ProtTeX introduces a novel approach that enables large language models to understand and edit protein structures, going beyond simple amino acid sequences.

• Developed a structure-aware tokenization system that converts 3D protein structures into text
• Created a structure-in-context learning framework that allows LLMs to reason about and edit proteins
• Achieved superior performance in protein structure prediction and editing compared to existing methods
• Demonstrated practical applications in protein design and functional analysis

This research represents a significant breakthrough for computational biology by allowing AI to truly understand protein structures, potentially accelerating drug discovery and protein engineering efforts.

ProtTeX: Structure-In-Context Reasoning and Editing of Proteins with Large Language Models