Conformational ensembles reveal the origins of serine protease catalysis

• 成果类型: Article
• 署名作者: Du, Siyuan; Kretsch, Rachael C.; Parres-Gold, Jacob; Pieri, Elisa; Cruzeiro, Vinicius Wilian D.; Zhu, Mingning; Pinney, Margaux M.; Yabukarski, Filip; Schwans, Jason P.; Martinez, Todd J.; Herschlag, Daniel
• 署名单位: Stanford University; Stanford University; Stanford University; Stanford University; Stanford University; United States Department of Energy (DOE); SLAC National Accelerator Laboratory; Stanford University; Stanford University; California Institute of Technology; California Institute of Technology; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of California System; University of California San Francisco; Bristol-Myers Squibb; California State University System; California State University Long Beach
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-13197
• DOI: 10.1126/science.ado5068
• 发表日期: 2025-02-14
• 页码: 735-+
• 关键词: alpha-lytic protease; nuclear-magnetic-resonance; barrier hydrogen-bond; sunflower trypsin inhibitor-1; enzyme-substrate interactions; escherichia-coli mutase; general base catalysis; active-site; transition-state; oxyanion hole

摘要

Enzymes exist in ensembles of states that encode the energetics underlying their catalysis. Conformational ensembles built from 1231 structures of 17 serine proteases revealed atomic-level changes across their reaction states. By comparing the enzymatic and solution reaction, we identified molecular features that provide catalysis and quantified their energetic contributions to catalysis. Serine proteases precisely position their reactants in destabilized conformers, creating a downhill energetic gradient that selectively favors the motions required for reaction while limiting off-pathway conformational states. The same catalytic features have repeatedly evolved in proteases and additional enzymes across multiple distinct structural folds. Our ensemble-function analyses revealed previously unknown catalytic features, provided quantitative models based on simple physical and chemical principles, and identified motifs recurrent in nature that may inspire enzyme design.