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DaVinci-Switches

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Designing Allosteric Protein Switches by In Vivo Directed Evolution and Computat...

Designing Allosteric Protein Switches by In Vivo Directed Evolution and Computational Inference Proteins are molecular machines that drive all major functions in cells. Controlling the activity of proteins in real time via light or chemicals is a central goal in synthetic biology. The design of switchable proteins, in partic... ver más

31/08/2027

UHEI

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

RUPRECHTKARLSUNIVERSITAET HEIDELBERG No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-09-01

ERC-2021-STG: ERC STARTING GRANTS Scope:Objectives

I+D

Cerrada hace 3 años

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Duración del proyecto: 59 meses Fecha Inicio: 2022-09-01
Fecha Fin: 2027-08-31

Líder del proyecto

RUPRECHTKARLSUNIVERSITAET HEIDELBERG No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Proteins are molecular machines that drive all major functions in cells. Controlling the activity of proteins in real time via light or chemicals is a central goal in synthetic biology. The design of switchable proteins, in particular single-chain, allosteric variants, however, is a challenging engineering problem thus-far mostly addressed by trial-and-error. DaVinci-Switches takes a radically new, data-driven perspective to fundamentally advance our understanding of protein allostery and accelerate and eventually rationalize the engineering of switchable proteins by interfacing synthetic biology with machine learning. We will establish a 'design by directed evolution' approach to create switchable proteins through receptor and effector fusion followed by phage-assisted in vivo directed evolution using synthetic gene circuits for selection. We will apply this novel pipeline to a diverse set of effector proteins and monitor the evolutionary process by next-generation sequencing (Objective 1). In parallel, we will perform an in-depth computational analysis of domain insertions within the natural protein repertoire. The combined, rich datasets will be used to train machine learning models to infer sequence patterns predictive of domain insertion tolerance and allosteric coupling between receptor-effector pairs (Objective 2). Finally, we will employ this unique model to design light- and drug-inducible variants of the Yamanaka cell reprogramming factors. These will provide the foundation of an Adeno-associated virus-based platform for cyclic, partial in vivo reprogramming of somatic cells with enormous potential for regenerative medicine, which will be evaluated in a murine model of drug-induced liver injury (Objective 3). DaVinci-Switches harnesses our key competences in protein engineering, synthetic biology and computation to reveal fundamental principles of allostery and enable transformative advances in the design of switchable proteins for research and medicine.

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