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NMR spectroscopy is an indispensably powerful technique for the analysis of biomolecules under ambient conditions, both for structural- and functional studies. However, in practice the complexity of the technique has often frustrated its application by non-specialists. In this paper, we present CcpNmr version-3, the latest software release from the Collaborative Computational Project for NMR, for all aspects of NMR data analysis, including liquid- and solid-state NMR data. This software has been designed to be simple, functional and flexible, and aims to ensure that routine tasks can be performed in a straightforward manner. We have designed the software according to modern software engineering principles and leveraged the capabilities of modern graphics libraries to simplify a variety of data analysis tasks. We describe the process of backbone assignment as an example of the flexibility and simplicity of implementing workflows, as well as the toolkit used to create the necessary graphics for this workflow. The package can be downloaded from www.ccpn.ac.uk/v3-software/downloads and is freely available to all non-profit organisations.

A new program, TALOS-N, is introduced for predicting protein backbone torsion angles from NMR chemical shifts. The program relies far more extensively on the use of trained artificial neural networks than its predecessor, TALOS+. Validation on an independent set of proteins indicates that backbone torsion angles can be predicted for a larger, ≥90 % fraction of the residues, with an error rate smaller than ca 3.5 %, using an acceptance criterion that is nearly two-fold tighter than that used previously, and a root mean square difference between predicted and crystallographically observed ( ϕ , ψ ) torsion angles of ca 12º. TALOS-N also reports sidechain χ 1 rotameric states for about 50 % of the residues, and a consistency with reference structures of 89 %. The program includes a neural network trained to identify secondary structure from residue sequence and chemical shifts.

The automated assignment of NOESY cross peaks has become a fundamental technique for NMR protein structure analysis. A widely used algorithm for this purpose is implemented in the program CYANA. It has been used for a large number of structure determinations of proteins in solution but was so far not described in full detail. In this paper we present a complete description of the CYANA implementation of automated NOESY assignment, which differs extensively from its predecessor CANDID by the use of a consistent probabilistic treatment, and we discuss its performance in the second round of the critical assessment of structure determination by NMR.

Implementation of a new algorithm, SMILE, is described for reconstruction of non-uniformly sampled two-, three- and four-dimensional NMR data, which takes advantage of the known phases of the NMR spectrum and the exponential decay of underlying time domain signals. The method is very robust with respect to the chosen sampling protocol and, in its default mode, also extends the truncated time domain signals by a modest amount of non-sampled zeros. SMILE can likewise be used to extend conventional uniformly sampled data, as an effective multidimensional alternative to linear prediction. The program is provided as a plug-in to the widely used NMRPipe software suite, and can be used with default parameters for mainstream application, or with user control over the iterative process to possibly further improve reconstruction quality and to lower the demand on computational resources. For large data sets, the method is robust and demonstrated for sparsities down to ca 1%, and final all-real spectral sizes as large as 300 Gb. Comparison between fully sampled, conventionally processed spectra and randomly selected NUS subsets of this data shows that the reconstruction quality approaches the theoretical limit in terms of peak position fidelity and intensity. SMILE essentially removes the noise-like appearance associated with the point-spread function of signals that are a default of five-fold above the noise level, but impacts the actual thermal noise in the NMR spectra only minimally. Therefore, the appearance and interpretation of SMILE-reconstructed spectra is very similar to that of fully sampled spectra generated by Fourier transformation.

In the present study, we used low-field nuclear magnetic resonance (LF-NMR) measurements and mercury intrusion porosimetry (MIP) to evaluate the influence of the water–binder ( w / b ) ratio, fly ash (FA) replacement and curing regimes on the pore structure of concrete. The main advantage of LF-NMR is that it is nondestructive and suitable for large concrete samples compared with other traditional methods, such as MIP, adsorption methods and scanning electron microscopy methods. Hence, the LF-NMR relaxometry method measures the pore structures that are closer to reality. The LF-NMR relaxation time, T 2 , represents the change in the pore structure during the hydration and hardening processes of concrete. The results showed that the T 2 spectrum of the concrete sample was mainly composed of 3–5 signal peaks. Additionally, the w / b ratio, FA replacement and the curing regimes have significant effects on the T 2 spectrum, porosity, and pore size distribution of concrete. In addition, the compressive strength of concrete has a close relationship with its pore structure. Based on the LF-NMR test results, the relationship between the compressive strength and the porosity, pore size distribution of concrete was established.

