An automated framework for NMR resonance assignment through simultaneous slice picking and spin system forming
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Imaging and Characterization Core Lab
Computational Bioscience Research Center (CBRC)
Advanced Nanofabrication, Imaging and Characterization Core Lab
Structural and Functional Bioinformatics Group
Online Publication Date2014-04-19
Print Publication Date2014-06
Permanent link to this recordhttp://hdl.handle.net/10754/563505
MetadataShow full item record
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.
SponsorsWe 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).
JournalJournal of Biomolecular NMR
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