Computational Modeling of a BiTE Targeting CD22 and the TCR Complex for Acute Lymphoblastic Leukemia

Overview

Acute Lymphoblastic Leukemia is the most common childhood cancer and has over 300,000 cases globablly, rapidly progressing in the blood and bone marrow. ALL affected cells often overexpress the CD22 receptor on the surface. Bispecific T-Cell Engagers (BiTEs) are engineered antibodies that bind the T-cell Receptor (TCR) and cancer cell receptors (CD22), promoting cytotoxicity. Current BiTE research relies on in vitro and in vivo studies which take years and cost hundreds of millions. I hypothesize that if scFvs bind to the predicted binding sites of their respective receptors, they can form strong and stable interactions with each receptor, enabling the formation of a CD22-TCR BiTE molecule that brings the two receptors into close proximity, resulting in T-cell–mediated cancer cell death. Alphafold 3 generated receptor structures. Twenty humanized scFvs from SAbDab were docked to both receptors and albumin as a control using HDock. Binding regions were validated with machine learning tools, and PLIP/PRODIGY quantified H-bonds and binding affinities. MD simulations evaluated stability of the best complexes, and a complete BiTE model was created to assess toxicity and penetration. scFv 9mfn exhibited strong binding to CD22(ΔG = −21.2 kcal/mol) and 9b7r to the TCR (ΔG = −13.3 kcal/mol). Both showed stable, favorable interactions compared to albumin controls. Significant interactions with these scFvs suggest utility in CD22-TCR BiTE molecules for ALL therapy. Sources of error include differences in computational and in vivo results. This study supports the hypothesis and highlights this computational pipeline as an accurate, cost-effective method to accelerate antibody and drug development targeting cancer.