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Quantitative high-throughput analysis of transcription factor binding specificities.

ABSTRACT: We present a general high-throughput approach to accurately quantify DNA-protein interactions, which can facilitate the identification of functional genetic polymorphisms. The method tested here on two structurally distinct transcription factors (TFs), NF-kappaB and OCT-1, comprises three steps: (i) optimized selection of DNA variants to be tested experimentally, which we show is superior to selecting variants at random; (ii) a quantitative protein-DNA binding assay using microarray and surface plasmon resonance technologies; (iii) prediction of binding affinity for all DNA variants in the consensus space using a statistical model based on principal coordinates analysis. For the protein-DNA binding assay, we identified a polyacrylamide/ester glass activation chemistry which formed exclusive covalent bonds with 5'-amino-modified DNA duplexes and hindered non-specific electrostatic attachment of DNA. Full accessibility of the DNA duplexes attached to polyacrylamide-modified slides was confirmed by the high degree of data correlation with the electromobility shift assay (correlation coefficient 93%). This approach offers the potential for high-throughput determination of TF binding profiles and predicting the effects of single nucleotide polymorphisms on TF binding affinity. New DNA binding data for OCT-1 are presented.

SUBMITTER: Linnell J

PROVIDER: S-EPMC390317 | biostudies-literature | 2004

REPOSITORIES: biostudies-literature

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Quantitative high-throughput analysis of transcription factor binding specificities.

Linnell Jane J Mott Richard R Field Simon S Kwiatkowski Dominic P DP Ragoussis Jiannis J Udalova Irina A IA

Nucleic acids research 20040227 4

We present a general high-throughput approach to accurately quantify DNA-protein interactions, which can facilitate the identification of functional genetic polymorphisms. The method tested here on two structurally distinct transcription factors (TFs), NF-kappaB and OCT-1, comprises three steps: (i) optimized selection of DNA variants to be tested experimentally, which we show is superior to selecting variants at random; (ii) a quantitative protein-DNA binding assay using microarray and surface ...[more]

PMID: 14990752

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Project description:Accurate predictions of the DNA binding specificities of transcription factors (TFs) are necessary for understanding gene regulatory mechanisms. Traditionally, predictive models are built based on nucleotide sequence features. Here, we employed three- dimensional DNA shape information obtained on a high-throughput basis to integrate intuitive DNA structural features into the modeling of TF binding specificities using support vector regression. We performed quantitative predictions of DNA binding specificities, using the DREAM5 dataset for 65 mouse TFs and genomic-context protein binding microarray data for three human basic helix-loop-helix TFs. DNA shape-augmented models compared favorably with sequence-based models for these predictions. Although both k-mer and DNA shape features encoded the interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space compared to k-mer use. Finally, analyzing the weights of DNA shape-augmented models uncovered TF family- specific structural readout mechanisms that were not obvious from the nucleotide sequence. Three genomic-context protein binding microarray (gcPBM) experiments of human transcription factors were performed. Briefly, the gcPBMs involved binding his-tagged transcription factors c-Myc, Max, and Mad1(Mxd1) to double-stranded 180K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 36-bp sequences: 1) bound probes, 2) unbound probes (or negative controls), and 3) test probes. Bound probes corresponded to genomic regions bound in vivo by c-Myc, Max, or Mad2 (ChIP-seq P < 10^(-10) in HeLaS3 or K562 celld (ENCODE)) that contain at least two consecutive 8-mers with universal PBM E-score > 0.4 (Munteanu and Gordan, LNCS 2013). All putative binding sites occur at the same position within the probes on the array. M-bM-^@M-^\UnboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-seq P < 10^(-10) and a maximum 8-mer E-score < 0.2. We also designed test probes that contain, within constant flanking regions, all nnCACGTGnn 10-mers and 18 nnnCACGTGnnn 12-mers (where n = A, C, G, or T). Each DNA sequence represented on the array is present in 6 replicate spots. We report the gcPBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).

Dataset Information

Quantitative high-throughput analysis of transcription factor binding specificities.

Publications

Quantitative high-throughput analysis of transcription factor binding specificities.

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