Patented signal analytic algorithms applied to hydrophobically transformed, numerical amino acid sequences have previously been used to design short, protein-targeted, L or D retro-inverso peptides. These peptides have demonstrated allosteric and/or indirect agonist effects on a variety of G-protein and tyrosine kinase coupled membrane receptors with 30% to over 80% hit rates. Here we extend these approaches to a globular protein target. We designed eight peptide ligands targeting an ELISA antibody responsive protein, beta-galactosidase, betaGAL. Three of the eight 14mer peptides allosterically activated betaGAL with ELISA methodology. Using Bayesian statistics, this 38% hit rate would have occurred 2 x 10(-9) by chance. These peptides demonstrated binding site competitive or noncompetitive interactions, suggesting allosteric site multiplicity with respect to their betaGAL binding-mediated ELISA signal. Kinetic studies demonstrated the temperature dependence of the betaGAL peptide binding functions. Using the van't Hoff relation, we found evidence for enthalpy-entropy compensation. This relation is often found for hydrophobic interactions in aqueous media, and is consistent with the postulated hydrophobic series encoding underlying our protein-targeted, peptide design methods. It appears that our algorithmic, hydrophobic autocovariance eigenvector template approach to the design of allosteric peptides targeting membrane receptors may also be applicable to the design of peptide ligands targeting nonmembrane involved globular proteins.

Download full-text PDF

Source
http://dx.doi.org/10.1002/bip.20607DOI Listing

Publication Analysis

Top Keywords

peptide ligands
12
ligands targeting
12
globular protein
8
peptides demonstrated
8
membrane receptors
8
peptide
5
peptides
5
designing allosteric
4
allosteric peptide
4
targeting
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!