Solution-state methyl NMR spectroscopy of large non-deuterated proteins enabled by deep neural networks.

Methyl-TROSY nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for characterising large biomolecules in solution. However, preparing samples for these experiments is demanding and entails deuteration, limiting its use. Here we demonstrate that NMR spectra recorded on protonated, uniformly 13C labelled samples can be processed using deep neural networks to yield spectra that are of similar quality to typical deuterated methyl-TROSY spectra, potentially providing information for proteins that cannot be produced in bacterial systems. We validate the methodology experimentally on three proteins with molecular weights in the range 42-360 kDa. We further demonstrate the applicability of our methodology to 3D NOESY spectra of Escherichia coli Malate Synthase G (81 kDa), where observed NOE cross-peaks are in good agreement with the available structure. The method represents an advance in the field of using deep learning to analyse complex magnetic resonance data and could have an impact on the study of large biomolecules in years to come.

Measuring Protein-Protein Interactions and Quantifying Their Dissociation Constants with Mass Photometry.

Protein-protein interactions underlie most biological processes, and determining the affinity and abundance of binding partners for each interaction is often a challenging task because these interactions often involve multiple ligands and binding sites. Standard methods for determining the affinity of protein interactions often require a large amount of starting material in addition to potentially disruptive labeling or immobilization of the binding partners. Mass photometry is a bioanalytical technique that measures the mass of single biomolecules in solution, quickly and with minimal sample requirements. This article describes how mass photometry can be used to determine the mass distribution of binding partners, the complexes they form, the relative abundance of each species, and, accordingly, the dissociation constant (KD ) of their interactions. © 2024 Refeyn Ltd. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Using mass photometry to measure protein-protein binding and quantify the KD of this interaction.

Biocatalytic reductive amination as a route to isotopically labelled amino acids suitable for analysis of large proteins by NMR.

We demonstrate an atom-efficient and easy to use H2-driven biocatalytic platform for the enantioselective incorporation of 2H-atoms into amino acids. By combining the biocatalytic deuteration catalyst with amino acid dehydrogenase enzymes capable of reductive amination, we synthesised a library of multiply isotopically labelled amino acids from low-cost isotopic precursors, such as 2H2O and 15NH4+. The chosen approach avoids the use of pre-labeled 2H-reducing agents, and therefore vastly simplifies product cleanup. Notably, this strategy enables 2H, 15N, and an asymmetric centre to be introduced at a molecular site in a single step, with full selectivity, under benign conditions, and with near 100% atom economy. The method facilitates the preparation of amino acid isotopologues on a half-gram scale. These amino acids have wide applicability in the analytical life sciences, and in particular for NMR spectroscopic analysis of proteins. To demonstrate the benefits of the approach for enabling the workflow of protein NMR chemists, we prepared l-[α-2H,15N, ß-13C]-alanine and integrated it into a large (>400 kDa) heat-shock protein oligomer, which was subsequently analysable by methyl-TROSY techniques, revealing new structural information.

Towards autonomous analysis of chemical exchange saturation transfer experiments using deep neural networks.

Macromolecules often exchange between functional states on timescales that can be accessed with NMR spectroscopy and many NMR tools have been developed to characterise the kinetics and thermodynamics of the exchange processes, as well as the structure of the conformers that are involved. However, analysis of the NMR data that report on exchanging macromolecules often hinges on complex least-squares fitting procedures as well as human experience and intuition, which, in some cases, limits the widespread use of the methods. The applications of deep neural networks (DNNs) and artificial intelligence have increased significantly in the sciences, and recently, specifically, within the field of biomolecular NMR, where DNNs are now available for tasks such as the reconstruction of sparsely sampled spectra, peak picking, and virtual decoupling. Here we present a DNN for the analysis of chemical exchange saturation transfer (CEST) data reporting on two- or three-site chemical exchange involving sparse state lifetimes of between approximately 3-60 ms, the range most frequently observed via experiment. The work presented here focuses on the 1H CEST class of methods that are further complicated, in relation to applications to other nuclei, by anti-phase features. The developed DNNs accurately predict the chemical shifts of nuclei in the exchanging species directly from anti-phase 1HN CEST profiles, along with an uncertainty associated with the predictions. The performance of the DNN was quantitatively assessed using both synthetic and experimental anti-phase CEST profiles. The assessments show that the DNN accurately determines chemical shifts and their associated uncertainties. The DNNs developed here do not contain any parameters for the end-user to adjust and the method therefore allows for autonomous analysis of complex NMR data that report on conformational exchange.

