Specific Transcription Factor Binding Research Articles

The Effects of Sequence Variation on Genome-wide NRF2 Binding--New Target Genes and Regulatory SNPs.

Transcription factor binding specificity is crucial for proper target gene regulation. Motif discovery algorithms identify the main features of the binding patterns, but the accuracy on the lower affinity sites is often poor. Nuclear factor E2-related factor 2 (NRF2) is a ubiquitous redox-activated transcription factor having a key protective role against endogenous and exogenous oxidant and electrophile stress. Herein, we decipher the effects of sequence variation on the DNA binding sequence of NRF2, in order to identify both genome-wide binding sites for NRF2 and disease-associated regulatory SNPs (rSNPs) with drastic effects on NRF2 binding. Interactions between NRF2 and DNA were studied using molecular modelling, and NRF2 chromatin immunoprecipitation-sequence datasets together with protein binding microarray measurements were utilized to study binding sequence variation in detail. The binding model thus generated was used to identify genome-wide binding sites for NRF2, and genomic binding sites with rSNPs that have strong effects on NRF2 binding and reside on active regulatory elements in human cells. As a proof of concept, miR-126–3p and -5p were identified as NRF2 target microRNAs, and a rSNP (rs113067944) residing on NRF2 target gene (Ferritin, light polypeptide, FTL) promoter was experimentally verified to decrease NRF2 binding and result in decreased transcriptional activity.

Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features.

BackgroundUnderstanding the mechanisms by which transcription factors (TF) are recruited to their physiological target sites is crucial for understanding gene regulation. DNA sequence intrinsic features such as predicted binding affinity are often not very effective in predicting in vivo site occupancy and in any case could not explain cell-type specific binding events. Recent reports show that chromatin accessibility, nucleosome occupancy and specific histone post-translational modifications greatly influence TF site occupancy in vivo. In this work, we use machine-learning methods to build predictive models and assess the relative importance of different sequence-intrinsic and chromatin features in the TF-to-target-site recruitment process.MethodsOur study primarily relies on recent data published by the ENCODE consortium. Five dissimilar TFs assayed in multiple cell-types were selected as examples: CTCF, JunD, REST, GABP and USF2. We used two types of candidate target sites: (a) predicted sites obtained by scanning the whole genome with a position weight matrix, and (b) cell-type specific peak lists provided by ENCODE. Quantitative in vivo occupancy levels in different cell-types were based on ChIP-seq data for the corresponding TFs. In parallel, we computed a number of associated sequence-intrinsic and experimental features (histone modification, DNase I hypersensitivity, etc.) for each site. Machine learning algorithms were then used in a binary classification and regression framework to predict site occupancy and binding strength, for the purpose of assessing the relative importance of different contextual features.ResultsWe observed striking differences in the feature importance rankings between the five factors tested. PWM-scores were amongst the most important features only for CTCF and REST but of little value for JunD and USF2. Chromatin accessibility and active histone marks are potent predictors for all factors except REST. Structural DNA parameters, repressive and gene body associated histone marks are generally of little or no predictive value.ConclusionsWe define a general and extensible computational framework for analyzing the importance of various DNA-intrinsic and chromatin-associated features in determining cell-type specific TF binding to target sites. The application of our methodology to ENCODE data has led to new insights on transcription regulatory processes and may serve as example for future studies encompassing even larger datasets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0846-z) contains supplementary material, which is available to authorized users.

Transcription factor motif quality assessment requires systematic comparative analysis

Transcription factor (TF) binding site prediction remains a challenge in gene regulatory research due to degeneracy and potential variability in binding sites in the genome. Dozens of algorithms designed to learn binding models (motifs) have generated many motifs available in research papers with a subset making it to databases like JASPAR, UniPROBE and Transfac. The presence of many versions of motifs from the various databases for a single TF and the lack of a standardized assessment technique makes it difficult for biologists to make an appropriate choice of binding model and for algorithm developers to benchmark, test and improve on their models. In this study, we review and evaluate the approaches in use, highlight differences and demonstrate the difficulty of defining a standardized motif assessment approach. We review scoring functions, motif length, test data and the type of performance metrics used in prior studies as some of the factors that influence the outcome of a motif assessment. We show that the scoring functions and statistics used in motif assessment influence ranking of motifs in a TF-specific manner. We also show that TF binding specificity can vary by source of genomic binding data. Finally, we demonstrate that information content of a motif is not in isolation a measure of motif quality but is influenced by TF binding behaviour. We conclude that there is a need for an easy-to-use tool that presents all available evidence for a comparative analysis.

