MHC I Stabilizing Potential of Computer-Designed Octapeptides

Wiśniewska, Joanna; Jäger, Natalie; Freier, Anja; Losch, Florian; Wiesmüller, Karl‐Heinz; Walden, Peter; Wrede, Paul; Schneider, Gisbert; Hiss, Jan A.

doi:10.1155/2010/396847

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…In this role they have been successfully used in the QSAR field, generating hypotheses in the drug design cycle for GPCRs and other target classes and in automated feature extraction, yielding convincing results in numerous projects for small molecule drug development [19] – [25,25] . Artificial neural networks have also generated very substantial progress in the optimization of peptides for various purposes in molecular biology and pharmaceutical design, for instance in MHC I binding and stabilizing peptides [26] – [28] , for the identification and biological activity of signal peptidase and viral proteinase cleavage sites [29] – [31] , with cell-interacting peptides [32] , and in modifying peptide transport across the blood-brain barrier [33] . Schneider et al [34] have used artificial neural networks and a computer-based evolutionary search to design autoantibody-binding peptides by a cyclic variation-selection process.…”

Section: Introductionmentioning

confidence: 99%

Molecular Evolution of a Peptide GPCR Ligand Driven by Artificial Neural Networks

et al. 2012

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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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Section: Introductionmentioning

confidence: 99%

Molecular Evolution of a Peptide GPCR Ligand Driven by Artificial Neural Networks

et al. 2012

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“…The work most similar to our study was published by Wisniewska and co-workers. They combined an ant colony optimization algorithm with an artificial neural network classifier to iteratively adapt octapeptides for MHC class I stabilization [ 55 ]. However, to our knowledge our study is the first study which investigates the operators of a GA in relation to maximizing peptide/MHC binding affinity.…”

Section: Discussionmentioning

confidence: 99%

PeptX: Using Genetic Algorithms to optimize peptides for MHC binding

et al. 2011

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BackgroundThe binding between the major histocompatibility complex and the presented peptide is an indispensable prerequisite for the adaptive immune response. There is a plethora of different in silico techniques for the prediction of the peptide binding affinity to major histocompatibility complexes. Most studies screen a set of peptides for promising candidates to predict possible T cell epitopes. In this study we ask the question vice versa: Which peptides do have highest binding affinities to a given major histocompatibility complex according to certain in silico scoring functions?ResultsSince a full screening of all possible peptides is not feasible in reasonable runtime, we introduce a heuristic approach. We developed a framework for Genetic Algorithms to optimize peptides for the binding to major histocompatibility complexes. In an extensive benchmark we tested various operator combinations. We found that (1) selection operators have a strong influence on the convergence of the population while recombination operators have minor influence and (2) that five different binding prediction methods lead to five different sets of "optimal" peptides for the same major histocompatibility complex. The consensus peptides were experimentally verified as high affinity binders.ConclusionWe provide a generalized framework to calculate sets of high affinity binders based on different previously published scoring functions in reasonable runtime. Furthermore we give insight into the different behaviours of operators and scoring functions of the Genetic Algorithm.

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Peptide Design by Nature‐Inspired Algorithms

Hiss

Schneider

2013

De Novo Molecular Design

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MHC I Stabilizing Potential of Computer-Designed Octapeptides

Cited by 3 publications

References 34 publications

Molecular Evolution of a Peptide GPCR Ligand Driven by Artificial Neural Networks

Molecular Evolution of a Peptide GPCR Ligand Driven by Artificial Neural Networks

PeptX: Using Genetic Algorithms to optimize peptides for MHC binding

Peptide Design by Nature‐Inspired Algorithms

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