Descrizione del progetto
Prevedere mutazioni benefiche per l’attività degli enzimi
Gli enzimi sono ottimi catalizzatori che accelerano notevolmente la velocità di reazioni chimiche complesse in condizioni fisiologiche. Comprendere il modo in cui progettare gli enzimi per massimizzarne la funzione andrà a vantaggio sia della medicina che della biotecnologia. Il progetto DeepZyme, finanziato dall’UE, si propone di affrontare ciò attraverso un modello che possa prevedere l’impatto delle modifiche degli enzimi quali le mutazioni. Il modello utilizzerà tecniche di apprendimento profondo per valutare le informazioni su sequenza, struttura e attività catalitica degli enzimi. Sfruttando il potere della pressione selettiva imposta sugli enzimi nel corso dell’evoluzione, il progetto si propone di perfezionare le proprietà di enzimi importanti.
Obiettivo
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.
Campo scientifico
- natural sciencesphysical sciencesquantum physics
- natural sciencesbiological sciencesgeneticsmutation
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural scienceschemical sciencescatalysis
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteinsenzymes
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinatore
14195 Berlin
Germania