title: Computational approaches for the design of peptides with anti-breast cancer properties.
abstract: 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.
  
  
  METHODS
  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.
authors.affiliation: Department of Chemistry and Physics, College of Science and Technology, Armstrong Atlantic State University, 11935 Abercorn Street, Savannah, GA 31419, USA. george.shields@armstrong.edu
authors.forename: George C
authors.initials: GC
authors.lastname: Shields
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D000970:Antineoplastic Agents / Q000737:chemistry* / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy*; D019295:Computational Biology; D003500:Cyclization; D015195:Drug Design; D004967:Estrogens / Q000378:metabolism; D005260:Female; D006801:Humans; D051379:Mice; D056004:Molecular Dynamics Simulation; D010455:Peptides / Q000737:chemistry* / Q000627:therapeutic use; D017433:Protein Structure, Secondary; D011789:Quantum Theory
chemical_list: D000970:Antineoplastic Agents; D004967:Estrogens; D010455:Peptides