Predictive understanding for how a particular amino acid sequence encodes enzymatic function is a grand challenge in molecular biology, with profound impacts in fields ranging from industrial biotechnology, computational protein design, and agriculture to predictive identification of disease mutations (1) and medicinal chemistry. Innovative methods for high-throughput and quantitative measurements of different aspects of enzymatic function are needed to achieve this goal. On page 411 of this issue, Markin et al. (2) describe a laboratory-on-a-chip platform called High-Throughput Microfluidic Enzyme Kinetics (HT-MEK) as a step in this direction. The technique allows high-fidelity in vitro biochemical and biophysical characterization of more than 1000 mutants of the model enzyme PafA (phosphate-irrepressible alkaline phosphatase of Flavobacterium). HT-MEK identifies partially overlapping yet distinct networks of amino acids that undergird individual reaction steps of PafA, illuminating the mechanistic basis of catalysis for this enzyme.

对特定氨基酸序列如何编码酶功能的预测理解是分子生物学中的一项重大挑战，在工业生物技术、计算蛋白质设计和农业到疾病突变的预测识别 ( 1 ) 和药物化学等领域具有深远的影响。需要对酶功能的不同方面进行高通量和定量测量的创新方法来实现这一目标。在本期第 411 页，Markin等人。( 2) 描述了一个称为高通量微流体酶动力学 (HT-MEK) 的芯片实验室平台，作为朝这个方向迈出的一步。该技术允许对模型酶 PafA（黄杆菌的磷酸盐不可抑制碱性磷酸酶）的 1000 多个突变体进行高保真体外生化和生物物理表征。HT-MEK 鉴定了部分重叠但不同的氨基酸网络，这些网络支持 PafA 的各个反应步骤，阐明了该酶催化的机制基础。