Site pharmacophoric feature

Why should Gox2 activity be at all related to the presence or absence of certain pharmacophore feature pairs in a molecule Certainly, it can be argued - using the typical interpretation given to QSAR models based on autocorrelogram-type descriptors [63] - that the selected pharmacophore feature pairs stand for pairs of functional groups that are involved in direct interactions with the site, and these functional groups must be found at a fixed relative distance. However, this model... 

Fig. 5.5 Putative interactions of the cocrystallized selective Cox2 ligand SC-558 with its active site, assuming a neutral sulfonamide group - the state used to assign pharmacophore feature flags by the used software. Dotted lines stand for hydrogen bonds, the other residues being responsible for hydrophobic contacts. From a physicochemical point of view, an ionized SO2 NH involved in a salt bridge with Arg 513 and hydrogen bonding to the other tautomer of His 90 would make more sense.

The SCF of the protein binding sites can be represented with colored bar-codes for which each color bit represents a 3D SCF pharmacophore feature. Binding sites with the highest number of matches between these triplets or quadruplets of SCF have the highest similarity with the query binding site (Fig. 5). The larger... 

Similarly to other software packages such as DISCO and Catalyst, Phase uses chemical features (hydrophobic, H-bond acceptors, H-bond donors, negative charge, positive charge, aromatic ring) to define the pharmacophore points called sites. These features are encoded in SMARTS and can be edited. H-bond-ing features are vectorized features (their directionality is considered). 

Figure 13.5 Topographical interaction model of ana adrenergic receptor generated based on public site-directed mutagenesis. Both pharmacophore models have been mapped into the topographical model of the receptor. The model reveals putative receptor interaction sites for most of the pharmacophoric features observed within each antagonist class, (a) Class I pharmacophore with prazosin as reference compound (b) class II pharmacophore with compound 10 as reference. Pharmacophoric features are red for posi-...

First, the available dataset is the foremost limitation on pharmacophore research. Ideally, it should contain compounds of various structures and activities with evidence that they bind to the same binding site. Potent, feature-poor and conformationally constrained compounds greatly help in reducing the number of solutions. In reality, however, rare are the cases that meet all these criteria together. Nevertheless, this does not mean that pharmacophore research is unthinkable for difficult cases. 

A powerful extension to the potential pharmacophore method has been developed, in which one of the points is forced to contain a special pharmacophore feature, as illustrated in figure 4. All the potential pharmacophores in the pharmacophore key must contain this feature, thus making it possible to reference the pharmacophoric shapes of the molecule relative to the special feature. This gives an internally referenced or relative measure of molecular similarity/diversity. The special feature can be assigned to any atom-type or site-point, or to special dummy atoms, such as those added as centroids of privileged substructures [7, 10]. With one of the points being reserved for this special feature, it would seem even more necessary to use the 4-point definition to capture enough of the... 

In structure-based pharmacophore modeling, excluded volume spheres can be placed at atoms forming the binding site, a feature that has also been included in LigandScout. For faster screening and less restrictive models, LigandScout alternatively allows for the placement of only a few excluded volume spheres at the lipophilic side-chain residues that are in contact with hydrophobic features in the ligand. We have found that excluded volume... 

Proteins can also be compared, with or without using information from ligands that bind to them. From the protein site points FLAP identifies the pharmacophores that are in common on-the-fly. Then complementarities between the proteins are evaluated maximizing the site-point features and the shape complementary between protein cavities. The resultant matches are then written out to a file. 

H-bond acceptor (C=0), acid (COj), base (NH and lipophilic (aromatic CH). Figure 4.7 illustrates the contours and the atoms which were added (with associated pharmacophore features) for the Factor Xa serine protease active site. 

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