MixChIP: a probabilistic method for cell type specific protein-DNA binding analysis.

BackgroundTranscription factors (TFs) are proteins that bind to DNA and regulate gene expression. To understand details of gene regulation, characterizing TF binding sites in different cell types, diseases and among individuals is essential. However, sometimes TF binding can only be measured from biological samples that contain multiple cell or tissue types. Sample heterogeneity can have a considerable effect on TF binding site detection. While manual separation techniques can be used to isolate a cell type of interest from heterogeneous samples, such techniques are challenging and can change intra-cellular interactions, including protein-DNA binding. Computational deconvolution methods have emerged as an alternative strategy to study heterogeneous samples and numerous methods have been proposed to analyze gene expression. However, no computational method exists to deconvolve cell type specific TF binding from heterogeneous samples.ResultsWe present a probabilistic method, MixChIP, to identify cell type specific TF binding sites from heterogeneous chromatin immunoprecipitation sequencing (ChIP-seq) data. Our method simultaneously estimates the binding strength in different cell types as well as the proportions of different cell types in each sample when only partial prior information about cell type composition is available. We demonstrate the utility of MixChIP by analyzing ChIP-seq data from two cell lines which we artificially mix to generate (simulated) heterogeneous samples and by analyzing ChIP-seq data from breast cancer patients measuring oestrogen receptor (ER) binding in primary breast cancer tissues. We show that MixChIP is more accurate in detecting TF binding sites from multiple heterogeneous ChIP-seq samples than the standard methods which do not account for sample heterogeneity.ConclusionsOur results show that MixChIP can estimate cell-type proportions and identify cell type specific TF binding sites from heterogeneous ChIP-seq samples. Thus, MixChIP can be an invaluable tool in analyzing heterogeneous ChIP-seq samples, such as those originating from cancer studies. R implementation is available at http://research.ics.aalto.fi/csb/software/mixchip/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0834-3) contains supplementary material, which is available to authorized users.

Mining for novel candidate clock genes in the circadian regulatory network.

BackgroundMost physiological processes in mammals are temporally regulated by means of a master circadian clock in the brain and peripheral oscillators in most other tissues. A transcriptional-translation feedback network of clock genes produces near 24 h oscillations in clock gene and protein expression. Here, we aim to identify novel additions to the clock network using a meta-analysis of public chromatin immunoprecipitation sequencing (ChIP-seq), proteomics and protein-protein interaction data starting from a published list of 1000 genes with robust transcriptional rhythms and circadian phenotypes of knockdowns.ResultsWe identified 20 candidate genes including nine known clock genes that received significantly high scores and were also robust to the relative weights assigned to different data types. Our scoring was consistent with the original ranking of the 1000 genes, but also provided novel complementary insights. Candidate genes were enriched for genes expressed in a circadian manner in multiple tissues with regulation driven mainly by transcription factors BMAL1 and REV-ERB α,β. Moreover, peak transcription of candidate genes was remarkably consistent across tissues. While peaks of the 1000 genes were distributed uniformly throughout the day, candidate gene peaks were strongly concentrated around dusk. Finally, we showed that binding of specific transcription factors to a gene promoter was predictive of peak transcription at a certain time of day and discuss combinatorial phase regulation.ConclusionsCombining complementary publicly-available data targeting different levels of regulation within the circadian network, we filtered the original list and found 11 novel robust candidate clock genes. Using the criteria of circadian proteomic expression, circadian expression in multiple tissues and independent gene knockdown data, we propose six genes (Por, Mtss1, Dgat2, Pim3, Ppp1r3b, Upp2) involved in metabolism and cancer for further experimental investigation. The availability of public high-throughput databases makes such meta-analysis a promising approach to test consistency between sources and tap their entire potential.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0227-2) contains supplementary material, which is available to authorized users.

Whole-genome bisulfite sequencing maps from multiple human tissues reveal novel CpG islands associated with tissue-specific regulation.

