Computational Chemistry studies of Proteins for CO2 Fixation and Sequestration
Technische Universität Berlin, Straße des 17. Juni, 135, 10623, Berlin, GER
There are two major energy-related problems the world is facing in the next 50 years, 1) the increased competition for fossil fuels reserves because of their depletion and 2) the increasing level of atmospheric CO2 which could produce large and uncontrollable impacts on the climate. A solution to these problems is to provide a future energy supply that is secure and CO2-neutral, switching to non fossil energy sources 1. Carbon dioxide, through carbon fixation process of inorganic carbon to organic compounds by living organisms, is the ultimate source of the fossil fuels. Different pathways for CO2 fixation exist, and they use different mechanisms and enzymes to process CO2 making C-H and C-C bonds. One of the major obstacles to an efficient conversion of CO2 into fuels is the lack of catalysts. In the light of this, the importance of a synergistic contribution of the catalysis and biological communities to the problem of converting carbon dioxide directly into fuels becomes clear. The carbonic anhydrases (CAs) are mostly zinc-containing metalloenzymes which catalyse the reversible hydration of carbon dioxide to bicarbonate2. Carbon monoxide dehydrogenases (CODHs) are enzymes responsible for the interconversion between CO and CO2 following the water-gas shift reaction3. A fundamental role in the study of the mechanisms taking place during the catalytic activity of these bioenzymes is covered by computational chemistry studies, in particular Quantum Mechanical/Molecular Mechanics methods (QM/MM). This method in fact represents an efficient way of calculating localised chemistry in both complex molecular systems, in metalloenzymes, as well as in solids. Within this approach a region of interest is identified where an electronic description is necessary, and it is treated at a quantum mechanical level, while the environment around it is described via a classical molecular mechanics force field4. A recent attempt to combine QM/MM schemes with potentials, describing energy and forces on each atom of a system, based on Machine Learning (ML) which is based on the use of statistical algorithms whose performance improves with training, has been done and it opened to a potential application of QM/ML methods to different chemical problems5.
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