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Fig 1.

The energy matrix, derived probabilistic models and corresponding logos of a typical simulation.

(a) The energy logo, with the average energy for each position set to 0. (b) The energy matrix generated from simulation. (c) The information logo when μ = −3. (d) The probabilistic model derived from all binding sites when μ = −3. (Matrix elements are frequency of each base at each position; probability × 100.) (e) The information logo when μ = 3. (f) The probabilistic model derived from all binding sites when μ = 3.

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Table 1.

The rank correlation between the predicted and true all sequence distributions.

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Table 1 Expand

Fig 2.

The correlation between the predicted and true all-sequence distributions.

(a) The correlation between the true distribution (in logarithm with highest affinity site set to 0) and that predicted by the PM generated from the weighted all binding sites. (b) The correlation between the true distribution (in logarithm) and that predicted by the PM generated from the weighted top 1% binding sites. (c) The correlation between the true distribution (in logarithm) and that predicted by the PM generated from the unweighted top 1% binding sites.

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Fig 2 Expand

Table 2.

The rank correlation between the predicted and true top 1% sequence distributions.

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Table 2 Expand

Fig 3.

The correlation between the predicted and true top 1% sequence distributions.

(a) The correlation between the true distribution (in logarithm with highest affinity site set to 0) and that predicted by the PM generated from the weighted all binding sites. (b) The correlation between the true distribution (in logarithm) and that predicted by the PM generated from the weighted top 1% binding sites. (c) The correlation between the true distribution (in logarithm) and that predicted by the PM generated from the unweighted top 1% binding sites.

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Fig 3 Expand

Fig 4.

The non-linear relationship between binding affinity and probability.

The C to A mutation that occurs at the same position but in two different sequence contexts causes dramatically different changes in the binding probability of the whole sequence.

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Fig 4 Expand