Presentation Information
[2ASBA-15]Computationally Guided Development of an Alkane-producing Enzyme with High Performance
○Hisashi Kudo1, Akihiko Kondo1, Tomohisa Hasunuma1 (1. Engineering Biology Research Center., Kobe univ. (Japan))
Keywords:
bioalkane,enzyme engineering,docking simulation
[Purpose]
In microbial bio-manufacturing, the selection of enzymes that constitute the biosynthetic pathways of target products is critically important. However, these enzymes have not been sufficiently systematized, making enzyme selection time-consuming. In addition, the enzyme selection often depends on the knowledge and experience of individual researchers, which tends to limit the diversity of enzymes employed. In this research, we aimed to efficiently identify high performance enzyme candidates from enzyme databases and to generate improved enzymes by using computational design.
[Method]
We constructed an integrated enzyme development system that combines enzyme search and clustering from enzyme databases, experimental evaluation, and computational design of the high performance mutants. In this system, the sequence data of enzymes related to a target reaction were collected from the NCBI protein database. These sequences were classified using our original enzyme clustering method, called by the MUSASHI method. Representative candidates were selected from each cluster and their enzymatic activities were evaluated in vitro to identify highly active enzymes. To improve the enzyme activity, two mutation designs were applied: (i) identification of subfamily specific residues (SSRs), followed by mutagenesis based on these residues, and (ii) structure-based mutant design using docking simulations.
[Results]
To validate the usefulness of this system, we focused on aldehyde deformylating oxygenase (ADO), an enzyme important for biofuel production that catalyzes the conversion of aldehydes into alkanes. ADO primarily produces C11–C17 alkanes, which correspond to hydrocarbons used in jet fuel and diesel fuel. Using the MUSASHI method, 150 ADO sequences were classified into three groups. Representative ADOs from each group were evaluated in vitro. Although no major differences in substrate specificity were observed among the groups, group 1 was suggested to contain highly active ADOs. By focusing on group 1 and increasing the number of evaluated ADOs, we identified NpADO, which exhibited high activity toward four aldehyde substrates with different carbon chain lengths. Further data expansion enabled the identification of a subset of ADOs within group 1 that showed particularly high activity, demonstrating the effectiveness of the MUSASHI method for discovering high performance enzymes. Next, the NpADO activity was increased through the mutagenesis of the SSRs. The comparison of ADOs with differing activities within group 1 enabled the identification of SSRs, which were located in loop regions between helices rather than around the active center. Saturation mutagenesis at these SSR positions resulted in mutants with up to a 1.3-fold increase in activity compared to WT. In parallel, the docking simulations using C12 aldehyde as the substrate were performed to design mutants with high affinity for the substrate. Experimental evaluation of these mutants revealed mutants with up to a two-fold increase in activity relative to WT, as well as mutants exhibiting activity specific to C12aldehyde.
[Conclusion]
The enzyme development system established in this research provides an effective framework for the efficient discovery and rational design of high performance enzymes for microbial bio-manufacturing.
In microbial bio-manufacturing, the selection of enzymes that constitute the biosynthetic pathways of target products is critically important. However, these enzymes have not been sufficiently systematized, making enzyme selection time-consuming. In addition, the enzyme selection often depends on the knowledge and experience of individual researchers, which tends to limit the diversity of enzymes employed. In this research, we aimed to efficiently identify high performance enzyme candidates from enzyme databases and to generate improved enzymes by using computational design.
[Method]
We constructed an integrated enzyme development system that combines enzyme search and clustering from enzyme databases, experimental evaluation, and computational design of the high performance mutants. In this system, the sequence data of enzymes related to a target reaction were collected from the NCBI protein database. These sequences were classified using our original enzyme clustering method, called by the MUSASHI method. Representative candidates were selected from each cluster and their enzymatic activities were evaluated in vitro to identify highly active enzymes. To improve the enzyme activity, two mutation designs were applied: (i) identification of subfamily specific residues (SSRs), followed by mutagenesis based on these residues, and (ii) structure-based mutant design using docking simulations.
[Results]
To validate the usefulness of this system, we focused on aldehyde deformylating oxygenase (ADO), an enzyme important for biofuel production that catalyzes the conversion of aldehydes into alkanes. ADO primarily produces C11–C17 alkanes, which correspond to hydrocarbons used in jet fuel and diesel fuel. Using the MUSASHI method, 150 ADO sequences were classified into three groups. Representative ADOs from each group were evaluated in vitro. Although no major differences in substrate specificity were observed among the groups, group 1 was suggested to contain highly active ADOs. By focusing on group 1 and increasing the number of evaluated ADOs, we identified NpADO, which exhibited high activity toward four aldehyde substrates with different carbon chain lengths. Further data expansion enabled the identification of a subset of ADOs within group 1 that showed particularly high activity, demonstrating the effectiveness of the MUSASHI method for discovering high performance enzymes. Next, the NpADO activity was increased through the mutagenesis of the SSRs. The comparison of ADOs with differing activities within group 1 enabled the identification of SSRs, which were located in loop regions between helices rather than around the active center. Saturation mutagenesis at these SSR positions resulted in mutants with up to a 1.3-fold increase in activity compared to WT. In parallel, the docking simulations using C12 aldehyde as the substrate were performed to design mutants with high affinity for the substrate. Experimental evaluation of these mutants revealed mutants with up to a two-fold increase in activity relative to WT, as well as mutants exhibiting activity specific to C12aldehyde.
[Conclusion]
The enzyme development system established in this research provides an effective framework for the efficient discovery and rational design of high performance enzymes for microbial bio-manufacturing.
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