| Autor: |
Sirin S; Schrödinger, Inc. , 120 West 45th Street, 29th Floor, New York, New York 10036, United States., Kumar R, Martinez C, Karmilowicz MJ, Ghosh P, Abramov YA, Martin V, Sherman W |
| Jazyk: |
angličtina |
| Zdroj: |
Journal of chemical information and modeling [J Chem Inf Model] 2014 Aug 25; Vol. 54 (8), pp. 2334-46. Date of Electronic Publication: 2014 Jul 22. |
| DOI: |
10.1021/ci5002185 |
| Abstrakt: |
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 ω-aminotransferase (ω-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 ω-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 (20-60-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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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