SNP Analysis Combined with Protein Structure Prediction Defines Structure- Functional Relationships in Cancer Related Cytochrome P450 Estrogen Metabolism

Igor   F.   Tsigelny; Vladimir      Kotlovyi; Linda      Wasserman

Abstract

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.

Keywords: P450, breast cancer, proteins, postmenopausal