold2-Augmented Integrative Computational Modeling. bioRxiv. 2023. URL: https://www.semanticscholar.org/paper/3fc0a91925264048e9245d9a96f5f510108052d3 *** 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.