title: New computational strategy to analyze the interactions of ERalpha and ERbeta with different ERE sequences.
abstract: The importance of computational methods for the simulation and analysis of biological systems has increased during the last years. In particular, methods to predict binding energies are developing not only with the aim of ranking the affinities between two or more complexes, but also to quantify the contribution of different types of interaction. In this work, we present the application of HINT, a non Newtonian force field, to rank the affinities of complexes formed by estrogen receptors (ER) alpha and beta and different estrogen responsive elements (ERE) near the estrogen-regulated genes. We used the crystallographic coordinates of the DNA binding domain of ERalpha complexed to a consensus ERE as a starting point to simulate several complexes in which some nucleotides in the ERE sequence were mutated. Moreover, we used homology modeling methods to create the structure of the complexes between the DNA binding domain of ERbeta (for which no experimental structures are currently available) and the same ERE sequences. Our results show that HINT is able to rank the affinities of ERalpha and ERbeta for different ERE sequences, and to correctly identify the positions on the DNA sequence that are most important for binding affinity. Moreover, the HINT output gives us the opportunity to identify and quantify the role played by each single atom of amino acids and nucleotides in the binding event, as well as to predict the effect on the binding affinity for other nucleotide mutations.
authors.affiliation: Laboratory of Bioinformatics and Computational Biology, Institute of Food Science, National Research Council, Avellino, Italy. amarabotti@isa.cnr.it
authors.forename: Anna
authors.initials: A
authors.lastname: Marabotti
authors.forename: Giovanni
authors.initials: G
authors.lastname: Colonna
authors.forename: Angelo
authors.initials: A
authors.lastname: Facchiano
mesh_terms: D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D001483:Base Sequence; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000737:chemistry / Q000378:metabolism; D047628:Estrogen Receptor alpha / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D047629:Estrogen Receptor beta / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism; D006801:Humans; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D009711:Nucleotides / Q000737:chemistry; D011485:Protein Binding; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D020218:Response Elements; D017386:Sequence Homology, Amino Acid; D013816:Thermodynamics
chemical_list: D000596:Amino Acids; D047628:Estrogen Receptor alpha; D047629:Estrogen Receptor beta; D009711:Nucleotides; D004247:DNA