Main Conference Day 2Thursday, 28 May 2026 - European Time GMT+1

Targeting pHLA class I and II at therapeutic resolution has been largely restricted to approaches building on the native TCR ligand as biologics or cell therapy. We here show how the special pIX phage display system has been optimized and combined with in silico guidance and deep sequencing as a unique platform allowing TCR-Like antibodies to enter this stage beyond the current state of the art.

Bispecific antibodies (bsAbs) can enable therapeutic mechanisms, such as dual antigen targeting or receptor agonism, that are impossible using monoclonal antibodies. BsAbs with IgG-like format (bsIgG) are comprised of two unique heavy chains, each having a cognate light chain. Co-expression of these four unique polypeptides often leads to several mispaired species that are difficult to separate from the target bsIgG due to their similar biophysical properties. Here we describe a set of mutations called ProAla that exploit a the unfolded protein response pathway of cells. ProAla heavy chains are engineered with higher folding energy barriers such that only the cognate light and heavy chains can induce folding, chaperone release and secretion. The structures of the ProAla Fab and Fc regions are identical in structure to normal antibodies, enabling maintenance of half-life and function. Mispaired polypeptides fail to secrete from the cell due to enhanced interaction with the endoplasmic reticulum chaperone BiP, resulting in increased purity of secreted bsIgGs.

Current Treg-depleting strategies often affect both tumour and effector T cells, limiting therapeutic benefit. A target-agnostic cell-panning approach using the n-CoDeR phage display library identified antibodies that selectively target tumour-associated Tregs. These antibodies recognised both familiar and novel Treg targets, including a distinct ICAM-1 domain, achieving tumour-specific Treg depletion while sparing peripheral cells. This suggests potential for improved cancer immunotherapy and supports unbiased discovery of therapeutic antibodies.   

TCR specificity to peptide-HLA antigens is central to immunology, impacting responses in infection, autoimmunity and cancer. Achieving precise recognition while avoiding off-target reactivity is critical for effective immunity and safe therapeutic interventions. Comprehensive, proteome-wide specificity profiling of TCRs is challenging with current methods, which notably lack integrated machine learning for large-scale analysis. Here, we report a synthetic immune cell system coupled with machine learning to enable TCR functional and specificity mapping of peptide-HLA antigens at proteome-scale.