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Definition and prediction of the full range of transcription factor binding sites--the hepatocyte nuclear factor 1 dimeric site.

ABSTRACT: In animals, transcription factor binding sites are hard to recognize because of their extensive variation. We therefore characterized the general relationship between a specific protein-binding site and its DNA sequence and used this relationship to generate a predictive algorithm for searching other DNA sequences. The experimental process was defined by studying hepatocyte nuclear factor 1 (HNF1), which binds DNA as a dimer on two inverted-repeat 7-bp half sites separated by one base. The binding model was based on the equivalence of the two half sites, which was confirmed in examples where specific modified sites were compared. Binding competition analysis was used to determine the effects of substitution of all four bases at each position in the half site. From these data, a weighted half-site matrix was generated and the full site was evaluated as the sum of two half-site scores. This process accurately predicted even weak binding sites that were significantly different from the consensus sequence. The predictions also showed a direct correlation with measured protein binding.

SUBMITTER: Locker J

PROVIDER: S-EPMC137408 | biostudies-literature | 2002 Sep

REPOSITORIES: biostudies-literature

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Definition and prediction of the full range of transcription factor binding sites--the hepatocyte nuclear factor 1 dimeric site.

Locker Joseph J Ghosh David D Luc Phuong-Van PV Zheng Jianhua J

Nucleic acids research 20020901 17

In animals, transcription factor binding sites are hard to recognize because of their extensive variation. We therefore characterized the general relationship between a specific protein-binding site and its DNA sequence and used this relationship to generate a predictive algorithm for searching other DNA sequences. The experimental process was defined by studying hepatocyte nuclear factor 1 (HNF1), which binds DNA as a dimer on two inverted-repeat 7-bp half sites separated by one base. The bindi ...[more]

PMID: 12202766

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Project description:Computational prediction of nucleotide binding specificity for transcription factors remains a fundamental and largely unsolved problem. Determination of binding positions is a prerequisite for research in gene regulation, a major mechanism controlling phenotypic diversity. Furthermore, an accurate determination of binding specificities from high-throughput data sources is necessary to realize the full potential of systems biology. Unfortunately, recently performed independent evaluation showed that more than half the predictions from most widely used algorithms are false. We introduce a graph-theoretical framework to describe local sequence similarity as the pair-wise distances between nucleotides in promoter sequences, and hypothesize that densely connected subgraphs are indicative of transcription factor binding sites. Using a well-established sampling algorithm coupled with simple clustering and scoring schemes, we identify sets of closely related nucleotides and test those for known TF binding activity. Using an independent benchmark, we find our algorithm predicts yeast binding motifs considerably better than currently available techniques and without manual curation. Importantly, we reduce the number of false positive predictions in yeast to less than 30%. We also develop a framework to evaluate the statistical significance of our motif predictions. We show that our approach is robust to the choice of input promoters, and thus can be used in the context of predicting binding positions from noisy experimental data. We apply our method to identify binding sites using data from genome scale ChIP-chip experiments. Results from these experiments are publicly available at http://cagt10.bu.edu/BSG. The graphical framework developed here may be useful when combining predictions from numerous computational and experimental measures. Finally, we discuss how our algorithm can be used to improve the sensitivity of computational predictions of transcription factor binding specificities.

Dataset Information

Definition and prediction of the full range of transcription factor binding sites--the hepatocyte nuclear factor 1 dimeric site.

Publications

Definition and prediction of the full range of transcription factor binding sites--the hepatocyte nuclear factor 1 dimeric site.

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