Chemical shifts contain important site-specific information on the structure and dynamics of proteins. Deviations from statistical average values, known as random coil chemical shifts (RCCSs), are extensively used to infer these relationships. Unfortunately, the use of imprecise reference RCCSs leads to biased inference and obstructs the detection of subtle structural features. Here we present a new method, POTENCI, for the prediction of RCCSs that outperforms the currently most authoritative methods. POTENCI is parametrized using a large curated database of chemical shifts for protein segments with validated disorder; It takes pH and temperature explicitly into account, and includes sequence-dependent nearest and next-nearest neighbor corrections as well as second-order corrections. RCCS predictions with POTENCI show root-mean-square values that are lower by 25–78%, with the largest improvements observed for 1Hα and 13C′. It is demonstrated how POTENCI can be applied to analyze subtle deviations from RCCSs to detect small populations of residual structure in intrinsically disorder proteins that were not discernible before. POTENCI source code is available for download, or can be deployed from the URL http://www.protein-nmr.org.

We report linewidth and proton T1, T1ρ and T2′ relaxation data of the model protein ubiquitin acquired at MAS frequencies up to 126 kHz. We find a predominantly linear improvement in linewidths and coherence decay times of protons with increasing spinning frequency in the range from 93 to 126 kHz. We further attempt to gain insight into the different contributions to the linewidth at fast MAS using site-specific analysis of proton relaxation parameters and present bulk relaxation times as a function of the MAS frequency. For microcrystalline fully-protonated ubiquitin, inhomogeneous contributions are only a minor part of the proton linewidth, and at 126 kHz MAS coherent effects are still dominating. We furthermore present site-specific proton relaxation rate constants during a spinlock at 126 kHz MAS, as well as MAS-dependent bulk T1ρ (1HN).

Non-uniform and sparse sampling of multi-dimensional NMR spectra has over the last decade become an important tool to allow for fast acquisition of multi-dimensional NMR spectra with high resolution. The success of non-uniform sampling NMR hinge on both the development of algorithms to accurately reconstruct the sparsely sampled spectra and the design of sampling schedules that maximise the information contained in the sampled data. Traditionally, the reconstruction tools and algorithms have aimed at reconstructing the full spectrum and thus ‘fill out the missing points’ in the time-domain spectrum, although other techniques are based on multi-dimensional decomposition and extraction of multi-dimensional shapes. Also over the last decade, machine learning, deep neural networks, and artificial intelligence have seen new applications in an enormous range of sciences, including analysis of MRI spectra. As a proof-of-principle, it is shown here that simple deep neural networks can be trained to reconstruct sparsely sampled NMR spectra. For the reconstruction of two-dimensional NMR spectra, reconstruction using a deep neural network performs as well, if not better than, the currently and widely used techniques. It is therefore anticipated that deep neural networks provide a very valuable tool for the reconstruction of sparsely sampled NMR spectra in the future to come.

NMR chemical shifts in proteins depend strongly on local structure. The program TALOS establishes an empirical relation between ¹³C, ¹⁵N and ¹H chemical shifts and backbone torsion angles [Greek Phi symbol] and ψ (Cornilescu et al. J Biomol NMR 13 289-302, 1999). Extension of the original 20-protein database to 200 proteins increased the fraction of residues for which backbone angles could be predicted from 65 to 74%, while reducing the error rate from 3 to 2.5%. Addition of a two-layer neural network filter to the database fragment selection process forms the basis for a new program, TALOS+, which further enhances the prediction rate to 88.5%, without increasing the error rate. Excluding the 2.5% of residues for which TALOS+ makes predictions that strongly differ from those observed in the crystalline state, the accuracy of predicted [Greek Phi symbol] and ψ angles, equals ±13°. Large discrepancies between predictions and crystal structures are primarily limited to loop regions, and for the few cases where multiple X-ray structures are available such residues are often found in different states in the different structures. The TALOS+ output includes predictions for individual residues with missing chemical shifts, and the neural network component of the program also predicts secondary structure with good accuracy.