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The propensity values can be calculated using the accessible surface area of residues, as was done by Ellis et al. [10], or using the frequencies of residues, as was done by Lejeune et al. [16].
In order to answer on this question we calculate the propensity values for protein-protein and protein-DNA secondary structure motifs from the group-MultiProteins∶DNA (shown in Figure 1).
For protein-DNA interfaces, ARG and LYS have the highest propensity values (>1.2), which indicates that they occur greater than 20% higher frequently in the interfaces than in the whole dataset.
Sequences were converted to numerical profiles comprising propensity values.
Aromatic pairs have propensity values close to or slightly below 1.00.
We extracted 544 useable propensity values from AAIndex (Kawashima et al., 2008).
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In order to evaluate whether a particular propensity value was significantly different from 1 (either above or below), a statistical bootstrapping method was implemented similar to [10].
These methods assign a propensity value to each AA in the queried protein sequence based on knowledge of the AAs physical and chemical properties.
In the model, the lower the mimetic propensity (value of parameter ε), the less the decision-making depends upon conspecific activity and the more it depends on personal information.
These scales assign a propensity value to every amino acid based on a priori studies of their physico-chemical properties.
Aggrescan data were normalized by running the program on all sequences available in Swiss-Prot and taking the resulting highest a4vAHS value as the maximum aggregation propensity value.
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