Modeling Host-Pathogen Protein-Protein Interaction Networks: Computational Approaches for Veterinary Virology

Introduction

The molecular interface between a pathogen and its host is fundamentally governed by protein-protein interactions (PPIs) [1, 2]. These interactions determine tropism, pathogenesis, and the success of viral replication [3, 4]. In veterinary virology, understanding host-pathogen PPIs is essential for elucidating mechanisms of disease emergence, identifying diagnostic biomarkers, and designing targeted interventions [5, 6]. The dense network of contacts between viral virulence factors and host cellular machinery can be systematically interrogated using computational methods that integrate sequence, structure, and systems-level data [7, 8]. This article reviews the current state of modeling host-pathogen PPI networks, with emphasis on docking simulations, interface hotspot prediction, and the use of 3D molecular coordinate viewers in a veterinary medicine context.

Biological Underpinnings of Host-Pathogen Interactions

Viral proteins exploit host cellular processes by mimicking host interaction motifs, binding to receptor complexes, or co-opting signaling pathways [1, 9]. For example, African swine fever virus (ASFV) proteins engage the unfolded protein response (UPR) pathway to enhance replication [10], while influenza A virus targets host metabolism-associated networks [11]. The interface between a viral protein and its host partner is characterized by specific “hotspot” residues that contribute disproportionately to binding free energy [12]. These hotspots are often evolutionarily conserved and represent prime targets for therapeutic disruption [12, 13]. Structural studies of intact infected cells using crosslinking mass spectrometry have revealed the spatial organization of viral-host complexes in situ [7]. The phosphoproteome of host cells following bacterial infection also provides dynamic snapshots of signaling rewiring [14], and similar approaches are now applied to viral systems [15, 14].

Computational Screening of Viral-Host Interactions

Computational prediction of PPIs has evolved from sequence-based homology transfer to sophisticated machine learning models that integrate multiple data modalities [16, 5, 17]. Graph neural networks (GNNs) have been particularly effective, as they naturally encode the relational structure of interaction networks [18, 19]. Hyperbolic graph embeddings capture hierarchy in the interactome [18], while multiview GNNs combine sequence, structural, and functional features [19]. For specific veterinary pathogens, such as white spot syndrome virus (WSSV) in shrimp, homology-based and topology-based integration improves prediction accuracy [20]. Ensemble machine learning architectures, such as those implemented in PlantPathoPPI, demonstrate the utility of combined feature spaces for plant-pathogen systems, with direct methodological parallels to animal host-virus systems [21]. Supervised learning approaches trained on known Ebola-human interactions have validated feature sets that generalize to other viral families [22]. The VHI-Pred tool uses multi-feature encoding to predict human-virus interactions [23]. Deep-HPI-pred provides an R-Shiny framework for network-based classification and prediction [17].

Table 1 summarizes representative computational methods and their input features.

Molecular Docking and Interface Hotspot Identification

Once candidate PPIs are predicted, molecular docking provides a means to generate three-dimensional models of the viral-host complex [8, 24]. Rigid-body docking (e.g., ZDOCK algorithms) and flexible docking (e.g., HADDOCK) can be applied to refine orientations [8]. Interface hotspots are identified through alanine scanning or energy decomposition, both computationally [12] and through directed evolution experiments [25]. The ppIRIS method enables proteome-wide discovery of PPIs via rapid mass spectrometry, accelerating experimental validation [25]. For veterinary viruses that lack high-resolution structures, homology models built from related viral families are used [20, 24]. The integration of AlphaFold-predicted structures with docking pipelines has improved the reliability of interface predictions [12]. The structural characterization of viral-host complexes can be visualized in 3D molecular coordinate viewers (e.g., PyMOL, UCSF Chimera), allowing researchers to inspect residue-level contacts and rationalize the impact of mutations [8].

