A new general-purpose fully automatic baseline-correction procedure for 1D and 2D NMR data

A new procedure for automatic baseline correction of NMR data sets is presented. It is based on an improved automatic recognition of signal-free regions that uses a Continuous Wavelet transform derivative calculation, followed by a baseline modelling procedure based on the Whittaker smoother algorithm. The method has been proven to automatically flatten 1D and 2D NMR spectra with large baseline distortions arising from different sources, is tolerant to low signal-to-noise ratio spectra, and to signals of varying widths in a single spectrum. Even though this procedure has so far only been applied to NMR spectra, we believe it to also be applicable to other spectroscopies having relatively narrow peaks (e.g., mass spectrometry), and potentially to those with broad peaks (e.g., near infrared or ultraviolet).     

The equilibrium unfolding of MerP characterized by multivariate analysis of 2D NMR data

A general problem when analysing NMR spectra that reflect variations in the environment of target molecules is that different resonances are affected to various extents. Often a few resonances that display the largest frequency changes are selected as probes to reflect the examined variation, especially in the case, where the NMR spectra contain numerous resonances. Such a selection is dependent on more or less intuitive judgements and relying on the observed spectral variation being primarily caused by changes in the NMR sample. Second, recording changes observed for a few (albeit significant) resonances is inevitably accompanied by not using all available information in the analysis. Likewise, the commonly used chemical shift mapping (CSM) [Biochemistry 39 (2000) 26, Biochemistry 39 (2000) 12595] constitutes a loss of information since the total variation in the data is not retained in the projection into this single variable. Here, we describe a method for subjecting 2D NMR time-domain data to multivariate analysis and illustrate it with an analysis of multiple NMR experiments recorded at various folding conditions for the protein MerP. The calculated principal components provide an unbiased model of variations in the NMR spectra and they can consequently be processed as NMR data, and all the changes as reflected in the principal components are thereby made available for visual inspection in one single NMR spectrum. This approach is much less laborious than consideration of large numbers of individual spectra, and it greatly increases the interpretative power of the analysis.     

The fumarate sensor DcuS: progress in rapid protein fold elucidation by combining protein structure prediction methods with NMR spectroscopy

We illustrate how moderate resolution protein structures can be rapidly obtained by interlinking computational prediction methodologies with un- or partially assigned NMR data. To facilitate the application of our recently described method of ranking and subsequent refining alternative structural models using unassigned NMR data [Proc. Natl. Acad. Sci. USA 100 (2003) 15404] for such "structural genomics"-type experiments it is combined with protein models from several prediction techniques, enhanced to utilize partial assignments, and applied on a protein with an unknown structure and fold. From the original NMR spectra obtained for the 140 residue fumarate sensor DcuS, 1100 1H, 13C, and 15N chemical shift signals, 3000 1H-1H NOESY cross peak intensities, and 209 backbone residual dipolar couplings were extracted and used to rank models produced by de novo structure prediction and comparative modeling methods. The ranking proceeds in two steps: first, an optimal assignment of the NMR peaks to atoms is found for each model independently, and second, the models are ranked based on the consistency between the NMR data and the model assuming these optimal assignments. The low-resolution model selected using this ranking procedure had the correct overall fold and a global backbone RMSD of 6.0 angstrom, and was subsequently refined to 3.7 angstrom RMSD. With the incorporation of a small number of NOE and residual dipolar coupling constraints available very early in the traditional spectral assignment process, a model with an RMSD of 2.8 angstrom could rapidly be built. The ability to generate moderate resolution models within days of NMR data collection should facilitate large scale NMR structure determination efforts.     

