A Computational Approach to Enzyme Design: Predicting 蠅-Aminotransferase Catalytic Activity Using Docking and MM-GBSA Scoring
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- 作者：Sarah Sirin ; Rajesh Kumar ; Carlos Martinez ; Michael J. Karmilowicz ; Preeyantee Ghosh ; Yuriy A. Abramov ; Van Martin ; Woody Sherman
- 刊名：Journal of Chemical Information and Modeling
- 出版年：2014
- 出版时间：August 25, 2014
- 年：2014
- 卷：54
- 期：8
- 页码：2334-2346
- 全文大小：628K
- ISSN：1549-960X
文摘
Enzyme design is an important area of ongoing research with a broad range of applications in protein therapeutics, biocatalysis, bioengineering, and other biomedical areas; however, significant challenges exist in the design of enzymes to catalyze specific reactions of interest. Here, we develop a computational protocol using an approach that combines molecular dynamics, docking, and MM-GBSA scoring to predict the catalytic activity of enzyme variants. Our primary focuses are to understand the molecular basis of substrate recognition and binding in an S-stereoselective 蠅-aminotransferase (蠅-AT), which naturally catalyzes the transamination of pyruvate into alanine, and to predict mutations that enhance the catalytic efficiency of the enzyme. The conversion of (R)-ethyl 5-methyl-3-oxooctanoate to (3S,5R)-ethyl 3-amino-5-methyloctanoate in the context of several 蠅-AT mutants was evaluated using the computational protocol developed in this work. We correctly identify the mutations that yield the greatest improvements in enzyme activity (20鈥?0-fold improvement over wild type) and confirm that the computationally predicted structure of a highly active mutant reproduces key structural aspects of the variant, including side chain conformational changes, as determined by X-ray crystallography. Overall, the protocol developed here yields encouraging results and suggests that computational approaches can aid in the redesign of enzymes with improved catalytic efficiency.