Identification Of DNA-binding Proteins Research Articles

NDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.

BackgroundDNA-binding proteins are vital for the study of cellular processes. In recent genome engineering studies, the identification of proteins with certain functions has become increasingly important and needs to be performed rapidly and efficiently. In previous years, several approaches have been developed to improve the identification of DNA-binding proteins. However, the currently available resources are insufficient to accurately identify these proteins. Because of this, the previous research has been limited by the relatively unbalanced accuracy rate and the low identification success of the current methods.ResultsIn this paper, we explored the practicality of modelling DNA binding identification and simultaneously employed an ensemble classifier, and a new predictor (nDNA-Prot) was designed. The presented framework is comprised of two stages: a 188-dimension feature extraction method to obtain the protein structure and an ensemble classifier designated as imDC. Experiments using different datasets showed that our method is more successful than the traditional methods in identifying DNA-binding proteins. The identification was conducted using a feature that selected the minimum Redundancy and Maximum Relevance (mRMR). An accuracy rate of 95.80% and an Area Under the Curve (AUC) value of 0.986 were obtained in a cross validation. A test dataset was tested in our method and resulted in an 86% accuracy, versus a 76% using iDNA-Prot and a 68% accuracy using DNA-Prot.ConclusionsOur method can help to accurately identify DNA-binding proteins, and the web server is accessible at http://datamining.xmu.edu.cn/~songli/nDNA. In addition, we also predicted possible DNA-binding protein sequences in all of the sequences from the UniProtKB/Swiss-Prot database.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-298) contains supplementary material, which is available to authorized users.

EnDNA-Prot: identification of DNA-binding proteins by applying ensemble learning.

DNA-binding proteins are crucial for various cellular processes, such as recognition of specific nucleotide, regulation of transcription, and regulation of gene expression. Developing an effective model for identifying DNA-binding proteins is an urgent research problem. Up to now, many methods have been proposed, but most of them focus on only one classifier and cannot make full use of the large number of negative samples to improve predicting performance. This study proposed a predictor called enDNA-Prot for DNA-binding protein identification by employing the ensemble learning technique. Experiential results showed that enDNA-Prot was comparable with DNA-Prot and outperformed DNAbinder and iDNA-Prot with performance improvement in the range of 3.97–9.52% in ACC and 0.08–0.19 in MCC. Furthermore, when the benchmark dataset was expanded with negative samples, the performance of enDNA-Prot outperformed the three existing methods by 2.83–16.63% in terms of ACC and 0.02–0.16 in terms of MCC. It indicated that enDNA-Prot is an effective method for DNA-binding protein identification and expanding training dataset with negative samples can improve its performance. For the convenience of the vast majority of experimental scientists, we developed a user-friendly web-server for enDNA-Prot which is freely accessible to the public.

Screening for Protein-DNA Interactions by Automatable DNA-Protein Interaction ELISA

DNA-binding proteins (DBPs), such as transcription factors, constitute about 10% of the protein-coding genes in eukaryotic genomes and play pivotal roles in the regulation of chromatin structure and gene expression by binding to short stretches of DNA. Despite their number and importance, only for a minor portion of DBPs the binding sequence had been disclosed. Methods that allow the de novo identification of DNA-binding motifs of known DBPs, such as protein binding microarray technology or SELEX, are not yet suited for high-throughput and automation. To close this gap, we report an automatable DNA-protein-interaction (DPI)-ELISA screen of an optimized double-stranded DNA (dsDNA) probe library that allows the high-throughput identification of hexanucleotide DNA-binding motifs. In contrast to other methods, this DPI-ELISA screen can be performed manually or with standard laboratory automation. Furthermore, output evaluation does not require extensive computational analysis to derive a binding consensus. We could show that the DPI-ELISA screen disclosed the full spectrum of binding preferences for a given DBP. As an example, AtWRKY11 was used to demonstrate that the automated DPI-ELISA screen revealed the entire range of in vitro binding preferences. In addition, protein extracts of AtbZIP63 and the DNA-binding domain of AtWRKY33 were analyzed, which led to a refinement of their known DNA-binding consensi. Finally, we performed a DPI-ELISA screen to disclose the DNA-binding consensus of a yet uncharacterized putative DBP, AtTIFY1. A palindromic TGATCA-consensus was uncovered and we could show that the GATC-core is compulsory for AtTIFY1 binding. This specific interaction between AtTIFY1 and its DNA-binding motif was confirmed by in vivo plant one-hybrid assays in protoplasts. Thus, the value and applicability of the DPI-ELISA screen for de novo binding site identification of DBPs, also under automatized conditions, is a promising approach for a deeper understanding of gene regulation in any organism of choice.

