SNP analysis combined with protein structure prediction defines structure-functional relationships in cancer related cytochrome P450 estrogen metabolism.

The P450 family of proteins has more than 1000 representatives, which despite sometimes relatively low sequence identity have a surprisingly high level of structural similarity. This fact makes this family of proteins ideal candidate for various types of modeling based on protein structure prediction. A number of P450 proteins, including CYPs 1A1, 1A2, 1B1, 3A4, 11B2, 17, and 19, play a role in the metabolism of estrogen. Inhibitors of these proteins could be very promising drugs for hormonal treatment of postmenopausal breast cancer. Population studies have yielded a significant amount of data describing the relationship between single nucleotide polymorphisms (SNP) in DNA and cancer risk related to these proteins. A combination of SNP analysis with protein structure prediction can be a very useful strategy in investigations of structure-functional relations of P450 proteins and structure-based drug design. Here we will demonstrate how protein structure prediction combined with genetic SNP analysis can be useful for potential drug design and possibly, individual treatment of breast cancer.