Sequence-specific transcription factors (TFs) regulate gene expression through their interactions with DNA sequences in the genome. The goals of this project are to continue developing approaches and data sets for understanding the DNA binding specificities of TFs and to identify the effects of coding polymorphisms within TFs' DNA binding domains on their DNA binding preferences. Identification of TFs' DNA binding specificities is important in understanding transcriptional regulatory networks, in particular in the prediction of cis regulator modules, inference of cis regulatory codes, and interpretation of in vivo TF binding data and gene expression data. Identification of the DNA binding effects of such polymorphic TF variants will be essential in studies aimed at understanding the gene regulatory effects resulting from natural genetic variation. This project will focus on human TFs, with an emphasis on those with known mutations or polymorphisms identified in exome or whole-genome sequencing projects. Our results will provide data that will likely be of importance to other systems, and more generally, our data, approaches, technologies, and database will be useful not only for human TFs but also for model organism studies. Specifically, we will: (1) develop a computational pipeline to predict the effects of coding mutations or polymorphisms within TF DNA binding domains (DBDs) for TFs of major structural classes; (2) determine the DNA binding specificities of mutant TFs designed to test our computational pipeline; (3) experimentally determine the effects of known mutations or coding polymorphisms within human TF DBDs; (4) further develop and maintain the UniPROBE database of universal protein binding microarray (PBM) data on TFs' DNA binding specificities.
The interactions between transcription factors (TFs) and their DNA binding sites are an integral part of gene regulatory networks within cells; however, it is not well understood how mutations or polymorphisms within TFs affect their DNA binding activities. In this project, we will develop a computational pipeline to identify with greater accuracy potentially damaging mutations or polymorphisms within TFs, and we will test such predictions experimentally. The resulting data are anticipated to improve the ability to understand the potential effects of such TF mutations or polymorphisms on gene regulation.
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