Computational approaches for the design of peptides with anti-breast cancer properties

Background: Breast cancer is the most common cancer among women. Tamoxifen is the preferred drug for estrogen receptor-positive breast cancer treatment, yet many of these cancers are intrinsically resistant to tamoxifen or acquire resistance during treatment. Therefore, scientists are searching for breast cancer drugs that have different molecular targets. Methodology: Recently, a computational approach was used to successfully design peptides that are new lead compounds against breast cancer. We used replica exchange molecular dynamics to predict the structure and dynamics of active peptides, leading to the discovery of smaller bioactive peptides. Conclusions: These analogs inhibit estrogen-dependent cell growth in a mouse uterine growth assay, a test showing reliable correlation with human breast cancer inhibition. We outline the computational methods that were tried and used along with the experimental information that led to the successful completion of this research.

Structural Analysis of alpha-Fetoprotein (AFP)-like Peptides with Anti-Breast-Cancer Properties

The abundance of alpha-fetoprotein (AFP), a natural protein produced by the fetal yolk sac during pregnancy, correlates with lower incidence of estrogen receptor positive (ER+) breast cancer. The pharmacophore region of AFP has been narrowed down to a four amino acid (AA) region in the third domain of the 591 AA peptide. Our computational study focuses on a 4-mer segment consisting of the amino acids threonine-proline-valine-asparagine (TPVN). We have run replica exchange molecular dynamics (REMD) simulations and used 120 configurational snapshots from the total trajectory as starting configurations for quantum chemical calculations. We optimized structures using semiempirical (PM3, PM6, PM6-D2, PM6-H2, PM6-DH+, PM6-DH2) and density functional methods (TPSS, PBE0, M06-2X). By comparing the accuracy of these methods against RI-MP2 benchmarks, we devised a protocol for calculating the lowest energy conformers of these peptides accurately and efficiently. This protocol screens out high-energy conformers using lower levels of theory and outlines a general method for predicting small peptide structures.