Abbas Ahmed, Guo Xianrong, Jing Bing-Yi, Gao Xin
Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
J Biomol NMR. 2014 Jun;59(2):75-86. doi: 10.1007/s10858-014-9828-0. Epub 2014 Apr 19.
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 ([Formula: see text]) 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.
尽管基于自动核磁共振的蛋白质结构测定取得了重大进展，但全自动方法选择的峰中存在大量假阳性和假阴性仍然是一个问题。这些假阳性和假阴性会影响共振归属方法的性能。造成这个问题的主要原因之一是，计算研究界通常将峰挑选和共振归属视为两个独立的问题，而光谱学家会利用专业知识同时挑选峰并归属其共振。我们提出了一种新颖的框架，该框架同时进行切片挑选和自旋系统形成，这是共振归属中的一个关键步骤。然后，我们的框架采用遗传算法，在自旋系统的连接性信息和氨基酸类型信息的指导下，将自旋系统归属到残基。我们框架的输入可以少至两种常用光谱，即CBCA(CO)NH和HNCACB。与现有的将峰作为单位的峰挑选和共振归属方法不同，我们的方法基于“切片”，切片是三维光谱中对应于特定（[公式：见原文]）值的一维向量。在基准模拟数据集和四个真实蛋白质数据集上的实验结果表明，我们的方法在使用比那些方法更少数量光谱的情况下，显著优于现有方法。我们的方法可在http://sfb.kaust.edu.sa/Pages/Software.aspx免费获取。
