dc.creator | Asencio Cortés, Gualberto | es |
dc.creator | Aguilar Ruiz, Jesús Salvador | es |
dc.creator | Márquez Chamorro, Alfonso Eduardo | es |
dc.creator | Ruiz, Roberto | es |
dc.creator | Santiesteban Toca, Cosme E. | es |
dc.date.accessioned | 2022-05-23T08:54:45Z | |
dc.date.available | 2022-05-23T08:54:45Z | |
dc.date.issued | 2012 | |
dc.identifier.citation | Asencio Cortés, G., Aguilar Ruiz, J.S., Márquez Chamorro, A.E., Ruiz, R. y Santiesteban Toca, C.E. (2012). Prediction of Mitochondrial Matrix Protein Structures Based on Feature Selection and Fragment Assembly. En EvoBIO 2012: 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (156-167), Málaga, España: Springer. | |
dc.identifier.isbn | 978-3-642-29065-7 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | https://hdl.handle.net/11441/133515 | |
dc.description.abstract | Protein structure prediction consists in determining the thre e-dimensional conformation of a protein based only on its amino acid se quence. This is currently a difficult and significant challenge in structural
bioinformatics because these structures are necessary for drug designing.
This work proposes a method that reconstructs protein structures from
protein fragments assembled according to their physico-chemical simi larities, using information extracted from known protein structures. Our
prediction system produces distance maps to represent protein struc tures, which provides more information than contact maps, which are
predicted by many proposals in the literature. Most commonly used
amino acid physico-chemical properties are hydrophobicity, polarity and
charge. In our method, we performed a feature selection on the 544 prop erties of the AAindex repository, resulting in 16 properties which were
used to predictions. We tested our proposal on 74 mitochondrial ma trix proteins with a maximum sequence identity of 30% obtained from
the Protein Data Bank. We achieved a recall of 0.80 and a precision
of 0.79 with an 8-angstrom cut-off and a minimum sequence separation
of 7 amino acids. Finally, we compared our system with other relevant
proposal on the same benchmark and we achieved a recall improvement
of 50.82%. Therefore, for the studied proteins, our method provides a
notable improvement in terms of recall. | es |
dc.format | application/pdf | es |
dc.format.extent | 12 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | EvoBIO 2012: 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (2012), pp. 156-167. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Protein structure prediction | es |
dc.subject | Physico-chemical amino acid properties | es |
dc.subject | Fragment assembly | es |
dc.subject | Protein distance map | es |
dc.subject | Feature selection | es |
dc.title | Prediction of Mitochondrial Matrix Protein Structures Based on Feature Selection and Fragment Assembly | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-642-29066-4_14 | es |
dc.identifier.doi | 10.1007/978-3-642-29066-4_14 | es |
dc.contributor.group | Universidad de Sevilla. TIC205: Ingeniería del Software Aplicada | es |
dc.publication.initialPage | 156 | es |
dc.publication.endPage | 167 | es |
dc.eventtitle | EvoBIO 2012: 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics | es |
dc.eventinstitution | Málaga, España | es |
dc.relation.publicationplace | Berlin, Germany | es |
dc.identifier.sisius | 20132129 | es |