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  • Articles  (8)
  • Protein-nucleic acid interaction, Computational Methods  (8)
  • Oxford University Press  (8)
  • 2015-2019  (8)
  • Biology  (8)
  • 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
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    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
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    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
    Details
    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
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 5
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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
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 6
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    Computational learning on specificity-determining residue-nucleotide interactions (2015)
    Oxford University Press
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    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
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 7
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    A structural view of microRNA-target recognition (2016)
    Oxford University Press
    Details
    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
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 8
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    A human transcription factor in search mode (2016)
    Oxford University Press
    Details
    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
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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