Integration with Omics Data for Network Construction

Single-method predictions benefit from multi-omics integration [26, 27, 10]. Temporal transcriptomics identifies early-response modules during Ebola virus infection [28], while integrated transcriptomic analysis of STEC-infected intestinal epithelial cells reveals miRNA-mRNA-TF regulatory hubs [27]. Quantitative proteomics of ASFV-infected cells highlights co-opted UPR components [10]. In feline calicivirus infection, transcriptome profiling of CRFK cells uncovers pathways modulated during lytic replication [29]. Multi-omics profiling of capsaicin-treated EBV reveals suppression of lytic reactivation through targeting both viral and host networks [26]. Serum proteomics of parasite-infected macaques identifies host immune response biomarkers [30]. These datasets are used as features in PPI prediction or as validation for network models [28, 11]. The construction of virus-host PPI networks that integrate metabolomic data, as done for influenza [11], provides a more holistic view of infection.

Network Analysis and Community Detection

After constructing a PPI network, graph-theoretic analysis identifies functional communities and key topological nodes [31, 32, 2]. The viral-host interactome often exhibits a scale-free topology, with hub proteins that are essential for both the virus and host [31]. Community detection algorithms, such as Louvain or stochastic block models, partition the network into modules that correspond to biological processes [31, 32]. In SARS-CoV-2, multimodal interactome mapping revealed spatial organization of viral proteins with host organelles [32]. Large language models have been applied to decode critical signaling pathways in EBV-mediated diseases by mining the literature and network databases [13]. For mpox virus, computational analysis of pathogen-host interactome enabled rapid drug repurposing [6]. These approaches are directly transferable to veterinary pathogens such as ASFV, avian influenza virus, and porcine reproductive and respiratory syndrome virus (PRRSV) [6].

graph TD
    A[Genomic/Proteomic Data], > B[Sequence-based PPI Prediction]
    A, > C[Structure-based Docking]
    A, > D[Omics Integration]
    B, > E[Candidate PPI List]
    C, > E
    D, > E
    E, > F[Network Construction & Analysis]
    F, > G[Community Detection]
    F, > H[Hub Identification]
    G, > I[Functional Module Annotation]
    H, > I
    I, > J[Therapeutic Target Prioritization]
    I, > K[Biomarker Discovery]
    J, > L[In Silico Screening / Drug Repurposing]
    K, > L
    L, > M[Experimental Validation]

Applications in Veterinary Medicine

Understanding host-pathogen PPIs has direct translational value for veterinary diagnostics and therapeutics [6, 4]. Drug repurposing screens based on interactome networks can identify approved veterinary drugs that disrupt critical viral-host interactions [11, 6]. For example, metabolism-associated protein network analysis identified host-directed anti-influenza compounds [11]. In shrimp aquaculture, gene network analysis combined with machine learning classified Enterocytozoon hepatopenaei infection stages [33]. The Bombyx mori-BmNPV model in silkworms has revealed regulatory mechanisms that inform antiviral strategies [3]. Cross-species interactome comparisons can explain host range restrictions, such as why certain avian influenza strains are poorly adapted to mammals [22]. Yeast two-hybrid high-throughput sequencing approaches have been applied to baculovirus-host systems [4]. For viral diseases of poultry, such as highly pathogenic avian influenza (H5N1) or infectious bronchitis, PPI networks can identify conserved targets across serotypes [29]. The prediction of virus-host protein-lncRNA interactions using transfer learning (CBIL-VHPLI) expands the scope of regulatory networks [34].

Challenges and Future Directions

Despite methodological advances, several challenges remain. The dynamic nature of PPIs (transient versus stable) is often neglected in static network models [1, 7]. Structural coverage of veterinary viral proteins is sparse, requiring extensive homology modeling [20, 24]. False-positive rates in high-throughput interaction screens necessitate rigorous validation [25]. Future work should integrate time-resolved omics [28] and incorporate conformational ensembles from molecular dynamics simulations [24]. The application of foundation models (e.g., protein language models) to PPI prediction for non-model organisms will accelerate veterinary applications [12, 9]. The development of community standards for sharing PPI data across veterinary species is also needed [2].

Conclusion

Computational modeling of host-pathogen protein-protein interaction networks has matured into a powerful suite of tools that integrate sequence, structure, and systems biology data. For veterinary virology, these methods offer a rational path to understanding pathogenesis, identifying drug targets, and developing diagnostics. The continued expansion of computational resources and experimental validation techniques will enable rapid response to emerging zoonotic threats and improve animal health.

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