1 research outputs found
A Computational Approach to Enzyme Design: Predicting ω‑Aminotransferase Catalytic Activity Using Docking and MM-GBSA Scoring
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 <i>S</i>-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 (<i>R</i>)-ethyl 5-methyl-3-oxooctanoate
to (3<i>S</i>,5<i>R</i>)-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