MOTIVATION: Traditional and high-throughput techniques for determining transcription factor (TF) binding specificities are generating large volumes of data of uneven quality, which are scattered across individual databases. RESULTS: FootprintDB integrates some of the most comprehensive freely available libraries of curated DNA binding sites and systematically annotates the binding interfaces of the corresponding TFs. The first release contains 2422 unique TF sequences, 10 112 DNA binding sites and 3662 DNA motifs. A survey of the included data sources, organisms and TF families was performed together with proprietary database TRANSFAC, finding that footprintDB has a similar coverage of multicellular organisms, while also containing bacterial regulatory data. A search engine has been designed that drives the prediction of DNA motifs for input TFs, or conversely of TF sequences that might recognize input regulatory sequences, by comparison with database entries. Such predictions can also be extended to a single proteome chosen by the user, and results are ranked in terms of interface similarity. Benchmark experiments with bacterial, plant and human data were performed to measure the predictive power of footprintDB searches, which were able to correctly recover 10, 55 and 90% of the tested sequences, respectively. Correctly predicted TFs had a higher interface similarity than the average, confirming its diagnostic value. AVAILABILITY AND IMPLEMENTATION: Web site implemented in PHP,Perl, MySQL and Apache. Freely available from http://floresta.eead.csic.es/footprintdb.

BACKGROUND: The specific recognition of genomic cis-regulatory elements by transcription factors (TFs) plays an essential role in the regulation of coordinated gene expression. Studying the mechanisms determining binding specificity in protein-DNA interactions is thus an important goal. Most current approaches for modeling TF specific recognition rely on the knowledge of large sets of cognate target sites and consider only the information contained in their primary sequence. RESULTS: Here we describe a structure-based methodology for predicting sequence motifs starting from the coordinates of a TF-DNA complex. Our algorithm combines information regarding the direct and indirect readout of DNA into an atomistic statistical model, which is used to estimate the interaction potential. We first measure the ability of our method to correctly estimate the binding specificities of eight prokaryotic and eukaryotic TFs that belong to different structural superfamilies. Secondly, the method is applied to two homology models, finding that sampling of interface side-chain rotamers remarkably improves the results. Thirdly, the algorithm is compared with a reference structural method based on contact counts, obtaining comparable predictions for the experimental complexes and more accurate sequence motifs for the homology models. CONCLUSION: Our results demonstrate that atomic-detail structural information can be feasibly used to predict TF binding sites. The computational method presented here is universal and might be applied to other systems involving protein-DNA recognition.

UNLABELLED: Interactions between proteins and DNA molecules lie at the core of the fundamental cellular processes such as transcriptional regulation. Some of these interactions have been experimentally described at atomic scale, but the molecular details of many others remain to be discovered. TFmodeller exploits the current knowledge about protein-DNA interfaces contained in the Protein Data Bank and uses it to model similar interfaces related by homology. Results are emailed to the user and include an evolutionary contact matrix, a schematic representation of the putative binding interface and atomic coordinates of the modelled complex. The library of complexes used by TFmodeller is updated on a weekly basis and is available for download. AVAILABILITY: TFmodeller and its web service interface are free for academic users at http://www.ccg.unam.mx/tfmodeller.

Abstract Summary: To optimize the search for structural templates in protein comparative modelling, the query sequence is split into domains. The initial list of templates for each domain, extracted from PFAM plus PDB and SCOP, is then ranked according to sequence identity (%ID), coverage and resolution. If %ID is less than 30, secondary structure matching is used to filter out false templates. Availability: http://www.bmm.icnet.uk/~3djigsaw/dom_fish Contact: [email protected]@cancer.org.uk * To whom correspondence should be addressed.