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  • Articles  (18)
  • Latest Papers from Table of Contents or Articles in Press  (18)
  • Protein-nucleic acid interaction, Computational Methods  (18)
  • Oxford University Press  (18)
  • Biology  (18)
  • 1
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    ConBind: motif-aware cross-species alignment for the identification of functional transcription factor binding sites (2016)
    Oxford University Press
    Details
    Publication Date: 2016-05-06
    Description: Eukaryotic gene expression is regulated by transcription factors (TFs) binding to promoter as well as distal enhancers. TFs recognize short, but specific binding sites (TFBSs) that are located within the promoter and enhancer regions. Functionally relevant TFBSs are often highly conserved during evolution leaving a strong phylogenetic signal. While multiple sequence alignment (MSA) is a potent tool to detect the phylogenetic signal, the current MSA implementations are optimized to align the maximum number of identical nucleotides. This approach might result in the omission of conserved motifs that contain interchangeable nucleotides such as the ETS motif (IUPAC code: GGAW). Here, we introduce ConBind, a novel method to enhance alignment of short motifs, even if their mutual sequence similarity is only partial. ConBind improves the identification of conserved TFBSs by improving the alignment accuracy of TFBS families within orthologous DNA sequences. Functional validation of the Gfi1b + 13 enhancer reveals that ConBind identifies additional functionally important ETS binding sites that were missed by all other tested alignment tools. In addition to the analysis of known regulatory regions, our web tool is useful for the analysis of TFBSs on so far unknown DNA regions identified through ChIP-sequencing.
    Keywords: Protein-nucleic acid interaction, Computational Methods
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 2
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    ROTS: reproducible RNA-seq biomarker detector--prognostic markers for clear cell renal cell cancer (2016)
    Oxford University Press
    Details
    Publication Date: 2016-01-09
    Description: Recent comprehensive assessments of RNA-seq technology support its utility in quantifying gene expression in various samples. The next step of rigorously quantifying differences between sample groups, however, still lacks well-defined best practices. Although a number of advanced statistical methods have been developed, several studies demonstrate that their performance depends strongly on the data under analysis, which compromises practical utility in real biomedical studies. As a solution, we propose to use a data-adaptive procedure that selects an optimal statistic capable of maximizing reproducibility of detections. After demonstrating its improved sensitivity and specificity in a controlled spike-in study, the utility of the procedure is confirmed in a real biomedical study by identifying prognostic markers for clear cell renal cell carcinoma (ccRCC). In addition to identifying several genes previously associated with ccRCC prognosis, several potential new biomarkers among genes regulating cell growth, metabolism and solute transport were detected.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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    Topics: Biology
    Published by Oxford University Press
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  • 3
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    DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool (2015)
    Oxford University Press
    Details
    Publication Date: 2015-12-16
    Description: There are currently 151 plants with draft genomes available but levels of functional annotation for putative protein products are low. Therefore, accurate computational predictions are essential to annotate genomes in the first instance, and to provide focus for the more costly and time consuming functional assays that follow. DNA-binding proteins are an important class of proteins that require annotation, but current computational methods are not applicable for genome wide predictions in plant species. Here, we explore the use of species and lineage specific models for the prediction of DNA-binding proteins in plants. We show that a species specific support vector machine model based on Arabidopsis sequence data is more accurate (accuracy 81%) than a generic model (74%), and based on this we develop a plant specific model for predicting DNA-binding proteins. We apply this model to the tomato proteome and demonstrate its ability to perform accurate high-throughput prediction of DNA-binding proteins. In doing so, we have annotated 36 currently uncharacterised proteins by assigning a putative DNA-binding function. Our model is publically available and we propose it be used in combination with existing tools to help increase annotation levels of DNA-binding proteins encoded in plant genomes.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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    Topics: Biology
    Published by Oxford University Press
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  • 4
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    Prediction of nucleic acid binding probability in proteins: a neighboring residue network based score (2015)
    Oxford University Press
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    Publication Date: 2015-06-24
    Description: We describe a general binding score for predicting the nucleic acid binding probability in proteins. The score is directly derived from physicochemical and evolutionary features and integrates a residue neighboring network approach. Our process achieves stable and high accuracies on both DNA- and RNA-binding proteins and illustrates how the main driving forces for nucleic acid binding are common. Because of the effective integration of the synergetic effects of the network of neighboring residues and the fact that the prediction yields a hierarchical scoring on the protein surface, energy funnels for nucleic acid binding appear on protein surfaces, pointing to the dynamic process occurring in the binding of nucleic acids to proteins.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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    Topics: Biology
    Published by Oxford University Press
