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dc.contributor.authorJang, Richard
dc.contributor.authorGao, Xin
dc.contributor.authorLi, Ming
dc.date.accessioned2014-08-27T09:52:49Z
dc.date.available2014-08-27T09:52:49Z
dc.date.issued2012-03-22
dc.identifier.citationJang R, Gao X, Li M (2012) Combining automated peak tracking in SAR by NMR with structure-based backbone assignment from 15N-NOESY. BMC Bioinformatics 13: S4. doi:10.1186/1471-2105-13-S3-S4.
dc.identifier.issn14712105
dc.identifier.pmid22536902
dc.identifier.doi10.1186/1471-2105-13-S3-S4
dc.identifier.urihttp://hdl.handle.net/10754/325471
dc.description.abstractBackground: Chemical shift mapping is an important technique in NMR-based drug screening for identifying the atoms of a target protein that potentially bind to a drug molecule upon the molecule's introduction in increasing concentrations. The goal is to obtain a mapping of peaks with known residue assignment from the reference spectrum of the unbound protein to peaks with unknown assignment in the target spectrum of the bound protein. Although a series of perturbed spectra help to trace a path from reference peaks to target peaks, a one-to-one mapping generally is not possible, especially for large proteins, due to errors, such as noise peaks, missing peaks, missing but then reappearing, overlapped, and new peaks not associated with any peaks in the reference. Due to these difficulties, the mapping is typically done manually or semi-automatically, which is not efficient for high-throughput drug screening.Results: We present PeakWalker, a novel peak walking algorithm for fast-exchange systems that models the errors explicitly and performs many-to-one mapping. On the proteins: hBclXL, UbcH5B, and histone H1, it achieves an average accuracy of over 95% with less than 1.5 residues predicted per target peak. Given these mappings as input, we present PeakAssigner, a novel combined structure-based backbone resonance and NOE assignment algorithm that uses just 15N-NOESY, while avoiding TOCSY experiments and 13C-labeling, to resolve the ambiguities for a one-to-one mapping. On the three proteins, it achieves an average accuracy of 94% or better.Conclusions: Our mathematical programming approach for modeling chemical shift mapping as a graph problem, while modeling the errors directly, is potentially a time- and cost-effective first step for high-throughput drug screening based on limited NMR data and homologous 3D structures. 2012 Jang et al.; licensee BioMed Central Ltd.
dc.language.isoen
dc.publisherSpringer Nature
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttp://creativecommons.org/licenses/by/2.0
dc.subject3D Structure
dc.subjectAssignment algorithms
dc.subjectBound proteins
dc.subjectChemical shift mapping
dc.subjectDrug molecules
dc.subjectDrug screening
dc.subjectGraph problems
dc.subjectHigh-throughput
dc.subjectHistone h1
dc.subjectMany-to-one
dc.subjectNMR data
dc.subjectOne-to-one mappings
dc.subjectReference spectrum
dc.subjectStructure-based
dc.subjectTarget proteins
dc.subjectTarget spectrum
dc.subjectAlgorithms
dc.subjectChemical shift
dc.subjectErrors
dc.subjectMapping
dc.subjectMathematical programming
dc.subjectMolecules
dc.subjectProteins
dc.subjectprotein
dc.subjectalgorithm
dc.subjectbacterium
dc.subjectchemical model
dc.subjectchemical structure
dc.subjectchemistry
dc.subjectmetabolism
dc.subjectmethodology
dc.subjectnuclear magnetic resonance
dc.subjectplant
dc.subjectstructure activity relation
dc.subjectAlgorithms
dc.subjectBacteria
dc.subjectModels, Chemical
dc.subjectModels, Molecular
dc.subjectNuclear Magnetic Resonance, Biomolecular
dc.subjectPlants
dc.subjectProteins
dc.subjectStructure-Activity Relationship
dc.titleCombining automated peak tracking in SAR by NMR with structure-based backbone assignment from 15N-NOESY
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalBMC Bioinformatics
dc.identifier.pmcidPMC3402924
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDavid R Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personGao, Xin
refterms.dateFOA2018-06-13T15:30:20Z
dc.date.published-online2012-03-22
dc.date.published-print2012


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This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.