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

酶动力学 氨基酸残基 突变体 计算生物学 化学 突变率 动力学 中立 氨基酸 催化效率 蛋白质数据库 对接(动物) 仿形(计算机编程) 生物化学 蛋白质结构 计算机科学 活动站点 生物 催化作用 肽序列 物理 基因 护理部 量子力学 医学 哲学 操作系统 认识论
作者
Bailu Yan,Xinchun Ran,Yaoyukun Jiang,Sarah K. Torrence,Yuan Li,Qianzhen Shao,Zhongyue Yang
出处
期刊:Journal of Physical Chemistry B [American Chemical Society]
卷期号:125 (38): 10682-10691 被引量:19
标识
DOI:10.1021/acs.jpcb.1c05901
摘要

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.
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