Identification of DNA-Binding and Protein-Binding Proteins Using Enhanced Graph Wavelet Features

Interactions between biomolecules play an essential role in various biological processes. For predicting DNA-binding or protein-binding proteins, many machine-learning-based techniques have used various types of features to represent the interface of the complexes, but they only deal with the properties of a single atom in the interface and do not take into account the information of neighborhood atoms directly. This paper proposes a new feature representation method for biomolecular interfaces based on the theory of graph wavelet. The enhanced graph wavelet features (EGWF) provides an effective way to characterize interface feature through adding physicochemical features and exploiting a graph wavelet formulation. Particularly, graph wavelet condenses the information around the center atom, and thus enhances the discrimination of features of biomolecule binding proteins in the feature space. Experiment results show that EGWF performs effectively for predicting DNA-binding and protein-binding proteins in terms of Matthew's correlation coefficient (MCC) score and the area value under the receiver operating characteristic curve (AUC).

Identification of centromeric and telomeric DNA-binding proteins in rice

Centromeres and telomeres are DNA/protein complexes and essential functional components of eukaryotic chromosomes. Previous studies have shown that rice centromeres and telomeres are occupied by CentO (rice centromere satellite DNA) satellite and G-rich telomere repeats, respectively. However, the protein components are not fully understood. DNA-binding proteins associated with centromeric or telomeric DNAs will most likely be important for the understanding of centromere and telomere structure and functions. To capture DNA-specific binding proteins, affinity pull-down technique was applied in this study to isolate rice centromeric and telomeric DNA-binding proteins. Fifty-five proteins were identified for their binding affinity to rice CentO repeat, and 80 proteins were identified for their binding to telomere repeat. One CentO-binding protein, Os02g0288200, was demonstrated to bind to CentO specifically by in vitro assay. A conserved domain, DUF573 with unknown functions was identified in this protein, and proven to be responsible for the specific binding to CentO in vitro. Four proteins identified as telomere DNA-binding proteins in this study were reported by different groups previously. These results demonstrate that DNA affinity pull-down technique is effective in the isolation of sequence-specific binding proteins and will be applicable in future studies of centromere and telomere proteins.

Identification of DNA-Binding Proteins Using Support Vector Machine with Sequence Information

DNA-binding proteins are fundamentally important in understanding cellular processes. Thus, the identification of DNA-binding proteins has the particularly important practical application in various fields, such as drug design. We have proposed a novel approach method for predicting DNA-binding proteins using only sequence information. The prediction model developed in this study is constructed by support vector machine-sequential minimal optimization (SVM-SMO) algorithm in conjunction with a hybrid feature. The hybrid feature is incorporating evolutionary information feature, physicochemical property feature, and two novel attributes. These two attributes use DNA-binding residues and nonbinding residues in a query protein to obtain DNA-binding propensity and nonbinding propensity. The results demonstrate that our SVM-SMO model achieves 0.67 Matthew's correlation coefficient (MCC) and 89.6% overall accuracy with 88.4% sensitivity and 90.8% specificity, respectively. Performance comparisons on various features indicate that two novel attributes contribute to the performance improvement. In addition, our SVM-SMO model achieves the best performance than state-of-the-art methods on independent test dataset.

