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Proc Natl Acad Sci U S A. 1998 Oct 13;95(21):12179-84.

Peptide design by artificial neural networks and computer-based evolutionary search.

Author information

1
Freie Universität Berlin, Universitätsklinikum Benjamin Franklin, Institut für Medizinische/Technische Physik und Lasermedizin, Krahmerstrasse 6-10, D-12207 Berlin, Germany. gisbert.schneider@roche.com

Abstract

A technique for systematic peptide variation by a combination of rational and evolutionary approaches is presented. The design scheme consists of five consecutive steps: (i) identification of a "seed peptide" with a desired activity, (ii) generation of variants selected from a physicochemical space around the seed peptide, (iii) synthesis and testing of this biased library, (iv) modeling of a quantitative sequence-activity relationship by an artificial neural network, and (v) de novo design by a computer-based evolutionary search in sequence space using the trained neural network as the fitness function. This strategy was successfully applied to the identification of novel peptides that fully prevent the positive chronotropic effect of anti-beta1-adrenoreceptor autoantibodies from the serum of patients with dilated cardiomyopathy. The seed peptide, comprising 10 residues, was derived by epitope mapping from an extracellular loop of human beta1-adrenoreceptor. A set of 90 peptides was synthesized and tested to provide training data for neural network development. De novo design revealed peptides with desired activities that do not match the seed peptide sequence. These results demonstrate that computer-based evolutionary searches can generate novel peptides with substantial biological activity.

PMID:
9770460
PMCID:
PMC22805
[Indexed for MEDLINE]
Free PMC Article
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