AbFUSE: Joint Antibody Optimization for Discriminating Similar Targets

Advances in protein structure prediction enable model-guided antibody sequence optimization, yet suppressing binding to related negative targets can also reduce predicted binding to the intended target. We present AbFUSE, a training-free framework that optimizes one antibody sequence against a positive target and multiple negative targets using frozen structure and affinity predictors. A reference-score penalty limits decreases in predicted positive-target binding; an optional static local relation term compares mapped target neighborhoods. For chimeric antigen receptor (CAR) design, the objective can additionally include a sequence-level positively charged patch (PCP) term from CAR-Tonic Signal Tuner (CAR-Toner). We evaluate candidate quality on a computational benchmark and explore multi-target predictions in a case study involving STEAP family members. No updated AbFUSE benchmark candidate passed the prespecified interface-confidence gate. In an exploratory AlphaFold Server assessment, designs targeting STEAP family member 1 (STEAP1) showed lower predicted binder–target pairwise alignment error against STEAP1 than against STEAP2/3; this pattern also appeared in the baseline designs. These findings characterize the framework and its current validation limits without establishing experimental affinity or selectivity.