Computational Methods for DNA-binding Protein and Binding Residue Prediction

Protein-DNA interactions are involved in many essential biological processes such as transcription, splicing, replication and DNA repair. It is of great value to identify DNA-binding proteins as well as their binding sites in order to study the mechanisms of these biological processes. A number of experimental methods have been developed for the identification of DNA-binding proteins, such as DNAase foot printing, EMSA, X-ray crystallography, NMR spectroscopy and CHIP-on-Chip. However, with the increasingly greater number of suspected protein-DNA interactions, identification by experimental methods is expensive, labor-intensive and time-consuming. Hence, in the past decades researchers have developed many computational approaches to predict in silico the interactions of proteins and DNA. Machine learning technology has been widely used and become dominant in this field. In this article, we focus on reviewing recent machine learning-based progresses in DNA-binding protein and binding residue prediction methods, the most commonly used features in these predictions, machine learning classifier comparison and selection, evaluation method comparison, and existing problems and future directions for the field.

Toward detection of DNA-bound proteins using solid-state nanopores: insights from computer simulations.

Through all-atom molecular dynamics simulations, we explore the use of nanopores in thin synthetic membranes for detection and identification of DNA binding proteins. Reproducing the setup of a typical experiment, we simulate electric field driven transport of DNA-bound proteins through nanopores smaller in diameter than the proteins. As model systems, we use restriction enzymes EcoRI and BamHI specifically and nonspecifically bound to a fragment of dsDNA, and streptavidin and NeutrAvidin proteins bound to dsDNA and ssDNA via a biotin linker. Our simulations elucidate the molecular mechanics of nanopore-induced rupture of a protein-DNA complex, the effective force applied to the DNA-protein bond by the electrophoretic force in a nanopore, and the role of DNA-surface interactions in the rupture process. We evaluate the ability of the nanopore ionic current and the local electrostatic potential measured by an embedded electrode to report capture of DNA, capture of a DNA-bound protein, and rupture of the DNA-protein bond. We find that changes in the strain on dsDNA can reveal the rupture of a protein-DNA complex by altering both the nanopore ionic current and the potential of the embedded electrode. Based on the results of our simulations, we suggest a new method for detection of DNA binding proteins that utilizes peeling of a nicked double strand under the electrophoretic force in a nanopore.

Unbiased Discovery of Interactions at a Control Locus Driving Expression of the Cancer-Specific Therapeutic and Diagnostic Target, Mesothelin

Although significant effort is expended on identifying transcripts/proteins that are up-regulated in cancer, there are few reports on systematic elucidation of transcriptional mechanisms underlying such druggable cancer-specific targets. The mesothelin (MSLN) gene offers a promising subject, being expressed in a restricted pattern normally, yet highly overexpressed in almost one-third of human malignancies and a target of cancer immunotherapeutic trials. CanScript, a cis promoter element, appears to control MSLN cancer-specific expression; its related genomic sequences may up-regulate other cancer markers. CanScript is a 20-nt bipartite element consisting of an SP1-like motif and a consensus MCAT sequence. The latter recruits TEAD (TEA domain) family members, which are universally expressed. Exploration of the active CanScript element, especially the proteins binding to the SP1-like motif, thus could reveal cancer-specific features having diagnostic or therapeutic interest. The efficient identification of sequence-specific DNA-binding proteins at a given locus, however, has lagged in biomarker explorations. We used two orthogonal proteomics approaches--unbiased SILAC (stable isotope labeling by amino acids in cell culture)/DNA affinity-capture/mass spectrometry survey (SD-MS) and a large transcription factor protein microarray (TFM)--and functional validation to explore systematically the CanScript interactome. SD-MS produced nine candidates, and TFM, 18. The screens agreed in confirming binding by TEAD proteins and by newly identified NAB1 and NFATc. Among other identified candidates, we found functional roles for ZNF24, NAB1 and RFX1 in MSLN expression by cancer cells. Combined interactome screens yield an efficient, reproducible, sensitive, and unbiased approach to identify sequence-specific DNA-binding proteins and other participants in disease-specific DNA elements.

Identification of novel DNA-binding proteins using DNA-affinity chromatography/pull down.

This units presents methods through which one may isolate and identify novel bacterial DNA-binding proteins. Briefly, the DNA sequence of interest is affixed to beads, and then incubated with bacterial cytoplasmic extract. Washes with buffers containing nonspecific DNA and low-salt concentrations will remove non-adhering and low-specificity DNA-binding proteins, while subsequent washes with higher salt concentrations will elute more specific DNA-binding proteins. Eluted proteins may then be identified by standard proteomic techniques.

