Using Large Language Model to Optimize Protein Purification: Insights from Protein Structure Literature Associated...

Obtaining pure and homogeneous protein samples is vital for protein biology studies, yet optimizing protein expression and purification methods can be time-consuming because of variations in factors like expression conditions, buffer components, and fusion tags. With over 81 000 Protein Data Bank (PDB)-associated articles as of October 2024, manual extraction of relevant methods is impractical. To streamline this process, an automated tool is developed by incorporating a large language model (LLM) to extract and classify key data from these articles. The information extraction accuracy is enhanced by a 2-step-LLM and a 3-step-prompt. The key findings include: 1) Tris buffer is used in 49.2% of cases, followed by 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) and phosphate buffers. 2) Polyhistidine tags dominate at 82.5%, followed by glutathione S-transferase (GST) and maltose-binding protein (MBP) tags. 3) E. coli expression is done at 16-20 °C, with induction period favoring 12-16 h (69.0%) over 3-6 h (14.3%). The statistical analyses highlight the correlation between protein properties and purification strategies. This tool is validated through two case studies: method bias for membrane protein purification, and crosslinker/detergent preferences for Cryo-Electron Microscopy sample preparation. These findings provide a valuable resource for designing protein expression and purification experiments.