FID-Net: A Versatile Deep Neural Network Architecture for NMR Spectral Reconstruction and Virtual Decoupling

In recent years, the transformative potential of deep neural networks (DNNs) for analysing and interpreting NMR data has clearly been recognised. However, most applications of DNNs in NMR to date either struggle to outperform existing methodologies or are limited in scope to a narrow range of data that closely resemble the data that the network was trained on. These limitations have prevented a widescale uptake of DNNs in NMR. Addressing this, we introduce FID-Net, a deep neural network architecture inspired by WaveNet, for performing analyses on time domain NMR data. We first demonstrate the effectiveness of this architecture in reconstructing non-uniformly sampled (NUS) biomolecular NMR spectra. It is shown that a single network is able to reconstruct a diverse range of 2D NUS spectra that have been obtained with arbitrary sampling schedules, with a range of sweep widths, and a variety of other acquisition parameters. The performance of the trained FID-Net in this case exceeds or matches existing methods currently used for the reconstruction of NUS NMR spectra. Secondly, we present a network based on the FID-Net architecture that can efficiently virtually decouple 13Cα-13Cβ couplings in HNCA protein NMR spectra in a single shot analysis, while at the same time leaving glycine residues unmodulated. The ability for these DNNs to work effectively in a wide range of scenarios, without retraining, paves the way for their widespread usage in analysing NMR data

Virtual Homonuclear Decoupling in Direct Detection NMR 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 methods, one-bond 13C-13C couplings significantly reduce sensitivity and resolution. The solution to this problem has typically been to record in-phase and anti-phase (IPAP) or spin state selective excitation (S3E) spectra and take linear combinations to yield singlet resolved resonances. This however, results in a doubling of the effective phase cycle and requires additional delays and pulses to create the necessary magnetisation. Here, we propose an alternative method of virtual decoupling using deep neural networks. This methodology requires only the in-phase spectra, halving the experimental time and, by decoupling signals, gives a significant boost in resolution while concomitantly doubling sensitivity relative to the in-phase spectrum. We successfully apply this methodology to virtually decouple in-phase CON (13CO-15N) protein NMR spectra and 13C-13C correlation spectra of protein side chains

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

Cell-permeable lanthanide-platinum(iv) anti-cancer prodrugs

Platinum compounds are a vital part of our anti-cancer arsenal, and determining the location and speciation of platinum compounds is crucial. We have synthesised a lanthanide complex bearing a salicylic group (Ln = Gd, Eu) which demonstrates excellent cellular accumulation and minimal cytotoxicity. Derivatisation enabled access to bimetallic lanthanide–platinum(II) and lanthanide–platinum(IV) complexes. Luminescence from the europium–platinum(IV) system was quenched, and reduction to platinum(II) with ascorbic acid resulted in a “switch-on” luminescence enhancement. We used diffusion-based 1H NMR spectroscopic methods to quantify cellular accumulation. The gadolinium–platinum(II) and gadolinium–platinum(IV) complexes demonstrated appreciable cytotoxicity. A longer delay following incubation before cytotoxicity was observed for the gadolinium–platinum(IV) compared to the gadolinium–platinum(II) complex. Functionalisation with octanoate ligands resulted in enhanced cellular accumulation and an even greater latency in cytotoxicity

Dynamic design: manipulation of millisecond timescale motions on the energy landscape of cyclophilin A

Proteins need to interconvert between many conformations in order to function, many of which are formed transiently, and sparsely populated. Particularly when the lifetimes of these states approach the millisecond timescale, identifying the relevant structures and the mechanism by which they interconvert remains a tremendous challenge. Here we introduce a novel combination of accelerated MD (aMD) simulations and Markov state modelling (MSM) to explore these ‘excited’ conformational states. Applying this to the highly dynamic protein CypA, a protein involved in immune response and associated with HIV infection, we identify five principally populated conformational states and the atomistic mechanism by which they interconvert. A rational design strategy predicted that the mutant D66A should stabilise the minor conformations and substantially alter the dynamics, whereas the similar mutant H70A should leave the landscape broadly unchanged. These predictions are confirmed using CPMG and R1ρ solution state NMR measurements. By efficiently exploring functionally relevant, but sparsely populated conformations with millisecond lifetimes in silico, our aMD/MSM method has tremendous promise for the design of dynamic protein free energy landscapes for both protein engineering and drug discovery

Development of NMR methodology for the study of complex molecular and biomolecular systems

Nuclear magnetic resonance (NMR) is amongst the most powerful and versatile spectroscopic tools for characterising biological systems. A key advantage of NMR techniques is that in addition to being sensitive to structural features of biomolecules through their chemical shift, chemical exchange and relaxation processes allow dynamic molecular properties to be studied. Techniques have also been developed to facilitate the characterisation of translational diffusion with NMR, through the application of pulsed field gradients. In this thesis we develop and apply NMR methods for studying biomolecular systems. Firstly, in Chapter 2, we introduce an experimental protocol and model for the analysis of cellular systems from the diffusional properties of the water within them. In addition to reporting diffusion coefficients, this analysis also provides information on cell radii, abundance and permeability. A drawback of NMR compared to other spectroscopic techniques is its relative insensitivity. One way of improving this is through chemical exchange saturation transfer (CEST) experiments where the presence of lowly concentrated species is effectively read out through its interactions with a highly concentrated species. In Chapter 3 we show how this experiment can be used to follow changes in a range of solution properties. The diffusion and CEST experiments are combined in Chapter 4, where we introduce the novel diffusion-weighted CEST experiment. Here, the diffusion weighting is used to differentiate between compartments, while the CEST component of the experiment characterises properties of each of the compartments. In Chapters 5 and 6, modern NMR techniques are used to characterise dynamical features of 15N-labelled proteins. Experiments employed in this section include a fast dynamics analysis as well as CPMG and R1ρ relaxation dispersion experiments. Collectively, these experiments provide important insights into the behaviour of the proteins with atomic-level resolution.</p

An HPLC method for the determination of the kinetics of hydrolysis of testosterone esters

Journal of Pharmaceutical and Biomedical Analysis34375-379JPBA

Norethisterone and levonorgestrel esters: A novel synthetic method

Steroids461639-647STED