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Discovering gapped binding sites of yeast transcription factors.

ABSTRACT: A gapped transcription factor-binding site (TFBS) contains one or more highly degenerate positions. Discovering gapped motifs is difficult, because allowing highly degenerate positions in a motif greatly enlarges the search space and complicates the discovery process. Here, we propose a method for discovering TFBSs, especially gapped motifs. We use ChIP-chip data to judge the binding strength of a TF to a putative target promoter and use orthologous sequences from related species to judge the degree of evolutionary conservation of a predicted TFBS. Candidate motifs are constructed by growing compact motif blocks and by concatenating two candidate blocks, allowing 0-15 degenerate positions in between. The resultant patterns are statistically evaluated for their ability to distinguish between target and nontarget genes. Then, a position-based ranking procedure is proposed to enhance the signals of true motifs by collecting position concurrences. Empirical tests on 32 known yeast TFBSs show that the method is highly accurate in identifying gapped motifs, outperforming current methods, and it also works well on ungapped motifs. Predictions on additional 54 TFs successfully discover 11 gapped and 38 ungapped motifs supported by literature. Our method achieves high sensitivity and specificity for predicting experimentally verified TFBSs.

SUBMITTER: Chen CY

PROVIDER: S-EPMC2268170 | biostudies-literature | 2008 Feb

REPOSITORIES: biostudies-literature

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Discovering gapped binding sites of yeast transcription factors.

Chen Chien-Yu CY Tsai Huai-Kuang HK Hsu Chen-Ming CM May Chen Mei-Ju MJ Hung Hao-Geng HG Huang Grace Tzu-Wei GT Li Wen-Hsiung WH

Proceedings of the National Academy of Sciences of the United States of America 20080213 7

A gapped transcription factor-binding site (TFBS) contains one or more highly degenerate positions. Discovering gapped motifs is difficult, because allowing highly degenerate positions in a motif greatly enlarges the search space and complicates the discovery process. Here, we propose a method for discovering TFBSs, especially gapped motifs. We use ChIP-chip data to judge the binding strength of a TF to a putative target promoter and use orthologous sequences from related species to judge the de ...[more]

PMID: 18272477

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Project description:Discovering amino acid (AA) patterns on protein binding sites has recently become popular. We propose a method to discover the association relationship among AAs on binding sites. Such knowledge of binding sites is very helpful in predicting protein-protein interactions. In this paper, we focus on protein complexes which have protein-protein recognition. The association rule mining technique is used to discover geographically adjacent amino acids on a binding site of a protein complex. When mining, instead of treating all AAs of binding sites as a transaction, we geographically partition AAs of binding sites in a protein complex. AAs in a partition are treated as a transaction. For the partition process, AAs on a binding site are projected from three-dimensional to two-dimensional. And then, assisted with a circular grid, AAs on the binding site are placed into grid cells. A circular grid has ten rings: a central ring, the second ring with 6 sectors, the third ring with 12 sectors, and later rings are added to four sectors in order. As for the radius of each ring, we examined the complexes and found that 10Å is a suitable range, which can be set by the user. After placing these recognition complexes on the circular grid, we obtain mining records (i.e. transactions) from each sector. A sector is regarded as a record. Finally, we use the association rule to mine these records for frequent AA patterns. If the support of an AA pattern is larger than the predetermined minimum support (i.e. threshold), it is called a frequent pattern. With these discovered patterns, we offer the biologists a novel point of view, which will improve the prediction accuracy of protein-protein recognition. In our experiments, we produced the AA patterns by data mining. As a result, we found that arginine (arg) most frequently appears on the binding sites of two proteins in the recognition protein complexes, while cysteine (cys) appears the fewest. In addition, if we discriminate the shape of binding sites between concave and convex further, we discover that patterns {arg, glu, asp} and {arg, ser, asp} on the concave shape of binding sites in a protein more frequently (i.e. higher probability) make contact with {lys} or {arg} on the convex shape of binding sites in another protein. Thus, we can confidently achieve a rate of at least 78%. On the other hand {val, gly, lys} on the convex surface of binding sites in proteins is more frequently in contact with {asp} on the concave site of another protein, and the confidence achieved is over 81%. Applying data mining in biology can reveal more facts that may otherwise be ignored or not easily discovered by the naked eye. Furthermore, we can discover more relationships among AAs on binding sites by appropriately rotating these residues on binding sites from a three-dimension to two-dimension perspective. We designed a circular grid to deposit the data, which total to 463 records consisting of AAs. Then we used the association rules to mine these records for discovering relationships. The proposed method in this paper provides an insight into the characteristics of binding sites for recognition complexes.

Dataset Information

Discovering gapped binding sites of yeast transcription factors.

Publications

Discovering gapped binding sites of yeast transcription factors.

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