Rate-Perturbing Single Amino Acid Mutation for Hydrolases: A Statistical Profiling

Hydrolases
are a critical component for modern chemical, pharmaceutical,
and environmental sciences. Identifying mutations that enhance catalytic
efficiency presents a roadblock to design and to discover new hydrolases
for broad academic and industrial uses. Here, we report the statistical
profiling for rate-perturbing mutant hydrolases with a single amino
acid substitution. We constructed an integrated structure−kinetics
database for hydrolases, IntEnzyDB, which contains 3907 kcats, 4175 KMs, and 2715 Protein
Data Bank IDs. IntEnzyDB adopts a relational architecture with a flattened
data structure, enabling facile and efficient access to clean and
tabulated data for machine learning uses. We conducted statistical
analyses on how single amino acids mutations influence the turnover
number (i.e., kcat) and efficiency (i.e., kcat/KM), with a
particular emphasis on profiling the features for rate-enhancing mutations.
The results show that mutation to bulky nonpolar residues with a hydrocarbon
chain involves a higher likelihood for rate acceleration than to other
types of residues. Linear regression models reveal geometric descriptors
of substrate and mutation residues that mediate rate-perturbing outcomes
for hydrolases with bulky nonpolar mutations. On the basis of the
analyses of the structure−kinetics relationship, we observe
that the propensity for rate enhancement is independent of protein
sizes. In addition, we observe that distal mutations (i.e., >10
Å
from the active site) in hydrolases are significantly more prone to
induce efficiency neutrality and avoid efficiency deletion but involve
similar propensity for rate enhancement. The studies reveal the statistical
features for identifying rate-enhancing mutations in hydrolases, which
will potentially guide hydrolase discovery in biocatalysis.

水解酶（Hydrolases）是现代化学、制药与环境科学领域的关键组成部分。筛选可提升催化效率的突变体，是开发并获取可广泛应用于学术与工业领域的新型水解酶的一大阻碍。本研究针对携带单氨基酸替换的速率扰动型水解酶突变体开展了统计特征分析。我们构建了一款整合了水解酶结构-动力学数据的数据库IntEnzyDB，该库包含3907个催化常数（kcat）、4175个米氏常数（KM）以及2715个蛋白质数据库（Protein Data Bank, PDB）编号。IntEnzyDB采用关系型架构与扁平化数据结构，可便捷高效地获取经过整理的结构化数据，适配机器学习应用需求。我们针对单氨基酸突变如何影响周转数（即kcat）与催化效率（即kcat/KM）开展了统计分析，重点梳理了可提升催化速率的突变体的特征。研究结果表明，相较于其他类型的氨基酸残基，将残基突变为带有烃链的大体积非极性残基时，催化速率提升的概率更高。线性回归模型揭示了底物与突变残基的几何描述符，这类描述符可调控携带大体积非极性突变的水解酶的速率扰动效应。基于结构-动力学关联的分析结果，我们发现催化速率提升的倾向性与蛋白质分子量无关。此外，我们发现水解酶中的远端突变（即距活性位点大于10埃（Å）的突变）更易导致催化效率保持中性，且不会引发效率下降，但这类突变同样具备相似的速率提升倾向性。本研究明确了可用于筛选水解酶速率提升突变体的统计特征，有望为生物催化领域的水解酶开发提供指导。