Organoarsenic probes to study proteins by NMR spectroscopy

Arsenical probes enable structural studies of proteins. We report the first organoarsenic probes for nuclear magnetic resonance (NMR) spectroscopy to study proteins in solutions. These probes can be attached to irregular loop regions. A lanthanide-binding tag induces sizable pseudocontact shifts in protein NMR spectra of a magnitude never observed for small paramagnetic probes before.

The Path to Actinorhodin: Regio- and Stereoselective Ketone Reduction by a Type II Polyketide Ketoreductase Revealed in Atomistic Detail

In type II polyketide synthases (PKSs), which typically biosynthesize several antibiotic and antitumor compounds, the substrate is a growing polyketide chain, shuttled between individual PKS enzymes whilst covalently tethered to an acyl carrier protein (ACP): this requires the ACP interacting with a series of different enzymes in succession. During biosynthesis of the antibiotic actinorhodin, produced by Streptomyces cœlicolor, one such key binding event is between an ACP carrying a 16-carbon octaketide chain (actACP) and a ketoreductase (actKR). Once the octaketide is bound inside actKR, it is likely cyclized between C7 and C12 and regioselective reduction of the ketone at C9 occurs: how these elegant chemical and conformational changes are controlled is not yet known. Here, we perform protein-protein docking, protein NMR, and extensive molecular dynamics simulations to reveal a likely mode of association between actACP and actKR; we obtain and analyze a detailed model of the C7-C12-cyclized octaketide within actKR’s active site; and confirm this model through multiscale (QM/MM) reaction simulations of the key ketoreduction step. Molecular dynamics simulations show that the most thermodynamically stable cyclized octaketide isomer (7S,12R) also gives rise to the most ‘reactive conformations’ for ketoreduction. Subsequent reaction simulations show that ketoreduction is stereoselective as well as regioselective, resulting in an S-alcohol. Our simulations further indicate several conserved residues that may be involved in selectivity of C7-12 cyclisation and C9 ketoreduction. The detailed insights obtained on ACP-based substrate presentation in type II PKSs will help with the design of nonendogenous ACP-ketoreductase systems capable of biosynthesizing non-natural polyketides.

Iain Donald Campbell. 24 April 1941—5 March 2014

Iain Campbell defined the study of proteins by nuclear magnetic resonance spectroscopy (NMR) in the UK, and was a towering international figure in biophysics and structural cell biology. His scientific career spanned nearly 50 years, almost entirely spent at the University of Oxford. As a PhD student he recorded electron spin resonance spectra, then later became a pioneer in the application of NMR methodology to whole cells, determining the world's second and UK's first protein structure by NMR. He ended his career as one of the leading scientific lights in integrin adhesion and focal cell assembly. His scientific contributions are characterized by intellectual rigour and a desire to solve the problem by applying the most appropriate tools. All who knew Iain noted his incredible work ethic, his precision and in particular his wry humour. The co-workers trained by Iain form the backbone of protein NMR internationally today, a tribute to his mentorship. His loss was deeply felt by colleagues across the world and of course most of all by his loving family.

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.

Cryptic SARS-CoV2-spike-with-sugar interactions revealed by 'universal' saturation transfer analysis

Host-expressed proteins on both host-cell and pathogen surfaces are widely exploited by pathogens, mediating cell entry (and exit) and influencing disease progression and transmission. This is highlighted by the diverse modes of coronavirus entry into cells and their consequent differing pathogenicity that is of direct relevance to the current SARS-CoV-2 pandemic. Host-expressed viral surface proteins bear post-translational modifications such as glycosylation that are essential for function but can confound or limit certain current biophysical methods used for dissecting key interactions. Several human coronaviruses attach to host cell-surface N-linked glycans that include forms of sialic acid. There remains, however, conflicting evidence as to if or how SARS-associated coronaviruses might use such a mechanism. Here, we show that novel protein NMR methods allow a complete and comprehensive analysis of the magnetization transfer caused by interactions between even heavily modified proteins and relevant ligands to generate quantitative binding data in a general manner. Our method couples direct, objective resonance-identification via a deconvolution algorithm with quantitative analysis using Bloch-McConnell equations to obtain interaction parameters (e.g. KD, kEx), which together enable structural modelling. By using an automated and openly available workflow, this method can be readily applied in a range of systems. This complete treatment of so-called 'saturation transfer' between protein and ligand now enables a general analysis of solution-phase ligand-protein binding beyond previously perceived limits of exchange rates, concentration or system - this allows 'universal' saturation transfer analysis (uSTA). uSTA proves critical in mapping direct interaction between natural sialoside sugar ligands and SARS-CoV-2-spike glycoprotein by quantitating ligand signal in spectral regions otherwise occluded by resonances from mobile spike-protein glycans (that also include sialosides). Using uSTA, 'end on'-binding by SARS-CoV-2-spike protein to sialoside glycan is revealed, which contrasts with an observed 'extended surface'-binding for previously validated heparin sugar ligands. Quantitative use of uSTA-derived restraints pinpoints likely binding modes to an intrinsically disordered region of the N-terminal domain of SARS-CoV-2-spike trimer. Consistent with this, glycan binding is minimally perturbed by antibodies that neutralize via binding the ACE2-binding domain (RBD) but strongly disrupted in the B1.1.7 and B1.351 variants-of-concern that possess hotspot mutations around the identified site. An analysis of beneficial genetic variances in cohorts of patients from early 2020 suggests a possible model in which A-lineage-SARS-CoV-2 may have exploited a specific sialylated-polylactosamine motif found on tetraantennary human N-linked-glycoproteins in deeper lung. Since cell-surface glycans are widely relevant to biology and pathology, uSTA can now provide a ready, quantitative method for widespread analysis of complex, host-derived and post-translationally modified proteins with putative ligands relevant to disease even in previously confounding complex systems.

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

<p>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 ¹³C_α-¹³C_β 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. </p>

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. <br>

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. <br>