CpG islands (CGIs) are one of the most widely studied regulatory features of the human genome, with critical roles in development and disease. Despite such significance and the original epigenetic definition, currently used CGI sets are typically predicted from DNA sequence characteristics. Although CGIs are deeply implicated in practical analyses of DNA methylation, recent studies have shown that such computational annotations suffer from inaccuracies. Here we used whole-genome bisulfite sequencing from 10 diverse human tissues to identify a comprehensive, experimentally obtained, single-base resolution CGI catalog. In addition to the unparalleled annotation precision, our method is free from potential bias due to arbitrary sequence features or probe affinity differences. In addition to clarifying substantial false positives in the widely used University of California Santa Cruz (UCSC) annotations, our study identifies numerous novel epigenetic loci. In particular, we reveal significant impact of transposable elements on the epigenetic regulatory landscape of the human genome and demonstrate ubiquitous presence of transcription initiation at CGIs, including alternative promoters in gene bodies and non-coding RNAs in intergenic regions. Moreover, coordinated DNA methylation and chromatin modifications mark tissue-specific enhancers at novel CGIs. Enrichment of specific transcription factor binding from ChIP-seq supports mechanistic roles of CGIs on the regulation of tissue-specific transcription. The new CGI catalog provides a comprehensive and integrated list of genomic hotspots of epigenetic regulation.

Evolutionary conserved gene co-expression drives generation of self-antigen diversity in medullary thymic epithelial cells

Promiscuous expression of a plethora of tissue-restricted antigens (TRAs) in medullary thymic epithelial cells (mTECs) is essential for central tolerance. This promiscuous gene expression (pGE) is characterized by inclusion of a broad range of TRAs and by its mosaic expression patterns, i.e. each antigen is only expressed in 1–3% of mTECs. It is currently unclear to which extent random and/or deterministic mechanisms are involved in the regulation of pGE. In order to address this issue, we deconstructed the transcriptional heterogeneity in mTEC to minor subsets expressing a particular TRA. We identified six delineable co-expression groups in mouse mTECs. These co-expression groups displayed a variable degree of mutual overlap and mapped to different stages of mTEC development. Co-expressed genes showed chromosomal preference and clustered within delimited genomic regions. Moreover, co-expression groups in mice and humans selected by a pair of orthologous genes preferentially co-expressed sets of orthologous genes attesting to the species conservation of pGE between mouse and human. Furthermore, co-expressed genes were enriched for specific transcription factor binding motifs concomitant with up-regulation of the corresponding transcription factors, implicating additional factors in the regulation of pGE besides the Autoimmune Regulator (Aire). Thus promiscuous transcription of self-antigens in mTECs entails a highly coordinated process, which is evolutionary strictly conserved between species.

ID: 139: Reactivation of latent HIV-1 by iron-mediated redox stress

HIV pathogenesis includes dysregulated immune responses and cytokines production, resulting in an increased redox stress. Cytokines, namely TNF-alpha, and redox-stress promote replication of HIV-1. We have previously observed that heme arginate, a drug approved for human use in treatment of hepatic porphyria, can reactivate the latent provirus in synergism with PKC inducers like TNF-alpha, phorbol myristate acetate (PMA), prostratin or bryostatin-1, while inhibiting HIV-1 replication during the acute infection. Heme is physiologically decomposed by action of heme oxygenases into 3 degradation products: iron (Fe2+), carbon monoxide (CO) and biliverdin that is further converted to bilirubin by biliverdin reductase. Therefore, we have studied the effects of heme degradation products on latent HIV-1 reactivation when added individually to ACH-2 cells harboring integrated HIV-1 provirus or to a clone of Jurkat cells harboring HIV-minivirus expressing EGFP. Addition of iron increased expression of both HIV-1 p24 Ag and EGFP, while addition of a CO-donor or bilirubin decreased the PMA-stimulated p24 and EGFP expression. The reactivation was inhibited by antioxidants N-acetyl cysteine and vitamin E or by an iron chelator desferrioxamine, decreasing iron-induced redox stress. Desferrioxamine also inhibited the effect of heme arginate on latent HIV-1 reactivation. Redox stress was shown to affect epigenetic mechanisms regulating gene expression as well as to activate redox-sensitive transcription factors. Therefore, we propose a model in which redox-modulating agents induce chromatin remodeling, affect binding of specific transcription factors to HIV-LTR and allow HIV-1 expression. These results may point towards a new direction in the latent HIV-1 reactivation and therapy.

Nonconsensus Protein Binding to Repetitive DNA Sequence Elements Significantly Affects Eukaryotic Genomes.

