CAR-Toner: an AI-driven approach for CAR tonic signaling prediction and optimization
Shizhen Qiu, Jian Chen, Tao Wu, Li Li, Gang Wang, Haitao Wu, Xianmin Song, Xuesong Liu, Haopeng Wang
Abstract
Tonic signaling of chimeric antigen receptors (CARs) plays a pivotal role in governing CAR-T cell fitness: inefficient tonic signaling results in poor CAR-T persistence, while excessive tonic signaling leads to CAR-T exhaustion. 1 , 2 , 3 Our previous work has elucidated that positively charged patches (PCPs) on the surface of the CAR antigen-binding domain facilitate CAR clustering, thereby triggering CAR tonic signals. To quantify these PCPs, which are indicative of CAR tonic signaling, we previously developed a bioinformatic method to determine the PCP score. 1 This calculation method starts with constructing three-dimensional (3D) homology models for CAR’s single-chain variable fragments (scFvs) using the SWISS homology modeler. Subsequently, the BindUP web server is used to determine the total count of residues within the top three largest patches containing continuous positively charged residues on the surface of CAR scFv. However, this PCP score calculation method has several limitations: 1. reliance on two external servers; 2. each calculation taking a few days, significantly hindering efficiency; 3. lack of batch calculation capability; 4. no optimization strategies provided for fine-tuning PCP scores. Given these constraints, we aimed to develop an artificial intelligence (AI)-based PCP score calculator and optimizer to overcome these bottlenecks.