Catalyst design from genetic information

May 15, 2018

Approaches towards data-driven catalyst design based on a combination of catalytic chemistry and genetics

Researchers from RIKEN CSRS and National Institute for Materials Science have succeeded in identifying a design strategy for artificial catalysts based on the genetic information of biological enzymes.

Data-driven catalyst design requires information of catalyst properties in both high quality and quantity. However, such information is currently lacking, hindering the usage of catalyst informatics towards the development of suitable materials. In this study, the joint research team focused on the relationship between the genetic structure of biological enzymes and the energy required for their repair, which allows usage of genetic information towards catalyst development for the first time.

Specifically, the researchers selected enzymes from cyanobacteria that catalyze the oxygen evolution (2H2O → O2 + 4H+ + 4e-) and oxygen reduction reactions (O2 + 4H+ + 4e- → 2H20), which are key processes in artificial photosynthesis and fuel cells. The inter-species genetic analysis revealed that the oxygen evolution enzyme has specifically optimized its genetic structure to minimize its repair cost, suggesting that increasing the stability, even at the expense of activity, may be beneficial even for artificial oxygen evolution catalysts.

The results of this work indicate that large-scale biochemical databases can be used in combination with machine learning and statistical processing to promote data-driven catalyst design.

 

Original article
Molecular Informatics doi:10.1002/minf.201700139
H. Ooka, K. Hashimoto, R. Nakamura,
"Design Strategy of Multi-electron Transfer Catalysts Based on a Bioinformatic Analysis of Oxygen Evolution and Reduction Enzymes".

Contact
Ryuhei Nakamura; Team Leader
Hideshi Ooka; Postdoctoral Researcher
Biofunctional Catalyst Research Team