| Title |
Differential evolution for protein folding optimization based on a three-dimensional AB off-lattice model
|
|---|---|
| Published in |
Journal of Molecular Modeling, September 2016
|
| DOI | 10.1007/s00894-016-3104-z |
| Pubmed ID | |
| Authors |
Borko Bošković, Janez Brest |
| Abstract |
This paper presents a differential evolution algorithm that is adapted for the protein folding optimization on a three-dimensional AB off-lattice model. The proposed algorithm is based on a self-adaptive differential evolution that improves the algorithm efficiency and reduces the number of control parameters. A mutation strategy for the fast convergence is used inside the algorithm. A temporal locality is used in order to speed up the algorithm convergence additionally and to find amino-acid conformations with the lowest free energy values. Within this mechanism a new vector is calculated when the trial vector is better than the corresponding vector from the population. This new vector is likely better than the trial vector and this accelerates convergence speed. Because of the fast convergence the algorithm has some chance to be trapped into the local optima. To mitigate this problem the algorithm includes reinitialization. The proposed algorithm was tested on amino-acid sequences that are used frequently in literature. The obtained results show that the proposed algorithm is superior to the algorithms from the literature and the obtained amino-acid sequences have significantly lower free energy values. Graphical Abstract Protein folding optimization on a three-dimensional AB off-lattice model using the differential evolution algorithm. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Slovenia | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 8 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor > Associate Professor | 2 | 25% |
| Professor | 1 | 13% |
| Student > Bachelor | 1 | 13% |
| Researcher | 1 | 13% |
| Student > Master | 1 | 13% |
| Other | 0 | 0% |
| Unknown | 2 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 63% |
| Unknown | 3 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 February 2018.
All research outputs
#13,471,180
of 22,962,258 outputs
Outputs from Journal of Molecular Modeling
#355
of 820 outputs
Outputs of similar age
#170,137
of 322,945 outputs
Outputs of similar age from Journal of Molecular Modeling
#3
of 13 outputs
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