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  • 5
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    A new method for evaluating the specificity of indirect readout in protein-DNA recognition (2012)
    Oxford University Press
    Details
    Publication Date: 2012-09-27
    Description: Proteins recognize a specific DNA sequence not only through direct contact (direct readout) with base pairs but also through sequence-dependent conformation and/or flexibility of DNA (indirect readout). However, it is difficult to assess the contribution of indirect readout to the sequence specificity. What is needed is a straightforward method for quantifying its contributions to specificity. Using Bayesian statistics, we derived the probability of a particular sequence for a given DNA structure from the trajectories of molecular dynamics (MD) simulations of DNAs containing all possible tetramer sequences. Then, we quantified the specificity of indirect readout based on the information entropy associated with the probability. We tested this method with known structures of protein–DNA complexes. This method enabled us to correctly predict those regions where experiments suggested the involvement of indirect readout. The results also indicated new regions where the indirect readout mechanism makes major contributions to the recognition. The present method can be used to estimate the contribution of indirect readout without approximations to the distributions in the conformational ensembles of DNA, and would serve as a powerful tool to study the mechanism of protein–DNA recognition.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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    Topics: Biology
    Published by Oxford University Press
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  • 6
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    Probing binding hot spots at protein-RNA recognition sites (2016)
    Oxford University Press
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    Publication Date: 2016-01-30
    Description: We use evolutionary conservation derived from structure alignment of polypeptide sequences along with structural and physicochemical attributes of protein–RNA interfaces to probe the binding hot spots at protein–RNA recognition sites. We find that the degree of conservation varies across the RNA binding proteins; some evolve rapidly compared to others. Additionally, irrespective of the structural class of the complexes, residues at the RNA binding sites are evolutionary better conserved than those at the solvent exposed surfaces. For recognitions involving duplex RNA, residues interacting with the major groove are better conserved than those interacting with the minor groove. We identify multi-interface residues participating simultaneously in protein–protein and protein–RNA interfaces in complexes where more than one polypeptide is involved in RNA recognition, and show that they are better conserved compared to any other RNA binding residues. We find that the residues at water preservation site are better conserved than those at hydrated or at dehydrated sites. Finally, we develop a Random Forests model using structural and physicochemical attributes for predicting binding hot spots. The model accurately predicts 80% of the instances of experimental G values in a particular class, and provides a stepping-stone towards the engineering of protein–RNA recognition sites with desired affinity.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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    Topics: Biology
    Published by Oxford University Press
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  • 7
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    Computational learning on specificity-determining residue-nucleotide interactions (2015)
    Oxford University Press
    Details
    Publication Date: 2015-12-02
    Description: The protein–DNA interactions between transcription factors and transcription factor binding sites are essential activities in gene regulation. To decipher the binding codes, it is a long-standing challenge to understand the binding mechanism across different transcription factor DNA binding families. Past computational learning studies usually focus on learning and predicting the DNA binding residues on protein side. Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families. The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance. In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein–DNA interactions across different DNA binding families.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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    Topics: Biology
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  • 8
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    SIOMICS: a novel approach for systematic identification of motifs in ChIP-seq data (2014)
    Oxford University Press
    Details
    Publication Date: 2014-03-13
    Description: The identification of transcription factor binding motifs is important for the study of gene transcriptional regulation. The chromatin immunoprecipitation (ChIP), followed by massive parallel sequencing (ChIP-seq) experiments, provides an unprecedented opportunity to discover binding motifs. Computational methods have been developed to identify motifs from ChIP-seq data, while at the same time encountering several problems. For example, existing methods are often not scalable to the large number of sequences obtained from ChIP-seq peak regions. Some methods heavily rely on well-annotated motifs even though the number of known motifs is limited. To simplify the problem, de novo motif discovery methods often neglect underrepresented motifs in ChIP-seq peak regions. To address these issues, we developed a novel approach called SIOMICS to de novo discover motifs from ChIP-seq data. Tested on 13 ChIP-seq data sets, SIOMICS identified motifs of many known and new cofactors. Tested on 13 simulated random data sets, SIOMICS discovered no motif in any data set. Compared with two recently developed methods for motif discovery, SIOMICS shows advantages in terms of speed, the number of known cofactor motifs predicted in experimental data sets and the number of false motifs predicted in random data sets. The SIOMICS software is freely available at http://eecs.ucf.edu/~xiaoman/SIOMICS/SIOMICS.html .
