Learning from nature: Understanding hydrogenase enzyme using computational approach

Learning from nature is a strategy for catalyst development. Its philosophy is that creative designs of high performance catalysts can be obtained from advanced understanding of catalysts selected by nature after billions of years of evolution, such as enzyme. A typical demonstration of such strategy is the developing of catalysts for large-scale hydrogen production. Hydrogenases due to their impressive performance in catalyzing hydrogen oxidation/production, have been selected as a prototype for human being's learning to achieve better design. Fully understand the structures of hydrogenases and their catalysis mechanisms are essential to reproduce and even outperform this prototype. This article reviews the computational efforts in recent years, focusing on density functional theory calculations on [NiFe] hydrogenases. It summarizes the current knowledge regarding the identification of active sites in [NiFe] hydrogenases and reaction cycles of hydrogen oxidation, followed with a brief collection and discussion of bio-inspired molecular catalysts derived from [NiFe] hydrogenase model. The capacity of computational calculations for the clarification of catalyst geometries and reaction mechanisms has been highlighted. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Structure and Mechanism > Computational Materials Science Structure and Mechanism > Reaction Mechanisms and Catalysis