Description du projet
Prédire les mutations bénéfiques pour l’activité enzymatique
Les enzymes sont de grands catalyseurs qui accélèrent considérablement la vitesse des réactions chimiques complexes en conditions physiologiques. Il sera bénéfique pour la médecine et la biotechnologie de comprendre comment concevoir des enzymes, afin de maximiser leur fonction. Le projet DeepZyme, financé par l’UE, propose d’aborder cette question par le biais d’un modèle pouvant prédire l’impact des modifications enzymatiques telles que les mutations. Ce modèle utilisera des techniques d’apprentissage profond, afin d’évaluer les informations sur la séquence, la structure et l’activité catalytique enzymatique. En exploitant le potentiel de la pression de sélection imposée aux enzymes au cours de l’évolution, le projet vise à affiner les propriétés des enzymes importantes.
Objectif
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.
Champ scientifique
- natural sciencesphysical sciencesquantum physics
- natural sciencesbiological sciencesgeneticsmutation
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural scienceschemical sciencescatalysis
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteinsenzymes
Mots‑clés
Programme(s)
Régime de financement
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinateur
14195 Berlin
Allemagne