Abstract
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.
| Language | English |
|---|---|
| Title of host publication | Computational Intelligence in Biomedicine and Bioinformatics |
| Pages | 317-336 |
| Number of pages | 20 |
| Volume | 151 |
| DOIs | |
| Publication status | Published - 1 Dec 2008 |
Publication series
| Name | Studies in Computational Intelligence |
|---|---|
| Volume | 151 |
| ISSN (Print) | 1860-949X |
Fingerprint
Keywords
- travel salesman problem
- hydrophobic amino acid
- protein structure prediction
- polar amino acid
- immune algorithm
Cite this
}
Flexible protein folding by ant colony optimization. / Hu, Xiao Min; Zhang, Jun; Li, Yun.
Computational Intelligence in Biomedicine and Bioinformatics. Vol. 151 2008. p. 317-336 (Studies in Computational Intelligence; Vol. 151).Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - Flexible protein folding by ant colony optimization
AU - Hu, Xiao Min
AU - Zhang, Jun
AU - Li, Yun
PY - 2008/12/1
Y1 - 2008/12/1
N2 - 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.
AB - 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.
KW - travel salesman problem
KW - hydrophobic amino acid
KW - protein structure prediction
KW - polar amino acid
KW - immune algorithm
UR - http://www.scopus.com/inward/record.url?scp=59549098190&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-70778-3_13
DO - 10.1007/978-3-540-70778-3_13
M3 - Chapter
SN - 9783540707769
VL - 151
T3 - Studies in Computational Intelligence
SP - 317
EP - 336
BT - Computational Intelligence in Biomedicine and Bioinformatics
ER -