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ENPDA: an evolutionary structure-based de novo peptide design algorithm

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Summary

One of the goals of computational chemists is to automate the de novo design of bioactive molecules. Despite significant advances in computational approaches to ligand design and binding energy evaluation, novel procedures for ligand design are required. Evolutionary computation provides a new approach to this design endeavor. We propose an evolutionary tool for de novo peptide design, based on the evaluation of energies for peptide binding to a user-defined protein surface patch. Special emphasis has been placed on the evaluation of the proposed peptides, leading to two different evaluation heuristics. The software developed was successfully tested on the design of ligands for the proteins prolyl oligopeptidase, p53, and DNA gyrase.

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Acknowledgments

The authors thank I. Traus for his creative input on the computational aspects of this work, and Prof. X. Vilasis for his mathematical support in the implementation of Bayesian network learning algorithms. The authors would also like to thank the Parc Científic de Barcelona for providing the␣computational resources used for this research.

This work was partially supported by grants from Fundación BBVA, Fundació Marató TV3 and the Ministerio de Ciencia y Tecnología FEDER (BIO2002-2301 and EET2001-4813), the Air Force Office of Scientific Research, Air Force Materiel Command, USAF (F49620-03-1-0129), and by the Technology Research, Education, and Commercialization Center (TRECC), at the University of Illinois at Urbana–Champaign, administered by the National Center for Supercomputing Applications (NCSA) and funded by the Office of Naval Research (N00014-01-1-0175). The US Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon.

The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force Office of Scientific Research, the Technology Research, Education, and Commercialization Center, the Office of Naval Research, or the U.S. Government.

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Authors and Affiliations

  1. Institut de Recerca Biomèdica de Barcelona, Parc Científic de Barcelona, Universitat de Barcelona, Josep Samitier, 1-5, Barcelona, E 08028, Spain

    Ignasi Belda, Sergio Madurga, Marc Martinell, Teresa Tarragó & Ernest Giralt

  2. Illinois Genetic Algorithms Laboratory, National Center for Supercomputing Applications, University of Illinois at Urbana–Champaign, 117 Transportation Building, 104 South Mathews Urbana, Illinois, IL, 61801-2996, USA

    Xavier Llorà

  3. Departament de Química Orgànica, Universitat de Barcelona, Martí i Franquès, 1-11, Barcelona, E 08028, Spain

    Mireia G. Piqueras, Ernesto Nicolás & Ernest Giralt

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  1. Ignasi Belda
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  2. Sergio Madurga
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  3. Xavier Llorà
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  4. Marc Martinell
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  5. Teresa Tarragó
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  6. Mireia G. Piqueras
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  7. Ernesto Nicolás
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  8. Ernest Giralt
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Correspondence to Ernest Giralt.

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Belda, I., Madurga, S., Llorà, X. et al. ENPDA: an evolutionary structure-based de novo peptide design algorithm. J Comput Aided Mol Des 19, 585–601 (2005). https://doi.org/10.1007/s10822-005-9015-1

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  • DOI: https://doi.org/10.1007/s10822-005-9015-1

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