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dc.contributor.authorRashid, Mahmood A
dc.contributor.authorNewton, MA Hakim
dc.contributor.authorHoque, Md Tamjidul
dc.contributor.authorSattar, Abdul
dc.contributor.editorTatsuya Akutsu
dc.date.accessioned2017-05-03T15:42:57Z
dc.date.available2017-05-03T15:42:57Z
dc.date.issued2013
dc.date.modified2014-04-02T04:02:39Z
dc.identifier.issn2314-6133
dc.identifier.doi10.1155/2013/924137
dc.identifier.urihttp://hdl.handle.net/10072/57694
dc.description.abstractProtein structure prediction (PSP) is computationally a very challenging problem. The challenge largely comes from the fact that the energy function that needs to be minimised in order to obtain the native structure of a given protein is not clearly known. A high resolution 20x20 energy model could better capture the behaviour of the actual energy function than a low resolution energy model such as hydrophobic polar. However, the fine grained details of the high resolution interaction energy matrix are often not very informative for guiding the search. In contrast, a low resolution energy model could effectively bias the search towards certain promising directions. In this paper, we develop a genetic algorithm that mainly uses a high resolution energy model for protein structure evaluation but uses a low resolution HP energy model in focussing the search towards exploring structures that have hydrophobic cores. We experimentally show that this mixing of energy models leads to significant lower energy structures compared to the state-of-the-art results.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent2770995 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.publisherHindawi Publishing Corporation
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom924137-1
dc.relation.ispartofpageto924137-15
dc.relation.ispartofjournalBioMed Research International
dc.relation.ispartofvolume2013
dc.rights.retentionY
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchBiological Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchTechnology
dc.subject.fieldofresearchcode080199
dc.subject.fieldofresearchcode06
dc.subject.fieldofresearchcode08
dc.subject.fieldofresearchcode10
dc.titleMixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by-nc-sa/2.1/au/
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© The Author(s) 2013. The attached file is posted here with permission of the copyright owners for your personal use only. No further distribution permitted.For information about this journal please refer to the journal’s website. The online version of this work is licensed under a Creative Commons License, available at http://creativecommons.org/licenses/by-nc-sa/2.1/au/
gro.date.issued2013
gro.hasfulltextFull Text
gro.griffith.authorSattar, Abdul
gro.griffith.authorRashid, Mahmood A.
gro.griffith.authorNewton, MAHakim A.
gro.griffith.authorHoque, Md T.


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