    Keywords: Protein-nucleic acid interaction, Computational Methods
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    Topics: Biology
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  • 9
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    An online bioinformatics tool predicts zinc finger and TALE nuclease off-target cleavage (2014)
    Oxford University Press
    Details
    Publication Date: 2014-04-03
    Description: Although engineered nucleases can efficiently cleave intracellular DNA at desired target sites, major concerns remain on potential ‘off-target’ cleavage that may occur throughout the genome. We developed an online tool: predicted report of genome-wide nuclease off-target sites (PROGNOS) that effectively identifies off-target sites. The initial bioinformatics algorithms in PROGNOS were validated by predicting 44 of 65 previously confirmed off-target sites, and by uncovering a new off-target site for the extensively studied zinc finger nucleases (ZFNs) targeting C-C chemokine receptor type 5. Using PROGNOS, we rapidly interrogated 128 potential off-target sites for newly designed transcription activator-like effector nucleases containing either Asn-Asn (NN) or Asn-Lys (NK) repeat variable di-residues (RVDs) and 3- and 4-finger ZFNs, and validated 13 bona fide off-target sites for these nucleases by DNA sequencing. The PROGNOS algorithms were further refined by incorporating additional features of nuclease–DNA interactions and the newly confirmed off-target sites into the training set, which increased the percentage of bona fide off-target sites found within the top PROGNOS rankings. By identifying potential off-target sites in silico , PROGNOS allows the selection of more specific target sites and aids the identification of bona fide off-target sites, significantly facilitating the design of engineered nucleases for genome editing applications.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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    Topics: Biology
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  • 10
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    Identification of co-occurring transcription factor binding sites from DNA sequence using clustered position weight matrices (2012)
    Oxford University Press
    Details
    Publication Date: 2012-03-14
    Description: Accurate prediction of transcription factor binding sites (TFBSs) is a prerequisite for identifying cis -regulatory modules that underlie transcriptional regulatory circuits encoded in the genome. Here, we present a computational framework for detecting TFBSs, when multiple position weight matrices (PWMs) for a transcription factor are available. Grouping multiple PWMs of a transcription factor (TF) based on their sequence similarity improves the specificity of TFBS prediction, which was evaluated using multiple genome-wide ChIP-Seq data sets from 26 TFs. The Z-scores of the area under a receiver operating characteristic curve (AUC) values of 368 TFs were calculated and used to statistically identify co-occurring regulatory motifs in the TF bound ChIP loci. Motifs that are co-occurring along with the empirical bindings of E2F, JUN or MYC have been evaluated, in the basal or stimulated condition. Results prove our method can be useful to systematically identify the co-occurring motifs of the TF for the given conditions.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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    Topics: Biology
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  • 11
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    Prediction of DNA binding motifs from 3D models of transcription factors; identifying TLX3 regulated genes (2014)
    Oxford University Press
    Details
    Publication Date: 2014-12-17
    Description: 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.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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    Topics: Biology
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  • 12
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    A knowledge-based scoring function for protein-RNA interactions derived from a statistical mechanics-based iterative method (2014)
    Oxford University Press
    Details
    Publication Date: 2014-04-15
    Description: Protein-RNA interactions play important roles in many biological processes. Given the high cost and technique difficulties in experimental methods, computationally predicting the binding complexes from individual protein and RNA structures is pressingly needed, in which a reliable scoring function is one of the critical components. Here, we have developed a knowledge-based scoring function, referred to as ITScore-PR, for protein-RNA binding mode prediction by using a statistical mechanics-based iterative method. The pairwise distance-dependent atomic interaction potentials of ITScore-PR were derived from experimentally determined protein–RNA complex structures. For validation, we have compared ITScore-PR with 10 other scoring methods on four diverse test sets. For bound docking, ITScore-PR achieved a success rate of up to 86% if the top prediction was considered and up to 94% if the top 10 predictions were considered, respectively. For truly unbound docking, the respective success rates of ITScore-PR were up to 24 and 46%. ITScore-PR can be used stand-alone or easily implemented in other docking programs for protein–RNA recognition.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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    Topics: Biology
