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TherMos: Estimating protein-DNA binding energies from in vivo binding profiles.

Sun W, Hu X, Lim MH, Ng CK, Choo SH, Castro DS, Drechsel D, Guillemot F, Kolatkar PR, Jauch R, Prabhakar S - Nucleic Acids Res. (2013)

Bottom Line: We experimentally validated TherMos binding energy models for Klf4 and Esrrb, using a novel protocol to measure PSEMs in vitro.Strikingly, our measurements revealed strong non-additivity at multiple positions within the two PSEMs. Among the algorithms tested, only TherMos was able to model the entire binding energy landscape of Klf4 and Esrrb.Our study reveals new insights into the energetics of TF-DNA binding in vivo and provides an accurate first-principles approach to binding energy inference from ChIP-seq and ChIP-exo data.

View Article: PubMed Central - PubMed

Affiliation: Computational and Systems Biology, Genome Institute of Singapore, 60 Biopolis St, Singapore 138672, Singapore.

ABSTRACT
Accurately characterizing transcription factor (TF)-DNA affinity is a central goal of regulatory genomics. Although thermodynamics provides the most natural language for describing the continuous range of TF-DNA affinity, traditional motif discovery algorithms focus instead on classification paradigms that aim to discriminate 'bound' and 'unbound' sequences. Moreover, these algorithms do not directly model the distribution of tags in ChIP-seq data. Here, we present a new algorithm named Thermodynamic Modeling of ChIP-seq (TherMos), which directly estimates a position-specific binding energy matrix (PSEM) from ChIP-seq/exo tag profiles. In cross-validation tests on seven genome-wide TF-DNA binding profiles, one of which we generated via ChIP-seq on a complex developing tissue, TherMos predicted quantitative TF-DNA binding with greater accuracy than five well-known algorithms. We experimentally validated TherMos binding energy models for Klf4 and Esrrb, using a novel protocol to measure PSEMs in vitro. Strikingly, our measurements revealed strong non-additivity at multiple positions within the two PSEMs. Among the algorithms tested, only TherMos was able to model the entire binding energy landscape of Klf4 and Esrrb. Our study reveals new insights into the energetics of TF-DNA binding in vivo and provides an accurate first-principles approach to binding energy inference from ChIP-seq and ChIP-exo data.

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In vitro binding energy model for Esrrb and comparison with algorithmic predictions from ChIP-seq. (A) Sequence logos of Esrrb motifs predicted by TherMos, MatrixREDUCE, Weeder, MEME, DREME and ChIPMunk. (B) Results of the EMSA competition assays. (C) The sequence logo of the Esrrb affinity model measured in vitro by EMSA competition assays. (D) Euclidean distance between in vitro motif and the motifs predicted by various algorithms.
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gkt250-F3: In vitro binding energy model for Esrrb and comparison with algorithmic predictions from ChIP-seq. (A) Sequence logos of Esrrb motifs predicted by TherMos, MatrixREDUCE, Weeder, MEME, DREME and ChIPMunk. (B) Results of the EMSA competition assays. (C) The sequence logo of the Esrrb affinity model measured in vitro by EMSA competition assays. (D) Euclidean distance between in vitro motif and the motifs predicted by various algorithms.

Mentions: To experimentally benchmark the performance of TherMos in predicting the intrinsic binding energy of TFs, we developed a competitive EMSA protocol that can measure PSEMs in vitro (‘Materials and Methods’ section and Supplementary Information). We first applied this validation approach to the nuclear receptor Esrrb. As in the standard EMSA competition assay, we mixed a labeled high-affinity DNA fragment with the purified Esrrb DNA-binding domain and multiple unlabeled competitor DNA fragments, and then quantified the fraction of labeled DNA fragments that bound Esrrb. The bound fractions were then used to infer the dissociation constants of TF binding to the competitor fragments (‘Materials and Methods’ section). Using the 9-bp Esrrb ‘consensus’ element CCAAGGTCA as the core of the labeled fragment, we tested 28 competitors: the consensus sequence itself, plus all 27 (3 × 9) singly mutated variants of the consensus (Figure 3B). From the resulting bound-fraction data, we estimated an additive in vitro PSEM for Esrrb. The equivalent sequence logo is shown in Figure 3C.Figure 3.


