Shao Qianzhen, Hollenbeak Asher C, Jiang Yaoyukun, Ran Xinchun, Bachmann Brian O, Yang Zhongyue J
Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.
Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee 37235, United States.
Chem Catal. 2025 Jun 19;5(6). doi: 10.1016/j.checat.2025.101334. Epub 2025 Mar 28.
We developed SubTuner, a physics-based computational tool that tackles the challenge of identifying enzyme mutants with enhanced activity for specified non-native substrates. To test the performance of SubTuner, we designed three tasks - all aiming to identify beneficial anion methyltransferase mutants for synthesis of non-native S-adenosyl-l-methionine analogs: first in the conversion of ethyl iodide from a pool of 190 AtHOL1 single-point mutants for an initial test of accuracy and speed; second of ethyl, n-propyl, cyclopropylmethyl, and phenethyl iodide from a pool of 600 acl-MT multi-point mutants for a test of generalizability; and eventually of bulkier substrates for AtHOL1 combined with experimental characterization for a test of predictivity. All tests demonstrated SubTuner's ability to accelerate enzyme engineering for non-native substrates, superior to existing bioinformatics and machine learning-based tools. SubTuner, with its physical hypothesis, quantitative accuracy, and mechanism-informing ability, holds a significant potential to aid enzyme engineering for substrate scope expansion.
我们开发了SubTuner，这是一种基于物理学的计算工具，用于应对识别对特定非天然底物具有增强活性的酶突变体这一挑战。为了测试SubTuner的性能，我们设计了三项任务——所有任务都旨在识别用于合成非天然S-腺苷-L-甲硫氨酸类似物的有益阴离子甲基转移酶突变体：首先是从190个AtHOL1单点突变体库中筛选能够将碘乙烷转化的突变体，用于初步测试准确性和速度；其次是从600个acl-MT多点突变体库中筛选能够转化碘乙烷、正丙基碘、环丙基甲基碘和苯乙基碘的突变体，用于测试通用性；最后是针对AtHOL1的更大体积底物进行筛选，并结合实验表征来测试预测性。所有测试都证明了SubTuner在加速非天然底物酶工程方面的能力，优于现有的生物信息学和基于机器学习的工具。SubTuner凭借其物理假设、定量准确性和机制解读能力，在协助酶工程扩大底物范围方面具有巨大潜力。
