News Summary

• Pinal is a 16-billion-parameter foundation model that designs candidate proteins from natural-language descriptions of desired function.
• Trained on 1.7 billion synthetically annotated protein–text pairs, Pinal links functional intent to protein sequence and structure.
• We applied Pinal to four distinct design tasks—a fluorescent protein, a polyethylene terephthalate hydrolase, an alcohol dehydrogenase and a metabolic H-protein.
• These findings support natural language as a high-level interface for candidate generation in protein design.