In silico Mutagenesis and Modeling of Decoy Peptides Targeting CIB1 to Obscure its Role in Triple-negative Breast Cancer Progression

Abstract

Background:: Cancer is recognized globally as the second-most dominating and leading cause of morbidities. Breast cancer is the most often diagnosed disease in women and one of the leading causes of cancer mortality. In women, 287,850, and in males, 2710 cases were reported in 2022. Approximately 10–20% of all new cases of breast cancer diagnosed in the United States in 2017 were triple-negative breast cancers (TNBCs), which lack the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Objective:: This study aims to adopt different strategies for targeting calcium integrin-binding protein 1 by computer-aided drug design methods. Our results showed that the top four selected peptides interact with CIB1 more strongly than the reference peptide and restore normal cell function by engaging CIB1. Our binding affinity analyses explore an innovative approach to planning a new peptide to inhibit triple-negative breast cancer. Methods:: Molecular dynamic simulation of the CIB1-UNC10245092 interaction highlights the potential peptide inhibitors through In-silico mutagenesis and designs novel peptide inhibitors from the reference peptide (UNC10245092) through residue scan methodology. Results:: The top four designed peptides (based on binding free energy) were subjected to molecular dynamics simulations using AMBER to evaluate stability. Conclusion:: Our results indicate that among the top five selected peptides, the mutant 2nd mutants have more potential to inhibit CIB1 than the reference peptide (UNC10245092) and have the potency to prevent or restore the tumor suppressor function of UNC10245092.