Anticancer Efficacy of Artiodactyla Milk Peptides Targeting MCL-1 and ERα Receptors in Breast Cancer: Insights from In-Silico Screening and Molecular Dynamics Studies

The data in this project utilizes computational methods to identify potential anti-cancer peptides within artiodactyla milk protein hydrolysates. Through an extensive in silico analysis, bioinformatics tools were employed to predict and analyze the structure, stability, and binding affinity of candidate peptides. By leveraging molecular dynamics simulations and machine learning algorithms, we seek to prioritize peptides with optimal therapeutic properties. The data set consists of pdb structure files with all relevant parameter plots of the predicted peptides, crystal structure pdb files of the protein receptor, raw and output files of the molecular docking analysis, and raw and output files of the MD simulation run.