Bandholtz S.,Charité - Medical University of Berlin |
Wichard J.,Leibnitz Institute For Molekulare Pharmakologie Fmp |
Kuhne R.,Leibnitz Institute For Molekulare Pharmakologie Fmp |
Grotzinger C.,Charité - Medical University of Berlin
PLoS ONE | Year: 2012
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. © 2012 Bandholtz et al.
Bleif S.,Saarland University |
Hannemann F.,Saarland University |
Lisurek M.,Leibnitz Institute For Molekulare Pharmakologie Fmp |
Von Kries J.P.,Leibnitz Institute For Molekulare Pharmakologie Fmp |
And 4 more authors.
ChemBioChem | Year: 2011
The cytochrome P450 monooxygenase CYP106A2 from Bacillus megaterium ATCC 13368 catalyzes hydroxylations of a variety of 3-oxo-Δ 4-steroids such as progesterone and deoxycorticosterone (DOC), mainly in the 15β-position. We combined a high-throughput screening and a rational approach for identifying new substrates of CYP106A2. The diterpene resin acid abietic acid was found to be a substrate and was docked into the active site of a CYP106A2 homology model to provide further inside into the structural basis of the regioselectivity of hydroxylation. The products of the hydroxylation reaction were analyzed by HPLC and the V max and K m values were calculated. The corresponding reaction products were analyzed by NMR spectroscopy and identified as 12α- and 12β-hydroxyabietic acid. CYP106A2 was therefore identified as the first reported bacterial cytochrome P450 diterpene hydroxylase. Furthermore, an effective whole-cell catalyst for the selective allylic 12α- and 12β-hydroxylation was applied to produce the hydroxylated products. Bacterial allylic hydroxylation: CYP106A2 has been identified as the first reported bacterial cytochrome P450 diterpene hydroxylase. It is able to carry out a one-step regioselective allylic hydroxylation of the diterpene abietic acid. An effective whole-cell catalyst for the selective allylic 12α- and 12β-hydroxylation was developed. © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.