Computational design of serine hydrolases

• 成果类型: Article
• 署名作者: Lauko, Anna; Pellock, Samuel J.; Sumida, Kiera H.; Anishchenko, Ivan; Juergens, David; Ahern, Woody; Jeung, Jihun; Shida, Alexander F.; Hunt, Andrew; Kalvet, Indrek; Norn, Christoffer; Humphreys, Ian R.; Jamieson, Cooper; Krishna, Rohith; Kipnis, Yakov; Kang, Alex; Brackenbrough, Evans; Bera, Asim K.; Sankaran, Banumathi; Houk, K. N.; Baker, David
• 署名单位: University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; Howard Hughes Medical Institute; University of Washington; University of Washington Seattle; University of California System; University of California Los Angeles
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-10715
• DOI: 10.1126/science.adu2454
• 发表日期: 2025-04-18
• 关键词: catalytic triad; protease mechanism; energy landscape; enzyme; hydrolysis; EVOLUTION; proteins; acetylcholinesterase; subtilisin

摘要

The design of enzymes with complex active sites that mediate multistep reactions remains an outstanding challenge. With serine hydrolases as a model system, we combined the generative capabilities of RFdiffusion with an ensemble generation method for assessing active site preorganization at each step in the reaction to design enzymes starting from minimal active site descriptions. Experimental characterization revealed catalytic efficiencies (k(cat)/K-m) up to 2.2 x 10(5) M-1 s(-1) and crystal structures that closely match the design models (C alpha root mean square deviations <1 angstrom). Selection for structural compatibility across the reaction coordinate enabled identification of new catalysts remove with five different folds distinct from those of natural serine hydrolases. Our de novo approach provides insight into the geometric basis of catalysis and a roadmap for designing enzymes that catalyze multistep transformations.