molecular evolution of a peptide gpcr ligand driven by artificial neural networks分子进化的肽gpcr配体由人工神经网络.pdfVIP

Molecular Evolution of a Peptide GPCR Ligand Driven by Artificial Neural Networks 1 ¨ 2 ¨ 2 ¨ 1 Sebastian Bandholtz , Jorg Wichard , Ronald Kuhne , Carsten Grotzinger * ´ ¨ 1 Charite – Universitatsmedizin Berlin, Campus Virchow-Klinikum, Department of Hepatology and Gastroenterology and Molecular Cancer Research Center (MKFZ), Tumor ¨ Targeting Lab, Berlin, Germany, 2 Leibnitz-Institut fur Molekulare Pharmakologie (fmp), Berlin, Germany 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 calculated weights for each cell. The resulting network calculated the fitness function in a genetic algorithm to explore the virtual space of all possible peptides. The network training was based on gradient descent techniques which rely on the efficient calculation of the gradient by back- propagation. After three consecutive cycles of sequence design by the neural network, peptide synthesis and b