Evolutionary GRSA for Protein Structure Prediction
Protein folding problem (PFP) is a challenge in some areas, such as molecular biology, computational biology, combinatorial optimi zation , and computer s cience . This is due to the large number of conformational structures that a protein can take from its primary structure to the native structure...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | México |
| Institución: | Universidad Autónoma de Coahuila |
| Repositorio: | Redalyc-UADEC |
| OAI Identifier: | oai:redalyc.org:265254330008 |
| Acceso en línea: | https://www.redalyc.org/articulo.oa?id=265254330008 |
| Access Level: | acceso abierto |
| Palabra clave: | Computación GRSA Evolutionary algorithms Protein Structure Prediction |
| Sumario: | Protein folding problem (PFP) is a challenge in some areas, such as molecular biology, computational biology, combinatorial optimi zation , and computer s cience . This is due to the large number of conformational structures that a protein can take from its primary structure to the native structure (NS). The aim of PFP is to find the NS of a protein target sequence. In general, the NS which has the lowest Gib bs energy or an energy close to it. In this paper, a Simulated Annealing like algorithm is presented, using the Golden Ratio search strategy and evolutionary techniques for PFP in small peptides. This method looks for the NS using only the protein's amino acid sequence, and determines the three - dimensional structure with the minimum energy or a close value of it. |
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