Jang Richard, Gao Xin, Li Ming
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada.
J Comput Biol. 2011 Mar;18(3):347-63. doi: 10.1089/cmb.2010.0251.
In NMR resonance assignment, an indispensable step in NMR protein studies, manually processed peaks from both N-labeled and C-labeled spectra are typically used as inputs. However, the use of homologous structures can allow one to use only N-labeled NMR data and avoid the added expense of using C-labeled data. We propose a novel integer programming framework for structure-based backbone resonance assignment using N-labeled data. The core consists of a pair of integer programming models: one for spin system forming and amino acid typing, and the other for backbone resonance assignment. The goal is to perform the assignment directly from spectra without any manual intervention via automatically picked peaks, which are much noisier than manually picked peaks, so methods must be error-tolerant. In the case of semi-automated/manually processed peak data, we compare our system with the Xiong-Pandurangan-Bailey-Kellogg's contact replacement (CR) method, which is the most error-tolerant method for structure-based resonance assignment. Our system, on average, reduces the error rate of the CR method by five folds on their data set. In addition, by using an iterative algorithm, our system has the added capability of using the NOESY data to correct assignment errors due to errors in predicting the amino acid and secondary structure type of each spin system. On a publicly available data set for human ubiquitin, where the typing accuracy is 83%, we achieve 91% accuracy, compared to the 59% accuracy obtained without correcting for such errors. In the case of automatically picked peaks, using assignment information from yeast ubiquitin, we achieve a fully automatic assignment with 97% accuracy. To our knowledge, this is the first system that can achieve fully automatic structure-based assignment directly from spectra. This has implications in NMR protein mutant studies, where the assignment step is repeated for each mutant.
在核磁共振（NMR）共振归属中，这是NMR蛋白质研究中不可或缺的一步，通常将来自N标记和C标记光谱的手动处理峰用作输入。然而，使用同源结构可以使人们仅使用N标记的NMR数据，从而避免使用C标记数据带来的额外费用。我们提出了一种新颖的整数规划框架，用于基于结构的主链共振归属，该框架使用N标记数据。其核心由一对整数规划模型组成：一个用于自旋系统形成和氨基酸类型确定，另一个用于主链共振归属。目标是直接从光谱进行归属，无需通过自动挑选的峰进行任何人工干预，而自动挑选的峰比人工挑选的峰噪声大得多，因此方法必须容错。对于半自动/手动处理的峰数据，我们将我们的系统与熊 - 潘杜兰甘 - 贝利 - 凯洛格的接触置换（CR）方法进行了比较，该方法是基于结构的共振归属中最具容错性的方法。在他们的数据集上，我们的系统平均将CR方法的错误率降低了五倍。此外，通过使用迭代算法，我们的系统还具有利用核欧沃豪斯效应光谱（NOESY）数据来纠正由于预测每个自旋系统的氨基酸和二级结构类型错误而导致的归属错误的能力。在一个公开可用的人类泛素数据集上，其中类型确定准确率为83%，我们实现了91%的准确率，而不校正此类错误时的准确率为59%。对于自动挑选的峰，利用来自酵母泛素的归属信息，我们实现了97%准确率的全自动归属。据我们所知，这是第一个能够直接从光谱实现基于结构的全自动归属的系统。这在NMR蛋白质突变体研究中具有重要意义，其中每个突变体都要重复归属步骤。
