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

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 iterativ...

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Autores principales:	Sebastian Bandholtz, Jörg Wichard, Ronald Kühne, Carsten Grötzinger
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0805680bab184e97b215ad6c2f0cb4f2
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spelling	oai:doaj.org-article:0805680bab184e97b215ad6c2f0cb4f22021-11-18T07:18:54ZMolecular evolution of a peptide GPCR ligand driven by artificial neural networks.1932-620310.1371/journal.pone.0036948https://doaj.org/article/0805680bab184e97b215ad6c2f0cb4f22012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22606313/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Peptide 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 bioassay this new approach yielded a ligand with 70fold higher metabolic stability compared to the wild type peptide without loss of the subnanomolar activity in the biological assay. Combining specialized neural networks with an exploration of the combinatorial amino acid sequence space by genetic algorithms represents a novel rational strategy for peptide design and optimization.Sebastian BandholtzJörg WichardRonald KühneCarsten GrötzingerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 5, p e36948 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sebastian Bandholtz Jörg Wichard Ronald Kühne Carsten Grötzinger Molecular evolution of a peptide GPCR ligand driven by artificial neural networks.
description	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 bioassay this new approach yielded a ligand with 70fold higher metabolic stability compared to the wild type peptide without loss of the subnanomolar activity in the biological assay. Combining specialized neural networks with an exploration of the combinatorial amino acid sequence space by genetic algorithms represents a novel rational strategy for peptide design and optimization.
format	article
author	Sebastian Bandholtz Jörg Wichard Ronald Kühne Carsten Grötzinger
author_facet	Sebastian Bandholtz Jörg Wichard Ronald Kühne Carsten Grötzinger
author_sort	Sebastian Bandholtz
title	Molecular evolution of a peptide GPCR ligand driven by artificial neural networks.
title_short	Molecular evolution of a peptide GPCR ligand driven by artificial neural networks.
title_full	Molecular evolution of a peptide GPCR ligand driven by artificial neural networks.
title_fullStr	Molecular evolution of a peptide GPCR ligand driven by artificial neural networks.
title_full_unstemmed	Molecular evolution of a peptide GPCR ligand driven by artificial neural networks.
title_sort	molecular evolution of a peptide gpcr ligand driven by artificial neural networks.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/0805680bab184e97b215ad6c2f0cb4f2
work_keys_str_mv	AT sebastianbandholtz molecularevolutionofapeptidegpcrliganddrivenbyartificialneuralnetworks AT jorgwichard molecularevolutionofapeptidegpcrliganddrivenbyartificialneuralnetworks AT ronaldkuhne molecularevolutionofapeptidegpcrliganddrivenbyartificialneuralnetworks AT carstengrotzinger molecularevolutionofapeptidegpcrliganddrivenbyartificialneuralnetworks
_version_	1718423577556942848

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

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