Seeded databases

In this exercise for the FGF target on the seeded database, we retrieved 70-80% of actives by screening 20-30% of the database. The best combination for retrieving maximum actives when 5% of the database was screened was nonbinary-all feature-Ochiai similarity coefficient . The effect of conformations is dependent on the flexibility of the molecules in the database. Based on our analysis, the maximum number of conformations set to 100 was sufficient for retrieving 70-80% of the actives. The hit list is 12-16 times enriched with respect to random selection from the database on screening only 5% of the database. 

What can be Really Learned from Virtual Screening Simulations of Seeded Databases ... 

Table 1 Examples of vitamin and cofactor biosynthesis subsystems in The SEED database... 

Fig numbers refer to the specific gene s ID number in the SEED database. ° Essentiaiity data from Uberati et al. ... 

Sonnhammer, E.L., Eddy, S.R., Durbin, R. Pfam a comprehensive database of protein domain families based on seed alignments. Proteins 28 405-420, 1997. 

For protection of domestic cattle, feeds should contain <0.05 mg diflubenzuron/kg FW. Cottonseed may be added to cattle diets provided that diflubenzuron concentrations in the seed do not exceed 0.2 mg/kg FW and that cottonseed composes <17% of the total diet bulk (Gartrell 1981). Diflubenzuron causes biochemical upset, as judged by lowered testosterone levels in chickens and rats (USEPA 1979), altered glutathione 5-transferase activity in mouse liver (which adversely affects the ability to detoxify foreign substances by way of conjugation Young et al. 1986), and disrupted hydroxylamine activity in human infants (USEPA 1979). Additional research seems needed on biochemical alterations induced by diflubenzuron. No diflubenzuron criteria are currently recommended for protection of avian and mammalian wildlife. All data available suggest that wildlife species are about as tolerant to diflubenzuron as are domestic poultry and livestock however, the wildlife database seems inadequate for practicable criteria formulation. 

SeeDs-2 library was generated from their in-house database called rCat of 1,622,763 unique chemical compounds assembled from 23 suppliers (25). The filtering cascade began with MW (same as SeeDs-1), then the functional groups and solubility filters which resulted in 43 unique compounds (no overlap with SeeDs-1). These were then clustered by 2D, 3-point pharma-cophoric features to provide 3 clusters, and the centroids of each cluster was submitted for chemist review. Of the 395 selected compounds that were ordered, 357 passed QC to become the SeeDs-2 fragment library. 

Among the EST database of ragi sequences, there are two groups of bifunctional proteinase inhibitor trypsin a-amylase from seeds of ragi sequences. The upper clade was further subdivided (Fig. 6.10). Wang et al. (2008) concluded that there was great diversity in the sequence of different Bowman-Birk inhibitors in emmer wheat both within and between populations. 

Sorensen, M. B., Rasmussen, S. K. (2005). Construction of an annotated EST resource for developing finger millet (Eleusine coracana L.) seeds. Unpublished. NCBI EST Database. 

If a model is to be used as a query to search for active molecules in a database, a common validation method is to demonstrate its performance on a database for which the pharmacological activity of each compound is known (or at least flagged as active or inactive). Most often, such databases are made artificially for this purpose. Thus, after gathering a set of active compounds, one would seed them in a larger database of randomly selected (and supposedly inactive) molecules, the idea being to mimic some HTS results. The model is finally evaluated according to its ability to search the database for the actives and perform better than a random search (enrichment). 

Dissimilarity analysis plays a major role in compound selection. Typical tasks include the selection of a maximally dissimilar subset of compounds from a large set or the identification of compounds that are dissimilar to an existing collection. Such issues have played a major role in compound acquisition in the pharmaceutical industry. A typical task would be to select a subset of maximally dissimilar compounds from a data set containing n molecules. This represents a non-trivial challenge because of the combinatorial problem involved in exploring all possible subsets. Therefore, other dissimilarity-based selection algorithms have been developed (Lajiness 1997). The basic idea of such approaches is to initially select a seed compound (either randomly or, better, based on dissimilarity to others), then calculate dissimilarity between the seed compound and all others and select the most dissimilar one. In the next step, the database compound most dissimilar to these two compounds is selected and added to the subset, and the process is repeated until a subset of desired size is obtained. 

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