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Biology Articles » Biophysics » Molecular Biophysics » Protein–DNA binding specificity predictions with structural models » Figures

Figures
- Protein–DNA binding specificity predictions with structural models

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Figure 1 {Delta}{Delta}G predictions (ddGcomp) versus experimental measurements (ddGexp). (A) Static model binding energy predictions for the set of experimental measurements used in fitting static model weights. (B) Dynamic model binding energy predictions for the same set of experimental measurements. Closed circles, static/dynamic model; red triangles, contact model; green triangles, number of mutations from the consensus sequence. r1-Linear correlation coefficient for the static/dynamic model and r2-linear correlation coefficient for the contact model. Three Zif268 datasets from Table 1 [two for Zif268 wild-type (42,67) and one for Zif268 D20A mutant (67)] are combined into one panel.

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Figure 2 {Delta}{Delta}G predictions (ddGcomp) versus experimental measurements (ddGexp). Static model binding energy predictions for Ndt80, (34) MAT a1/{alpha}2, (30) AtERF1 (45) and c-Myb. (47) Closed circles, static model; red triangles, contact model; green triangles, number of mutations from the consensus sequence. r1-Linear correlation coefficient for the static model and r2-linear correlation coefficient for the contact model.

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Figure 3 Experimental binding affinities conferred by indirect readout can be explained with DNA conformational energies alone. Dynamic model predictions of DNA base step energies (ddGdna) versus experimental binding free energies (ddGexp) for BamHI endonuclease (49) and PU.1 ETS domain (50).

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Figure 4 Degree of pairwise additivity in binding energies predicted with the dynamic model. Comparison of binding energies computed after making multiple base pairs substitutions (ddGcomp) with the sum of binding energies computed for corresponding one-point mutations of the DNA site from the protein–DNA structure (ddGpw).

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Figure 5 PWM predictions for Ndt80 (A) and Zif268 (B). From top to bottom: experiment, contact model based on the consensus sequence and the number of protein–DNA contacts, static model and dynamic model (see text for details). PWMs are displayed using the uniform height WebLogo representation(58): the height of each letter in the column is proportional to its probability in the PWM.

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Figure 6 PWM predictions by homology for D.melanogaster TF bicoid (Bcd) (A) and giant (Gt) (B). From top to bottom: (A) panel 1: experiment; panels 2–4: contact model, static model with full energy function and static model with DNA conformational energy only (with dna–bp and dna–bs weights multiplied by 5) using D.melanogaster Engrailed homeodomain Q50K (2hdd) as a template; panels 5–7: contact model, dynamic model with full energy function (reference DNA energies are not subtracted since DNA is bent in homeodomains) and static model with DNA conformational energy only (with dna–bp and dna–bs weights multiplied by 5) using D.melanogaster Engrailed wild-type homeodomain (3hdd) as a template. (B) Experiment, contact model, static model and dynamic model using Homo sapiens nuclear factor NF-IL6 (C/EBP-ß;1gu4) as a template. All amino acids substituted at the protein–DNA interface are repacked in the static model. PWMs are displayed using the uniform height WebLogo representation (58).

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