The MYC genes encode nuclear sequence specific-binding DNA-binding proteins that are pleiotropic regulators of cellular function, and the c-MYC proto-oncogene is deregulated and/or mutated in most human cancers. Experimental studies of MYC binding to the genome are not fully consistent. While many c-MYC recognition sites can be identified in c-MYC responsive genes, other motif matches-even experimentally confirmed sites-are associated with genes showing no c-MYC response. We have developed a computational model that integrates multiple sources of evidence to predict which genes will bind and be regulated by MYC in vivo. First, a Bayesian network classifier is used to predict those c-MYC recognition sites that are most likely to exhibit high-occupancy binding in chromatin immunoprecipitation studies. This classifier incorporates genomic sequence, experimentally determined genomic chromatin acetylation islands, and predicted methylation status from a computational model estimating the likelihood of genomic DNA methylation. We find that the predictions from this classifier are also applicable to other transcription factors, such as cAMP-response element-binding protein, whose binding sites are sensitive to DNA methylation. Second, the MYC binding probability is combined with the gene expression profile data from nine independent microarray datasets in multiple tissues. Finally, we may consider gene function annotations in Gene Ontology to predict the c-MYC targets. We assess the performance of our prediction results by comparing them with the c-myc targets identified in the biomedical literature. In total, we predict 460 likely c-MYC target genes in the human genome, of which 67 have been reported to be both bound and regulated by MYC, 68 are bound by MYC, and another 80 are MYC-regulated. The approach thus successfully identifies many known c-MYC targets and suggests many novel sites. Our findings suggest that to identify c-MYC genomic targets, integration of different data sources helps to improve the accuracy.

MOTIVATION: Current methods for identifying sequence specific binding sites in DNA sequence using position specific weight matrices are limited in both sensitivity and specificity. Double strand DNA helix exhibits sequence dependent variations in conformation. Interactions between macromolecules result from complementarity of the two tertiary structures. We hypothesize that this conformational variation plays a role in transcription factor binding site recognition, and that the use of this structure information will improve the predictive power of transcription factor binding site models. RESULTS: Conformation models for the sequence dependence of DNA helix distortion have been developed. Using our conformational models, we defined a tertiary structure template for the met operon repressor MetJ binding site. Both naturally occurring sites and precursor binding sites identified through in vitro selection were used as the basis for template definition. The conformational model appears to recognize features of protein binding sites that are distinct from the features recognized by primary sequence based profiles. Combining the conformational model and primary sequence profile yields a hybrid model with improved discriminatory power compared with either the conformational model or sequence profile alone. Using our hybrid model, we searched the E.coli genome. We are able to identify the documented MetJ sites in the promoter regions of metA, metB, metC, metR and metF. In addition, we find several novel loci with characteristics suggesting that they are functional MetJ repressor binding sites. Novel MetJ binding sites are found upstream of the metK gene, as well as upstream of a gene, abc, a gene that encodes for a component of a multifunction transporter which may transport amino acids across the membrane. The false positive rate is significantly lower than the sequence profile method. AVAILABILITY: The programs of implementation of this algorithm are available upon request. The list of crystal structures used for compiling the mean base step parameters of DNA is available by anonymous ftp at http://stateslab.wustl.edu/pub/helix/StructureList.

Double strand DNA exhibits sequence dependent variations in three-dimensional conformation.Crystallographic studies have shown that regulatory proteins bind to DNA and may use these structural effects to indirectly recognize binding sites in a sequence specific manner.In this paper we describe a sequence similarity scoring measure based on the energetics of helix distortion.A set of preferred conformations is compiled from data in the Nucleic Acid Database ( NDB ) for 16 dinucleotide steps.These conformational parameters are combined with elastic force constants derived from solution studies of DNA to compute a log likelihood score for one sequence adopting the preferred conformation of another.