Biological Foundation Models for Predicting Host Tropism in Emerging Zoonotic Viruses

Introduction

Host tropism, the capacity of a virus to infect specific host species, is a fundamental determinant of zoonotic potential and pandemic risk [1]. Emerging zoonotic viruses frequently originate from wildlife or livestock reservoirs and may cross species barriers to infect new hosts, including domestic animals and, occasionally, humans [1]. Predicting the host range of a novel virus from its genomic sequence alone remains a major challenge in veterinary virology and computational biology [1]. Traditional experimental methods, such as in vitro receptor binding assays and animal inoculation studies, are time‑consuming and often impractical for rapid outbreak response. Machine learning models, and more recently biological foundation models, offer a complementary approach by leveraging large‑scale sequence and structural data to infer host tropism directly from viral molecular features [1]. This article reviews the biological principles underlying host tropism, surveys computational strategies from random forest classifiers to transformer‑based architectures, and evaluates their utility for veterinary surveillance of emerging zoonotic threats.

Biological Mechanisms of Host Tropism

Host tropism is governed by a complex interplay between viral attachment proteins and host cell surface receptors, as well as intracellular factors that modulate replication efficiency [1]. For enveloped RNA viruses such as influenza A virus, the hemagglutinin (HA) protein binds to sialic acid receptors on the host cell membrane; the linkage specificity (α‑2,3 vs. α‑2,6) determines avian versus mammalian tropism [1]. Similarly, the neuraminidase (NA) protein facilitates viral release and can influence host range. Beyond receptor binding, host‑specific restriction factors (e.g., Mx proteins, tetherin) and the compatibility of viral polymerase subunits with host nuclear transport machinery also shape tropism [1]. In veterinary medicine, understanding these mechanisms is critical for assessing the risk that an avian influenza virus, for instance, may adapt to swine or domestic poultry (see Highly Pathogenic Avian Influenza (H5N1) in Poultry and Wild Birds) [1].

Computational Approaches for Host Tropism Prediction

Early computational efforts to predict host tropism relied on phylogenetic analysis and heuristic rules derived from known receptor binding preferences [1]. With the growth of sequence databases, machine learning classifiers became feasible [1]. Eng et al. (2014) applied a random forest algorithm to predict the host origin (avian vs. human) of influenza A virus proteins [1]. The model was trained on physicochemical features extracted from protein sequences, including amino acid composition, hydrophobicity, polarity, and secondary structure propensities [1]. Feature importance analysis revealed that residues in the receptor‑binding domain of HA were most discriminatory [1]. The random forest model achieved high accuracy, demonstrating that sequence‑derived features alone can distinguish avian from human influenza strains [1]. This proof‑of‑concept has since motivated the development of more sophisticated models.

The table below summarizes common algorithmic strategies for host tropism prediction.

Biological Foundation Models

Biological foundation models represent a paradigm shift in computational biology [1]. These large neural networks, pre‑trained on hundreds of millions of protein sequences, learn a general representation of protein structure and function that can be fine‑tuned for specific tasks such as host tropism prediction [1]. Unlike earlier classifiers that relied on manually engineered features, foundation models implicitly encode evolutionary and biophysical information from the training corpus [1]. For example, a model pre‑trained on viral proteomes could be adapted to classify host tropism by appending a lightweight classifier head and training on curated host‑label datasets [1]. The ability to transfer knowledge across viral families is particularly valuable for emerging zoonotic viruses where labelled data are scarce [1].

A typical workflow integrating foundation models is illustrated in the Mermaid diagram below.

graph TD
 A[Viral Protein Sequence] --> B[Pre-trained Foundation Model]
 B --> C[Sequence Embedding Vector]
 C --> D[Trained Classifier Head]
 D --> E[Host Tropism Prediction]
 E --> F{Validation}
 F -->|Experimentally Confirmed| G[Surveillance & Risk Assessment]
 F -->|Discrepancy| H[Iterative Fine-Tuning]
 H --> B

This pipeline first encodes the viral protein using a foundation model to produce a dense embedding [1]. The embedding is then passed to a classifier (e.g., logistic regression or a small neural network) that outputs the predicted host species [1]. Fine‑tuning may be required when the target virus diverges substantially from the pre‑training data [1].

Case Studies in Veterinary Virology

Influenza A Virus. The random forest model of Eng et al. (2014) remains a benchmark for host tropism prediction [1]. Applied to HA and NA sequences from avian, swine, and human isolates, the model assigns a host probability score that can flag strains with mammalian‑adaptive signatures [1]. This is directly relevant to surveillance of Highly Pathogenic Avian Influenza (HPAI) H5N1 in Poultry, where early detection of mammalian adaptation informs culling and biosecurity measures [1].

West Nile Virus. Although WNV is primarily mosquito‑borne, its envelope protein determines tropism for avian versus mammalian cells [1]. A foundation model pre‑trained on flavivirus polyproteins could be fine‑tuned to distinguish equine‑pathogenic variants from those that remain confined to birds (see West Nile Virus in Horses) [1].

Feline Coronaviruses. Feline enteric coronavirus and the highly virulent feline infectious peritonitis (FIP) virus differ by mutations in the spike protein that alter macrophage tropism [1]. Predictive models that incorporate both sequence and structural embeddings could assist in identifying FIP‑associated mutations, guiding diagnostic and management decisions (see Feline Coronavirus and FIP) [1].

Challenges and Future Directions

Despite progress, several obstacles remain [1]. First, high‑quality, species‑resolved training data are limited for many viral families, especially those circulating in wildlife reservoirs [1]. Second, host tropism can be polygenic: mutations in multiple viral proteins may collectively alter host range, a complexity that simple classifiers may not capture [1]. Third, foundation models require substantial computational resources and may embed biases from the training corpus (e.g., overrepresentation of well‑studied human viruses) [1]. Future work should focus on integrating multi‑omics data (e.g., host transcriptomic responses) and developing lightweight models deployable in field settings [1]. The integration of such models into routine veterinary diagnostics, as outlined in Antimicrobial Susceptibility Testing in Secondary Viral Co-infections, could enhance One Health surveillance efforts [1].

Conclusion

Biological foundation models offer a powerful framework for predicting host tropism in emerging zoonotic viruses [1]. By leveraging large‑scale pre‑training, these models can learn biophysically relevant representations that improve predictive accuracy over traditional machine learning approaches [1]. Coupled with high‑throughput sequencing and experimental validation, they promise to accelerate risk assessment for novel viral threats in livestock, poultry, and wildlife [1]. Continued collaboration between veterinary virologists, bioinformaticians, and model developers will be essential to realise this potential fully [1].

References

[1] Eng CL, Tong JC, Tan TW. Predicting host tropism of influenza A virus proteins using random forest. BMC Med Genomics. 2014. 7 Suppl 3:S8. URL: https://pubmed.ncbi.nlm.nih.gov/25521718/ *** 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.