Muir Duncan F, Asper Garrison P R, Notin Pascal, Posner Jacob A, Marks Debora S, Keiser Michael J, Pinney Margaux M
Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA.
Program in Biophysics, University of California, San Francisco, San Francisco, CA, USA.
Science. 2025 Jun 12;388(6752):eadu1058. doi: 10.1126/science.adu1058.
Quantitatively mapping enzyme sequence-catalysis landscapes remains a critical challenge in understanding enzyme function, evolution, and design. In this study, we leveraged emerging microfluidic technology to measure catalytic constants- and -for hundreds of diverse orthologs and mutants of adenylate kinase (ADK). We dissected this sequence-catalysis landscape's topology, navigability, and mechanistic underpinnings, revealing catalytically heterogeneous neighborhoods organized by domain architecture. These results challenge long-standing hypotheses in enzyme adaptation, demonstrating that thermophilic enzymes are not universally slower than their mesophilic counterparts. Semisupervised models that combine our data with the rich sequence representations from large protein language models predict orthologous ADK-sequence catalytic parameters better than existing approaches. Our work demonstrates a promising strategy for dissecting sequence-catalysis landscapes across enzymatic evolution, opening previously unexplored avenues for enzyme engineering and functional prediction.
对酶的序列-催化景观进行定量映射，仍然是理解酶的功能、进化和设计方面的一项关键挑战。在本研究中，我们利用新兴的微流控技术测量了腺苷酸激酶（ADK）数百种不同直系同源物和突变体的催化常数。我们剖析了这种序列-催化景观的拓扑结构、可导航性和机制基础，揭示了由结构域结构组织的催化异质邻域。这些结果挑战了酶适应性方面长期存在的假设，表明嗜热酶并不普遍比它们的中温对应物慢。将我们的数据与来自大型蛋白质语言模型的丰富序列表示相结合的半监督模型，比现有方法能更好地预测直系同源ADK序列的催化参数。我们的工作展示了一种剖析整个酶促进化过程中序列-催化景观的有前景的策略，为酶工程和功能预测开辟了以前未探索的途径。
