Library selection from large combinatorial

Lobanov VS, Agrafiotis DK. Stochastic similarity selections from large combinatorial libraries. J Chem Inf Comput Sci 2000 40 460-470. 

In vitro selection is a combinatorial approach in which functional molecules are selected from large libraries of randomized RNAs or DNAs by selection techniques that are suitable for the enrichment of a particular property such as the binding to a target molecule or a particular catalytic activity (Fig. 5). 

Figure 3.14 Top Design of the 11,325-member resin-bound dynamic combinatorial library (RBDCL). Bottom Left The HlVl frameshift-inducing stemloop RNA screened against the large RBDCL. Bottom Right Compound 3-3, the homodisulfide selected from the RBDCL that binds the HIV 1 frameshift-inducing stemloop with high affinity and selectivity.

It is in the realm of very large combinatorial libraries that selection rather than screening gains crucial importance. As the focus shifts from randomizing an eight-residue peptide to a 100 amino acid protein (the typical size of a small functional domain, for example a chorismate mutase domain), the number of sequence permutations rises to an astronomical 20100. The ability to assay even a tiny fraction of this sequence space in directed molecular evolution experiments demands selection, even though initial development of an appropriate system may be considerably more involved than the setup of a screening procedure. 

We screened peptides that recognize and bind to a partial structure of DCMU from a combinatorial library of tetrapeptides on solid-phase. We used the 3,4-dichloroaniline (DCA) group as bait after taking into consideration the chemical structure of DCMU and its simple synthesis. To screen peptides that bind to the DCA group, a fluorescent-labeled dichloroaniline molecule (NBD-DCA) was synthesized by conjugating a dye with the dichloroaniline through a linker (Fig. 8.4). We selected a NBD moiety for the dye because of its small chemical structure and large Stokes shift. 

Cell-based methods, as well as clustering or distance-based methods, aim at extracting representative structurally diverse subsets of compounds from large chemical databases [Cummins, Andrews et al, 1996 Mason and Pickett, 1997 Pearlman and Smith, 1999 Earnum, Desjarlais et al, 2003]. They are mainly used in design and optimization of combinatorial libraries the most important aspect being here to ensure maximum diversity within and between libraries before they are produced. Moreover, cell-based methods are used for lead discovery purposes allowing the selection of the compounds most similar to the active reference target. 

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