Application of Genetic Algorithms to Combinatorial Synthesis: A Computational Approach to Lead Identification and Lead Optimization,

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Abstract

A genetic algorithms (GA) based strategy is described for the identification or optimization of active leads. This approach does not require the synthesis and evaluation of huge libraries. Instead it involves iterative generations of smaller sample sets, which are assayed, and the “experimentally” determined biological response is used as an input for GA to rapidly find better leads. The GA described here has been applied to the identification of potent and selective stromelysin substrates from a combinatorial-based population of 206 or 64 000 000 possible hexapeptides. Using GA, we have synthesized less then 300 unique immobilized peptides in a total of five generations to achieve this end. The results show that each successive generation provided better and unique substrates. An additional strategy of utilizing the knowledge gained in each generation in a spin-off SAR activity is described here. Sequences from the first generations were evaluated for stromelysin and collagenase activity to identify stromelysin-selective substrates. GlyProSerThr-TyrThr with Tyr as the P1‘ residue is such an example. A number of peptides replacing Tyr with unusual monomers were synthesized and evaluated as stromelysin substrates. This led to the identification of Ser(OBn) as the best and most selective P1‘ residue for stromelysin.

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