In Silico Identification of scFv Targeting CD22 (Siglec-2) Receptor Targeting CAR T-Cell Therapy in Acute Lymphoblastic Leukemia

Abstract

Acute lymphoblastic leukemia (ALL) is a fast-growing cancer of the blood and bone marrow that origi- nates from immature lymphoid cells. The Siglec-2 (CD22) receptor is involved in cell signaling that pro- motes the proliferation, survival, and possibly immune evasion of ALL cells, making it a potential target for slowing disease progression. CAR T-cells (Chimeric Antigen Receptor T-cells) are immune cells that are genetically engineered to recognize and attack specific cancer cells more effectively. We hypothesize that the scFv used in this research can be attached to the surface of CAR T-cells, enabling them to target the CD22 receptor on ALL cells. In the current study, we have used computational simulation techniques to identify nanobodies that interact effectively with the CD22 receptor. The CD22 3D structure modeling was performed using AlphaFold 3, a machine learning-based technique. 9 humanized scFv structures were downloaded from the Protein Data Bank. These nanobodies were docked on the CD22 receptor using the HDOCK software. Based on the CD22-aptamer interaction analysis by PLIP software, I have identified scFv (PDB ID = 5DA0) as the most potent binding scFv to the CD22 receptor. This research  can be utilized to treat ALL by using the 5DA0 scFv to target the CD22 receptor accurately, which is more prevalent in these cells. The present findings provide a foundation for the rational design of CARs targeting CD22-positive malignant cells, thereby facilitating the development of more effective and specific immunotherapies for ALL.