Identification of essential and non-essential single-stranded DNA-binding proteins in a model archaeal organism

Single-stranded DNA-binding proteins (SSBs) play vital roles in all aspects of DNA metabolism in all three domains of life and are characterized by the presence of one or more OB fold ssDNA-binding domains. Here, using the genetically tractable euryarchaeon Haloferax volcanii as a model, we present the first genetic analysis of SSB function in the archaea. We show that genes encoding the OB fold and zinc finger-containing RpaA1 and RpaB1 proteins are individually non-essential for cell viability but share an essential function, whereas the gene encoding the triple OB fold RpaC protein is essential. Loss of RpaC function can however be rescued by elevated expression of RpaB, indicative of functional overlap between the two classes of haloarchaeal SSB. Deletion analysis is used to demonstrate important roles for individual OB folds in RpaC and to show that conserved N- and C-terminal domains are required for efficient repair of DNA damage. Consistent with a role for RpaC in DNA repair, elevated expression of this protein leads to enhanced resistance to DNA damage. Taken together, our results offer important insights into archaeal SSB function and establish the haloarchaea as a valuable model for further studies.

IDNA-Prot: Identification of DNA Binding Proteins Using Random Forest with Grey Model

DNA-binding proteins play crucial roles in various cellular processes. Developing high throughput tools for rapidly and effectively identifying DNA-binding proteins is one of the major challenges in the field of genome annotation. Although many efforts have been made in this regard, further effort is needed to enhance the prediction power.By incorporating the features into the general form of pseudo amino acid composition that were extracted from protein sequences via the “grey model” and by adopting the random forest operation engine, we proposed a new predictor, called iDNA-Prot, for identifying uncharacterized proteins as DNA-binding proteins or non-DNA binding proteins based on their amino acid sequences information alone. The overall success rate by iDNA-Prot was 83.96% that was obtained via jackknife tests on a newly constructed stringent benchmark dataset in which none of the proteins included has pairwise sequence identity to any other in a same subset. In addition to achieving high success rate, the computational time for iDNA-Prot is remarkably shorter in comparison with the relevant existing predictors. Hence it is anticipated that iDNA-Prot may become a useful high throughput tool for large-scale analysis of DNA-binding proteins.As a user-friendly web-server, iDNA-Prot is freely accessible to the public at the web-site on http://icpr.jci.edu.cn/bioinfo/iDNA-Prot or http://www.jci-bioinfo.cn/iDNA-Prot. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results.

IDBPs: a web server for the identification of DNA binding proteins

Summary: The iDBPs server uses the three-dimensional (3D) structure of a query protein to predict whether it binds DNA. First, the algorithm predicts the functional region of the protein based on its evolutionary profile; the assumption is that large clusters of conserved residues are good markers of functional regions. Next, various characteristics of the predicted functional region as well as global features of the protein are calculated, such as the average surface electrostatic potential, the dipole moment and cluster-based amino acid conservation patterns. Finally, a random forests classifier is used to predict whether the query protein is likely to bind DNA and to estimate the prediction confidence. We have trained and tested the classifier on various datasets and shown that it outperformed related methods. On a dataset that reflects the fraction of DNA binding proteins (DBPs) in a proteome, the area under the ROC curve was 0.90. The application of the server to an updated version of the N-Func database, which contains proteins of unknown function with solved 3D-structure, suggested new putative DBPs for experimental studies.Availability: http://idbps.tau.ac.il/Contact: NirB@tauex.tau.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online.

Understanding Protein-DNA Interactions through Dynamics

Transcriptional regulation is a key factor in controlling proper cellular behavior. For this reason, so-called regulation networks (quantifying the molecular interactions controlling the transcription), have been heavily studied. One goal is to enrich these networks through in silico identification of DNA-binding proteins and their respective binding sites. Often such work assumes a specific distance between atoms as constituting an interaction and construct models based on this assumption. However, this ad hoc rule fails to account for many of the complexities that lie behind the physical nature of binding. We present a framework for studying these interactions in more realistic settings accounting for both overall energy and dynamics of protein-DNA complex. We demonstrate that short molecular dynamics simulations better characterize biomolecular interactions and that a better definition of interactions improves the prediction of protein-DNA docking. Specifically, interacting residues are identified through the analysis of MD energy functions and results are compared with published experimental results. Further, we show how our novel definition of DNA-binding can be used for constructing improved machine learning classifiers for automatic identification of DNA-binding residues.

