Descripción del proyecto
Predicción de mutaciones beneficiosas para la actividad de las enzimas
Las enzimas son catalizadores eficaces que aceleran significativamente la velocidad de reacciones químicas complejas en condiciones naturales. Entender cómo diseñar enzimas para maximizar su función será beneficioso tanto para la medicina como para la biotecnología. El proyecto DeepZyme, financiado con fondos europeos, propone abordar esta cuestión mediante un modelo que puede predecir el efecto de modificaciones en las enzimas, como las mutaciones. El modelo utilizará técnicas de aprendizaje profundo para evaluar información sobre la secuencia, la estructura y la actividad catalítica de las enzimas. Al aprovechar el poder de la presión selectiva sobre las enzimas a lo largo de la evolución, el proyecto pretende perfeccionar las propiedades de enzimas importantes.
Objetivo
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.
Ámbito científico
- natural sciencesphysical sciencesquantum physics
- natural sciencesbiological sciencesgeneticsmutation
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural scienceschemical sciencescatalysis
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteinsenzymes
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Programa(s)
Régimen de financiación
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinador
14195 Berlin
Alemania