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Evolutionary computation and multimodal search: A good combination to tackle molecular diversity in the field of peptide design

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Summary

The awesome degree of structural diversity accessible in peptide design has created a demand for computational resources that can evaluate a multitude of candidate structures. In our specific case, we translate the peptide design problem to an optimization problem, and use evolutionary computation (EC) in tandem with docking to carry out a combinatorial search. However, the use of EC in huge search spaces with different optima may pose certain drawbacks. For example, EC is prone to focus a search in the first good region found. This is a problem not only because of the undesirable and automatic rejection of potentially good search space regions, but also because the found solution may be extremely difficult to synthesize chemically or may even be a false docking positive. In order to avoid rejecting potentially good solutions and to maximize the molecular diversity of the search, we have implemented evolutionary multimodal search techniques, as well as the molecular diversity metric needed by the multimodal algorithms to measure differences between various regions of the search space.

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Abbreviations

EC:

evolutionary computation

ENPDA:

Evolutionary structure-based de Novo Peptide Design Algorithm

POP:

prolyl oligopeptidase

ADME/Tox:

absorption, distribution, metabolism, excretion, and toxicity

QSAR:

quantitative structure-activity relationship

GA:

genetic algorithm

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Acknowledgments

The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the Barcelona Supercomputing Center - Centro Nacional de Supercomputación. The authors also acknowledge R.␣A. Rodríguez-Mías for his nice contributions to this article.

This work was partially supported by grants from Fundación BBVA, 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 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, Parc Científic de Barcelona, Universitat de Barcelona, Josep Samitier, 1-5, E 08028, Barcelona, Spain

    Ignasi Belda, Sergio Madurga, Teresa Tarragó & Ernest Giralt

  2. Department de Química Física, Universitat de Barcelona, E 08028, Barcelona, Spain

    Sergio Madurga

  3. Illinois Genetic Algorithms Laboratory, Department of General Engineering, University of Illinois, Urbana, IL, 61801, USA

    Xavier Llorà

  4. Department de Química Orgànica, Universitat de Barcelona, E 08028, Barcelona, Spain

    Ernest Giralt

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  1. Ignasi Belda
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  5. Ernest Giralt
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Correspondence to Ernest Giralt.

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Belda, I., Madurga, S., Tarragó, T. et al. Evolutionary computation and multimodal search: A good combination to tackle molecular diversity in the field of peptide design. Mol Divers 11, 7–21 (2007). https://doi.org/10.1007/s11030-006-9053-1

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