Qianzhen Shao, Asher C. Hollenbeak, Yaoyukun Jiang, Xinchun Ran, Brian O. Bachmann, Zhongyue J. Yang
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引用次数: 0
Abstract
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 Arabidopsis thaliana Harmless to Ozone Layer 1 (AtHOL1) single-point mutants for an initial test of accuracy and speed; second, of ethyl, n-propyl, cyclopropylmethyl, and phenethyl iodides from a pool of 600 Aspergillus clavatus methyltransferase multi-point mutants for a test of generalizability; and, eventually, of bulkier substrates for AtHOL1 combined with experimental characterization for a test of a priori 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 significant potential to aid enzyme engineering for substrate scope expansion.
期刊介绍:
Chem Catalysis is a monthly journal that publishes innovative research on fundamental and applied catalysis, providing a platform for researchers across chemistry, chemical engineering, and related fields. It serves as a premier resource for scientists and engineers in academia and industry, covering heterogeneous, homogeneous, and biocatalysis. Emphasizing transformative methods and technologies, the journal aims to advance understanding, introduce novel catalysts, and connect fundamental insights to real-world applications for societal benefit.