Context-Dependent Design of IInduced-Fit Enzymes using Deep Learning Generates Well-Expressed, Thermally Stable and Active Enzymes

Chen Brestel, Weizmann Institute of Science, Rehovot, Israel (chen.brestel@gmail.com)

Our study utilizes deep learning for enzyme design, creating well expressed in Escherichia coli, thermally stable variants with high enzymatic activity, validated by thermal and kinetic assays. Traditional computational design pipelines demand expert insight into protein structures. Our pipeline sees enzyme structures as modular components, simplifying structure creation and bypassing expert curation. Where traditional methods have low success and require extensive screening, our method produces designs that surpass natural enzymes in expression and thermostability, while preserving wild-type activity levels with just a few dozens of tested variants. The findings presented here are of high practical importance and offer a very simple to use, yet transformative approach to backbone remodeling in enzymes and other proteins. We will focus in lecture on the computational part of the study and specifically on the AI products that enabled us to predict protein properties solely based on its 1D sequence.

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Short Biography of Presenting Author

Entrepreneur, experienced Senior AI Research Lead & Researcher with a proven history of working with diverse teams across the biomedical, automotive and electro optics industries. Passionate about solving scientific and specifically biomedical problems with innovative computational tools. Experienced with kicking off novel innovative approaches and applications. Skilled in Product Development, Research and Development (R&D), Machine Learning, Computer Vision, and Algorithms. Strong research professional with a Doctor of Philosophy (Ph.D.) focused in Computer Vision from Weizmann Institute of Science.