A Handbook of Transcription Factors: 52 (Subcellular Biochemistry)

NF-kappa-B is controlled by various mechanisms of post-translational modification and subcellular compartmentalization as well as by interactions with other cofactors or corepressors. NF-kappa-B complexes are held in the cytoplasm in an inactive state complexed with members of the NF-kappa-B inhibitor I-kappa-B family. In a conventional activation pathway, I-kappa-B is phosphorylated by I-kappa-B kinases IKKs in response to different activators, subsequently degraded thus liberating the active NF-kappa-B complex which translocates to the nucleus.

The NF-kappa-B pp52 homodimer is a transcriptional repressor. NFKB2 appears to have dual functions such as cytoplasmic retention of attached NF-kappa-B proteins by p and generation of p52 by a cotranslational processing. The proteasome-mediated process ensures the production of both p52 and p and preserves their independent function. Translocation t 10;14 q24;q32 with IGHA1.

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The resulting oncogene is also called LytC alpha variant. Contains 7 ANK repeats. Contains 1 death domain. Contains 1 RHD Rel-like domain. The glycine-rich region GRR appears to be a critical element in the generation of p While translation occurs, the particular unfolded structure after the GRR repeat promotes the generation of p52 making it an acceptable substrate for the proteasome. This process is known as cotranslational processing.

The processed form is active and the unprocessed form acts as an inhibitor I kappa B-like , being able to form cytosolic complexes with NF-kappa B, trapping it in the cytoplasm. Complete folding of the region downstream of the GRR repeat precludes processing. Subsequent to MAP3Kdependent serine phosphorylation, p polyubiquitination occurs then triggering its proteasome-dependent processing. The above threshold was used as a first step in order to exclude unbound oligonucleotides. In the second step, a stricter threshold allowing one less mismatch was used to filter out oligonucleotides that have multiple motif occurrences, in order to exclude cooperative binding events from our analysis.

The stricter threshold ensures that not too many oligonucleotides are filtered out in the second step. Finally, the oligonucleotides were aligned according to the core motif. To reach reliable conclusions, we used multiple data filtering procedures to discard datasets of insufficient quality. We performed two stages of QC for these datasets. In the first stage, we used four QC criteria to ensure high counts for accurate score estimates, large sample size, and score variability.

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We filtered out datasets based on R 2 performance criteria. Due to their linearity, we would expect that, for MLR models, model A would perform at least as well as model B, given that B uses a subset of features used by A. This process reduced the number of valid datasets to The i th nucleotide in s was denoted s i. For nucleotide position i:. For these DNA shape features, the following normalization was performed:. As a control, we shuffled the pentamer query table and tested its effects on shape models.

Mapping for the other direction is as follows. Superscript numbers denote nucleotide positions in the DNA sequences. Subscript letters denote what features at those positions the learned weights are for. It is only true if we assume that the training process always ensures that the learned model has the highest generalization accuracy under the MLR framework, that is, the optimal solution. In practice, the solution is not necessarily the optimal one, despite being the goal of the regularization.

We gauged the importance of each DNA shape feature at each nucleotide position by adding this feature to the baseline 1mer model. LY and YO developed computational methods and analyzed data.

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RS and RR directed the computational study and overall project. This work was performed in part while Y. Sloan Research Fellowship to R. National Center for Biotechnology Information , U. Journal List Mol Syst Biol v. Published online Feb 6. Author information Article notes Copyright and License information Disclaimer.

Published under the terms of the CC BY 4. This is an open access article under the terms of the Creative Commons Attribution 4. This article has been cited by other articles in PMC.

Associated Data Supplementary Materials. Introduction Protein — DNA interactions play a central role in gene regulation. Open in a separate window. Each dot represents a TF. Dots of the same color belong to the same TF family. An ellipse was drawn for each TF family. The label 3merE2 represents 3mer features from the two end positions at the 5' and 3' terminal of each DNA sequence.

The labels 2merNoE2 and 3merNoE3 indicate that 2mer and 3mer features, respectively, were removed from the end positions. Datasets were tested for variable scores. The score of the 90 th percentile had to be at least 0. For nucleotide position i: Conflict of interest The authors declare that they have no conflict of interest.

Appendix Click here for additional data file.

Written from a genome-based perspective, this volume provides an objective overview of transcription factor biochemistry. Topics include known transcription factor classes, origins and evolution of transcription factor types, and mechanisms of interaction with chromatin. Read more Read less.

Here's how restrictions apply. From the Back Cover Transcription factors are - in the most common use of the term - proteins that possess sequence-specific DNA-binding activity, and either directly or indirectly influence the transcription of genes in proximity to the binding site.

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