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In this article, we focus on geometrical structures based methods.
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Protein-structure based methods combine structural features of both the ligand and the interacting biological molecule to predict the binding affinity of the compound, usually quantified as the free energy of binding ((Delta G_{bind})) [3].
Structure based methods were used with and without consensus sequence information.
Seven B cell epitopes (B-P1 to B-P7) were predicted by sequence and structure based methods.
In this study we have applied multiple ligand and structure based methods to the PknB problem and then combined these results using data fusion.
In the past, both, ligand and structure based methods intended to predict if a small molecule could cause fatal heart arrhythmias, "torsades de pointe" and sudden death.
However, ligand-based approaches often cover a broader range of binding conformations, but only a small part of possible binding site space at the protein, which is a reason to prefer structure based methods.
Such methods, similar to the more common structure based methods [57, 58], generally do not allow precise (e.g. binding constant error <10-fold) prediction of binding properties of individual compound-target interactions, but they do provide guidance to focus efforts on compound classes or libraries with the best chances for success [59 64].
We found shape based similarity search methods (ROCS and EON) performed significantly better than structure based methods.
We employed domain sequence and structure based methods to infer a model for the evolutionary history of the MAGUKs.
Our method can be used to support other, e.g. structure based, methods for the discovery of (specific) ncRNA classes by incorporating our similarity scores into the respective probabilistic model or machine learning scheme.
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