Negative impact of noise on the principal component analysis of NMR data

Principal component analysis (PCA) is routinely applied to the study of NMR based metabolomic data. PCA is used to simplify the examination of complex metabolite mixtures obtained from biological samples that may be composed of hundreds or thousands of chemical components. PCA is primarily used to identify relative changes in the concentration of metabolites to identify trends or characteristics within the NMR data that permits discrimination between various samples that differ in their source or treatment. A common concern with PCA of NMR data is the potential over emphasis of small changes in high concentration metabolites that would over-shadow significant and large changes in low-concentration components that may lead to a skewed or irrelevant clustering of the NMR data. We have identified an additional concern, very small and random fluctuations within the noise of the NMR spectrum can also result in large and irrelevant variations in the PCA clustering. Alleviation of this problem is obtained by simply excluding the noise region from the PCA by a judicious choice of a threshold above the spectral noise.     

An efficient peak assignment algorithm for two-dimensional NMR correlation spectra of framework structures

An algorithm is described for efficiently assigning the resonances in NMR spectra to the inequivalent atoms in the structure under study based on the information in two-dimensional NMR correlation experiments and the 'connectivities' known from the structure. The algorithm, which is based on basic graph theory concepts, finds all possible assignments sets which are consistent with the experimentally observed correlations and known connectivities in a very efficient manner. It is designed to deal with less than ideal experimental data in which there may be overlapping peaks and uncertainty about the presence or absence of correlation peaks. The algorithm was primarily developed for assigning the peaks in the high-resolution solid-state 29Si MAS NMR spectra of highly siliceous zeolites based on two-dimensional 29Si INADEQUATE spectra and is described using the zeolites ZSM-12 and ZSM-5 as working examples. Peak assignment for zeolite frameworks is particularly challenging since there is often little or no information to distinguish peaks from one another such as characteristic chemical shifts, relative intensities, or different relaxation times. The algorithm may be a useful tool for easily, reliably, and efficiently working out peak assignments from other types of correlation experiments on other types of systems and further examples are provided in the Supplementary material.     

NMR spectral quantitation by principal component analysis. III. A generalized procedure for determination of lineshape variations

We present a general procedure for automatic quantitation of a series of spectral peaks based on principal component analysis (PCA). PCA has been previously used for spectral quantitation of a single resonant peak of constant shape but variable amplitude. Here we extend this procedure to estimate all of the peak parameters: amplitude, position (frequency), phase and linewidth. The procedure consists of a series of iterative steps in which the estimates of position and phase from one stage of iteration are used to correct the spectra prior to the next stage. The process is convergent to a stable result, typically in less than 5 iterations. If desired, remaining linewidth variations can then be corrected. Correction of (typically) unwanted variations of these types is important not only for direct peak quantitation, but also as a preprocessing step for spectral data prior to application of pattern recognition/classification techniques. The procedure is demonstrated on simulated data and on a set of 992 (31)P NMR in vivo spectra taken from a kinetic study of rat muscle energetics. The proposed procedure is robust, makes very limited assumptions about the lineshape, and performs well with data of low signal-to-noise ratio.     

Automated Signal Detection as Tool to Evaluate Magnetic Field Homogeneity from Fourier Transformed Proton NMR Spectra

Off line analysis of proton nuclear magnetic resonance (NMR) spectra by computerized peak detection is used to determine the shim quality from Fourier transformed spectra. The method is designed to analyze spectra independently from any further information about the measurement. For this purpose, the number of maxima in a spectral region is determined and compared to the number of peaks identified by an automatic routine utilizing a curvature-based method. The resulting “shim quality quotient” represents a good indicator for the magnetic field homogeneity present during the measurement. This method can be applied to separated single signals as well as to entire spectral regions. Hence, its brought use as tool in automatic interpretation of NMR spectra is possible. An example is shown for an in situ proton NMR measurement of glucose mutarotation.     

