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DeepZyme

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DeepZyme Learning Deep Representations of Enzymes for Predicting Catalytically...

DeepZyme Learning Deep Representations of Enzymes for Predicting Catalytically Beneficial Mutations During the course of evolution nature has created and optimized extraordinary protein catalysts, named enzymes, that are fundamental in all reigns of life. Enzymes facilitate complex chemical reactions at physiological conditions,... ver más

31/12/2022

Freie Universitaet...

163K€

Presupuesto del proyecto: 163K€

Líder del proyecto

FREIE UNIVERSITAET BERLIN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-12-31

MSCA-IF-2019: Individual Fellowships

I+D

Cerrada hace 5 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

Freie Universitaet Berlin

162.81K€ | Lider

CERTIFICACION DE LESIONES ES...

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Duración del proyecto: 33 meses Fecha Inicio: 2020-03-28
Fecha Fin: 2022-12-31

Líder del proyecto

FREIE UNIVERSITAET BERLIN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 163K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto During the course of evolution nature has created and optimized extraordinary protein catalysts, named enzymes, that are fundamental in all reigns of life. Enzymes facilitate complex chemical reactions at physiological conditions, accelerating their rates by several orders of magnitude and being highly selective over alternative –undesired– chemical transformations. Understanding how enzymes work and how to engineer their functions is essential for many disciplines, with applications ranging from medical therapies to biotechnological devices. The main challenge towards the rational control of enzymes is that given their complexity, it is not trivial to predict modifications –known as mutations– that are beneficial for their activity. The DeepZyme project aims to develop a model for the prediction of such modifications, taking advantage of revolutionary techniques in the field of deep learning. We propose to obtain condensed representations of enzymes by leveraging their sequence, structure and catalytic information. These representations can be suitably designed to describe enzymatic information that is available in nature, and learn how enzymes have been tuned by selection pressures along evolution. Navigating in the space of enzyme representations will allow us to finely tune their properties, and thereby guide a rational design process. Our model will be used together with other state-of-the-art techniques (including molecular dynamics, Markov state models and quantum mechanics / molecular mechanics) to generate from scratch an enzyme able to catalyze chemical reactions along the synthesis of drug-like molecules.

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