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MULTI-evolve: Rapid Evolution of Complex Multi-mutant Proteins | Arc Institute

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The search space for protein engineering grows exponentially with complexity. To address this problem, we developed MULTI-evolve, a framework for efficient protein evolution that applies machine learning models trained on datasets of ~200 variants focused specifically on pairs of function-enhancing mutations. Published today in Science, this work represents Arc Institute's first lab-in-the-loop framework for biological design.

Patrick Hsu Vincent Tran February 19, 2026 MULTI-evolve: Rapid Evolution of Complex Multi-mutant Proteins The search space for protein engineering grows exponentially with complexity. A protein of just 100 amino acids has 20^100 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

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