The proteins' PDB files were obtained through the server AlphaFold 3, an advanced AI system developed by DeepMind to predict protein structures . Next, we used a Graph Neural Network (GNN) to find the site of the protein involved in binding . Adding onto the binding site prediction, it was necessary to determine the electrostatic surface potential (ESP) because it would help to determine if and where the aptamer would bind as aptamers are negatively charged, meaning they would bind to positive areas. With the use of HDOCK software, molecular docking was done for each of the structures of the receptors . To analyze the eight aptamers, their base pair sequences were used to convert them into three-dimensional figures to analyze for molecular docking in a three-step process. First, the aptamer's RNA code was taken to convert it into a two-dimensional format through the Vfold2D server . Second, the Vfold3D 2.0 server was used to convert the 2D structures of the aptamers to 3D . The RNA sequence and the dot-bracket for each aptamer were pasted into the input box, and the number of clusters was set to one. The aptamer sequences were converted to 3D structures in PDB files downloaded for docking. Molecular docking of protein-aptamer was done using HDOCK software, where the receptor molecule was γδ TCR, and the ligand molecules were the following eight aptamers. Each paired aptamer with the receptor molecule showed various binding predictions, so the first prediction for the pairing was used for each of them as it was deemed to make the most sense in terms of receptor-ligand complexes. PDA-Pred was used to calculate the binding affinity between the aptamers and the γδ TCR receptor molecule . Conversely, PLIP was used to check for interactions between the receptor-ligand complexes and record the distances between the hydrogen bonds for each amino acid in the protein .