Molecular evolution of a peptide GPCR ligand driven by artificial neural networks.

ABSTRACT: Peptide ligands of G protein-coupled receptors constitute valuable natural lead structures for the development of highly selective drugs and high-affinity tools to probe ligand-receptor interaction. Currently, pharmacological and metabolic modification of natural peptides involves either an iterative trial-and-error process based on structure-activity relationships or screening of peptide libraries that contain many structural variants of the native molecule. Here, we present a novel neural network architecture for the improvement of metabolic stability without loss of bioactivity. In this approach the peptide sequence determines the topology of the neural network and each cell corresponds one-to-one to a single amino acid of the peptide chain. Using a training set, the learning algorithm

SUBMITTER: Bandholtz S 

PROVIDER: S-EPMC3351444 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature