An automated framework for NMR resonance assignment through simultaneous slice picking and spin system forming

Handle URI:
http://hdl.handle.net/10754/563505
Title:
An automated framework for NMR resonance assignment through simultaneous slice picking and spin system forming
Authors:
Abbas, Ahmed; Guo, Xianrong; Jing, Bingyi; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
Despite significant advances in automated nuclear magnetic resonance-based protein structure determination, the high numbers of false positives and false negatives among the peaks selected by fully automated methods remain a problem. These false positives and negatives impair the performance of resonance assignment methods. One of the main reasons for this problem is that the computational research community often considers peak picking and resonance assignment to be two separate problems, whereas spectroscopists use expert knowledge to pick peaks and assign their resonances at the same time. We propose a novel framework that simultaneously conducts slice picking and spin system forming, an essential step in resonance assignment. Our framework then employs a genetic algorithm, directed by both connectivity information and amino acid typing information from the spin systems, to assign the spin systems to residues. The inputs to our framework can be as few as two commonly used spectra, i.e., CBCA(CO)NH and HNCACB. Different from the existing peak picking and resonance assignment methods that treat peaks as the units, our method is based on 'slices', which are one-dimensional vectors in three-dimensional spectra that correspond to certain (N, H) values. Experimental results on both benchmark simulated data sets and four real protein data sets demonstrate that our method significantly outperforms the state-of-the-art methods while using a less number of spectra than those methods. Our method is freely available at http://sfb.kaust.edu.sa/Pages/Software.aspx. © 2014 Springer Science+Business Media.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Advanced Nanofabrication, Imaging and Characterization Core Lab; Computer Science Program; Computational Bioscience Research Center (CBRC); Core Labs; Structural and Functional Bioinformatics Group
Publisher:
Springer Science + Business Media
Journal:
Journal of Biomolecular NMR
Issue Date:
19-Apr-2014
DOI:
10.1007/s10858-014-9828-0
PubMed ID:
24748536
Type:
Article
ISSN:
09252738
Sponsors:
We thank Dr. Ad Bax's group for making CS-ROSETTA available. We are grateful to Dr. Yang Shen for answering our questions regarding CS-ROSETTA server. The spectra for TM1112 were generated by Cheryl Arrowsmith's Lab at the University of Toronto. The spectra for CASKIN, VRAR, and HACS1 were provided by Logan Donaldson's Lab at York University. We thank Virginia Unkefer for editorial assistance. This work was supported by Award No. GRP-CF-2011-19-P-Gao-Huang and a GMSV-OCRF award from King Abdullah University of Science and Technology (KAUST).
Appears in Collections:
Articles; Advanced Nanofabrication, Imaging and Characterization Core Lab; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAbbas, Ahmeden
dc.contributor.authorGuo, Xianrongen
dc.contributor.authorJing, Bingyien
dc.contributor.authorGao, Xinen
dc.date.accessioned2015-08-03T11:53:07Zen
dc.date.available2015-08-03T11:53:07Zen
dc.date.issued2014-04-19en
dc.identifier.issn09252738en
dc.identifier.pmid24748536en
dc.identifier.doi10.1007/s10858-014-9828-0en
dc.identifier.urihttp://hdl.handle.net/10754/563505en
dc.description.abstractDespite significant advances in automated nuclear magnetic resonance-based protein structure determination, the high numbers of false positives and false negatives among the peaks selected by fully automated methods remain a problem. These false positives and negatives impair the performance of resonance assignment methods. One of the main reasons for this problem is that the computational research community often considers peak picking and resonance assignment to be two separate problems, whereas spectroscopists use expert knowledge to pick peaks and assign their resonances at the same time. We propose a novel framework that simultaneously conducts slice picking and spin system forming, an essential step in resonance assignment. Our framework then employs a genetic algorithm, directed by both connectivity information and amino acid typing information from the spin systems, to assign the spin systems to residues. The inputs to our framework can be as few as two commonly used spectra, i.e., CBCA(CO)NH and HNCACB. Different from the existing peak picking and resonance assignment methods that treat peaks as the units, our method is based on 'slices', which are one-dimensional vectors in three-dimensional spectra that correspond to certain (N, H) values. Experimental results on both benchmark simulated data sets and four real protein data sets demonstrate that our method significantly outperforms the state-of-the-art methods while using a less number of spectra than those methods. Our method is freely available at http://sfb.kaust.edu.sa/Pages/Software.aspx. © 2014 Springer Science+Business Media.en
dc.description.sponsorshipWe thank Dr. Ad Bax's group for making CS-ROSETTA available. We are grateful to Dr. Yang Shen for answering our questions regarding CS-ROSETTA server. The spectra for TM1112 were generated by Cheryl Arrowsmith's Lab at the University of Toronto. The spectra for CASKIN, VRAR, and HACS1 were provided by Logan Donaldson's Lab at York University. We thank Virginia Unkefer for editorial assistance. This work was supported by Award No. GRP-CF-2011-19-P-Gao-Huang and a GMSV-OCRF award from King Abdullah University of Science and Technology (KAUST).en
dc.publisherSpringer Science + Business Mediaen
dc.subjectPeak pickingen
dc.subjectResonance assignmenten
dc.subjectSpin systemen
dc.subjectWaveleten
dc.titleAn automated framework for NMR resonance assignment through simultaneous slice picking and spin system formingen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentAdvanced Nanofabrication, Imaging and Characterization Core Laben
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentCore Labsen
dc.contributor.departmentStructural and Functional Bioinformatics Groupen
dc.identifier.journalJournal of Biomolecular NMRen
dc.contributor.institutionDepartment of Mathematics, Hong Kong University of Science and Technology, Kowloon, Hong Kongen
kaust.authorAbbas, Ahmeden
kaust.authorGuo, Xianrongen
kaust.authorGao, Xinen

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