Recent genome-wide experiments in different eukaryotic genomes provide an unprecedented view of transcription factor (TF) binding locations and of nucleosome occupancy. These experiments revealed that a large fraction of TF binding events occur in regions where only a small number of specific TF binding sites (TFBSs) have been detected. Furthermore, in vitro protein-DNA binding measurements performed for hundreds of TFs indicate that TFs are bound with wide range of affinities to different DNA sequences that lack known consensus motifs. These observations have thus challenged the classical picture of specific protein-DNA binding and strongly suggest the existence of additional recognition mechanisms that affect protein-DNA binding preferences. We have previously demonstrated that repetitive DNA sequence elements characterized by certain symmetries statistically affect protein-DNA binding preferences. We call this binding mechanism nonconsensus protein-DNA binding in order to emphasize the point that specific consensus TFBSs do not contribute to this effect. In this paper, using the simple statistical mechanics model developed previously, we calculate the nonconsensus protein-DNA binding free energy for the entire C. elegans and D. melanogaster genomes. Using the available chromatin immunoprecipitation followed by sequencing (ChIP-seq) results on TF-DNA binding preferences for ~100 TFs, we show that DNA sequences characterized by low predicted free energy of nonconsensus binding have statistically higher experimental TF occupancy and lower nucleosome occupancy than sequences characterized by high free energy of nonconsensus binding. This is in agreement with our previous analysis performed for the yeast genome. We suggest therefore that nonconsensus protein-DNA binding assists the formation of nucleosome-free regions, as TFs outcompete nucleosomes at genomic locations with enhanced nonconsensus binding. In addition, here we perform a new, large-scale analysis using in vitro TF-DNA preferences obtained from the universal protein binding microarrays (PBM) for ~90 eukaryotic TFs belonging to 22 different DNA-binding domain types. As a result of this new analysis, we conclude that nonconsensus protein-DNA binding is a widespread phenomenon that significantly affects protein-DNA binding preferences and need not require the presence of consensus (specific) TFBSs in order to achieve genome-wide TF-DNA binding specificity.

14 Quantitative modeling of transcription factor binding specificities using DNA shape

DNA binding specificities of transcription factors (TFs) are a key component of gene regulatory processes. Underlying mechanisms that explain the highly specific binding of TFs to their genomic tar...

Survey of protein-DNA interactions in Aspergillus oryzae on a genomic scale.

The genome-scale delineation of in vivo protein–DNA interactions is key to understanding genome function. Only ∼5% of transcription factors (TFs) in the Aspergillus genus have been identified using traditional methods. Although the Aspergillus oryzae genome contains >600 TFs, knowledge of the in vivo genome-wide TF-binding sites (TFBSs) in aspergilli remains limited because of the lack of high-quality antibodies. We investigated the landscape of in vivo protein–DNA interactions across the A. oryzae genome through coupling the DNase I digestion of intact nuclei with massively parallel sequencing and the analysis of cleavage patterns in protein–DNA interactions at single-nucleotide resolution. The resulting map identified overrepresented de novo TF-binding motifs from genomic footprints, and provided the detailed chromatin remodeling patterns and the distribution of digital footprints near transcription start sites. The TFBSs of 19 known Aspergillus TFs were also identified based on DNase I digestion data surrounding potential binding sites in conjunction with TF binding specificity information. We observed that the cleavage patterns of TFBSs were dependent on the orientation of TF motifs and independent of strand orientation, consistent with the DNA shape features of binding motifs with flanking sequences.

Conservation of transcription factor binding specificities across 600 million years of bilateria evolution.

Divergent morphology of species has largely been ascribed to genetic differences in the tissue-specific expression of proteins, which could be achieved by divergence in cis-regulatory elements or by altering the binding specificity of transcription factors (TFs). The relative importance of the latter has been difficult to assess, as previous systematic analyses of TF binding specificity have been performed using different methods in different species. To address this, we determined the binding specificities of 242 Drosophila TFs, and compared them to human and mouse data. This analysis revealed that TF binding specificities are highly conserved between Drosophila and mammals, and that for orthologous TFs, the similarity extends even to the level of very subtle dinucleotide binding preferences. The few human TFs with divergent specificities function in cell types not found in fruit flies, suggesting that evolution of TF specificities contributes to emergence of novel types of differentiated cells.

