Journal of Mathematical Chemistry

, Volume 53, Issue 3, pp 794–806 | Cite as

An efficient approach for solving the HP protein folding problem based on UEGO

  • J. M. García-Martínez
  • E. M. Garzón
  • J. M. Cecilia
  • H. Pérez-Sánchez
  • P. M. Ortigosa
Original Paper
First Online:
Received:
Accepted:

Abstract

This work applies the methodology of the Universal Evolutionary Global Optimization, UEGO, to solve the protein structure optimization problem based on the HP model. The UEGO algorithm was initially designed to solve problems whose solutions were codified as real vectors. However, in this work the HP protein folding solutions have been defined as means of conformations encoded by relative coordinates. Consequently several main concepts in UEGO have been re-defined, i.e. the representation of a solution, the distance concept, the computation of a middle point, etc. In addition, a new efficient local optimizer has been designed based on the characteristics of the protein model. This work develops the adaptation and implementation of UEGO to the HP model and analyzes the UEGO solutions of HP protein folding for different 3D problems. Finally, obtained HP solutions are converted into all-atom models so that comparison with real proteins can be carried out, and a good agreement is obtained for small size proteins.

Keywords

Protein folding Evolutionary algorithm HP model  Multiscale modeling Algorithm acceleration 
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Notes

Acknowledgments

This work has been funded by grants from the Spanish Ministry of Science and Innovation (TIN2012-37483-C03-03), Junta de Andalucía (P10-TIC-6002 and P12-TIC301), Fundación Séneca (The Agency of Science and Technology of the Region of Murcia, 00003/CS/10, 15254/PI/10 and 18946/JLI/13) and by the Nils Coordinated Mobility under Grant 012-ABEL-CM-2014A, in part financed by the European Regional Development Fund (ERDF).

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • J. M. García-Martínez
    • 1
  • E. M. Garzón
    • 1
  • J. M. Cecilia
    • 2
  • H. Pérez-Sánchez
    • 2
  • P. M. Ortigosa
    • 1
  1. 1.Department of Informatics, ceiA3University of AlmeríaAlmeríaSpain
  2. 2.Bioinformatics and High Performance Computing Research Group (BIO-HPC), Computer Science DepartmentUniversidad Católica San Antonio de Murcia (UCAM)MurciaSpain

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