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

Bandholtz S, Wichard J, Kühne R, Grötzinger C - PLoS ONE (2012)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Charité-Universitätsmedizin Berlin, Campus Virchow-Klinikum, Department of Hepatology and Gastroenterology and Molecular Cancer Research Center (MKFZ), Tumor Targeting Lab, 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 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.

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Stability and activity of chemerin peptides improve over three cycles. Upper panel: The complete data set from three optimization rounds is shown, each dot representing one peptide and its properties. The trend points to the upper left part of the diagram, i.e. peptides that combine high receptor activity ( = low EC50) with high metabolic stability. Lower panel: separate representations of the data from all three rounds. While in the first round, the majority of data points is found in the lower right quadrant with poor stability and activity, in cycles 2 and 3 the data cloud is shifted towards the upper left, representing improved activity and stability. Quality is a measure of the likelihood of observing the substitutions in a particular column of the alignment [62].
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pone-0036948-g004: Stability and activity of chemerin peptides improve over three cycles. Upper panel: The complete data set from three optimization rounds is shown, each dot representing one peptide and its properties. The trend points to the upper left part of the diagram, i.e. peptides that combine high receptor activity ( = low EC50) with high metabolic stability. Lower panel: separate representations of the data from all three rounds. While in the first round, the majority of data points is found in the lower right quadrant with poor stability and activity, in cycles 2 and 3 the data cloud is shifted towards the upper left, representing improved activity and stability. Quality is a measure of the likelihood of observing the substitutions in a particular column of the alignment [62].

Mentions: The effectiveness of the GNN-based modulation of the peptide composition are visualized in plots for both parameters analyzed within this process (Fig. 4). While in the first round, the majority of data points were found in the lower right quadrant with poor stability and activity, in cycles 2 and 3 the data cloud was shifted towards the upper left, representing improved activity and stability.


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

Bandholtz S, Wichard J, Kühne R, Grötzinger C - PLoS ONE (2012)

Stability and activity of chemerin peptides improve over three cycles. Upper panel: The complete data set from three optimization rounds is shown, each dot representing one peptide and its properties. The trend points to the upper left part of the diagram, i.e. peptides that combine high receptor activity ( = low EC50) with high metabolic stability. Lower panel: separate representations of the data from all three rounds. While in the first round, the majority of data points is found in the lower right quadrant with poor stability and activity, in cycles 2 and 3 the data cloud is shifted towards the upper left, representing improved activity and stability. Quality is a measure of the likelihood of observing the substitutions in a particular column of the alignment [62].
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3351444&req=5

pone-0036948-g004: Stability and activity of chemerin peptides improve over three cycles. Upper panel: The complete data set from three optimization rounds is shown, each dot representing one peptide and its properties. The trend points to the upper left part of the diagram, i.e. peptides that combine high receptor activity ( = low EC50) with high metabolic stability. Lower panel: separate representations of the data from all three rounds. While in the first round, the majority of data points is found in the lower right quadrant with poor stability and activity, in cycles 2 and 3 the data cloud is shifted towards the upper left, representing improved activity and stability. Quality is a measure of the likelihood of observing the substitutions in a particular column of the alignment [62].
Mentions: The effectiveness of the GNN-based modulation of the peptide composition are visualized in plots for both parameters analyzed within this process (Fig. 4). While in the first round, the majority of data points were found in the lower right quadrant with poor stability and activity, in cycles 2 and 3 the data cloud was shifted towards the upper left, representing improved activity and stability.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Charité-Universitätsmedizin Berlin, Campus Virchow-Klinikum, Department of Hepatology and Gastroenterology and Molecular Cancer Research Center (MKFZ), Tumor Targeting Lab, 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 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.

Show MeSH
Related in: MedlinePlus