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    Combining ambiguous chemical shift mapping with structure-based backbone and NOE assignment from 15N-NOESY

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    Type
    Conference Paper
    Authors
    Jang, Richard
    Gao, Xin cc
    Li, Ming cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Computational Bioscience Research Center (CBRC)
    Structural and Functional Bioinformatics Group
    Date
    2011
    Permanent link to this record
    http://hdl.handle.net/10754/564350
    
    Metadata
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    Abstract
    Chemical shift mapping is an important technique in NMRbased 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. However, automated methods are necessary for high-throughput drug screening. 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-toone mapping. On the three proteins, it achieves an average accuracy of 94% or better. Copyright © 2011 ACM.
    Publisher
    Association for Computing Machinery (ACM)
    Journal
    Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine - BCB '11
    Conference/Event name
    2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011
    ISBN
    9781450307963
    DOI
    10.1145/2147805.2147814
    ae974a485f413a2113503eed53cd6c53
    10.1145/2147805.2147814
    Scopus Count
    Collections
    Conference Papers; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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