Structure-Based Pharmacophore Modeling
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Structure-Based Pharmacophore Modeling
by hiranyagarbh Fri Apr 25, 2008 9:43 pm
The molecular recognition properties of a binding pocket are determined by the amino acids forming the cavity.
The spatial arrangements of these amino acids and their physicochemical properties define the shape and the properties that a ligand has to complement in order to be qualified to bind to the pocket.
Therefore, the structure of the binding pocket can be used to map putative interaction sites for certain functional groups such as hydrogen-bond donors and acceptors and hydrophobic features into the binding site. These favorable interactions sites are also referred to as “hot spots”.
Up to now, there has been no method available to translate these interaction sites directly into chemically accessible new molecules. An indirect way is to derive a pharmacophore hypothesis based on the calculated “hot spots”.
This pharmacophore is then subsequently used for virtual database screening or to guide docking of a pre-assembled library. In addition, “hot spots” can also be used to tailor scoring functions for a specific target in order to improve their predictive power.
The spatial arrangements of these amino acids and their physicochemical properties define the shape and the properties that a ligand has to complement in order to be qualified to bind to the pocket.
Therefore, the structure of the binding pocket can be used to map putative interaction sites for certain functional groups such as hydrogen-bond donors and acceptors and hydrophobic features into the binding site. These favorable interactions sites are also referred to as “hot spots”.
Up to now, there has been no method available to translate these interaction sites directly into chemically accessible new molecules. An indirect way is to derive a pharmacophore hypothesis based on the calculated “hot spots”.
This pharmacophore is then subsequently used for virtual database screening or to guide docking of a pre-assembled library. In addition, “hot spots” can also be used to tailor scoring functions for a specific target in order to improve their predictive power.
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