Designing Allosteric Protein Switches by In Vivo Directed Evolution and Computational Inference

Introduction

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.

Project Overview

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.

Methodology

1. Design by Directed Evolution
   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.

2. Monitoring Evolutionary Process
   We will apply this novel pipeline to a diverse set of effector proteins and monitor the evolutionary process by next-generation sequencing (Objective 1).

3. Computational Analysis
   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).

Application

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).

Conclusion

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.