TY - JOUR
T1 - A Slice-based 13C-detected NMR Spin System Forming and Resonance Assignment Method
AU - Alazmi, Meshari
AU - Abbas, Ahmed
AU - Guo, Xianrong
AU - Fan, Ming
AU - Li, Lihua
AU - Gao, Xin
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The 13C-detected spectra for Ubiquitin were provided by the Imaging and Characterization Core Lab, King Abdullah University of Science and Technology (KAUST). The research reported in this paper is supported by the funding from King Abdullah University of Science and Technology (KAUST).
PY - 2018/6/25
Y1 - 2018/6/25
N2 - Nuclear magnetic resonance (NMR) spectroscopy is attracting more attention in the field of computational structural biology. Till recently, 1H-detected experiments are the dominant NMR technique used due to the high sensitivity of 1H nuclei. However, the current availability of high magnetic fields and cryogenically cooled probe heads allow researchers to overcome the low sensitivity of 13C nuclei. Consequently, 13C-detected experiments have become a popular technique in different NMR applications especially resonance assignment and structure determination of large proteins. In this paper, we propose the first spin system forming method for 13C-detected NMR spectra. Our method is able to accurately form spin systems based on as few as two 13C-detected spectra, CBCACON and CBCANCO. Our method picks slices from the more trusted spectrum and uses them as feedback to direct the slice picking in the less trusted one. This feedback leads to picking the accurate slices that consequently helps to form better spin systems. We tested our method on a real dataset of ‘Ubiquitin’ and a benchmark simulated dataset consisting of 12 proteins. We fed our spin systems as inputs to a genetic algorithm to generate the chemical shift assignment, and obtained 92% correct chemical shift assignment for Ubiquitin. For the simulated dataset, we obtained an average recall of 86% and an average precision of 88%. Finally, our chemical shift assignment of Ubiquitin was given as an input to CS-ROSETTA server that generated structures close to the experimentally determined structure
AB - Nuclear magnetic resonance (NMR) spectroscopy is attracting more attention in the field of computational structural biology. Till recently, 1H-detected experiments are the dominant NMR technique used due to the high sensitivity of 1H nuclei. However, the current availability of high magnetic fields and cryogenically cooled probe heads allow researchers to overcome the low sensitivity of 13C nuclei. Consequently, 13C-detected experiments have become a popular technique in different NMR applications especially resonance assignment and structure determination of large proteins. In this paper, we propose the first spin system forming method for 13C-detected NMR spectra. Our method is able to accurately form spin systems based on as few as two 13C-detected spectra, CBCACON and CBCANCO. Our method picks slices from the more trusted spectrum and uses them as feedback to direct the slice picking in the less trusted one. This feedback leads to picking the accurate slices that consequently helps to form better spin systems. We tested our method on a real dataset of ‘Ubiquitin’ and a benchmark simulated dataset consisting of 12 proteins. We fed our spin systems as inputs to a genetic algorithm to generate the chemical shift assignment, and obtained 92% correct chemical shift assignment for Ubiquitin. For the simulated dataset, we obtained an average recall of 86% and an average precision of 88%. Finally, our chemical shift assignment of Ubiquitin was given as an input to CS-ROSETTA server that generated structures close to the experimentally determined structure
UR - http://hdl.handle.net/10754/628390
UR - https://ieeexplore.ieee.org/document/8395030/
UR - http://www.scopus.com/inward/record.url?scp=85049092319&partnerID=8YFLogxK
U2 - 10.1109/tcbb.2018.2849728
DO - 10.1109/tcbb.2018.2849728
M3 - Article
SN - 1545-5963
VL - 15
SP - 1999
EP - 2008
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
IS - 6
ER -