In-Silico Studies of RBP7, CALML3, C35, LGALSL,S100A3 Proteins Involved in Breast Cancer

Mini Fernandez and D. Keerthana Goud

Chapter 13 in Convergence of Technology & Biology ─ Transforming Life Sciences, 2025, pp 127-142 from Shanlax Publications

Abstract: The most important reason for death caused by cancer in females around the globe is breast cancer. Early diagnosis, proper treatment are fundamental in order to enhance the outcomes of patients. This study aims to investigate various proteins that contribute to the development and progression of breast cancer. Proteins that were chosen for this work are: Retinoid-binding protein 7, Calmodulin-like protein 3, C35 protein, Galectinrelated protein and S100-A3 protein. In addition, three drugs: Raloxifene, Tamoxifen, and Fulvestrant were chosen to check if they have therapeutic effects on the above-mentioned proteins. By studying how these proteins interact with drugs, we can gain important information that could help create effective treatment. The protein sequences were retrieved from biological databases. Homology modelling was done to produce models for the selected proteins using SWISS-MODEL, then validated with help of ‘SAVES v6.0’ and ‘ProSA’. To identify places where drug would bind to the protein, surface pockets were characterized with the employment of ‘CASTp’. Each protein was then docked to three drugs using Vina in ‘PyRx’ and the most suitable conformation of the ligand was selected via splitting the ligand using ‘AutoDock Vina’. The protein-drug interactions were then visualised in ‘Biovia Discovery Studio’. The models were generated for proteins and were docked to the ligands. As predicted, the ligands successfully attached to amino acid residues in active site of the proteins by Hydrophobic bond, Hydrogen bond, Electrostatic Bond. By studying interactions between the proteins and the drugs, it is analysed about how the drug help to slow the progression of breast cancer. This highlights their potential as targeted treatments that could help stop tumor growth. This study also interprets which drug has more or less therapeutic effect for each protein that help in better treatment.

Date: 2025
ISBN: 978-93-6163-763-6
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