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.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): GRP-CF-2011-19-P-Gao-Huang, GMSV-OCRF
Acknowledgements: 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).
ASJC Scopus subject areas