Soft Computing Methods for Disulfide Connectivity 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...

Descripción completa

Detalles Bibliográficos
Autores: Márquez Chamorro, Alfonso Eduardo, Aguilar Ruiz, Jesús Salvador
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2015
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/133553
Acceso en línea:https://hdl.handle.net/11441/133553
https://doi.org/10.4137/EBO.S25349
Access Level:acceso abierto
Palabra clave:Disulfide connectivity prediction
Protein structure prediction
Soft computing
Support vector machines
Neural networks
id ES_4e1e148bb2373fd3419eeaf4e3951a54
oai_identifier_str oai:idus.us.es:11441/133553
network_acronym_str ES
network_name_str España
repository_id_str
spelling Soft Computing Methods for Disulfide Connectivity PredictionMárquez Chamorro, Alfonso EduardoAguilar Ruiz, Jesús SalvadorDisulfide connectivity predictionProtein structure predictionSoft computingSupport vector machinesNeural networksThe 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 methodsJunta de Andalucía P07-TIC-02611Ministerio de Educación y Ciencia TIN2011-28956-C02-01Libertas AcademicaLenguajes y Sistemas InformáticosTIC205: Ingeniería del Software AplicadaJunta de AndalucíaMinisterio de Educación y Ciencia (MEC). España2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/133553https://doi.org/10.4137/EBO.S25349reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésEvolutionary Bioinformatics, 11, 223-229.P07-TIC-02611TIN2011-28956-C02-01https://journals.sagepub.com/doi/10.4137/EBO.S25349info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1335532026-06-17T12:51:07Z
dc.title.none.fl_str_mv Soft Computing Methods for Disulfide Connectivity Prediction
title Soft Computing Methods for Disulfide Connectivity Prediction
spellingShingle Soft Computing Methods for Disulfide Connectivity Prediction
Márquez Chamorro, Alfonso Eduardo
Disulfide connectivity prediction
Protein structure prediction
Soft computing
Support vector machines
Neural networks
title_short Soft Computing Methods for Disulfide Connectivity Prediction
title_full Soft Computing Methods for Disulfide Connectivity Prediction
title_fullStr Soft Computing Methods for Disulfide Connectivity Prediction
title_full_unstemmed Soft Computing Methods for Disulfide Connectivity Prediction
title_sort Soft Computing Methods for Disulfide Connectivity Prediction
dc.creator.none.fl_str_mv Márquez Chamorro, Alfonso Eduardo
Aguilar Ruiz, Jesús Salvador
author Márquez Chamorro, Alfonso Eduardo
author_facet Márquez Chamorro, Alfonso Eduardo
Aguilar Ruiz, Jesús Salvador
author_role author
author2 Aguilar Ruiz, Jesús Salvador
author2_role author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
TIC205: Ingeniería del Software Aplicada
Junta de Andalucía
Ministerio de Educación y Ciencia (MEC). España
dc.subject.none.fl_str_mv Disulfide connectivity prediction
Protein structure prediction
Soft computing
Support vector machines
Neural networks
topic Disulfide connectivity prediction
Protein structure prediction
Soft computing
Support vector machines
Neural networks
description 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
publishDate 2015
dc.date.none.fl_str_mv 2015
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/133553
https://doi.org/10.4137/EBO.S25349
url https://hdl.handle.net/11441/133553
https://doi.org/10.4137/EBO.S25349
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Evolutionary Bioinformatics, 11, 223-229.
P07-TIC-02611
TIN2011-28956-C02-01
https://journals.sagepub.com/doi/10.4137/EBO.S25349
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Libertas Academica
publisher.none.fl_str_mv Libertas Academica
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869407735703928832
score 15,300719

Similares en LA Referencia