Computational design of serine hydrolases

IF 44.7 1区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Science Pub Date : 2025-02-13 DOI:10.1126/science.adu2454

Anna Lauko, Samuel J. Pellock, Kiera H. Sumida, Ivan Anishchenko, David Juergens, Woody Ahern, Jihun Jeung, Alex Shida, Andrew Hunt, Indrek Kalvet, Christoffer Norn, Ian R. Humphreys, Cooper Jamieson, Rohith Krishna, Yakov Kipnis, Alex Kang, Evans Brackenbrough, Asim K. Bera, Banumathi Sankaran, K. N. Houk, David Baker

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引用次数: 0

Abstract

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 to design enzymes starting from minimal active site descriptions. Experimental characterization revealed catalytic efficiencies ( k cat / K m ) up to 2.2x10 5 M −1 s −1 and crystal structures that closely match the design models (Cα RMSDs < 1 Å). Selection for structural compatibility across the reaction coordinate enabled identification of new catalysts in low-throughput screens 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.

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来源期刊

Science 综合性期刊-综合性期刊

CiteScore

61.10

自引率

0.90%

发文量

审稿时长

2.1 months

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