Quantitative modeling of transcription factor binding specificities using DNA shape

DNA binding specificities of transcription factors (TFs) are a key component of gene regulatory processes. Underlying mechanisms that explain the highly specific binding of TFs to their genomic target sites are poorly understood. A better understanding of TF-DNA binding requires the ability to quantitatively model TF binding to accessible DNA as its basic step, before additional in vivo components can be considered. Traditionally, these models were built based on nucleotide sequence. Here, we integrated 3D DNA shape information derived with a high-throughput approach into the modeling of TF binding specificities. Using support vector regression, we trained quantitative models of TF binding specificity based on protein binding microarray (PBM) data for 68 mammalian TFs. The evaluation of our models included cross-validation on specific PBM array designs, testing across different PBM array designs, and using PBM-trained models to predict relative binding affinities derived from in vitro selection combined with deep sequencing (SELEX-seq). Our results showed that shape-augmented models compared favorably to sequence-based models. Although both k-mer and DNA shape features can encode interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space. In addition, analyzing the feature weights of DNA shape-augmented models uncovered TF family-specific structural readout mechanisms that were not revealed by the DNA sequence. As such, this work combines knowledge from structural biology and genomics, and suggests a new path toward understanding TF binding and genome function.

The pluripotent regulatory circuitry connecting promoters to their long-range interacting elements

The mammalian genome harbors up to one million regulatory elements often located at great distances from their target genes. Long-range elements control genes through physical contact with promoters and can be recognized by the presence of specific histone modifications and transcription factor binding. Linking regulatory elements to specific promoters genome-wide is currently impeded by the limited resolution of high-throughput chromatin interaction assays. Here we apply a sequence capture approach to enrich Hi-C libraries for >22,000 annotated mouse promoters to identify statistically significant, long-range interactions at restriction fragment resolution, assigning long-range interacting elements to their target genes genome-wide in embryonic stem cells and fetal liver cells. The distal sites contacting active genes are enriched in active histone modifications and transcription factor occupancy, whereas inactive genes contact distal sites with repressive histone marks, demonstrating the regulatory potential of the distal elements identified. Furthermore, we find that coregulated genes cluster nonrandomly in spatial interaction networks correlated with their biological function and expression level. Interestingly, we find the strongest gene clustering in ES cells between transcription factor genes that control key developmental processes in embryogenesis. The results provide the first genome-wide catalog linking gene promoters to their long-range interacting elements and highlight the complex spatial regulatory circuitry controlling mammalian gene expression.

ChIP-nexus enables improved detection of in vivo transcription factor binding footprints.

Understanding how eukaryotic enhancers are bound and regulated by specific combinations of transcription factors is still a major challenge. To better map transcription factor binding genome-wide at nucleotide resolution in vivo, we have developed a robust ChIP-exo protocol called ChIP experiments with nucleotide resolution through exonuclease, unique barcode and single ligation (ChIP-nexus), which utilizes an efficient DNA self-circularization step during library preparation. Application of ChIP-nexus to four proteins—human TBP and Drosophila NFkB, Twist and Max— demonstrates that it outperforms existing ChIP protocols in resolution and specificity, pinpoints relevant binding sites within enhancers containing multiple binding motifs and allows the analysis of in vivo binding specificities. Notably, we show that Max frequently interacts with DNA sequences next to its motif, and that this binding pattern correlates with local DNA sequence features such as DNA shape. ChIP-nexus will be broadly applicable to studying in vivo transcription factor binding specificity and its relationship to cis-regulatory changes in humans and model organisms.

An Arabidopsis Transcriptional Regulatory Map Reveals Distinct Functional and Evolutionary Features of Novel Transcription Factors.

Transcription factors (TFs) play key roles in both development and stress responses. By integrating into and rewiring original systems, novel TFs contribute significantly to the evolution of transcriptional regulatory networks. Here, we report a high-confidence transcriptional regulatory map covering 388 TFs from 47 families in Arabidopsis. Systematic analysis of this map revealed the architectural heterogeneity of developmental and stress response subnetworks and identified three types of novel network motifs that are absent from unicellular organisms and essential for multicellular development. Moreover, TFs of novel families that emerged during plant landing present higher binding specificities and are preferentially wired into developmental processes and these novel network motifs. Further unveiled connection between the binding specificity and wiring preference of TFs explains the wiring preferences of novel-family TFs. These results reveal distinct functional and evolutionary features of novel TFs, suggesting a plausible mechanism for their contribution to the evolution of multicellular organisms.

Replicative senescence is associated with nuclear reorganization and with DNA methylation at specific transcription factor binding sites.

