This research applies large language models to decode and design proteins by treating amino acid sequences as biological languages. By identifying hidden structural and functional patterns across massive protein datasets, the work enables creation of novel proteins for medicine, cancer therapy, carbon capture, and environmental remediation beyond naturally evolved biological systems.

This research investigates whether regeneration can be induced in animals that normally lack regenerative abilities. Using nutrient factors such as amino acids and insulin, regeneration was stimulated in mice, jellyfish, and fruit flies. The findings reveal that regeneration is a coordinated whole-body process involving energy allocation, organ remodeling, and conserved nutrient signaling pathways.

This research uses natural language processing techniques to uncover evolutionary relationships between ancient proteins. By analyzing contextual patterns among amino acids, the new computational tool can identify connections between proteins that diverged billions of years ago, helping scientists reconstruct the history of early microbial life and Earth’s biological evolution.

This research designs simplified, custom-built proteins to understand how natural proteins work and to create new biocatalysts. By choosing a desired function and designing the amino-acid sequence and structure from scratch, the project aims to develop clean, efficient protein-based alternatives to environmentally harmful industrial chemistry.