Computational Simulations of CD19-targeted Chimeric Antigen Receptor (CAR) T Cells Therapy in Autoimmune Disease

Chimeric Antigen Receptor (CAR) T-cell therapy involves genetically engineering a pa- tient’s T cells to enhance their ability to target and eliminate specific cells in the body. This approach  has emerged as a promising treatment strategy for various cancers, particularly B-cell malignancies. In addition to its application in cancer, CAR T-cell therapy can potentially treat autoimmune diseases where autoreactive B cells are implicated. Severe Myositis and Systemic Sclerosis are autoimmune diseases that affect different body parts, with B cells playing a significant role in triggering and maintaining autoim- munity. CD19, a surface receptor in the B cells, presents an attractive target for CAR T-cells to eliminate these autoreactive B cells to the specific binding site on the surface of CD19 receptors, facilitating ef- fective targeting by CAR T-cells. The structure of the CD19 receptors was predicted using AlphaFold 3,  a machine learning-based method. The P2Rank web server was employed to identify binding sites on the surface of receptors. The single-chain variable fragment (ScFv) was retrieved from the Protein Data Bank (PDB), a global repository of 3D structures of biological macromolecules. The HDOCK software was effectively utilized to simulate docking interactions between antibodies and CD19 receptors. Results showed that receptor complexes formed strong interactions at the binding site predicted by the graph neu- ral network, confirming the docked results. Antibodies were selected based on visual inspection, binding energy calculation, and hydrogen bond analysis. This research will provide valuable advancements and insights into creating more effective CAR T-cells targeting autoimmune diseases, such as severe myositis and systemic sclerosis.