TherMos: Estimating protein-DNA binding energies from in vivo binding profiles.

Sun W, Hu X, Lim MH, Ng CK, Choo SH, Castro DS, Drechsel D, Guillemot F, Kolatkar PR, Jauch R, Prabhakar S - Nucleic Acids Res. (2013)

In vitro binding energy model for Esrrb and comparison with algorithmic predictions from ChIP-seq. (A) Sequence logos of Esrrb motifs predicted by TherMos, MatrixREDUCE, Weeder, MEME, DREME and ChIPMunk. (B) Results of the EMSA competition assays. (C) The sequence logo of the Esrrb affinity model measured in vitro by EMSA competition assays. (D) Euclidean distance between in vitro motif and the motifs predicted by various algorithms.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3675472&req=5

gkt250-F3: In vitro binding energy model for Esrrb and comparison with algorithmic predictions from ChIP-seq. (A) Sequence logos of Esrrb motifs predicted by TherMos, MatrixREDUCE, Weeder, MEME, DREME and ChIPMunk. (B) Results of the EMSA competition assays. (C) The sequence logo of the Esrrb affinity model measured in vitro by EMSA competition assays. (D) Euclidean distance between in vitro motif and the motifs predicted by various algorithms.
Mentions: To experimentally benchmark the performance of TherMos in predicting the intrinsic binding energy of TFs, we developed a competitive EMSA protocol that can measure PSEMs in vitro (‘Materials and Methods’ section and Supplementary Information). We first applied this validation approach to the nuclear receptor Esrrb. As in the standard EMSA competition assay, we mixed a labeled high-affinity DNA fragment with the purified Esrrb DNA-binding domain and multiple unlabeled competitor DNA fragments, and then quantified the fraction of labeled DNA fragments that bound Esrrb. The bound fractions were then used to infer the dissociation constants of TF binding to the competitor fragments (‘Materials and Methods’ section). Using the 9-bp Esrrb ‘consensus’ element CCAAGGTCA as the core of the labeled fragment, we tested 28 competitors: the consensus sequence itself, plus all 27 (3 × 9) singly mutated variants of the consensus (Figure 3B). From the resulting bound-fraction data, we estimated an additive in vitro PSEM for Esrrb. The equivalent sequence logo is shown in Figure 3C.Figure 3.

Bottom Line: We experimentally validated TherMos binding energy models for Klf4 and Esrrb, using a novel protocol to measure PSEMs in vitro.Strikingly, our measurements revealed strong non-additivity at multiple positions within the two PSEMs. Among the algorithms tested, only TherMos was able to model the entire binding energy landscape of Klf4 and Esrrb.Our study reveals new insights into the energetics of TF-DNA binding in vivo and provides an accurate first-principles approach to binding energy inference from ChIP-seq and ChIP-exo data.

View Article: PubMed Central - PubMed

Affiliation: Computational and Systems Biology, Genome Institute of Singapore, 60 Biopolis St, Singapore 138672, Singapore.

ABSTRACT
Accurately characterizing transcription factor (TF)-DNA affinity is a central goal of regulatory genomics. Although thermodynamics provides the most natural language for describing the continuous range of TF-DNA affinity, traditional motif discovery algorithms focus instead on classification paradigms that aim to discriminate 'bound' and 'unbound' sequences. Moreover, these algorithms do not directly model the distribution of tags in ChIP-seq data. Here, we present a new algorithm named Thermodynamic Modeling of ChIP-seq (TherMos), which directly estimates a position-specific binding energy matrix (PSEM) from ChIP-seq/exo tag profiles. In cross-validation tests on seven genome-wide TF-DNA binding profiles, one of which we generated via ChIP-seq on a complex developing tissue, TherMos predicted quantitative TF-DNA binding with greater accuracy than five well-known algorithms. We experimentally validated TherMos binding energy models for Klf4 and Esrrb, using a novel protocol to measure PSEMs in vitro. Strikingly, our measurements revealed strong non-additivity at multiple positions within the two PSEMs. Among the algorithms tested, only TherMos was able to model the entire binding energy landscape of Klf4 and Esrrb. Our study reveals new insights into the energetics of TF-DNA binding in vivo and provides an accurate first-principles approach to binding energy inference from ChIP-seq and ChIP-exo data.

Show MeSH
Related in: MedlinePlus