| pubmed-article:15751119 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:15751119 | lifeskim:mentions | umls-concept:C0162340 | lld:lifeskim |
| pubmed-article:15751119 | lifeskim:mentions | umls-concept:C0524637 | lld:lifeskim |
| pubmed-article:15751119 | lifeskim:mentions | umls-concept:C0376528 | lld:lifeskim |
| pubmed-article:15751119 | lifeskim:mentions | umls-concept:C1158478 | lld:lifeskim |
| pubmed-article:15751119 | lifeskim:mentions | umls-concept:C1140694 | lld:lifeskim |
| pubmed-article:15751119 | lifeskim:mentions | umls-concept:C1705422 | lld:lifeskim |
| pubmed-article:15751119 | lifeskim:mentions | umls-concept:C0441712 | lld:lifeskim |
| pubmed-article:15751119 | pubmed:issue | 1 | lld:pubmed |
| pubmed-article:15751119 | pubmed:dateCreated | 2005-3-7 | lld:pubmed |
| pubmed-article:15751119 | pubmed:abstractText | Transcription factors play essential role in the gene regulation in higher organisms, binding to multiple target sequences and regulating multiple genes in a complex manner. In order to decipher the mechanism of gene regulation, it is important to understand the molecular mechanism of protein-DNA recognition. Here we describe a strategy to approach this problem, using various methods in bioinformatics and computational biology. We have used a knowledge-based approach, utilizing rapidly increasing structural data of protein-DNA complexes, to derive empirical potential functions for the specific interactions between bases and amino acids as well as for DNA conformation, from the statistical analyses on the structural data. Then these statistical potentials are used to quantify the specificity of protein-DNA recognition. The quantification of specificity has enabled us to establish the structure-function analysis of transcription factors, such as the effects of binding cooperativity on target recognition. The method is also applied to real genome sequences, predicting potential target sites. We are also using computer simulations of protein-DNA interactions and DNA conformation in order to complement the empirical method. The integration of these approaches together will provide deeper insight into the mechanism of protein-DNA recognition and improve the target prediction of transcription factors. | lld:pubmed |
| pubmed-article:15751119 | pubmed:language | eng | lld:pubmed |
| pubmed-article:15751119 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:15751119 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:15751119 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:15751119 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:15751119 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:15751119 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:15751119 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:15751119 | pubmed:month | Feb | lld:pubmed |
| pubmed-article:15751119 | pubmed:issn | 0219-7200 | lld:pubmed |
| pubmed-article:15751119 | pubmed:author | pubmed-author:GromihaM... | lld:pubmed |
| pubmed-article:15751119 | pubmed:author | pubmed-author:SelvarajSamue... | lld:pubmed |
| pubmed-article:15751119 | pubmed:author | pubmed-author:SaraiAkinoriA | lld:pubmed |
| pubmed-article:15751119 | pubmed:author | pubmed-author:KonoHidetoshi... | lld:pubmed |
| pubmed-article:15751119 | pubmed:author | pubmed-author:SiebersJorgJ | lld:pubmed |
| pubmed-article:15751119 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:15751119 | pubmed:volume | 3 | lld:pubmed |
| pubmed-article:15751119 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:15751119 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:15751119 | pubmed:pagination | 169-83 | lld:pubmed |
| pubmed-article:15751119 | pubmed:dateRevised | 2006-11-15 | lld:pubmed |
| pubmed-article:15751119 | pubmed:meshHeading | pubmed-meshheading:15751119... | lld:pubmed |
| pubmed-article:15751119 | pubmed:meshHeading | pubmed-meshheading:15751119... | lld:pubmed |
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| pubmed-article:15751119 | pubmed:meshHeading | pubmed-meshheading:15751119... | lld:pubmed |
| pubmed-article:15751119 | pubmed:meshHeading | pubmed-meshheading:15751119... | lld:pubmed |
| pubmed-article:15751119 | pubmed:meshHeading | pubmed-meshheading:15751119... | lld:pubmed |
| pubmed-article:15751119 | pubmed:meshHeading | pubmed-meshheading:15751119... | lld:pubmed |
| pubmed-article:15751119 | pubmed:meshHeading | pubmed-meshheading:15751119... | lld:pubmed |
| pubmed-article:15751119 | pubmed:year | 2005 | lld:pubmed |
| pubmed-article:15751119 | pubmed:articleTitle | Integration of bioinformatics and computational biology to understand protein-DNA recognition mechanism. | lld:pubmed |
| pubmed-article:15751119 | pubmed:affiliation | Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Japan. sarai@bse.kyutech.ac.jp | lld:pubmed |
| pubmed-article:15751119 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:15751119 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
| pubmed-article:15751119 | pubmed:publicationType | Evaluation Studies | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:15751119 | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:15751119 | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:15751119 | lld:pubmed |
