RESUMO
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.
Assuntos
Sistema Enzimático do Citocromo P-450/metabolismo , Estradiol/metabolismo , Polimorfismo de Nucleotídeo Único/genética , Sequência de Aminoácidos , Sistema Enzimático do Citocromo P-450/química , Sistema Enzimático do Citocromo P-450/genética , Estradiol/química , Humanos , Isoenzimas , Modelos Moleculares , Dados de Sequência Molecular , Conformação Proteica , Alinhamento de Sequência , Relação Estrutura-AtividadeRESUMO
An algorithm for comparison of homologous protein structures and for study of conformational changes in proteins, has been developed. The method is based on identification of pieces of the two molecules that have similar shapes, as determined by the local conformation of the polypeptide chain. Pieces that superpose within a specified tolerance are assembled into domains based on similar transformations for superposition. The result is sets of pieces that represent conserved structural elements and conserved spatial relationships between structural elements within the proteins being compared. A similarity criterion based on maximum distance rather than on root mean square deviation reduces bias by outliers. The utility of the method is demonstrated by using examples from the protein kinase family.