Global climate and energy challenges require efficient, robust and scalable catalysts for the conversion of renewable energies. Nature has evolved extremely active catalysts (enzymes) for the conversion of small molecules relevant to energy (H2, CO2, N2). The scalability of these enzymes offers distinct advantages over the rare, precious metals that are currently used in energy conversion.
Unfortunately, the enzymes are unable to tolerate the extreme conditions of operating fuel cells or electrolyzers. Directed evolution is a powerful approach for improving enzymes, but is mostly restricted to natural amino acids and biological conditions, with limited compatibility for evolving enzymes toward enhanced resistance in abiotic systems. The project E-VOLUTION aims to establish directed evolution in fully abiotic systems, using artificial amino acids to make artificial enzymes that are stable even in extreme conditions. Towards this, I will establish new electrochemical peptide synthesis platforms to enable the generation of enzyme-length peptides using both natural and artificial amino acids. Extended libraries of artificial enzyme variants will be produced and screened directly on electrode microarrays. Top enzyme candidates for the conversion of H2 will be selected using fuel cell/electrolyzer conditions as the evolutionary criteria. A new procedure for synthesizing libraries of full-length artificial proteins will enable the creation of thousands of enzyme variants using artificial building blocks. The generation of high-quality datasets will be transformative to drive future machine learning-based evolution steps for both full size enzymes and small-molecule catalysts with applications beyond H2 evolution. This will support the discovery of highly active catalysts able to sustain conditions of large-scale energy conversion devices, accelerating breakthroughs toward the economically competitive use of renewable energies for fuel and chemical production.
