Flexible protein folding by ant colony optimization

Xiao Min Hu, Jun Zhang*, Yun Li

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Citations (Scopus)


Protein structure prediction is one of the most challenging topics in bioinformatics. As the protein structure is found to be closely related to its functions, predicting the folding structure of a protein to judge its functions is meaningful to the humanity. This chapter proposes a flexible ant colony (FAC) algorithm for solving protein folding problems (PFPs) based on the hydrophobic-polar (HP) square lattice model. Different from the previous ant algorithms for PFPs, the pheromones in the proposed algorithm are placed on the arcs connecting adjacent squares in the lattice. Such pheromone placement model is similar to the one used in the traveling salesmen problems (TSPs), where pheromones are released on the arcs connecting the cities. Moreover, the collaboration of effective heuristic and pheromone strategies greatly enhances the performance of the algorithm so that the algorithm can achieve good results without local search methods. By testing some benchmark two-dimensional hydrophobic-polar (2D-HP) protein sequences, the performance shows that the proposed algorithm is quite competitive compared with some other well-known methods for solving the same protein folding problems.

Original languageEnglish
Title of host publicationComputational Intelligence in Biomedicine and Bioinformatics
Number of pages20
Publication statusPublished - 1 Dec 2008

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


  • travel salesman problem
  • hydrophobic amino acid
  • protein structure prediction
  • polar amino acid
  • immune algorithm


Dive into the research topics of 'Flexible protein folding by ant colony optimization'. Together they form a unique fingerprint.

Cite this