NetMoDEzyme
Financiado
Network models for the computational design of proficient enzymes
Billions of years of evolution have made enzymes superb catalysts capable of accelerating reactions by several orders of magnitude. The underlying physical principles of their extraordinary catalytic power still remains highly deb...
Billions of years of evolution have made enzymes superb catalysts capable of accelerating reactions by several orders of magnitude. The underlying physical principles of their extraordinary catalytic power still remains highly debated, which makes the alteration of natural enzyme activities towards synthetically useful targets a tremendous challenge for modern chemical biology. The routine design of enzymes will, however, have large socio-economic benefits, as because of the enzymatic advantages the production costs of many drugs will be reduced and will allow industries to use environmentally friendly alternatives. The goal of this project is to make the routine design of proficient enzymes possible. 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. The groundbreaking nature of NetMoDEzyme relies on the application of network models to reduce the complexity of the enzyme design paradigm and completely reformulate previous computational design approaches. The new protocol proposed accurately characterizes the enzyme conformational dynamics and customizes the included mutations by exploiting the correlated movement of the enzyme active site residues with distal regions. The guidelines for mutation are withdrawn from the costly directed evolution experimental technique, and the most proficient enzymes are easily identified via chemoinformatic models. The new strategy will be applied to develop proficient enzymes for the synthesis of enantiomerically pure β-blocker drugs for treating cardiovascular problems at a reduced cost. The experimental assays of our computational predictions will finally elucidate the potential of this genuinely new approach for mimicking Nature’s rules of evolution.
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Duración del proyecto: 82 meses
Fecha Inicio: 2016-02-09
Fecha Fin: 2022-12-31
Fecha Fin: 2022-12-31
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2022-12-31
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-StG-2015:
ERC Starting Grant
Cerrada
hace 9 años
Presupuesto
El presupuesto total del proyecto asciende a
1M€
Líder del proyecto
UNIVERSITAT DE GIRONA
No se ha especificado una descripción o un objeto social para esta compañía.
Total investigadores
226
Total investigadores
226