Global Analysis of a Plasmid-Cured Shigella flexneri Strain: New Insights into the Interaction between the Chromosome and a Virulence Plasmid

Shigella flexneri is an important human pathogen that causes dysentery, and remains a significant threat to public health, particularly in developing countries. The virulence of this pathogen is dependent on an acquired virulence plasmid. To investigate the crosstalk between the bacterial chromosome and the exogenous virulence plasmid, a virulence plasmid-cured strain was constructed using plasmid incompatibility. The global patterns of gene expression of this strain compared with the wild-type strain were analyzed using 2-DE combined with MALDI-TOF MS. Most known virulence factors of S. flexneri were identified in the 2-DE gels. Interestingly, the expression of the glycerol 3-phosphate (glp) regulon-encoded proteins was increased when the virulence plasmid was absent. Microarray analysis confirmed that regulation occurred at the transcriptional level. Purification and identification of DNA binding proteins with affinity for the regulatory region of the glp genes revealed that regulation mediated by the virulence plasmid to control the expression of the glp regulon might in turn be mediated by protein GlpR. To our knowledge, this is the first study analyzing the interaction between a pathogen chromosome and a virulence plasmid at the proteomic level.

DNA-Prot: Identification of DNA Binding Proteins from Protein Sequence Information using Random Forest

DNA-binding proteins (DNABPs) are important for various cellular processes, such as transcriptional regulation, recombination, replication, repair, and DNA modification. So far various bioinformatics and machine learning techniques have been applied for identification of DNA-binding proteins from protein structure. Only few methods are available for the identification of DNA binding proteins from protein sequence. In this work, we report a random forest method, DNA-Prot, to identify DNA binding proteins from protein sequence. Training was performed on the dataset containing 146 DNA-binding proteins and 250 non DNA-binding proteins. The algorithm was tested on the dataset containing 92 DNA-binding proteins and 100 non DNA-binding proteins. We obtained 80.31% accuracy from training and 84.37% accuracy from testing. Benchmarking analysis on the independent of 823 DNA-binding proteins and 823 non DNA-binding proteins shows that our approach can distinguish DNA-binding proteins from non DNA-binding proteins with more than 80% accuracy. We also compared our method with DNAbinder method on test dataset and two independent datasets. Comparable performance was observed from both methods on test dataset. In the benchmark dataset containing 823 DNA-binding proteins and 823 non DNA-binding proteins, we obtained significantly better performance from DNA-Prot with 81.83% accuracy whereas DNAbinder achieved only 61.42% accuracy using amino acid composition and 63.5% using PSSM profile. Similarly, DNA-Prot achived better performance rate from the benchmark dataset containing 88 DNA-binding proteins and 233 non DNA-binding proteins. This result shows DNA-Prot can be efficiently used to identify DNA binding proteins from sequence information. The dataset and standalone version of DNA-Prot software can be obtained from http://www3.ntu.edu.sg/home/EPNSugan/index_files/dnaprot.htm.

Identification of DNA-binding Proteins Using Structural, Electrostatic and Evolutionary Features

DNA-binding proteins (DBPs) participate in various crucial processes in the life-cycle of the cells, and the identification and characterization of these proteins is of great importance. We present here a random forests classifier for identifying DBPs among proteins with known 3D structures. First, clusters of evolutionarily conserved regions (patches) on the surface of proteins were detected using the PatchFinder algorithm; earlier studies showed that these regions are typically the functionally important regions of proteins. Next, we trained a classifier using features like the electrostatic potential, cluster-based amino acid conservation patterns and the secondary structure content of the patches, as well as features of the whole protein, including its dipole moment. Using 10-fold cross-validation on a dataset of 138 DBPs and 110 proteins that do not bind DNA, the classifier achieved a sensitivity and a specificity of 0.90, which is overall better than the performance of published methods. Furthermore, when we tested five different methods on 11 new DBPs that did not appear in the original dataset, only our method annotated all correctly.The resulting classifier was applied to a collection of 757 proteins of known structure and unknown function. Of these proteins, 218 were predicted to bind DNA, and we anticipate that some of them interact with DNA using new structural motifs. The use of complementary computational tools supports the notion that at least some of them do bind DNA.

