Breast cancer is a prevalent disease that primarily affects women, with significant implications for public health. Early detection improves survival rates, and technological advancements can enhance early diagnosis. Protein structure prediction methods can provide valuable insights into the structure of proteins involved in breast cancer, including human epidermal growth factor receptor-2 (HER2). HER2 is a known protein receptor that plays a critical role in breast cancer development and is a target for therapy. Predicting the binding affinity between HER2 and potential ligands can help identify novel treatment options. This study aimed to predict the structure of HER2 using I-TASSER and determine potential ligands using empirical graph neural network-based scoring functions. Molecular docking simulations were performed to evaluate the binding conformation and stability of the HER2-ligand complexes. The results showed the potential ligands identified by I-TASSER: AEE, UUU, and Mg2+. Afterward, their binding affinity with HER2 was assessed, yielding AEE as the best binding with the lowest vina score.
HER2, I-TASSER, ligand, novel treatment, breast cancer, binding affinity
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