Geometric deep learning for protein sequence design

The geometric transformer samples the sequence space of the beta-lactamase TEM-1 enzyme (in grey) complexed a natural substrate (in cyan) to produce new well folded and active enzymes. Designing proteins that can perform specific functions involves understanding and manipulating their sequences and structures. This task is crucial for developing targeted treatments for diseases and creating enzymes for industrial applications. One of the grand challenges in protein engineering is designing proteins de novo, meaning from scratch, to tailor their properties for specific tasks. This has profound implications for biology, medicine, and materials science.