Project
Title: AI-driven de novo design followed by an AI-guided DMTA (Design–Make–Test–Analyze) workflow for the development of novel proteases with a broader substrate spectrum
Description of the Project
The project aims to develop novel proteases with a broad substrate spectrum and high activity through experimentally supported AI-based de novo design, combined with an AI-driven DMTA cycle (Design–Make–Test–Analyze). The study investigates to what extent the specificity of proteases can be altered using AI methods. Proteases serve as a model system for exploring the modifiability of catalytic enzymes. Current research indicates that AI designs require experimental feedback; therefore, they are combined here with a DMTA cycle. The project will use automated high-throughput methods to synthesize and measure large protein datasets in order to train AI models. A universal AI communication protocol is intended to flexibly integrate different algorithms. The project seeks to elucidate functional relationships in proteases and to validate an AI-agnostic platform for the development of new biomolecules.
Personnel
