A gradient descent algorithm for minimizing amino acid coupling reactions when synthesizing cyclic-peptide libraries

Darwen, Paul J., Tran, Tran T., Bourne, Gregory T., Nielson, Jonathan L., and Smythe, Mark L. (2006) A gradient descent algorithm for minimizing amino acid coupling reactions when synthesizing cyclic-peptide libraries. Combinatorial Chemistry and High Throughput Screening, 9 (7). pp. 559-563.

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Combinatorial chemistry has become an invaluable tool in medicinal chemistry for the identification of new drug leads. For example, libraries of predetermined sequences and head-to-tail cyclized peptides are routinely synthesized in our laboratory using the IRORI approach. Such libraries are used as molecular toolkits that enable the development of pharmacophores that define activity and specificity at receptor targets. These libraries can be quite large and difficult to handle, due to physical and chemical constraints imposed by their size. Therefore, smaller sub-libraries are often targeted for synthesis. The number of coupling reactions required can be greatly reduced if the peptides having common amino acids are grouped into the same sub-library (batching). This paper describes a schedule optimizer to minimize the number of coupling reactions by rotating and aligning sequences while simultaneously batching. The gradient descent method thereby reduces the number of coupling reactions required for synthesizing cyclic peptide libraries. We show that the algorithm results in a 75% reduction in the number of coupling reactions for a typical cyclic peptide library.

Item ID: 38932
Item Type: Article (Research - C1)
ISSN: 1386-2073
Keywords: cyclic peptides, alignment, batch synthesis, coupling reaction, gradient descent, hill climbing, combinatorial chemistry
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Funders: Novo Nordisk Foundation (NNF), Protagonist, Australian Research Council (ARC)
Date Deposited: 29 Nov 2016 01:23
FoR Codes: 03 CHEMICAL SCIENCES > 0307 Theoretical and Computational Chemistry > 030799 Theoretical and Computational Chemistry not elsewhere classified @ 40%
01 MATHEMATICAL SCIENCES > 0103 Numerical and Computational Mathematics > 010303 Optimisation @ 60%
SEO Codes: 92 HEALTH > 9299 Other Health > 929999 Health not elsewhere classified @ 80%
97 EXPANDING KNOWLEDGE > 970108 Expanding Knowledge in the Information and Computing Sciences @ 20%
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