Predicting the effects of amino acid replacements in peptide hormones on their binding affinities for class B GPCRs and application to the design of secretin receptor antagonists.

Journal of Computer-aided Molecular Design
Jerez A TeAndrew J Bordner

Abstract

Computational prediction of the effects of residue changes on peptide-protein binding affinities, followed by experimental testing of the top predicted binders, is an efficient strategy for the rational structure-based design of peptide inhibitors. In this study we apply this approach to the discovery of competitive antagonists for the secretin receptor, the prototypical member of class B G protein-coupled receptors (GPCRs). Proteins in this family are involved in peptide hormone-stimulated signaling and are implicated in several human diseases, making them potential therapeutic targets. We first validated our computational method by predicting changes in the binding affinities of several peptides to their cognate class B GPCRs due to alanine replacement and compared the results with previously published experimental values. Overall, the results showed a significant correlation between the predicted and experimental ΔΔG values. Next, we identified candidate inhibitors by applying this method to a homology model of the secretin receptor bound to an N-terminal truncated secretin peptide. Predictions were made for single residue replacements to each of the other nineteen naturally occurring amino acids at peptide residues within t...Continue Reading

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Citations

Jan 30, 2013·Biochemical Society Transactions·Laurence J Miller, Maoqing Dong
Feb 11, 2020·Biotechnology Letters·Akane Sakaguchi-MikamiTomohiko Yamazaki
Mar 29, 2020·Biochemical Pharmacology·Maoqing DongLaurence J Miller

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