北京大学发表最新Cell论文

Core Viewpoint - The research introduces a machine-learning-assisted strategy called CAGE-Prox vivo for precise protein activation in living organisms, providing a universal platform for time-resolved biological studies and on-demand therapeutic interventions [1][13]. Group 1: Research Background - The study emphasizes the importance of gain-of-function research in understanding biological processes and disease pathology, highlighting various protein engineering techniques that have been developed to manipulate proteins [4]. - Current techniques, while effective, often rely on complex protein constructs that may alter the natural function of target proteins [4][5]. Group 2: CAGE-Prox Strategy - CAGE-Prox is a more universal strategy for controlled activation of a wide range of protein targets, independent of the amino acid residue type at the active site [5]. - The strategy utilizes a light-degradable tyrosine residue (ONBY) to temporarily mask protein activity, allowing for high temporal resolution in studying stimulated cellular processes [5][6]. Group 3: CAGE-Prox vivo Development - The CAGE-Prox vivo strategy incorporates a non-natural amino acid, trans-cyclooctene-tyrosine (TCOY), which can be introduced near the active site of target proteins to temporarily deactivate their function [7][9]. - The research team developed an integrated machine learning process to evolve an aminoacyl-tRNA synthetase (aaRS) that can efficiently incorporate TCOY into proteins [10][11]. Group 4: Applications of CAGE-Prox vivo - The CAGE-Prox vivo system enables precise killing of tumor cells by temporarily inactivating the anthrax lethal factor (LF) and then restoring its activity through a small molecule-triggered bioorthogonal reaction [9][10]. - The strategy also allows for the construction of safer bispecific antibodies that only regain their tumor-targeting function upon specific chemical activation, reducing the risk of cytokine storms and related toxicities [11][12].