DBD2BS: connecting a DNA-binding protein with its binding sites

Chien, Ting-Ying; Lin, Cheng-Kai; Lin, Chih‐Wei; Weng, Yi-Zhong; Chen, C.-Y.; Chang, Darby Tien-Hao

doi:10.1093/nar/gks564

Cited by 6 publications

(8 citation statements)

References 26 publications

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“…Of these, ChIP is the only one that is based on in vivo analysis and that can demonstrate the binding of a transcriptional regulator to its target gene(s) vividly and in real time. [54][55][56] Therefore, ChIP was used to monitor the binding of NF-jB to the iNOS promoter in this study. To support our findings, we used the conservative H2A.X antibody as a positive control and an IgG antibody as a negative control.…”

Section: Discussionmentioning

confidence: 99%

Regulation of iNOS Expression by NF-κB in Human Lens Epithelial Cells Treated With High Levels of Glucose

Jia

Liu

Zhang

et al. 2013

Invest. Ophthalmol. Vis. Sci.

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NF-κB regulates iNOS expression in a time- and concentration-dependent manner. Under high glucose conditions, NF-κB is activated and rapidly translocates to the nucleus, leading to increased binding to the iNOS promoter and a consequent increase in iNOS expression. The findings of this study provide important experimental evidence that clarifies the pathogenesis of cataracts associated with diabetes and contributes to the search for therapeutic targets of these cataracts.

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Section: Discussionmentioning

confidence: 99%

Regulation of iNOS Expression by NF-κB in Human Lens Epithelial Cells Treated With High Levels of Glucose

Jia

Liu

Zhang

et al. 2013

Invest. Ophthalmol. Vis. Sci.

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“…These predictors often work well for a specific complex, and hence, Nagarajan et al designed a Web server for selecting the best predictor for predicting DNA binding site based on the structure/function of protein/DNA. Structure‐based tools utilize position weight matrix constructed from atomic level information and knowledge‐based statistical potential …”

Section: Introductionmentioning

confidence: 99%

“…Structure-based tools utilize position weight matrix constructed from atomic level information and knowledge-based statistical potential. [23][24][25] To understand the dynamics of protein-DNA interactions, study of thermodynamic factors such as binding affinity, free energy, and enthalpy is crucial. These thermodynamic parameters for protein-DNA complexes are available in ProNIT database.…”

Section: Introductionmentioning

confidence: 99%

Dissecting and analyzing key residues in protein‐DNA complexes

Kulandaisamy

Srivastava

Raju

et al. 2017

J of Molecular Recognition

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Protein-DNA interactions are involved in various fundamental biological processes such as replication, transcription, DNA repair, and gene regulation. To understand the interaction in protein-DNA complexes, the integrative study of binding and stabilizing residues is important. In the present study, we have identified key residues that play a dual role in both binding and stability from a nonredundant dataset of 319 protein-DNA complexes. We observed that key residues are identified in very less number of complexes (29%) and only about 4% of stabilizing/binding residues are identified as key residues. Specifically, stabilizing residues have higher preference to be key residues than binding residues. These key residues include polar, nonpolar, aliphatic, aromatic, and charged amino acids. Moreover, we have analyzed and discussed the key residues in different protein-DNA complexes, which are classified based on protein structural class, function, DNA strand, and their conformations. Especially, Ser, Thr, Tyr, Arg, and Lys residues are commonly found in most of the subclasses of protein-DNA complexes. Further, we analyzed atomic contacts, which show that polar-nonpolar is more enriched than other types of contacts. In addition, the charged contacts are highly preferred in protein-DNA complexes compared with protein-protein and protein-RNA complexes. Finally, we have discussed the sequence and structural features of key residues such as conservation score, surrounding hydrophobicity, solvent accessibility, secondary structure, and long-range order. This study will be helpful to understand the recognition mechanism and structural and functional aspects of protein-DNA complexes.

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“…On the other hand, Web servers that use protein–DNA complexes to predict PWMs or sequence specificity of TFs are still limited. Our previous work, DBD2BS, was developed for predicting PWMs from protein unbound structures (24). A more similar work to PiDNA is a recently published Web server, 3DTF, which also uses knowledge-based potential on mutated structures to produce PWMs (25).…”

Section: Introductionmentioning

confidence: 99%

PiDNA: predicting protein–DNA interactions with structural models

Lin

Chen

2013

Nucleic Acids Research

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Predicting binding sites of a transcription factor in the genome is an important, but challenging, issue in studying gene regulation. In the past decade, a large number of protein–DNA co-crystallized structures available in the Protein Data Bank have facilitated the understanding of interacting mechanisms between transcription factors and their binding sites. Recent studies have shown that both physics-based and knowledge-based potential functions can be applied to protein–DNA complex structures to deliver position weight matrices (PWMs) that are consistent with the experimental data. To further use the available structural models, the proposed Web server, PiDNA, aims at first constructing reliable PWMs by applying an atomic-level knowledge-based scoring function on numerous in silico mutated complex structures, and then using the PWM constructed by the structure models with small energy changes to predict the interaction between proteins and DNA sequences. With PiDNA, the users can easily predict the relative preference of all the DNA sequences with limited mutations from the native sequence co-crystallized in the model in a single run. More predictions on sequences with unlimited mutations can be realized by additional requests or file uploading. Three types of information can be downloaded after prediction: (i) the ranked list of mutated sequences, (ii) the PWM constructed by the favourable mutated structures, and (iii) any mutated protein–DNA complex structure models specified by the user. This study first shows that the constructed PWMs are similar to the annotated PWMs collected from databases or literature. Second, the prediction accuracy of PiDNA in detecting relatively high-specificity sites is evaluated by comparing the ranked lists against in vitro experiments from protein-binding microarrays. Finally, PiDNA is shown to be able to select the experimentally validated binding sites from 10 000 random sites with high accuracy. With PiDNA, the users can design biological experiments based on the predicted sequence specificity and/or request mutated structure models for further protein design. As well, it is expected that PiDNA can be incorporated with chromatin immunoprecipitation data to refine large-scale inference of in vivo protein–DNA interactions. PiDNA is available at: http://dna.bime.ntu.edu.tw/pidna.

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DBD2BS: connecting a DNA-binding protein with its binding sites

Cited by 6 publications

References 26 publications

Regulation of iNOS Expression by NF-κB in Human Lens Epithelial Cells Treated With High Levels of Glucose

Regulation of iNOS Expression by NF-κB in Human Lens Epithelial Cells Treated With High Levels of Glucose

Dissecting and analyzing key residues in protein‐DNA complexes

PiDNA: predicting protein–DNA interactions with structural models

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