A 13C-detected 15N double-quantum NMR experiment to probe arginine side-chain guanidinium 15Nη chemical shifts.

Arginine side-chains are often key for enzyme catalysis, protein-ligand and protein-protein interactions. The importance of arginine stems from the ability of the terminal guanidinium group to form many key interactions, such as hydrogen bonds and salt bridges, as well as its perpetual positive charge. We present here an arginine 13Cζ-detected NMR experiment in which a double-quantum coherence involving the two 15Nη nuclei is evolved during the indirect chemical shift evolution period. As the precession frequency of the double-quantum coherence is insensitive to exchange of the two 15Nη; this new approach is shown to eliminate the previously deleterious line broadenings of 15Nη resonances caused by the partially restricted rotation about the Cζ-Nε bond. Consequently, sharp and well-resolved 15Nη resonances can be observed. The utility of the presented method is demonstrated on the L99A mutant of the 19 kDa protein T4 lysozyme, where the measurement of small chemical shift perturbations, such as one-bond deuterium isotope shifts, of the arginine amine 15Nη nuclei becomes possible using the double-quantum experiment

Relaxation Dispersion NMR Spectroscopy

Relaxation dispersion nuclear magnetic resonance (NMR) spectroscopy has been developed since the 1950s and has now evolved into a very sensitive and versatile tool to study chemical and conformational exchange processes on the micro- to milliseconds (µs–ms) time scale. While relaxation dispersion NMR was originally designed with small molecules in mind, it has become a very attractive tool to also study the dynamics of biological macromolecules, after major advances had been made in hardware, experimental design and isotope labelling

Methodological Advancements for Characterising Protein Side Chains by NMR Spectroscopy

The surface of proteins is covered by side chains of polar amino acids that are imperative for modulating protein functionality through the formation non-covalent intermolecular interactions. However, despite their tremendous importance, the unique structures of protein side chains require tailored approaches for investigation by NMR spectroscopy, and so have traditionally been understudied compared to the protein backbone. Here, we review substantial recent methodological advancements within NMR spectroscopy to address this issue. Specifically, we consider advancements that provide new insight into methyl-bearing side chains, show the potential of using non-natural amino acids, and reveal the actions of charged side chains. Combined, the new methods promise unprecedented characterisations of side chains that will further elucidate protein function

Virtual Homonuclear Decoupling in Direct Detection Nuclear Magnetic Resonance Experiments using Deep Neural Networks

Nuclear magnetic resonance (NMR) experiments are frequently complicated by the presence of homonuclear scalar couplings. For the growing body of biomolecular 13C-detected NMR methods, one-bond 13C–13C couplings significantly reduce sensitivity and resolution. The solution to this problem has typically been to perform virtual decoupling by recording multiple spectra and taking linear combinations. Here, we propose an alternative method of virtual decoupling using deep neural networks, which only requires a single spectrum and gives a significant boost in resolution while reducing the minimum effective phase cycles of the experiments by at least a factor of 2. We successfully apply this methodology to virtually decouple in-phase CON (13CO–15N) protein NMR spectra, 13C–13C correlation spectra of protein side chains, and 13Cα-detected protein 13Cα–13CO spectra where two large homonuclear couplings are present. The deep neural network approach effectively decouples spectra with a high degree of flexibility, including in cases where existing methods fail, and facilitates the use of simpler pulse sequences

Multi-Quantum Chemical Exchange Saturation Transfer NMR to Quantify Symmetrical Exchange: Application to Rotational Dynamics of the Guanidinium Group in Arginine Side Chains

Chemical exchange saturation transfer (CEST) NMR experiments have emerged as a powerful tool for characterizing dynamics in proteins. We show here that the CEST approach can be extended to systems with symmetrical exchange, where the NMR signals of all exchanging species are severely broadened. To achieve this, multiquantum CEST (MQ-CEST) is introduced, where the CEST pulse is applied to a longitudinal multispin order density element and the CEST profiles are encoded onto nonbroadened nuclei. The MQCEST approach is demonstrated on the restricted rotation of guanidinium groups in arginine residues within proteins. These groups and their dynamics are essential for many enzymes and for noncovalent interactions through the formation of hydrogen bonds, salt-bridges, and πstacking interactions, and their rate of rotation is highly indicative of the extent of interactions formed. The MQ-CEST method is successfully applied to guanidinium groups in the 19 kDa L99A mutant of T4 lysozyme

Using Deep Neural Networks to Reconstruct Non-uniformly Sampled NMR Spectra

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

Intra-residue methyl-methyl correlations for valine and leucine residues in large proteins from a 3D-HMBC-HMQC experiment

Methyl-TROSY based NMR experiments have over the last two decades become one of the most important means to characterise dynamics and functional mechanisms of large proteins and macromolecular machines in solution. The chemical shift assignment of methyl groups in large proteins is, however, still not trivial and it is typically performed using backbone-dependent experiments in a ‘divide and conquer’ approach, mutations, structure-based assignments or a combination of these. Structure-based assignment of methyl groups is an emerging strategy, which reduces the time and cost required as well as providing a method that is independent of a backbone assignment. One crucial step in available structure-based assignment protocols is linking the two prochiral methyl groups of leucine and valine residues. This has previously been achieved by recording NOESY spectra with short mixing times or by comparing NOESY spectra. Herein, we present a method based on through-bond scalar coupling transfers, a 3D-HMBC-HMQC experiment, to link the intra-residue methyl groups of leucine and valine. It is shown that the HMBC-HMQC method has several advantages over solely using NOESY spectra since a unique intra-residue cross-peak is observed. Moreover, overlap in the methyl-TROSY HMQC spectrum can easily be identified with the HMBC-HMQC experiment, thereby removing possible ambiguities in the assignment

Aromatic side-chain flips orchestrate the conformational sampling of functional loops in human histone deacetylase 8

Human histone deacetylase 8 (HDAC8) is a key hydrolase in gene regulation and an important drug-target. High-resolution structures of HDAC8 in complex with substrates or inhibitors are available, which have provided insights into the bound state of HDAC8 and its function. Here, using long all-atom unbiased molecular dynamics simulations and Markov state modelling, we show a strong correlation between the conformation of aromatic side chains near the active site and opening and closing of the surrounding functional loops of HDAC8. We also investigated two mutants known to allosterically downregulate the enzymatic activity of HDAC8. Based on experimental data, we hypothesise that I19S-HDAC8 is unable to release the product, whereas both product release and substrate binding are impaired in the S39E-HDAC8 mutant. The presented results deliver detailed insights into the functional dynamics of HDAC8 and provide a mechanism for the substantial downregulation caused by allosteric mutations, including a disease causing one