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  • 13
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    DBSI: DNA-binding site identifier (2013)
    Oxford University Press
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    Publication Date: 2013-09-06
    Description: In this study, we present the DNA-Binding Site Identifier (DBSI), a new structure-based method for predicting protein interaction sites for DNA binding. DBSI was trained and validated on a data set of 263 proteins (TRAIN-263), tested on an independent set of protein-DNA complexes (TEST-206) and data sets of 29 unbound (APO-29) and 30 bound (HOLO-30) protein structures distinct from the training data. We computed 480 candidate features for identifying protein residues that bind DNA, including new features that capture the electrostatic microenvironment within shells near the protein surface. Our iterative feature selection process identified features important in other models, as well as features unique to the DBSI model, such as a banded electrostatic feature with spatial separation comparable with the canonical width of the DNA minor groove. Validations and comparisons with established methods using a range of performance metrics clearly demonstrate the predictive advantage of DBSI, and its comparable performance on unbound (APO-29) and bound (HOLO-30) conformations demonstrates robustness to binding-induced protein conformational changes. Finally, we offer our feature data table to others for integration into their own models or for testing improved feature selection and model training strategies based on DBSI.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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  • 14
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    A structural view of microRNA-target recognition (2016)
    Oxford University Press
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    Publication Date: 2016-05-20
    Description: It is well established that the correct identification of the messenger RNA targeted by a given microRNA (miRNA) is a difficult problem, and that available methods all suffer from low specificity. We hypothesize that the correct identification of the pairing should take into account the effect of the Argonaute protein (AGO), an essential catalyst of the recognition process. Therefore, we developed a strategy named MiREN for building and scoring three-dimensional models of the ternary complex formed by AGO, a miRNA and 22 nt of a target mRNA that putatively interacts with it. We show here that MiREN can be used to assess the likelihood that an RNA molecule is the target of a given miRNA and that this approach is more accurate than other existing methods, usually based on sequence or sequence-related features. Our results also suggest that AGO plays a relevant role in the selection of the miRNA targets. Our method can represent an additional step for refining predictions made by faster but less accurate classical methods for the identification of miRNA targets.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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    Topics: Biology
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  • 15
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    Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters (2012)
    Oxford University Press
    Details
    Publication Date: 2012-08-23
    Description: We present a set of four parameters that in combination can predict DNA-binding residues on protein structures to a high degree of accuracy. These are the number of evolutionary conserved residues ( N cons ) and their spatial clustering ( e ), hydrogen bond donor capability ( D p ) and residue propensity ( R p ). We first used these parameters to characterize 130 interfaces in a set of 126 DNA-binding proteins (DBPs). The applicability of these parameters both individually and in combination, to distinguish the true binding region from the rest of the protein surface was then analyzed. R p shows the best performance identifying the true interface with the top rank in 83% cases. Importantly, we also used the unbound-bound test cases of the protein–DNA docking benchmark to test the efficacy of our method. When applied to the unbound form of the DBP s, R p can distinguish 86% cases. Finally, we have applied the SVM approach for recognizing the interface region using the above parameters along with the individual amino acid composition as attributes. The accuracy of prediction is 90.5% for the bound structures and 93.6% for the unbound form of the proteins.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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  • 16
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    Mixture models and wavelet transforms reveal high confidence RNA-protein interaction sites in MOV10 PAR-CLIP data (2012)
    Oxford University Press
