Knowledge-based three-body potential for transcription factor binding site prediction.
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ABSTRACT:
SUBMITTER: Qin W
PROVIDER: S-EPMC8687219 | biostudies-literature | 2016 Feb
REPOSITORIES: biostudies-literature
Publications
Knowledge-based three-body potential for transcription factor binding site prediction.
Qin Wenyi W Zhao Guijun G Carson Matthew M Jia Caiyan C Lu Hui H
IET systems biology 20160201 1
A structure-based statistical potential is developed for transcription factor binding site (TFBS) prediction. Besides the direct contact between amino acids from TFs and DNA bases, the authors also considered the influence of the neighbouring base. This three-body potential showed better discriminate powers than the two-body potential. They validate the performance of the potential in TFBS identification, binding energy prediction and binding mutation prediction. ...[more]
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Project description:Finding where transcription factors (TFs) bind to the DNA is of key importance to decipher gene regulation at a transcriptional level. Classically, computational prediction of TF binding sites (TFBSs) is based on basic position weight matrices (PWMs) which quantitatively score binding motifs based on the observed nucleotide patterns in a set of TFBSs for the corresponding TF. Such models make the strong assumption that each nucleotide participates independently in the corresponding DNA-protein interaction and do not account for flexible length motifs. We introduce transcription factor flexible models (TFFMs) to represent TF binding properties. Based on hidden Markov models, TFFMs are flexible, and can model both position interdependence within TFBSs and variable length motifs within a single dedicated framework. The availability of thousands of experimentally validated DNA-TF interaction sequences from ChIP-seq allows for the generation of models that perform as well as PWMs for stereotypical TFs and can improve performance for TFs with flexible binding characteristics. We present a new graphical representation of the motifs that convey properties of position interdependence. TFFMs have been assessed on ChIP-seq data sets coming from the ENCODE project, revealing that they can perform better than both PWMs and the dinucleotide weight matrix extension in discriminating ChIP-seq from background sequences. Under the assumption that ChIP-seq signal values are correlated with the affinity of the TF-DNA binding, we find that TFFM scores correlate with ChIP-seq peak signals. Moreover, using available TF-DNA affinity measurements for the Max TF, we demonstrate that TFFMs constructed from ChIP-seq data correlate with published experimentally measured DNA-binding affinities. Finally, TFFMs allow for the straightforward computation of an integrated TF occupancy score across a sequence. These results demonstrate the capacity of TFFMs to accurately model DNA-protein interactions, while providing a single unified framework suitable for the next generation of TFBS prediction.
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Project description:UnlabelledPromoter prediction has gained increased attention in studies related to transcriptional regulation of gene expression. We developed a web server named PMSearch (Poly Matrix Search) which utilizes Position Frequency Matrices (PFMs) to predict transcription factor binding sites (TFBSs) in DNA sequences. PMSearch takes PFMs (either user-defined or retrieved from local dataset which currently contains 507 PFMs from Transfac Public 7.0 and JASPAR) and DNA sequences of interest as the input, then scans the DNA sequences with PFMs and reports the sites of high scores as the putative binding sites. The output of the server includes 1) A plot for the distribution of predicted TFBS along the DNA sequence, 2) A table listing location, score and motif for each putative binding site, and 3) Clusters of predicted binding sites. PMSearch also provides links for accessing clusters of PFMs that are similar to the input PFMs to facilitate complicated promoter analysis.AvailabilityPMSearch is available for free at http://www.nicemice.cn/bioinfo/PMS.
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