BackgroundPrimary cells enter replicative senescence after a limited number of cell divisions. This process needs to be considered in cell culture experiments, and it is particularly important for regenerative medicine. Replicative senescence is associated with reproducible changes in DNA methylation (DNAm) at specific sites in the genome. The mechanism that drives senescence-associated DNAm changes remains unknown - it may involve stochastic DNAm drift due to imperfect maintenance of epigenetic marks or it is directly regulated at specific sites in the genome.ResultsIn this study, we analyzed the reorganization of nuclear architecture and DNAm changes during long-term culture of human fibroblasts and mesenchymal stromal cells (MSCs). We demonstrate that telomeres shorten and shift towards the nuclear center at later passages. In addition, DNAm profiles, either analyzed by MethylCap-seq or by 450k IlluminaBeadChip technology, revealed consistent senescence-associated hypermethylation in regions associated with H3K27me3, H3K4me3, and H3K4me1 histone marks, whereas hypomethylation was associated with chromatin containing H3K9me3 and lamina-associated domains (LADs). DNA hypermethylation was significantly enriched in the vicinity of genes that are either up- or downregulated at later passages. Furthermore, specific transcription factor binding motifs (e.g. EGR1, TFAP2A, and ETS1) were significantly enriched in differentially methylated regions and in the promoters of differentially expressed genes.ConclusionsSenescence-associated DNA hypermethylation occurs at specific sites in the genome and reflects functional changes in the course of replicative senescence. These results indicate that tightly regulated epigenetic modifications during long-term culture contribute to changes in nuclear organization and gene expression.Electronic supplementary materialThe online version of this article (doi:10.1186/s13148-015-0057-5) contains supplementary material, which is available to authorized users.

Chromatin immunoprecipitation assays: analyzing transcription factor binding and histone modifications in vivo.

Studies in the past decade have shown that differential gene expression depends not only on the binding of specific transcription factors to discrete promoter elements but also on the epigenetic modification of the DNA as well as histones associated with the promoter. While techniques like electrophoretic mobility shift assays could detect and characterize the binding of specific transcription factors present in cell lysates to DNA sequences in in vitro binding conditions, they were not effective in assessing the binding in intact cells. Development of chromatin immunoprecipitation technique in the past decade enabled the analysis of the association of regulatory molecules with specific promoters or changes in histone modifications in vivo, without overexpressing any component. ChIP assays can provide a snapshot of how a regulatory transcription factor affects the expression of a single gene, or a variety of genes at the same time. Availability of high quality antibodies that recognizes histones modified in a specific fashion further expanded the use of ChIP assays to analyze even minute changes in histone modification and nucleosomes structure. This chapter outlines the general strategies and protocols used to carry out ChIP assays to study the differential recruitment of transcription factors as well as histone modifications.

Protein-DNA Binding in the Absence of Consensus Binding Motif

Until now, it has been reasonably assumed that specific base-pair recognition is the only mechanism controlling the specificity of transcription factor (TF)-DNA binding. In our study we show that nonspecific DNA sequences possessing certain repeat symmetries, when present outside of specific TF binding sites (TFBSs), statistically control TF-DNA binding preferences. We used high-throughput protein-DNA binding assays to measure the binding levels for several human TFs to tens of thousands of short DNA sequences with varying repeat symmetries. Based on statistical mechanics modeling, we identify a new protein-DNA binding mechanism induced by DNA sequence symmetry in the absence of consensus binding motif, and experimentally demonstrate that this mechanism indeed highly affects protein-DNA binding preferences.

Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes.

Proper cell functioning depends on the precise spatio-temporal expression of its genetic material. Gene expression is controlled to a great extent by sequence-specific transcription factors (TFs). Our current knowledge on where and how TFs bind and associate to regulate gene expression is incomplete. A structure-based computational algorithm (TF2DNA) is developed to identify binding specificities of TFs. The method constructs homology models of TFs bound to DNA and assesses the relative binding affinity for all possible DNA sequences using a knowledge-based potential, after optimization in a molecular mechanics force field. TF2DNA predictions were benchmarked against experimentally determined binding motifs. Success rates range from 45% to 81% and primarily depend on the sequence identity of aligned target sequences and template structures, TF2DNA was used to predict 1321 motifs for 1825 putative human TF proteins, facilitating the reconstruction of most of the human gene regulatory network. As an illustration, the predicted DNA binding site for the poorly characterized T-cell leukemia homeobox 3 (TLX3) TF was confirmed with gel shift assay experiments. TLX3 motif searches in human promoter regions identified a group of genes enriched in functions relating to hematopoiesis, tissue morphology, endocrine system and connective tissue development and function.