Ai-augmented computational modeling of bispecific antibody targeting B7H4+ cancer cells and CD3e+ CAR T-cells for targeted therapy in solid tumor

A solid tumor is an abnormal mass of uncontrolled cell growth, typically originating in the breast, lung, or prostate. Its physiological features make treatment by traditional methods difficult. In Solid tumors, the B7H4 receptor is overexpressed on the surface of the cancer cells, i.e., the number of receptors on cancer cells increases. This makes them an essential target for cancer diagnosis and targeted therapy. DuoBody is a bispecific antibody with two halves of antibodies designed to target two specific receptors and enhance therapeutic effects by bringing T cells to cancer cells, resulting in cancer cell apoptosis by the T cell. In this paper, I am working on the B7H4 receptor, which is highly expressed in solid tumors, and the CD3e receptor, which plays a role in activating T-cell response. I hypothesize that these DuoBody antibodies can be used to target the B7H4+ solid cancer cells by inducing the CAR T-cells toward the cancer cells. In the current research, I have performed computational modeling to design DuoBody antibodies targeting cancer (B7H4 receptor) and CAR T cells (CD3e receptor). Initially, I got the 3D structures of the receptors by using the AlphaFold 3 web server. The 3D structure of antibodies was downloaded from the protein data bank. The downloaded antibodies were docked on the predicted receptor structures utilizing the HDOCK2.0 software to understand the antibodies' binding interaction and affinity. The docking results were validated using the graph neural network (GNN).  The antibodies were selected based on visual inspection, binding energy, and hydrogen bond interactions of the output obtained from the molecular docking simulations. The Binding energies calculated by the PRODIGY software showed that antibodies 4a6y and 2uyl strongly bond to the CD3e receptor and that 1il1 and 1f8t strongly bond to the B7H4 receptor. This research can be used in pharmaceutical drug development to engineer Duobodys targeting cancer cells.

Keywords: graph neural network, DuoBody, B7H4.