Name
Abstract | ||
|---|---|---|
Predicting the native structure of proteins is one of the most challenging prob- lems in molecular biology. The goal is to determine the three-dimensional structure from the one-dimensional amino acid sequence. De novo prediction algorithms seek to do this by devel- oping a representation of the proteins structure, an energy potential and some optimization algorithm that finds the structure with minimal energy. Bee Colony Optimization is a new metaheuristic approach to optimization based on the for- aging behaviour of bees. We have implemented the Bee Colony Optimization metaheuristic using hill-climbing as local search to generate good solutions to the protein structure pre- diction problem. With this method the choice of local search method can easily be changed, new solutions could be generated using evolutionary algorithms or the heuristic could be used to prioritize parallel runs of searches. The results show that Bee Colony Optimization generally finds better solutions than simulated annealing in the same amount of time. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s10852-010-9125-1 | J. Math. Model. Algorithms |
Keywords | Field | DocType |
protein structure prediction · bee colony optimization · metaheuristic,amino acid sequence,local search,molecular biology,evolutionary algorithm,simulated annealing,protein structure prediction,hill climbing,protein structure | Simulated annealing,Protein structure prediction,Mathematical optimization,Waggle dance,Prediction algorithms,Optimization algorithm,Local search (optimization),Optimization problem,Mathematics,Metaheuristic | Journal |
Volume | Issue | ISSN |
9 | 2 | 1572-9214 |
Citations | PageRank | References |
5 | 0.48 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rasmus Fonseca | 1 | 28 | 3.78 |
| Martin Paluszewski | 2 | 55 | 3.55 |
| Pawel Winter | 3 | 99 | 12.98 |