Simplified method to predict mutual interactions of human transcription factors based on their primary structure
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KAUST DepartmentComputational Bioscience Research Center (CBRC)
Permanent link to this recordhttp://hdl.handle.net/10754/325289
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AbstractBackground: Physical interactions between transcription factors (TFs) are necessary for forming regulatory protein complexes and thus play a crucial role in gene regulation. Currently, knowledge about the mechanisms of these TF interactions is incomplete and the number of known TF interactions is limited. Computational prediction of such interactions can help identify potential new TF interactions as well as contribute to better understanding the complex machinery involved in gene regulation. Methodology: We propose here such a method for the prediction of TF interactions. The method uses only the primary sequence information of the interacting TFs, resulting in a much greater simplicity of the prediction algorithm. Through an advanced feature selection process, we determined a subset of 97 model features that constitute the optimized model in the subset we considered. The model, based on quadratic discriminant analysis, achieves a prediction accuracy of 85.39% on a blind set of interactions. This result is achieved despite the selection for the negative data set of only those TF from the same type of proteins, i.e. TFs that function in the same cellular compartment (nucleus) and in the same type of molecular process (transcription initiation). Such selection poses significant challenges for developing models with high specificity, but at the same time better reflects real-world problems. Conclusions: The performance of our predictor compares well to those of much more complex approaches for predicting TF and general protein-protein interactions, particularly when taking the reduced complexity of model utilisation into account. © 2011 Schmeier et al.
CitationSchmeier S, Jankovic B, Bajic VB (2011) Simplified Method to Predict Mutual Interactions of Human Transcription Factors Based on Their Primary Structure. PLoS ONE 6: e21887. doi:10.1371/journal.pone.0021887.
PublisherPublic Library of Science (PLoS)
PubMed Central IDPMC3130058
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TcoF-DB: dragon database for human transcription co-factors and transcription factor interacting proteinsSchaefer, Ulf; Schmeier, Sebastian; Bajic, Vladimir B. (Nucleic Acids Research, Oxford University Press (OUP), 2010-10-21) [Article]The initiation and regulation of transcription in eukaryotes is complex and involves a large number of transcription factors (TFs), which are known to bind to the regulatory regions of eukaryotic DNA. Apart from TF-DNA binding, protein-protein interaction involving TFs is an essential component of the machinery facilitating transcriptional regulation. Proteins that interact with TFs in the context of transcription regulation but do not bind to the DNA themselves, we consider transcription co-factors (TcoFs). The influence of TcoFs on transcriptional regulation and initiation, although indirect, has been shown to be significant with the functionality of TFs strongly influenced by the presence of TcoFs. While the role of TFs and their interaction with regulatory DNA regions has been well-studied, the association between TFs and TcoFs has so far been given less attention. Here, we present a resource that is comprised of a collection of human TFs and the TcoFs with which they interact. Other proteins that have a proven interaction with a TF, but are not considered TcoFs are also included. Our database contains 157 high-confidence TcoFs and additionally 379 hypothetical TcoFs. These have been identified and classified according to the type of available evidence for their involvement in transcriptional regulation and their presence in the cell nucleus. We have divided TcoFs into four groups, one of which contains high-confidence TcoFs and three others contain TcoFs which are hypothetical to different extents. We have developed the Dragon Database for Human Transcription Co-Factors and Transcription Factor Interacting Proteins (TcoF-DB). A web-based interface for this resource can be freely accessed at http://cbrc.kaust.edu.sa/tcof/ and http://apps.sanbi.ac.za/tcof/. © The Author(s) 2010.
Targeted transcriptional repression using a chimeric TALE-SRDX repressor proteinMahfouz, Magdy M.; Li, Lixin; Piatek, Marek J.; Fang, Xiaoyun; Mansour, Hicham; Bangarusamy, Dhinoth K.; Zhu, Jian-Kang (Plant Molecular Biology, Springer Nature, 2011-12-14) [Article]Transcriptional activator-like effectors (TALEs) are proteins secreted by Xanthomonas bacteria when they infect plants. TALEs contain a modular DNA binding domain that can be easily engineered to bind any sequence of interest, and have been used to provide user-selected DNA-binding modules to generate chimeric nucleases and transcriptional activators in mammalian cells and plants. Here we report the use of TALEs to generate chimeric sequence-specific transcriptional repressors. The dHax3 TALE was used as a scaffold to provide a DNA-binding module fused to the EAR-repression domain (SRDX) to generate a chimeric repressor that targets the RD29A promoter. The dHax3. SRDX protein efficiently repressed the transcription of the RD29A
Simplified Method for Predicting a Functional Class of Proteins in Transcription Factor ComplexesPiatek, Marek J.; Schramm, Michael C.; Burra, Dharani Dhar; BinShbreen, Abdulaziz; Jankovic, Boris R.; Chowdhary, Rajesh; Archer, John A.C.; Bajic, Vladimir B. (PLoS ONE, Public Library of Science (PLoS), 2013-07-12) [Article]Background:Initiation of transcription is essential for most of the cellular responses to environmental conditions and for cell and tissue specificity. This process is regulated through numerous proteins, their ligands and mutual interactions, as well as interactions with DNA. The key such regulatory proteins are transcription factors (TFs) and transcription co-factors (TcoFs). TcoFs are important since they modulate the transcription initiation process through interaction with TFs. In eukaryotes, transcription requires that TFs form different protein complexes with various nuclear proteins. To better understand transcription regulation, it is important to know the functional class of proteins interacting with TFs during transcription initiation. Such information is not fully available, since not all proteins that act as TFs or TcoFs are yet annotated as such, due to generally partial functional annotation of proteins. In this study we have developed a method to predict, using only sequence composition of the interacting proteins, the functional class of human TF binding partners to be (i) TF, (ii) TcoF, or (iii) other nuclear protein. This allows for complementing the annotation of the currently known pool of nuclear proteins. Since only the knowledge of protein sequences is required in addition to protein interaction, the method should be easily applicable to many species.Results:Based on experimentally validated interactions between human TFs with different TFs, TcoFs and other nuclear proteins, our two classification systems (implemented as a web-based application) achieve high accuracies in distinguishing TFs and TcoFs from other nuclear proteins, and TFs from TcoFs respectively.Conclusion:As demonstrated, given the fact that two proteins are capable of forming direct physical interactions and using only information about their sequence composition, we have developed a completely new method for predicting a functional class of TF interacting protein partners with high precision and accuracy. © 2013 Piatek et al.