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.

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.

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 of noncovalent intermolecular interactions. However, despite their tremendous importance, the unique structures of protein side chains require tailored approaches for investigation by nuclear magnetic resonance spectroscopy and so have traditionally been understudied compared with the protein backbone. Here, we review substantial recent methodological advancements within nuclear magnetic resonance 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.

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-13Cß 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.

The anatomy of unfolding of Yfh1 is revealed by site-specific fold stability analysis measured by 2D NMR spectroscopy.

Most techniques allow detection of protein unfolding either by following the behaviour of single reporters or as an averaged all-or-none process. We recently added 2D NMR spectroscopy to the well-established techniques able to obtain information on the process of unfolding using resonances of residues in the hydrophobic core of a protein. Here, we questioned whether an analysis of the individual stability curves from each resonance could provide additional site-specific information. We used the Yfh1 protein that has the unique feature to undergo both cold and heat denaturation at temperatures above water freezing at low ionic strength. We show that stability curves inconsistent with the average NMR curve from hydrophobic core residues mainly comprise exposed outliers that do nevertheless provide precious information. By monitoring both cold and heat denaturation of individual residues we gain knowledge on the process of cold denaturation and convincingly demonstrate that the two unfolding processes are intrinsically different.

Multiquantum 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 MQ-CEST 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 π-stacking 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.

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.

INDIANA: An in-cell diffusion method to characterize the size, abundance and permeability of cells.

NMR and MRI diffusion experiments contain information describing the shape, size, abundance, and membrane permeability of cells although extracting this information can be challenging. Here we present the INDIANA (IN-cell DIffusion ANAlysis) method to simultaneously and non-invasively measure cell abundance, effective radius, permeability and intrinsic relaxation rates and diffusion coefficients within the inter- and intra-cellular populations. The method couples an experimental dataset comprising stimulated-echo diffusion measurements, varying both the gradient strength and the diffusion delay, together with software to fit a model based on the Kärger equations to robustly extract the relevant parameters. A detailed error analysis is presented by comparing the results from fitting simulated data from Monte Carlo simulations, establishing its effectiveness. We note that for parameters typical of mammalian cells the approach is particularly effective, and the shape of the underlying cells does not unduly affect the results. Finally, we demonstrate the performance of the experiment on systems of suspended yeast and mammalian cells. The extracted parameters describing cell abundance, size, permeability and relaxation are independently validated.

Formation of a Secretion-Competent Protein Complex by a Dynamic Wrap-around Binding Mechanism.

Bacterial virulence is typically initiated by translocation of effector or toxic proteins across host cell membranes. A class of gram-negative pathogenic bacteria including Yersinia pseudotuberculosis and Yersinia pestis accomplishes this objective with a protein assembly called the type III secretion system. Yersinia effector proteins (Yop) are presented to the translocation apparatus through formation of specific complexes with their cognate chaperones (Syc). In the complexes where the structure is available, the Yops are extended and wrap around their cognate chaperone. This structural architecture enables secretion of the Yop from the bacterium in early stages of translocation. It has been shown previously that the chaperone-binding domain of YopE is disordered in its isolation but becomes substantially more ordered in its wrap-around complex with its chaperone SycE. Here, by means of NMR spectroscopy, small-angle X-ray scattering and molecular modeling, we demonstrate that while the free chaperone-binding domain of YopH (YopHCBD) adopts a fully ordered and globular fold, it populates an elongated, wrap-around conformation when it engages in a specific complex with its chaperone SycH2. Hence, in contrast to YopE that is unstructured in its free state, YopH transits from a globular free state to an elongated chaperone-bound state. We demonstrate that a sparsely populated YopHCBD state has an elevated affinity for SycH2 and represents an intermediate in the formation of the protein complex. Our results suggest that Yersinia has evolved a binding mechanism where SycH2 passively stimulates an elongated YopH conformation that is presented to the type III secretion system in a secretion-competent conformation.

