Identification Of DNA Binding Sites Research Articles

Analysis and the identification of DNA binding sites for the cI repressor of Bacteriophage ɛ34

Gene 46 of ɛ34 encodes a protein that is similar to other phage repressors. It has a C‐terminal protease domain which is about 80% identical to the repressors of phage λ, but its DNA binding N‐terminal portion is only about 18% identical to its closest known relatives in phages λ, and Lex A. The N‐terminal DNA‐binding domain is homologous to the HTH_XRE family of DNA‐binding domains. In another study we have generated homology models of this proteins and its interaction with DNA [1].The main goal of this project was to initiate a structure‐function study on the cI repressor protein of phage ɛ34. The cI repressor of ɛ34 protein with a 6X polyhistidine tag was first isolated and purified to homogeneity. We have identified regions for cI binding by using bioinformatics and gel‐mobility shift assay using PCR fragments encompassing the intergenic region of the cI and cro gene. Our binding sites compare well with the known intergenic regions of other phages such as λ, Φ11, 434. This project was done as a collaborative venture between two laboratories, Huntingdon College (Dr. Doba Jackson) and Alabama State University (laboratory of Dr. Robert Villafane). This project is in line with Dr. Robert Villafane's research into the Genomic analysis of bacteriophage ɛ34 of Salmonella enteric serovar Anatum.Support or Funding InformationSupport Support for this work was provided by Alabama State University and Huntingdon College.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

MotifMark: Finding regulatory motifs in DNA sequences.

The interaction between proteins and DNA is a key driving force in a significant number of biological processes such as transcriptional regulation, repair, recombination, splicing, and DNA modification. The identification of DNA-binding sites and the specificity of target proteins in binding to these regions are two important steps in understanding the mechanisms of these biological activities. A number of high-throughput technologies have recently emerged that try to quantify the affinity between proteins and DNA motifs. Despite their success, these technologies have their own limitations and fall short in precise characterization of motifs, and as a result, require further downstream analysis to extract useful and interpretable information from a haystack of noisy and inaccurate data. Here we propose MotifMark, a new algorithm based on graph theory and machine learning, that can find binding sites on candidate probes and rank their specificity in regard to the underlying transcription factor. We developed a pipeline to analyze experimental data derived from compact universal protein binding microarrays and benchmarked it against two of the most accurate motif search methods. Our results indicate that MotifMark can be a viable alternative technique for prediction of motif from protein binding microarrays and possibly other related high-throughput techniques.

A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence

The knowledge of protein-DNA interactions is essential to fully understand the molecular activities of life. Many research groups have developed various tools which are either structure- or sequence-based approaches to predict the DNA-binding residues in proteins. The structure-based methods usually achieve good results, but require the knowledge of the 3D structure of protein; while sequence-based methods can be applied to high-throughput of proteins, but require good features. In this study, we present a new information theoretic feature derived from Jensen–Shannon Divergence (JSD) between amino acid distribution of a site and the background distribution of non-binding sites. Our new feature indicates the difference of a certain site from a non-binding site, thus it is informative for detecting binding sites in proteins. We conduct the study with a five-fold cross validation of 263 proteins utilizing the Random Forest classifier. We evaluate the functionality of our new features by combining them with other popular existing features such as position-specific scoring matrix (PSSM), orthogonal binary vector (OBV), and secondary structure (SS). We notice that by adding our features, we can significantly boost the performance of Random Forest classifier, with a clear increment of sensitivity and Matthews correlation coefficient (MCC).

PDNAsite: Identification of DNA-binding Site from Protein Sequence by Incorporating Spatial and Sequence Context.

Protein-DNA interactions are involved in many fundamental biological processes essential for cellular function. Most of the existing computational approaches employed only the sequence context of the target residue for its prediction. In the present study, for each target residue, we applied both the spatial context and the sequence context to construct the feature space. Subsequently, Latent Semantic Analysis (LSA) was applied to remove the redundancies in the feature space. Finally, a predictor (PDNAsite) was developed through the integration of the support vector machines (SVM) classifier and ensemble learning. Results on the PDNA-62 and the PDNA-224 datasets demonstrate that features extracted from spatial context provide more information than those from sequence context and the combination of them gives more performance gain. An analysis of the number of binding sites in the spatial context of the target site indicates that the interactions between binding sites next to each other are important for protein-DNA recognition and their binding ability. The comparison between our proposed PDNAsite method and the existing methods indicate that PDNAsite outperforms most of the existing methods and is a useful tool for DNA-binding site identification. A web-server of our predictor (http://hlt.hitsz.edu.cn:8080/PDNAsite/) is made available for free public accessible to the biological research community.

