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.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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
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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.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Enzymes are known to sample various conformations, many of which are critical for their biological function. However, structural characterizations of enzymes predominantly focus on the most populated ...conformation. As a result, single-point mutations often produce structures that are similar or essentially identical to those of the wild-type enzyme despite large changes in enzymatic activity. Here, we show for mutants of a histone deacetylase enzyme (HDAC8) that reduced enzymatic activities, reduced inhibitor affinities, and reduced residence times are all captured by the rate constants between intrinsically sampled conformations that, in turn, can be obtained independently by solution NMR spectroscopy. Thus, for the HDAC8 enzyme, the dynamic sampling of conformations dictates both enzymatic activity and inhibitor potency. Our analysis also dissects the functional role of the conformations sampled, where specific conformations distinct from those in available structures are responsible for substrate and inhibitor binding, catalysis, and product dissociation. Precise structures alone often do not adequately explain the effect of missense mutations on enzymatic activity and drug potency. Our findings not only assign functional roles to several conformational states of HDAC8 but they also underscore the paramount role of dynamics, which will have general implications for characterizing missense mutations and designing inhibitors.
Proteins are inherently plastic molecules, whose function often critically depends on excursions between different molecular conformations (conformers). However, a rigorous understanding of the ...relation between a protein's structure, dynamics and function remains elusive. This is because many of the conformers on its energy landscape are only transiently formed and marginally populated (less than a few per cent of the total number of molecules), so that they cannot be individually characterized by most biophysical tools. Here we study a lysozyme mutant from phage T4 that binds hydrophobic molecules and populates an excited state transiently (about 1 ms) to about 3% at 25 °C (ref. 5). We show that such binding occurs only via the ground state, and present the atomic-level model of the 'invisible', excited state obtained using a combined strategy of relaxation-dispersion NMR (ref. 6) and CS-Rosetta model building that rationalizes this observation. The model was tested using structure-based design calculations identifying point mutants predicted to stabilize the excited state relative to the ground state. In this way a pair of mutations were introduced, inverting the relative populations of the ground and excited states and altering function. Our results suggest a mechanism for the evolution of a protein's function by changing the delicate balance between the states on its energy landscape. More generally, they show that our approach can generate and validate models of excited protein states.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.Here, the authors show deep neural networks can be trained to transform crowded 1H,13C-NMR spectra of large proteins into readily interpretable spectra, negating the requirement for uniform deuteration to analyse complex NMR spectra.
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•The transverse relaxation rate of the minor state resonance R2,B plays a critical role in determining the B1 fields that lead to the most informative CEST datasets.•To obtain ...accurate exchange parameters, K=kexkex+R2,B12 rather than the exchange rate (kex) should guide the choice of B1 (~0.7K & ~1.7K) fields used to record CEST experiments.•When R2,B is greater than kex, CEST datasets recorded with ‘large’ B1 fields ≫kex/2π are required to obtain accurate exchange parameters.
Over the last decade chemical exchange saturation transfer (CEST) NMR methods have emerged as powerful tools to characterize biomolecular conformational dynamics occurring between a visible major state and ‘invisible’ minor states. The ability of the CEST experiment to detect these minor states, and provide precise exchange parameters, hinges on using appropriate B1 field strengths during the saturation period. Typically, a pair of B1 fields with ω1 (=2πB1) values around the exchange rate kex are chosen. Here we show that the transverse relaxation rate of the minor state resonance (R2,B) also plays a crucial role in determining the B1 fields that lead to the most informative datasets. Using K=kexkex+R2,B12 ≥ kex, to guide the choice of B1, instead of kex, leads to data wherefrom substantially more accurate exchange parameters can be derived. The need for higher B1 fields, guided by K, is demonstrated by studying the conformational exchange in two mutants of the 71 residue FF domain with kex ∼ 11 s−1 and ∼ 72 s−1, respectively. In both cases analysis of CEST datasets recorded using B1 field values guided by kex lead to imprecise exchange parameters, whereas using B1 values guided by K resulted in precise site-specific exchange parameters. The conclusions presented here will be valuable while using CEST to study slow processes at sites with large intrinsic relaxation rates, including carbonyl sites in small to medium sized proteins, amide 15N sites in large proteins and when the minor state dips are broadened due to exchange among the minor states.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Fishery of blue mussels
Mytilus edulis
constitutes a very important economic activity in Denmark, whereas mussel farming on long-lines or nets is a new, growing sector. Spawning from natural mussel ...beds takes place during early summer, and larvae disperse via water currents before settling on the bottom or on spat collectors in the farms. In the present study, we coupled a 3D physical model system (FlexSem) with an agent-based model in order to examine the connectivity of this marine system in terms of mussel larval dispersal and settling potential. To address this question, we (1) estimated the dispersal and connectivity between 17 areas in the Limfjorden, (2) identified the main donor and receiver areas of mussel larvae and (3) identified possible dispersal barriers. The results show that the central narrow strait in the Limfjorden was the main donor area in all the studied years, and that the adjacent eastern areas were the main receiver areas. Towards the inner basins of the Limfjorden, isolation increased and limited connectivity was observed. The results from the cluster analysis grouped the Limfjorden into 3 to 5 clusters, but there was still some exchange of simulated larvae observed among these clusters. Analysis of molecular markers revealed no genetic differentiation between areas and supports the model results, indicating that despite distinguishable hydrographic boundaries, the mussel populations in the Limfjorden are well connected. This study demonstrates how connectivity modeling can be used to support site selection processes in aquaculture.