Selective Radical Trifluoromethylation of Native Residues in Proteins.

The incorporation of fluorine can not only significantly facilitate the study of proteins but also potentially modulate their function. Though some biosynthetic methods allow global residue-replacement, post-translational fluorine incorporation would constitute a fast and efficient alternative. Here, we reveal a mild method for direct protein radical trifluoromethylation at native residues as a strategy for symmetric-multifluorine incorporation on mg scales with high recoveries. High selectivity toward tryptophan residues enhanced the utility of this direct trifluoromethylation technique allowing ready study of fluorinated protein constructs using 19F-NMR.

Monitoring the Disassembly of Virus-like Particles by 19F-NMR.

Virus-like particles (VLPs) are stable protein cages derived from virus coats. They have been used extensively as biomolecular platforms, e.g., nanocarriers or vaccines, but a convenient in situ technique is lacking for tracking functional status. Here, we present a simple way to monitor disassembly of 19F-labeled VLPs derived from bacteriophage Qß by 19F NMR. Analysis of resonances, under a range of conditions, allowed determination not only of the particle as fully assembled but also as disassembled, as well as detection of a degraded state upon digestion by cells. This in turn allowed mutational redesign of disassembly and testing in both bacterial and mammalian systems as a strategy for the creation of putative, targeted-VLP delivery systems.

Determination of an optimally sensitive and specific chemical exchange saturation transfer MRI quantification metric in relevant biological phantoms.

The purpose of this study was to develop realistic phantom models of the intracellular environment of metastatic breast tumour and naïve brain, and using these models determine an analysis metric for quantification of CEST MRI data that is sensitive to only labile proton exchange rate and concentration. The ability of the optimal metric to quantify pH differences in the phantoms was also evaluated. Novel phantom models were produced, by adding perchloric acid extracts of either metastatic mouse breast carcinoma cells or healthy mouse brain to bovine serum albumin. The phantom model was validated using 1 H NMR spectroscopy, then utilized to determine the sensitivity of CEST MRI to changes in pH, labile proton concentration, T1 time and T2 time; six different CEST MRI analysis metrics (MTRasym , APT*, MTRRex , AREX and CESTR* with and without T1 /T2 compensation) were compared. The new phantom models were highly representative of the in vivo intracellular environment of both tumour and brain tissue. Of the analysis methods compared, CESTR* with T1 and T2 time compensation was optimally specific to changes in the CEST effect (i.e. minimal contamination from T1 or T2 variation). In phantoms with identical protein concentrations, pH differences between phantoms could be quantified with a mean accuracy of 0.6 pH units. We propose that CESTR* with T1 and T2 time compensation is the optimal analysis method for these phantoms. Analysis of CEST MRI data with T1 /T2 time compensated CESTR* is reproducible between phantoms, and its application in vivo may resolve the intracellular alkalosis associated with breast cancer brain metastases without the need for exogenous contrast agents.

Harnessing NMR relaxation interference effects to characterise supramolecular assemblies.

Solution-state NMR spectroscopy remains the primary method for characterising synthetic supramolecular assemblies. Yet, in their NMR spectra, relaxation interference effects can significantly alter peak intensities hindering interpretation. Here, we present a simple experiment for synthetic chemists to analyse this effect, allowing interpretation of these distorted spectra and validation of spectral assignments. We apply this experiment to synthetic porphyrin oligomers with molecular weights approaching those of protein domains (10 kDa). Our experiment provides a simple means to gain additional structural and dynamical information that will become increasingly useful as chemists create larger molecular architectures.