PreDNA: accurate prediction of DNA-binding sites in proteins by integrating sequence and geometric structure information

Protein-DNA interactions often take part in various crucial processes, which are essential for cellular function. The identification of DNA-binding sites in proteins is important for understanding the molecular mechanisms of protein-DNA interaction. Thus, we have developed an improved method to predict DNA-binding sites by integrating structural alignment algorithm and support vector machine-based methods. Evaluated on a new non-redundant protein set with 224 chains, the method has 80.7% sensitivity and 82.9% specificity in the 5-fold cross-validation test. In addition, it predicts DNA-binding sites with 85.1% sensitivity and 85.3% specificity when tested on a dataset with 62 protein-DNA complexes. Compared with a recently published method, BindN+, our method predicts DNA-binding sites with a 7% better area under the receiver operating characteristic curve value when tested on the same dataset. Many important problems in cell biology require the dense non-linear interactions between functional modules be considered. Thus, our prediction method will be useful in detecting such complex interactions.

Improving ChIP-seq peak-calling for functional co-regulator binding by integrating multiple sources of biological information

BackgroundChromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) is increasingly being applied to study genome-wide binding sites of transcription factors. There is an increasing interest in understanding the mechanism of action of co-regulator proteins, which do not bind DNA directly, but exert their effects by binding to transcription factors such as the estrogen receptor (ER). However, due to the nature of detecting indirect protein-DNA interaction, ChIP-seq signals from co-regulators can be relatively weak and thus biologically meaningful interactions remain difficult to identify.ResultsIn this study, we investigated and compared different statistical and machine learning approaches including unsupervised, supervised, and semi-supervised classification (self-training) approaches to integrate multiple types of genomic and transcriptomic information derived from our experiments and public database to overcome difficulty of identifying functional DNA binding sites of the co-regulator SRC-1 in the context of estrogen response. Our results indicate that supervised learning with naïve Bayes algorithm significantly enhances peak calling of weak ChIP-seq signals and outperforms other machine learning algorithms. Our integrative approach revealed many potential ERα/SRC-1 DNA binding sites that would otherwise be missed by conventional peak calling algorithms with default settings.ConclusionsOur results indicate that a supervised classification approach enables one to utilize limited amounts of prior knowledge together with multiple types of biological data to enhance the sensitivity and specificity of the identification of DNA binding sites from co-regulator proteins.

DamIP: Using Mutant DNA Adenine Methyltransferase to Study DNA‐Protein Interactions In Vivo

DamIP is a new method for studying DNA-protein interaction in vivo. A mutant form of DNA adenine methyltransferase (DamK9A) from E. coli is fused to the protein of interest and expressed. The fusion protein will bind to target binding sites and introduce N(6)-adenine methylation in nearby sites in the genomic DNA. Methylated DNA fragments are enriched by immunopreciptation with an antibody that recognizes N(6)-methyladenine, and can then be used for further analysis, e.g., real-time PCR, microarray, or high-throughput sequencing. This method is simple and does not require protein-DNA crosslinking or a specific antibody to the protein of interest. This unit describes the application of this method for identification of DNA binding sites in vivo.

A novel ensemble learning method for de novo computational identification of DNA binding sites.

BackgroundDespite the diversity of motif representations and search algorithms, the de novo computational identification of transcription factor binding sites remains constrained by the limited accuracy of existing algorithms and the need for user-specified input parameters that describe the motif being sought.ResultsWe present a novel ensemble learning method, SCOPE, that is based on the assumption that transcription factor binding sites belong to one of three broad classes of motifs: non-degenerate, degenerate and gapped motifs. SCOPE employs a unified scoring metric to combine the results from three motif finding algorithms each aimed at the discovery of one of these classes of motifs. We found that SCOPE's performance on 78 experimentally characterized regulons from four species was a substantial and statistically significant improvement over that of its component algorithms. SCOPE outperformed a broad range of existing motif discovery algorithms on the same dataset by a statistically significant margin.ConclusionSCOPE demonstrates that combining multiple, focused motif discovery algorithms can provide a significant gain in performance. By building on components that efficiently search for motifs without user-defined parameters, SCOPE requires as input only a set of upstream sequences and a species designation, making it a practical choice for non-expert users. A user-friendly web interface, Java source code and executables are available at .

