A cellular hydrogen sensor for enzyme engineering: The project H2aseScanning aims to develop a new approach to improve the performance of hydrogenases as they interface between renewable energy and biotechnology. Climate change poses a severe threat to our society and natural resources; carbon dioxide emissions produced as a result of human activity are particularly harmful due to their warming effects. To this end, biotechnological CO2 utilization constitutes an emerging and promising strategy to convert these harmful emissions into valuable chemicals. Nature provides an avenue for CO2 utilization by way of autotrophic microorganisms which have CO2 fixing pathways and a metabolic energy module that provides the chemical energy for its fixation. Unfortunately, it is often difficult to couple these modules to renewable energy sources. In this context, hydrogenases which have the capacity to oxidize of molecular hydrogen to supply reduction equivalents for cellular processes stand out. Hydrogenases can thus provide a unique interface between hydrogen that can be produced from renewable energy and biotechnological processes. They can be applied in cellular processes for the production of valuable chemicals from CO2, in cell-free systems where the hydrogenase supplies electrons for biocatalytic reactions and are elements of biohybrid systems. Unfortunately, the activity and operational stability of hydrogenases leaves much to be desired. Protein engineering is an efficient approach to develop and advance enzymes for industrial applications. H2aseScanning aims to develop a system for the investigation of hydrogenases by deep mutational scanning. The complementation of inactivated genes of subunits of the native hydrogenase with large mutant libraries allows to couple the mutant’s hydrogenase activity to the growth of a hydrogen-utilizing bacterium. This massive high-throughput selection system will then be used to obtain a fitness landscape with comprehensive information of the role of all amino acids on the fitness of the enzyme. The resulting datasets will lay the groundwork for the improvement of hydrogenases by machine-learning methods.
|Effective start/end date||1/01/22 → 30/06/23|
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