IEEE/ACM Trans Comput Biol Bioinform. 2018 Nov-Dec;15(6):1999-2008. doi: 10.1109/TCBB.2018.2849728. Epub 2018 Jun 25.
Nuclear magnetic resonance (NMR) spectroscopy is attracting more attention in the field of computational structural biology. Till recently, H-detected experiments are the dominant NMR technique used due to the high sensitivity of H nuclei. However, the current availability of high magnetic fields and cryogenically cooled probe heads allow researchers to overcome the low sensitivity of C nuclei. Consequently, 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 C-detected NMR spectra. Our method is able to accurately form spin systems based on as few as two 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.
核磁共振(NMR)光谱学在计算结构生物学领域越来越受到关注。直到最近,由于氢核的高灵敏度,H 检测实验仍然是使用的主要 NMR 技术。然而,目前高磁场和低温探头的可用性使研究人员能够克服 C 核的低灵敏度。因此,C 检测实验已成为不同 NMR 应用中的一种流行技术,特别是在大蛋白质的共振分配和结构确定方面。在本文中,我们提出了用于 C 检测 NMR 光谱的第一个自旋系统形成方法。我们的方法能够仅基于两个 C 检测光谱(CBCACON 和 CBCANCO)准确地形成自旋系统。我们的方法从更可信的光谱中提取切片,并将其用作反馈,以指导在不太可信的光谱中进行切片提取。这种反馈导致提取准确的切片,从而有助于形成更好的自旋系统。我们在“泛素”的真实数据集和由 12 个蛋白质组成的基准模拟数据集上测试了我们的方法。我们将自旋系统作为输入提供给遗传算法,以生成化学位移分配,从而为泛素获得 92%的正确化学位移分配。对于模拟数据集,我们获得了 86%的平均召回率和 88%的平均精度。最后,我们将泛素的化学位移分配作为输入提供给 CS-ROSETTA 服务器,该服务器生成的结构与实验确定的结构接近。