The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20100 possible variants—more combinations than atoms in the observable universe. Traditional engineering methods might test hundreds of variants but limit exploration to narrow regions of the sequence space. Recent machine learning approaches enable broader searches through computational screening. However, these approaches still require tens of thousands of measurements, or 5–10 iterative rounds.
