Osuna, Sílvia

Billions of years of evolution have made enzymes superb catalysts capable of accelerating reactions by as many as seventeen orders of magnitude. This rate acceleration is achieved by decreasing the activation barriers of reactions, making them possible at lower temperatures and pressures. Enzymes (i.e. biocatalysts) are indeed the most efficient, specific and selective catalysts known. They operate under biological conditions, are biodegradable, non-toxic, their high selectivities and efficiencies reduce the number of work- up steps, and provide product in higher yields. These characteristics make enzyme-catalyzed processes an attractive alternative for chemical manufacturing. However, the use of enzymes in industry is limited, as most of processes do not present a biocatalyst to catalyze and accelerate the corresponding reactions. The ability of routinely designing enzymes for any target process will have large socio-economic impacts, as the production costs of many drugs will be reduced and will allow industries to use environmentally friendly alternatives. However, the routine design of enzymes for any target reaction has not yet been achieved. This is in part motivated by the imprecise knowledge of the underlying physical principles of biocatalysis, which makes the alteration of the natural activity of enzymes towards synthetically relevant targets a tremendous challenge for biochemistry. Current computational and experimental approaches are able to confer natural enzymes new functionalities but are economically unviable and the catalytic efficiencies lag far behind their natural counterparts.

We work in the design of new enzymes for distinct processes important for their potential applications in medicine. We explore the structural basis of improved catalysis achieved by the experimental directed evolution (DE) technique through computational modeling, and are currently developing a new computational protocol based on Molecular Dynamics and network models that reduce the complexity of the enzyme design paradigm. Our computational predictions are tested in the lab to finally elucidate the potential of this genuinely new computational approach for mimicking Nature’s rules of evolution.
We collaborate with many groups, being the most relevant ones: Prof. K. N. Houk (UCLA, USA), Prof. Y. Tang (UCLA, USA), Dr. G. Huisman (Codexis).

We additionally work on the computational exploration of the chemical reactivity and properties of carbon-based materials. This topic is related to Dr. Osuna’s PhD thesis and she has collaborations with the groups of Prof. L. Echegoyen (UTEP), Dr. Y. Yamakoshi (ETH Zurich), Prof. J. M. Poblet (URV), and Prof. N. Martín (UCM).