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dc.contributor.authorIslam, Md Lisul
dc.contributor.authorShatabda, Swakkhar
dc.contributor.authorRashid, Mahmood A
dc.contributor.authorKhan, MGM
dc.contributor.authorRahman, M Sohel
dc.date.accessioned2019-06-09T01:33:01Z
dc.date.available2019-06-09T01:33:01Z
dc.date.issued2019
dc.identifier.issn1476-9271
dc.identifier.doi10.1016/j.compbiolchem.2019.01.004
dc.identifier.urihttp://hdl.handle.net/10072/383443
dc.description.abstractNuclear Magnetic Resonance Spectroscopy (most commonly known as NMR Spectroscopy) is used to generate approximate and partial distances between pairs of atoms of the native structure of a protein. To predict protein structure from these partial distances by solving the Euclidean distance geometry problem from the partial distances obtained from NMR Spectroscopy, we can predict three-dimensional (3D) structure of a protein. In this paper, a new genetic algorithm is proposed to efficiently address the Euclidean distance geometry problem towards building 3D structure of a given protein applying NMR's sparse data. Our genetic algorithm uses (i) a greedy mutation and crossover operator to intensify the search; (ii) a twin removal technique for diversification in the population; (iii) a random restart method to recover from stagnation; and (iv) a compaction factor to reduce the search space. Reducing the search space drastically, our approach improves the quality of the search. We tested our algorithms on a set of standard benchmarks. Experimentally, we show that our enhanced genetic algorithms significantly outperforms the traditional genetic algorithms and a previously proposed state-of-the-art method. Our method is capable of producing structures that are very close to the native structures and hence, the experimental biologists could adopt it to determine more accurate protein structures from NMR data.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartoflocationEngland
dc.relation.ispartofpagefrom6
dc.relation.ispartofpageto15
dc.relation.ispartofjournalComputational Biology and Chemistry
dc.relation.ispartofvolume79
dc.subject.fieldofresearchChemical sciences
dc.subject.fieldofresearchBiological sciences
dc.subject.fieldofresearchcode34
dc.subject.fieldofresearchcode31
dc.subject.keywordsProtein structure prediction
dc.subject.keywordsSparse data
dc.subject.keywordsMolecular distance geometry
dc.subject.keywordsNuclear magnetic resonance spectroscopy
dc.subject.keywordsGenetic algorithms
dc.titleProtein structure prediction from inaccurate and sparse NMR data using an enhanced genetic algorithm
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
gro.rights.copyright© 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorRashid, Mahmood A.


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