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We found that the latter conformation represents the most reliable model for binding of buspirone analogues.
These results are discussed in the context of a two-receptor model for binding of AFPep and ring-and-tail analogs.
A LIE model for binding affinity toward the isoform CYP 1A2 was recently published, in which a similar protocol as presented here was adopted [24], but in which ligand preparation was only semi-automated.
Our choice of using PSFM model for binding sites is arbitrary.
Finally, as noted before, our choice of using a PSFM model for binding sites is arbitrary and the same framework can be easily extended to other binding site models as well.
However, van Nimwegen et al. [10] (supporting text) use a simple maximum-entropy argument to show that the additivity assumption on energy does imply the PWM model for binding sites, if one also makes the reasonable assumption that binding sites have a significantly higher expected binding energy than random sites.
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We construct our basic probabilistic framework using commonly used models for binding and non-binding sites, although generalizations to more complex models are straightforward.
The most commonly used probabilistic models for binding sites and background sequences, on which our methods are also built, are the position specific frequency matrix (PSFM) model [66], [21] and the Markovian model [67], respectively.
The QSAR models for binding to albumin and other proteins are summarized in the preceding sections.
Limitations of early seed-motif matching approaches led to the integration of thermodynamic models for binding strength.
To date, kinetic studies of the ATP sulfurylases from mammals, fungi and plants have led to two different models for binding of substrates [ 8, 36, 43].
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