Exploring DNA-Binding Proteins with In Vivo Chemical Cross-Linking and Mass Spectrometry

DNA-binding proteins are very important constituents of proteomes of all species and play crucial roles in transcription, DNA replication, recombination, repair, and other activities associated with DNA. Although a number of DNA-binding proteins have been identified, many proteins involved in gene regulation and DNA repair are likely still unknown because of their dynamic and/or weak interactions with DNA. In this report, we described an approach for the comprehensive identification of DNA-binding proteins with in vivo formaldehyde cross-linking and LC-MS/MS. DNA-binding proteins could be purified via the isolation of DNA-protein complexes and released from the complexes by reversing the cross-linking. By using this method, we were able to identify more than one hundred DNA-binding proteins, such as proteins involved in transcription, gene regulation, DNA replication and repair, and a large number of proteins that are potentially associated with DNA and DNA-binding proteins. This method should be generally applicable to the investigation of other nucleic acid-binding proteins, and hold great potential in the comprehensive study of gene regulation, DNA damage response and repair, as well as many other critical biological processes at proteomic level.

A Proteomics Approach for Identification of Single Strand DNA-binding Proteins Involved in Transcriptional Regulation of Mouse μ Opioid Receptor Gene

The pharmacological actions of morphine and morphine-like drugs such as heroin are mediated primarily through the μ opioid receptor. Previously a single strand DNA element of the mouse μ opioid receptor gene (Oprm1) proximal promoter was found to be important for regulating Oprm1 in neuronal cells. To identify proteins binding to the single strand DNA element as potential regulators for Oprm1, affinity column chromatography with the single strand DNA element was performed using neuroblastoma NS20Y cells followed by two-dimensional gel electrophoresis and MALDI-TOF mass spectrometry. We identified five poly(C)-binding proteins: heterogeneous nuclear ribonucleoprotein (hnRNP) K, α-complex proteins (αCP) αCP1, αCP2, αCP2-KL, and αCP3. Binding of these proteins to the single strand DNA element of Oprm1 was sequence-specific as confirmed by supershift assays. In cotransfection studies, hnRNP K, αCP1, αCP2, and αCP2-KL activated the Oprm1 promoter activity, whereas αCP3 acted as a repressor. Ectopic expression of hnRNP K, αCP1, αCP2, and αCP2-KL also led to activation of the endogenous Oprm1 transcripts, and αCP3 repressed endogenous Oprm1 transcripts. We demonstrate novel roles as transcriptional regulators in Oprm1 regulation for hnRNP K and αCP binding to the single strand DNA element.

Identification of DNA-binding proteins on human umbilical vein endothelial cell plasma membrane.

The binding of anti-DNA antibodies to the endothelial cell is mediated through DNA, which forms a bridge between the immunoglobulin and the plasma membrane. We have shown that 32P-labelled DNA bound to the plasma membrane of human umbilical vein endothelial cells (HUVEC) by a saturable process, which could be competitively inhibited by non-radiolabelled DNA. In addition, DNA-binding was enhanced in HUVEC that had been treated with IL-1 alpha or tumour necrosis factor-alpha (TNF-alpha). DNA-binding proteins of mol. wt 46,000, 92,000, and 84,000 were identified by the binding of 32P-labelled DNA to plasma membrane proteins separated on SDS-PAGE. DNA-binding proteins of mol. wt 46,000 and 84,000 were also present in the cytosol and nucleus. Murine anti-DNA MoAb410 bound to a single band, at mol. wt 46,000, of plasma membrane protein, in the presence of DNA. Our results showed that DNA-binding proteins are present in different cellular fractions of endothelial cells. DNA-binding proteins on the cell membrane could participate in the in situ formation of immune deposits; and their presence in the cell nucleus suggests a potential role in the modulation of cell function.