Combining automated peak tracking in SAR by NMR with structure-based backbone assignment from 15N-NOESY

Handle URI:
http://hdl.handle.net/10754/325471
Title:
Combining automated peak tracking in SAR by NMR with structure-based backbone assignment from 15N-NOESY
Authors:
Jang, Richard; Gao, Xin ( 0000-0002-7108-3574 ) ; Li, Ming
Abstract:
Background: 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Jang 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.
Publisher:
BioMed Central
Journal:
BMC Bioinformatics
Issue Date:
21-Mar-2012
DOI:
10.1186/1471-2105-13-S3-S4
PubMed ID:
22536902
PubMed Central ID:
PMC3402924
Type:
Article
ISSN:
14712105
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorJang, Richarden
dc.contributor.authorGao, Xinen
dc.contributor.authorLi, Mingen
dc.date.accessioned2014-08-27T09:52:49Z-
dc.date.available2014-08-27T09:52:49Z-
dc.date.issued2012-03-21en
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.en
dc.identifier.issn14712105en
dc.identifier.pmid22536902en
dc.identifier.doi10.1186/1471-2105-13-S3-S4en
dc.identifier.urihttp://hdl.handle.net/10754/325471en
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.en
dc.language.isoenen
dc.publisherBioMed Centralen
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.en
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en
dc.subject3D Structureen
dc.subjectAssignment algorithmsen
dc.subjectBound proteinsen
dc.subjectChemical shift mappingen
dc.subjectDrug moleculesen
dc.subjectDrug screeningen
dc.subjectGraph problemsen
dc.subjectHigh-throughputen
dc.subjectHistone h1en
dc.subjectMany-to-oneen
dc.subjectNMR dataen
dc.subjectOne-to-one mappingsen
dc.subjectReference spectrumen
dc.subjectStructure-baseden
dc.subjectTarget proteinsen
dc.subjectTarget spectrumen
dc.subjectAlgorithmsen
dc.subjectChemical shiften
dc.subjectErrorsen
dc.subjectMappingen
dc.subjectMathematical programmingen
dc.subjectMoleculesen
dc.subjectProteinsen
dc.subjectproteinen
dc.subjectalgorithmen
dc.subjectbacteriumen
dc.subjectchemical modelen
dc.subjectchemical structureen
dc.subjectchemistryen
dc.subjectmetabolismen
dc.subjectmethodologyen
dc.subjectnuclear magnetic resonanceen
dc.subjectplanten
dc.subjectstructure activity relationen
dc.subjectAlgorithmsen
dc.subjectBacteriaen
dc.subjectModels, Chemicalen
dc.subjectModels, Molecularen
dc.subjectNuclear Magnetic Resonance, Biomolecularen
dc.subjectPlantsen
dc.subjectProteinsen
dc.subjectStructure-Activity Relationshipen
dc.titleCombining automated peak tracking in SAR by NMR with structure-based backbone assignment from 15N-NOESYen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalBMC Bioinformaticsen
dc.identifier.pmcidPMC3402924en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDavid R Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canadaen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorGao, Xinen
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