Jianyang Michael Zeng, Chittaranjan Tripathy, Pei Zhou, Bruce R Donald
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In this paper we present a novel NOE assignment algorithm, called HAusdorff-based NOE Assignment (HANA), that starts with a high-resolution protein backbone computed using only two residual dipolar couplings (RDCs) per residue37, 39, employs a Hausdorff-based pattern matching technique to deduce similarity between experimental and back-computed NOE spectra for each rotamer from a statistically diverse library, and drives the selection of optimal position-specific rotamers for filtering ambiguous NOE assignments. Our algorithm runs in time O(tn(3) +tn log t), where t is the maximum number of rotamers per residue and n is the size of the protein. Application of our algorithm on biological NMR data for three proteins, namely, human ubiquitin, the zinc finger domain of the human DNA Y-polymerase Eta (pol η) and the human Set2-Rpb1 interacting domain (hSRI) demonstrates that our algorithm overcomes spectral noise to achieve more than 90% assignment accuracy. Additionally, the final structures calculated using our automated NOE assignments have backbone RMSD < 1.7 Å and all-heavy-atom RMSD < 2.5 Å from reference structures that were determined either by X-ray crystallography or traditional NMR approaches. These results show that our NOE assignment algorithm can be successfully applied to protein NMR spectra to obtain high-quality structures.</p>","PeriodicalId":72665,"journal":{"name":"Computational systems bioinformatics. 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引用次数: 0
摘要
基于溶液核磁共振(NMR)光谱的高通量结构测定在结构基因组学中发挥着重要作用。核磁共振结构确定的主要瓶颈之一是对核磁共振数据进行解释,以便通过为质子对分配核奥弗霍瑟效应(NOE)谱峰来获得足够数量的精确距离约束。NOE 自动赋值的难点主要在于化学位移的共振变性和 NOE 峰位置的实验误差造成的不确定性。本文提出了一种新颖的 NOE 赋值算法,称为基于 HAusdorff 的 NOE 赋值(HANA),该算法以高分辨率蛋白质骨架为起点,每个残基仅使用两个残余偶极耦合(RDC)进行计算37, 39 ,采用基于 Hausdorff 的模式匹配技术,从统计多样性库中推断出每个旋转体的实验 NOE 光谱与反向计算 NOE 光谱之间的相似性,并驱动选择特定位置的最佳旋转体,以过滤模糊的 NOE 赋值。我们的算法运行时间为 O(tn(3)+tn log t),其中 t 是每个残基的最大转子数量,n 是蛋白质的大小。我们的算法应用于三种蛋白质的生物核磁共振数据,即人类泛素、人类 DNA Y 聚合酶 Eta(pol η)的锌指结构域和人类 Set2-Rpb1 相互作用结构域(hSRI),结果表明我们的算法克服了光谱噪声,达到了 90% 以上的赋值准确率。此外,使用我们的自动 NOE 赋值计算出的最终结构与通过 X 射线晶体学或传统 NMR 方法确定的参考结构相比,骨干 RMSD < 1.7 Å,所有重原子 RMSD < 2.5 Å。这些结果表明,我们的 NOE 赋值算法可成功应用于蛋白质 NMR 光谱,从而获得高质量的结构。
A HAUSDORFF-BASED NOE ASSIGNMENT ALGORITHM USING PROTEIN BACKBONE DETERMINED FROM RESIDUAL DIPOLAR COUPLINGS AND ROTAMER PATTERNS.
High-throughput structure determination based on solution Nuclear Magnetic Resonance (NMR) spectroscopy plays an important role in structural genomics. One of the main bottlenecks in NMR structure determination is the interpretation of NMR data to obtain a sufficient number of accurate distance restraints by assigning nuclear Overhauser effect (NOE) spectral peaks to pairs of protons. The difficulty in automated NOE assignment mainly lies in the ambiguities arising both from the resonance degeneracy of chemical shifts and from the uncertainty due to experimental errors in NOE peak positions. In this paper we present a novel NOE assignment algorithm, called HAusdorff-based NOE Assignment (HANA), that starts with a high-resolution protein backbone computed using only two residual dipolar couplings (RDCs) per residue37, 39, employs a Hausdorff-based pattern matching technique to deduce similarity between experimental and back-computed NOE spectra for each rotamer from a statistically diverse library, and drives the selection of optimal position-specific rotamers for filtering ambiguous NOE assignments. Our algorithm runs in time O(tn(3) +tn log t), where t is the maximum number of rotamers per residue and n is the size of the protein. Application of our algorithm on biological NMR data for three proteins, namely, human ubiquitin, the zinc finger domain of the human DNA Y-polymerase Eta (pol η) and the human Set2-Rpb1 interacting domain (hSRI) demonstrates that our algorithm overcomes spectral noise to achieve more than 90% assignment accuracy. Additionally, the final structures calculated using our automated NOE assignments have backbone RMSD < 1.7 Å and all-heavy-atom RMSD < 2.5 Å from reference structures that were determined either by X-ray crystallography or traditional NMR approaches. These results show that our NOE assignment algorithm can be successfully applied to protein NMR spectra to obtain high-quality structures.