10: Computational modeling of scFv-based bispecific T-cell engagers targeting IL13Rα2 and T-cell receptors for glioblastoma immunotherapy

Abstract: Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor, characterized by rapid proliferation, infiltration into surrounding tissue, and high recurrence rates, making current treatments—surgery, chemotherapy, radiation, immunotherapy, and targeted therapy—largely ineffective in the long term. A promising therapeutic target in GBM is the interleukin-13 receptor alpha 2 (IL13Rα2), a decoy receptor that is highly overexpressed in tumor cells but minimally expressed in normal brain tissue, enabling selective targeting. This study explores the potential of single-chain variable fragment (scFv) antibodies to target IL13Rα2 in glioblastoma through computational modeling and molecular docking approaches. In this context, scFvs can be incorporated into bispecific T cell engagers (BiTEs), which connect tumor antigens to CD3 on T cells to trigger responses. Protein structures of IL13Rα2 and T-cell receptor chains were modeled and analyzed to identify candidate binding regions using machine-learning-based binding site prediction. scFv antibody structures were selected and optimized, followed by protein-protein docking simulations to evaluate binding orientation and interaction strength. The binding affinities of the resulting complexes were estimated to rank scFbs with the most favorable interactions for BiTE construction. In parallel, a supervised machine learning classifier was developed using physicochemical sequence features to predict amyloidogenic propensity, allowing assessment of scFv stability and aggregation risk. Docking and binding energy analysis identified multiple scFvs with strong predicted affinity for IL13Rα2 and T-cell receptors, along with low aggregation potential. Together, these results support a computational and machine-learning guided framework for designing scFv-based bispecific T-cell engagers with improved specificity, stability, and therapeutic potential for glioblastoma.