In-Depth Sequence–Function Characterization Reveals Multiple Pathways to Enhance Enzymatic Activity

Deep mutational scanning (DMS) has recently emerged as a powerful method to study protein sequence–function relationships but is not well-explored as a guide to enzyme engineering and identifying of pathways by which their catalytic cycle may be improved. We report such a demonstration in this work using a phenylalanine ammonia-lyase (PAL), which deaminates l-phenylalanine to trans-cinnamic acid and has widespread application in chemoenzymatic synthesis, agriculture, and medicine. In particular, the PAL from Anabaena variabilis (AvPAL*) has garnered significant attention as the active ingredient in Pegvaliase, the only FDA-approved drug for treating classical phenylketonuria (PKU). Although an extensive body of literature exists on the structure, substrate-specificity, and catalytic cycle, protein-wide sequence determinants of function remain unknown, as do intermediate reaction steps that limit turnover frequency, which has hindered the rational engineering of these enzymes. Here, we created a detailed sequence–function landscape of AvPAL* by performing DMS and revealed 112 mutations at 79 functionally relevant sites that affect a positive change in enzyme fitness. Using fitness values and structure–function analysis, we picked a subset of positions for comprehensive single- and multi-site saturation mutagenesis and identified combinations of mutations that led to improved reaction kinetics in cell-free and cellular contexts. We then performed quantum mechanics/molecular mechanics (QM/MM) and molecular dynamics (MD) studies to understand the mechanistic role of the most beneficial mutations and observed that different mutants confer improvements via different mechanisms, including stabilizing transition and intermediate states, improving substrate diffusion into the active site, and decreasing product inhibition. This work demonstrates how DMS can be combined with computational analysis to effectively identify significant mutations that enhance enzyme activity along with the underlying mechanisms by which these mutations confer their benefit.深度突变扫描（Deep mutational scanning, DMS）近年来已成为研究蛋白质序列-功能关系的有力方法，但作为酶工程指导工具与催化循环优化途径鉴定手段的应用尚未得到充分探索。本研究以苯丙氨酸解氨酶（phenylalanine ammonia-lyase, PAL）为对象开展相关验证：PAL可将L-苯丙氨酸脱氨生成反式肉桂酸，在化学酶法合成、农业及医药领域拥有广泛应用。其中，来自多变鱼腥藻的PAL（AvPAL*）作为培格司酶（Pegvaliase）的活性成分受到广泛关注——培格司酶是目前美国食品药品监督管理局（FDA）唯一获批用于治疗经典型苯丙酮尿症（classical phenylketonuria, PKU）的药物。尽管已有大量文献围绕PAL的结构、底物特异性与催化循环展开研究，但该酶全序列范围内决定其功能的关键位点仍未明确，限制周转频率的中间反应步骤也尚未被阐明，这一短板阻碍了这类酶的理性工程化改造。本研究通过开展DMS实验，构建了AvPAL*的高精度序列-功能图谱，鉴定出79个功能相关位点上的112个可提升酶功能适配性的突变。基于酶适配值与结构-功能分析，我们选取部分位点开展系统性单点及多位点饱和诱变，筛选出可在无细胞体系与细胞体系中改善反应动力学的突变组合。我们进一步通过量子力学/分子力学（QM/MM）与分子动力学（MD）研究，解析了最优突变体的功能机制，发现不同突变可通过多种途径提升酶活性：包括稳定过渡态与中间反应态、促进底物向活性中心扩散、降低产物抑制效应。本研究证明，将DMS与计算分析相结合可高效筛选可提升酶活性的关键突变，并阐明此类突变发挥功能的内在分子机制。