COCOZYME, PID2021-129034NB-I00


COCOZYME, PID2021-129034NB-I00
Diseño computacional de enzimas conformacionalmente dirigido para mejorar la actividad aislada o en complejo
Total: 157,300€. Period: 2022-2025

  • RESEARCH YEAR 2022-2025
  • Funding 157,300€


Enzymes are superb catalysts capable of accelerating the chemical reactions by as many as seventeen orders of magnitude. They achieved such impressive rate accelerations by decreasing the activation barriers of reactions, making them possible at lower temperatures and pressures. Apart from their high efficiency, enzymes are specific and selective, and operate under mild biological conditions. These features make enzyme-catalyzed processes an attractive alternative for chemical manufacturing. Still the application of enzymes in industry is quite limited, as most industrial processes lack a natural enzyme able to perform the desired transformations, to accommodate the non-natural substrate(s) of interest, and/or their low stability in non-optimal conditions.

Enzyme design focuses on the generation of new biocatalysts with improved properties by altering their natural constituting sequence of amino acids. The interest in enzyme design arises from their advantageous characteristics, but also from the intellectual challenge of creating novel enzymatic activities. Enzyme design is a stringent test to understand enzyme catalysis, evolution, folding and stability. Initial attempts to computationally design enzymes overlooked the fact that enzymes exist as ensembles of conformations that are important for function. Tuning these populations of conformational states through mutation enables evolution towards novel activity.

In this project, a general conformationally-driven enzyme design protocol will be developed based on extensively characterizing the ensemble of conformations and applying correlation-based tools for identifying the mutation hotspots (at the active site, but also at distal positions). The correlation-based tools will be combined with multiple sequence alignment techniques, and machine-learning techniques will be applied for rapidly ranking and evaluating the new generated variants. The computational protocol will be applied for conferring stand-alone activity to allosterically-regulated enzymes, and also for designing new allosterically-regulated enzymes. The ability to computationally design new enzymes that could be activated or deactivated using allosteric interactions with other effectors or binding partners would greatly expand the chemical biology toolbox.

This project will enhance our current understanding of allosteric regulation and its role in enzyme catalysis, and will additionally contribute to achieve the routine design of enzymes for any target reaction, which has a high industrial interest associated.