Work

Leveraging yeast sequestration to study and engineer posttranslational modification enzymes

Year: 2023

Type: review

Abstract: Enzymes that catalyze posttranslational modifications (PTMs) of peptides and proteins (PTM-enzymes)-proteases, protein ligases, oxidoreductases, kinases, and other transferases-are foundational to our understanding of health and disease and empower applications in chemical biology, synthetic biology, and biomedicine. To fully harness the potential of PTM-enzymes, there is a critical need to decipher their enzymatic and biological mechanisms, develop molecules that can probe and modulate them, and endow them with improved and novel functions. These objectives are contingent upon implementation of high-throughput functional screens and selections that interrogate large sequence libraries to isolate desired PTM-enzyme properties. This review discusses the principles of Saccharomyces cerevisiae organelle sequestration to study and engineer PTM-enzymes. These include outer membrane sequestration, specifically methods that modify yeast surface display, and cytoplasmic sequestration based on enzyme-mediated transcription activation. Furthermore, we present a detailed discussion of yeast endoplasmic reticulum sequestration for the first time. Where appropriate, we highlight the major features and limitations of different systems, specifically how they can measure and control enzyme catalytic efficiencies. Taken together, yeast-based high-throughput sequestration approaches significantly lower the barrier to understanding how PTM-enzymes function and how to reprogram them.

Source: Biotechnology and Bioengineering

DOI: 10.1002/bit.28621

Authors: Samantha G. Martinusen, Carl A. Denard

Institution: University of Florida

Language: English

Topic: Fungal and yeast genetics research

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: bronze

Funders: National Science Foundation, National Institutes of Health, National Institute of General Medical Sciences

Awards: Machine Learning-Guided Engineering of Protease Modulators, G4745128405, G6671297155, CAREER: Selectively Reprogramming Proteases through the High-Throughput Discovery of Functional Protein-Protein Interactions., Reprogramming proteases: tackling human diseases with next-generation modulators