Abstract
Nuclear magnetic resonance (NMR) spectroscopy is attracting more attention in the field of computational structural biology. Till recently, <inline-formula><tex-math notation="LaTeX">^1</tex-math> <inline-graphic xlink:href="alazmi-ieq2-2849728.gif"/> </inline-formula>H-detected experiments are the dominant NMR technique used due to the high sensitivity of <inline-formula><tex-math notation="LaTeX">^1</tex-math> <inline-graphic xlink:href="alazmi-ieq3-2849728.gif"/> </inline-formula>H nuclei. However, the current availability of high magnetic fields and cryogenically cooled probe heads allow researchers to overcome the low sensitivity of <inline-formula><tex-math notation="LaTeX">^{13}</tex-math> <inline-graphic xlink:href="alazmi-ieq4-2849728.gif"/> </inline-formula>C nuclei. Consequently, <inline-formula><tex-math notation="LaTeX">^{13}</tex-math> <inline-graphic xlink:href="alazmi-ieq5-2849728.gif"/> </inline-formula>C-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 <inline-formula><tex-math notation="LaTeX">^{13}</tex-math> <inline-graphic xlink:href="alazmi-ieq6-2849728.gif"/> </inline-formula>C-detected NMR spectra. Our method is able to accurately form spin systems based on as few as two <inline-formula><tex-math notation="LaTeX">^{13}</tex-math> <inline-graphic xlink:href="alazmi-ieq7-2849728.gif"/> </inline-formula>C-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 percent correct chemical shift assignment for Ubiquitin. For the simulated dataset, we obtained an average recall of 86 percent and an average precision of 88 percent. 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.