Name
Abstract | ||
|---|---|---|
A hybrid combination between differential evolution and a local refinement of protein structures provided by fragment replacements was performed for protein structure prediction. The coarse-grained protein conformation representation of the Rosetta environment was used. Given the deceptiveness of the Rosetta energy model, an evolutionary computing niching method, crowding, was incorporated in the evolutionary algorithm with the aim to obtain optimized solutions that at the same time provide a set of diverse protein folds. Thus, the probability to obtain optimized conformations close to the native structure is increased. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-19651-6_19 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Protein structure prediction,Evolutionary algorithm,Computer science,Crowding,Algorithm,Evolutionary computation,Differential evolution,Artificial intelligence,Machine learning,Protein structure | Conference | 11487 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Varela | 1 | 3 | 2.45 |
| José Santos | 2 | 97 | 14.77 |