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    Publication Date: 2012-11-04
    Description: The Photo-Activatable Ribonucleoside-enhanced CrossLinking and ImmunoPrecipitation (PAR-CLIP) method was recently developed for global identification of RNAs interacting with proteins. The strength of this versatile method results from induction of specific T to C transitions at sites of interaction. However, current analytical tools do not distinguish between non-experimentally and experimentally induced transitions. Furthermore, geometric properties at potential binding sites are not taken into account. To surmount these shortcomings, we developed a two-step algorithm consisting of a non-parametric two-component mixture model and a wavelet-based peak calling procedure. Our algorithm can reduce the number of false positives up to 24% thereby identifying high confidence interaction sites. We successfully employed this approach in conjunction with a modified PAR-CLIP protocol to study the functional role of nuclear Moloney leukemia virus 10, a putative RNA helicase interacting with Argonaute2 and Polycomb. Our method, available as the R package wavClusteR , is generally applicable to any substitution-based inference problem in genomics.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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  • 17
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    CLIP-based prediction of mammalian microRNA binding sites (2013)
    Oxford University Press
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    Publication Date: 2013-08-09
    Description: Prediction and validation of microRNA (miRNA) targets are essential for understanding functions of miRNAs in gene regulation. Crosslinking immunoprecipitation (CLIP) allows direct identification of a huge number of Argonaute-bound target sequences that contain miRNA binding sites. By analysing data from CLIP studies, we identified a comprehensive list of sequence, thermodynamic and target structure features that are essential for target binding by miRNAs in the 3' untranslated region (3' UTR), coding sequence (CDS) region and 5' untranslated region (5' UTR) of target messenger RNA (mRNA). The total energy of miRNA:target hybridization, a measure of target structural accessibility, is the only essential feature common for both seed and seedless sites in all three target regions. Furthermore, evolutionary conservation is an important discriminating feature for both seed and seedless sites. These features enabled us to develop novel statistical models for the predictions of both seed sites and broad classes of seedless sites. Through both intra-dataset validation and inter-dataset validation, our approach showed major improvements over established algorithms for predicting seed sites and a class of seedless sites. Furthermore, we observed good performance from cross-species validation, suggesting that our prediction framework can be valuable for broad application to other mammalian species and beyond. Transcriptome-wide binding site predictions enabled by our approach will greatly complement the available CLIP data, which only cover small fractions of transcriptomes and known miRNAs due to non-detectable levels of expression. Software and database tools based on the prediction models have been developed and are available through Sfold web server at http://sfold.wadsworth.org .
    Keywords: Protein-nucleic acid interaction, Computational Methods
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  • 18
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    A human transcription factor in search mode (2016)
    Oxford University Press
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    Publication Date: 2016-01-09
    Description: Transcription factors (TF) can change shape to bind and recognize DNA, shifting the energy landscape from a weak binding, rapid search mode to a higher affinity recognition mode. However, the mechanism(s) driving this conformational change remains unresolved and in most cases high-resolution structures of the non-specific complexes are unavailable. Here, we investigate the conformational switch of the human mitochondrial transcription termination factor MTERF1, which has a modular, superhelical topology complementary to DNA. Our goal was to characterize the details of the non-specific search mode to complement the crystal structure of the specific binding complex, providing a basis for understanding the recognition mechanism. In the specific complex, MTERF1 binds a significantly distorted and unwound DNA structure, exhibiting a protein conformation incompatible with binding to B-form DNA. In contrast, our simulations of apo MTERF1 revealed significant flexibility, sampling structures with superhelical pitch and radius complementary to the major groove of B-DNA. Docking these structures to B-DNA followed by unrestrained MD simulations led to a stable complex in which MTERF1 was observed to undergo spontaneous diffusion on the DNA. Overall, the data support an MTERF1-DNA binding and recognition mechanism driven by intrinsic dynamics of the MTERF1 superhelical topology.
    Keywords: Protein-nucleic acid interaction, Computational Methods
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