Next-generation proximity labeling enzymes for mapping spatial proteomes in living cells

About This Grant

Project Summary/Abstract Our research project aims to develop next-generation proximity labeling (PL) enzymes, specifically FlexID and LaccID, to enhance our understanding of molecular interactions and spatial compartmentation within living cells. These principles are foundational to cellular biology; however, current methodologies for mapping cellular interactomes and organelle proteomes, such as microscopy and biochemical fractionation, often lack accuracy and comprehensiveness. Proximity labeling has emerged as a powerful alternative, yet existing methods like APEX and TurboID face significant limitations in specificity, sensitivity, and practicality in vivo. Our proposal seeks to address these challenges. In Aim 1, we will optimize FlexID, an innovative PL enzyme capable of utilizing diverse non-biotin substrates for rapid, non-toxic labeling. This enzyme will be engineered for fast labeling times (within one minute) while maintaining high spatial specificity across various subcellular compartments. Rigorous characterization and improvement of FlexID1 will be conducted through computational design and directed evolution, with the goal of establishing FlexID2 as a versatile tool for spatial proteomics in cell biology, neuroscience, and immunology. In Aim 2, we will engineer LaccID, designed specifically to label surface proteins and reduce background labeling from intracellular pools. By enhancing LaccID’s speed and in vivo functionality, we will develop LaccID2 for mapping specific cell-type surface proteomes in various contexts. This aim will involve exploring alternative laccase templates and utilizing directed evolution under physiologically relevant conditions. The third aim is to conduct a quantitative comparison and benchmarking of PL enzymes, including FlexID, LaccID, APEX, and TurboID. We will systematically evaluate metrics such as sensitivity, specificity, and labeling radius through quantitative mass spectrometry-based proteomics across different cell models. This comprehensive analysis will produce guidelines that assist the scientific community in selecting the most effective PL methodologies for their specific research needs. Overall, this research has the potential to significantly advance the tools available for probing protein dynamics and molecular interactions in living cells. By addressing the limitations of existing PL methodologies, our work aims to facilitate transformative insights into cellular processes, enhancing the understanding of disease mechanisms and aiding therapeutic development. The successful implementation of these innovative PL enzymes will lead to exciting new applications across cell biology, neuroscience, and immunology, catalyzing further advancements in biotechnological and biomedical research.