Foundation Artificial Intelligence Models in Animal Biotechnology: From Protein Structure Prediction to Genomic Language Models and Autonomous Laboratory Systems

This research project provides a comprehensive methodology-oriented review and implementation framework for applying foundation artificial intelligence models within animal biotechnology, with a specific focus on livestock health and production. The framework evaluates the transition from traditional computational biology to large-scale pre-trained neural networks, including AlphaFold3, ESM-2, the Evo genomic language model, and the Nucleotide Transformer. It outlines systematic approaches for predicting the structures of critical livestock disease proteins, such as viral capsids and bacterial surface antigens, to facilitate rational vaccine design. Furthermore, the project details the utilization of genomic and epigenomic language models to decode regulatory sequence functions, predict variant effects, and optimize CRISPR guide RNA selection with minimized off-target

risks. By integrating generative protein design tools like RFdiffusion and ProteinMPNN, this work establishes a structured computational pipeline for engineering novel therapeutic proteins and enzymes. Ultimately, this research provides guidance on structuring autonomous laboratory workflows, offering a robust foundation for academic research and simulation-based validation in veterinary genomics and molecular design.

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