Focused hierarchical design of peptide libraries-follow the lead

• Published in: Journal of chemometrics Vol. 21; no. 10-11; pp. 486 - 495
• Main Authors: Muthas, Daniel, Lek, Per M., Nurbo, Johanna, Karlén, Anders, Lundstedt, Torbjörn
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.10.2007; Wiley; Wiley Subscription Services, Inc
• Subjects: ACE inhibitors; amino acid z-scales; Amino acids; Biological and medical sciences; Chemistry; Design of experiments; Exact sciences and technology; FARMACI; General and physical chemistry; hierarchical design; Medical sciences; PCA; peptide library design; Peptides; PHARMACY; Design; Peptide library; hierarchical design; Peptides; Experimental design; Aminoacid; amino acid z-scales; peptide library design; Chemometrics; design of experiments; PCA
• ISSN: 0886-9383; 1099-128X; 1099-128X
• DOI: 10.1002/cem.1069

A novel design strategy based on the hierarchical design of experiments (HDoE) method named focused hierarchical design of experiments (FHDoE) is presented. FHDoE combine two design layers and use focused substitutions to increase the probability of obtaining active peptides when designing libraries through a selection of compounds biased towards a lead structure. Increasing the number of peptides with measurable activity will increase the information gained and the likelihood of constructing good quantitative structure–activity relationship (QSAR) models. The utility of the novel design method is verified using two different approaches. First, a library designed with the novel FHDoE method was compared with libraries generated from classical positional scanning techniques (e.g., alanine scan) as well as with general and centered minimum analog peptide sets (MAPS) libraries by using an example found in the literature. Secondly, the same design strategies were applied to a dataset of 58 angiotensin converting enzyme (ACE) dipeptide inhibitors. QSAR models were generated from designed sublibraries and the activities of the remaining compounds were predicted. These two examples show that the use of FHDoE renders peptide libraries close in physicochemical space to the native ligand, yielding a more thorough screening of the area of interest as compared to the classical positional scans and fractional factorial design (FFD). It is also shown that an FHDoE library of six dipeptides could produce a QSAR model that better described the requisites of high activity ACE inhibitors than could QSAR models built from either a nine‐dipeptide library designed with MAPS or a 58‐dipeptide library. Copyright © 2007 John Wiley & Sons, Ltd.