光谱分析在煤结构研究中的进展

煤结构是各类煤相关研究的微观基础,光谱分析作为煤结构研究的重要方法被广泛应用,其在煤结构研究中的进展对光谱分析方法的普及、应用和发展有重要意义。光谱分析方法研究煤结构已成为煤化工领域使用的常规方法,能够快速无损检测,对煤分子结构的破坏小,可为不同环境条件下煤物理化学性质的变化提供有效的检测手段。从光谱分析在煤质、大分子结构、煤中元素三个方面介绍光谱分析方法,主要对傅里叶变换红外光谱（FTIR）、Raman光谱分析、核磁共振谱（NMR）进行综述介绍,阐明其在煤结构研究中的发展历史、应用的关键研究结果及其意义。综合国内外煤结构研究中的多种光谱分析方法及应用现状发现：目前研究并没有彻底解决煤结构特征及性质变化的问题,缺乏对煤结构光谱特征信息共性的总结,未能形成煤中官能团和元素的不同光谱信息数据库,存在光谱特征峰与煤结构信息不对等的问题,即在某波长存在特征峰但无法与煤中官能团匹配,或煤官能团受元素组成、键能等影响对多波长产生响应的问题。现阶段对原煤自然状态下结构的研究已经不能满足煤应用中产生的问题,单一的光谱分析方法不能全面分析煤结构特征,且对影响煤结构光谱特征变化因素的研究较少,尤其是煤样的前处理和煤在萃取等过程中,前处理液和萃取剂对煤光谱特征的影响。展望光谱分析在煤结构的研究中可以从以下几方面入手：光谱分析与其他方法的联用以综合描述煤结构,如化学方法、高分辨率透射电镜(HRTEM)、扫描轨道显微镜（STM）、质谱（MS）等方法的联用,定性、定量全面分析煤结构特征;多种条件下煤结构及光谱特征,现阶段应利用光谱分析方法研究煤在多种条件下的结构特征及性质变化,解决煤在实际应用中的问题。如对煤进行氧化、氢化、热解、燃烧、低温、液化、汽化等处理,分析过程变化和产物特征,有助于推测母体煤的结构,了解煤的性质,控制煤物理化学过程变化中的产物,获取煤的精细化学品;煤光谱分析特征信息库建立,网络大数据背景下建立煤光谱分析特征信息库及可视化数据查询平台,实现多条件模拟假设,演示和探索煤结构在不同条件下的动态变化,利用人工智能、云计算方法实现煤各类光谱数据的处理分析,增强光谱数据信息挖掘,提升数据有效性和实用性。     

Entropy minimization and spectral dissimilarity curve resolution technique applied to nuclear magnetic resonance data sets

The use of entropy minimization and spectral dissimilarity is applied to three nuclear magnetic resonance (NMR) data sets. The data sets contain 2, 2, and 3 observables each. It was found that without any a priori information the sets of pure component spectra underlying the NMR spectroscopic observations could be extracted. These successful spectral resolutions suggest that a combined entropy minimization and spectral dissimilarity approach can be further developed for even larger NMR data sets containing a larger number of observables. Brief comparison to DECRA and PMF curve resolution results is also presented.     

Phase-sensitive spectral estimation by the hybrid filter diagonalization method

A more robust way to obtain a high-resolution multidimensional NMR spectrum from limited data sets is described. The Filter Diagonalization Method (FDM) is used to analyze phase-modulated data and cast the spectrum in terms of phase-sensitive Lorentzian "phase-twist" peaks. These spectra are then used to obtain absorption-mode phase-sensitive spectra. In contrast to earlier implementations of multidimensional FDM, the absolute phase of the data need not be known beforehand, and linear phase corrections in each frequency dimension are possible, if they are required. Regularization is employed to improve the conditioning of the linear algebra problems that must be solved to obtain the spectral estimate. While regularization smoothes away noise and small peaks, a hybrid method allows the true noise floor to be correctly represented in the final result. Line shape transformation to a Gaussian-like shape improves the clarity of the spectra, and is achieved by a conventional Lorentzian-to-Gaussian transformation in the time-domain, after inverse Fourier transformation of the FDM spectra. The results obtained highlight the danger of not using proper phase-sensitive line shapes in the spectral estimate. The advantages of the new method for the spectral estimate are the following: (i) the spectrum can be phased by conventional means after it is obtained; (ii) there is a true and accurate noise floor; and (iii) there is some indication of the quality of fit in each local region of the spectrum. The method is illustrated with 2D NMR data for the first time, but is applicable to n-dimensional data without any restriction on the number of time/frequency dimensions.