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Inferring Binding Energies from Selected Binding Sites

Author

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  • Yue Zhao
  • David Granas
  • Gary D Stormo

Abstract

We employ a biophysical model that accounts for the non-linear relationship between binding energy and the statistics of selected binding sites. The model includes the chemical potential of the transcription factor, non-specific binding affinity of the protein for DNA, as well as sequence-specific parameters that may include non-independent contributions of bases to the interaction. We obtain maximum likelihood estimates for all of the parameters and compare the results to standard probabilistic methods of parameter estimation. On simulated data, where the true energy model is known and samples are generated with a variety of parameter values, we show that our method returns much more accurate estimates of the true parameters and much better predictions of the selected binding site distributions. We also introduce a new high-throughput SELEX (HT-SELEX) procedure to determine the binding specificity of a transcription factor in which the initial randomized library and the selected sites are sequenced with next generation methods that return hundreds of thousands of sites. We show that after a single round of selection our method can estimate binding parameters that give very good fits to the selected site distributions, much better than standard motif identification algorithms.Author Summary: The DNA binding sites of transcription factors that control gene expression are often predicted based on a collection of known or selected binding sites. The most commonly used methods for inferring the binding site pattern, or sequence motif, assume that the sites are selected in proportion to their affinity for the transcription factor, ignoring the effect of the transcription factor concentration. We have developed a new maximum likelihood approach, in a program called BEEML, that directly takes into account the transcription factor concentration as well as non-specific contributions to the binding affinity, and we show in simulation studies that it gives a much more accurate model of the transcription factor binding sites than previous methods. We also develop a new method for extracting binding sites for a transcription factor from a random pool of DNA sequences, called high-throughput SELEX (HT-SELEX), and we show that after a single round of selection BEEML can obtain an accurate model of the transcription factor binding sites.

Suggested Citation

  • Yue Zhao & David Granas & Gary D Stormo, 2009. "Inferring Binding Energies from Selected Binding Sites," PLOS Computational Biology, Public Library of Science, vol. 5(12), pages 1-8, December.
  • Handle: RePEc:plo:pcbi00:1000590
    DOI: 10.1371/journal.pcbi.1000590
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    References listed on IDEAS

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    1. Dana S F Homsi & Vineet Gupta & Gary D Stormo, 2009. "Modeling the Quantitative Specificity of DNA-Binding Proteins from Example Binding Sites," PLOS ONE, Public Library of Science, vol. 4(8), pages 1-9, August.
    2. Eilon Sharon & Shai Lubliner & Eran Segal, 2008. "A Feature-Based Approach to Modeling Protein–DNA Interactions," PLOS Computational Biology, Public Library of Science, vol. 4(8), pages 1-17, August.
    3. Mei-Ling Ting Lee & Martha L. Bulyk & G. A. Whitmore & George M. Church, 2002. "A Statistical Model for Investigating Binding Probabilities of DNA Nucleotide Sequences Using Microarrays," Biometrics, The International Biometric Society, vol. 58(4), pages 981-988, December.
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    Cited by:

    1. Claudia Coronnello & Ryan Hartmaier & Arshi Arora & Luai Huleihel & Kusum V Pandit & Abha S Bais & Michael Butterworth & Naftali Kaminski & Gary D Stormo & Steffi Oesterreich & Panayiotis V Benos, 2012. "Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density," PLOS Computational Biology, Public Library of Science, vol. 8(12), pages 1-13, December.
    2. Vishaka Datta & Sridhar Hannenhalli & Rahul Siddharthan, 2019. "ChIPulate: A comprehensive ChIP-seq simulation pipeline," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-32, March.
    3. Shuxiang Ruan & Gary D Stormo, 2017. "Inherent limitations of probabilistic models for protein-DNA binding specificity," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-15, July.

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    1. Dana S F Homsi & Vineet Gupta & Gary D Stormo, 2009. "Modeling the Quantitative Specificity of DNA-Binding Proteins from Example Binding Sites," PLOS ONE, Public Library of Science, vol. 4(8), pages 1-9, August.
    2. Shuxiang Ruan & Gary D Stormo, 2017. "Inherent limitations of probabilistic models for protein-DNA binding specificity," PLOS Computational Biology, Public Library of Science, vol. 13(7), pages 1-15, July.

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