title: Chemical principles additive model aligns low consensus DNA targets of p53 tumor suppressor protein.
abstract: Computational prediction of the interaction between protein transcription factors and their cognate DNA binding sites in genomic promoters constitutes a formidable challenge in two situations: when the number of discriminatory interactions is small compared to the length of the binding site, and when DNA binding sites possess a poorly conserved consensus binding motif. The transcription factor p53 tumor suppressor protein and its target DNA exhibit both of these issues. From crystal structure analysis, only three discriminatory amino acid side chains contact the DNA for a binding site spanning ten base pairs. Furthermore, our analysis of a dataset of genome wide fragments binding to p53 revealed many sequences lacking the expected consensus. The low information content leads to an overestimation of binding sites, and the lack of conservation equates to a computational alignment problem. Within a fragment of DNA known to bind to p53, computationally locating the position of the site equates to aligning the DNA with the binding interface. From a molecular perspective, that alignment implies a specification of which DNA bases are interacting with which amino acid side chains, and aligning many sequences to the same protein interface concomitantly produces a multiple sequence alignment. From this vantage, we propose to cast prediction of p53 binding sites as an alignment to the protein binding surface with the novel approach of optimizing the alignment of DNA fragments to the p53 binding interface based on chemical principles. A scoring scheme based on this premise was successfully implemented to score a dataset of biological DNA fragments known to contain p53 binding sites. The results illuminate the mechanism of recognition for the protein-DNA system at the forefront of cancer research. These findings implicate that p53 may recognize its target binding sites via several different mechanisms which may include indirect readout.
authors.affiliation: Department of Chemistry, Vassar College, 124 Raymond Avenue, Poughkeepsie, NY 12604, United States of America; Department of Chemistry, Wesleyan University, Hall-Atwater Laboratories, Middletown, CT 06459, United States of America. Electronic address: kthayer@wesleyan.edu.
authors.forename: Kelly M
authors.initials: KM
authors.lastname: Thayer
authors.affiliation: Department of Chemistry, Vassar College, 124 Raymond Avenue, Poughkeepsie, NY 12604, United States of America.
authors.forename: In Sub M
authors.initials: ISM
authors.lastname: Han
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D019295:Computational Biology; D004273:DNA, Neoplasm / Q000737:chemistry*; D006801:Humans; D006860:Hydrogen Bonding; D008958:Models, Molecular; D016159:Tumor Suppressor Protein p53 / Q000737:chemistry*
chemical_list: D004273:DNA, Neoplasm; D016159:Tumor Suppressor Protein p53
keywords: Additive energy; Bioinformatics of DNA; DNA binding site prediction; DNA sequence alignment; Low consensus; p53