Evolutionary GRSA for Protein Structure Prediction

  • Fanny Gabriela Maldonado-Nava Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero
  • Juan Frausto-Solís Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero
  • Juan Paulo Sánchez-Hernández Universidad Politécnica del Estado de Morelos
  • Juan Javier González-Barbosa Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero
  • Ernesto Liñán-García Universidad Autónoma de Coahuila. Facultad de Sistemas
  • Guadalupe Castilla Valdez Tecnológico Nacional de México, Instituto Tecnológico de Ciudad Madero
Keywords: Protein Structure Prediction, GRSA, Evolutionary algorithms

Abstract

Protein folding problem (PFP) is a challenge in some areas, such as molecular biology, computational biology, combinatorial optimization, and computer science. This is due to the large number of conformational structures that a protein can take from its primary structure to the native structure (NS). The aim of PFP is to find the NS of a protein target sequence. In general, the NS which has the lowest Gibbs energy or an energy close to it. In this paper, a Simulated Annealing like algorithm is presented, using the Golden Ratio search strategy and evolutionary techniques for PFP in small peptides. This method looks for the NS using only the protein's amino acid sequence, and determines the three-dimensional structure with the minimum energy or a close value of it.

Published
2016-11-14
How to Cite
Maldonado-Nava, F. G., Frausto-Solís, J., Sánchez-Hernández, J. P., González-Barbosa, J. J., Liñán-García, E., & Castilla Valdez, G. (2016). Evolutionary GRSA for Protein Structure Prediction. International Journal of Combinatorial Optimization Problems and Informatics, 7(3), 75-86. Retrieved from https://ijcopi.org/ojs/article/view/29
Section
Articles