Artículo
Soft Computing Methods for Disulfide Connectivity Prediction
Autor/es | Márquez Chamorro, Alfonso Eduardo
Aguilar Ruiz, Jesús Salvador |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2015 |
Fecha de depósito | 2022-05-24 |
Publicado en |
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Resumen | The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can
be divided into several subproblems. One of these subproblems is the prediction ... The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods |
Agencias financiadoras | Junta de Andalucía Ministerio de Educación y Ciencia (MEC). España |
Identificador del proyecto | P07-TIC-02611
TIN2011-28956-C02-01 |
Cita | Márquez Chamorro, A.E. y Aguilar Ruiz, J.S. (2015). Soft Computing Methods for Disulfide Connectivity Prediction. Evolutionary Bioinformatics, 11, 223-229. |
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