Identification of DNA binding sites for ComE, a key regulator of natural competence in Streptococcus pneumoniae.

Natural competence in Streptococcus pneumoniae is regulated by a quorum-sensing mechanism consisting of a competence-stimulating peptide (CSP), its dedicated secretion apparatus (ComAB), its histidine kinase receptor (ComD) and a response regulator (ComE). In this report, we show that ComE is a DNA-binding protein that acts autocatalytically by binding to a region in its own promoter. Two additional ComE binding sites were identified in the pneumococcal genome, one in the promoter region of comAB and the other upstream of an ABC transporter of unknown function. A comparison of the ComE-binding sequences with the sequence motif previously found to be involved in the co-ordinated expression of the late genes revealed that they are unrelated. These findings indicate that ComE activates transcription of the late genes indirectly, i.e. via one or more intermediate factors.

Identification of DNA binding sites for the v-erbA oncoprotein, the viral homolog to thyroid hormone receptor alpha

The v-erbA oncogene protein, P75 gag-v-erbA, is a mutant form of the thyroid hormone receptor α (TR α) which has sustained mutations both in the ligand binding and DNA binding domains. The oncoprotein has therefore lost its ability to bind ligand, and its heterodimerization with the retinoid-X receptor (RXR) is impaired. Here, we have investigated the effects of the mutations in the DNA binding domain. By applying a PCR-based screening assay we isolated DNA sequences to which P75 gag-v-erbA binds as a heterodimer with RXR, and characterized these with regard to their nucleotide sequence and ability to associate with RXR/P75 gag-v-erbA heterodimers in vitro and in vivo. In the PCR selection assay the heterodimer exhibited a preference for direct repeats with a 3′ half-site sequence AGGTCG and spacers of four or five nucleotides separating the two half-sites. These DNA binding data were confirmed by gel retardation assays with synthetic oligonucleotides as well as by transfection experiments using dominantly active VP16 fusion proteins with P75 gag-v-erbA and TR α. The comparison between RXR/P75 gag-v-erbA and RXR/TR α heterodimers demonstrated that although their DNA binding properties are very similar, however, a relaxed specificity of P75 gag-v-erbA for the spacer length may allow it to interfere with more hormone signalling pathways than only that of thyroid hormone.

Human 15-lipoxygenase gene promoter: analysis and identification of DNA binding sites for IL-13-induced regulatory factors in monocytes.

In order to study the transcriptional control of 15-LO expression, we have cloned and sequenced the human 15-LO promoter region. The 15-LO promoter is associated with a CpG island at the 5'-end of the gene, and sequence analysis reveals putative Sp1 and Ap2 binding site/s and absence of TATA or CAAT motifs. Transcription is initiated at one major site. Using deletion constructs, we have defined an active promoter region of 1056 bp. Gel-shift assays revealed that transcriptional factor(s) induced only in response to IL-13 treatment of human peripheral blood monocytes bind to the 15-LO promoter DNA. Two regions, DP1 (-140 to -92 bp) and DP2 (-353 to -304 bp) of the promoter were essential for transcription in HeLa cells and human peripheral monocytes. Hela nuclear extracts contained a specific nuclear factor(s) binding to 15-LO promoter DNA which are distinct from those derived from IL-13-treated human peripheral monocyte nuclear extracts. In addition, fluorescent in situ hybridization (FISH) results refined the previous localization of 15-LO to human chromosome 17p13.3.

Expectation maximization algorithm for identifying protein-binding sites with variable lengths from unaligned DNA fragments

An Expectation Maximization algorithm for identification of DNA binding sites is presented. The approach predicts the location of binding regions while allowing variable length spacers within the sites. In addition to predicting the most likely spacer length for a set of DNA fragments, the method identifies individual sites that differ in spacer size. No alignment of DNA sequences is necessary. The method is illustrated by application to 231 Escherichia coli DNA fragments known to contain promoters with variable spacings between their consensus regions. Maximum-likelihood tests of the differences between the spacing classes indicate that the consensus regions of the spacing classes are not distinct. Further tests suggest that several positions within the spacing region may contribute to promoter specificity.