Global warming is not uniformly distributed, the climate is expected to change most rapidly in the arctic regions. Large scale changes in the arctic biosphere is therefore of particular concern. ...Capelin (Mallotus villosus) is a keystone species in the many arctic marine ecosystem including the seas around Iceland in Greenland. Its summer/autumn distribution has shifted over thousands of kilometres and production has decreased substantially. Multiple drivers may be involved in this change. Here, we explore the roles of changes in larval drift and thermal conditions around spawning and the larvae. Using model based simulations of the period 1993–2015, we find that vertical behaviour of the larvae, geographical distribution of the spawning and to some extent also timing of the spawning affect the drift trajectories. Our results from the simulations indicate large interannual variation in westwards drift, but without a long-term trend. The shift in distribution of the capelin did consequently not appear to result from a large scale shift in currents. Unlike the current, ambient temperature had a long term trend, and the temperature level changed abruptly and significantly between 2002 and 2003. The long-term shift in distribution might have been driven by the warming through a combination of earlier and more northern spawning and mortality dynamics. However, the timing of the shift in temperature (from 2003) was not in agreement with the timing of the events during the shift (starting in 2002). The first year may, on the other hand, be explained by the strong westwards currents particularly in 2002. Also, we point out a striking match between the abrupt increase in ambient temperature and the observed decrease in recruitment from 2003.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Boron is absent in proteins, yet is a micronutrient. It possesses unique bonding that could expand biological function including modes of Lewis acidity not available to typical elements of life. Here ...we show that post-translational Cβ-Bγ bond formation provides mild, direct, site-selective access to the minimally sized residue boronoalanine (Bal) in proteins. Precise anchoring of boron within complex biomolecular systems allows dative bond-mediated, site-dependent protein Lewis acid-base-pairing (LABP) by Bal. Dynamic protein-LABP creates tunable inter- and intramolecular ligand-host interactions, while reactive protein-LABP reveals reactively accessible sites through migratory boron-to-oxygen Cβ-Oγ covalent bond formation. These modes of dative bonding can also generate de novo function, such as control of thermo- and proteolytic stability in a target protein, or observation of transient structural features via chemical exchange. These results indicate that controlled insertion of boron facilitates stability modulation, structure determination, de novo binding activities and redox-responsive 'mutation'.
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GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
In this work an improved stable isotope labeling protocol for nucleic acids is introduced. The novel building blocks eliminate/minimize homonuclear 13C and 1H scalar couplings thus allowing proton ...relaxation dispersion (RD) experiments to report accurately on the chemical exchange of nucleic acids. Using site‐specific 2H and 13C labeling, spin topologies are introduced into DNA and RNA that make 1H relaxation dispersion experiments applicable in a straightforward manner. The novel RNA/DNA building blocks were successfully incorporated into two nucleic acids. The A‐site RNA was previously shown to undergo a two site exchange process in the micro‐ to millisecond time regime. Using proton relaxation dispersion experiments the exchange parameters determined earlier could be recapitulated, thus validating the proposed approach. We further investigated the dynamics of the cTAR DNA, a DNA transcript that is involved in the viral replication cycle of HIV‐1. Again, an exchange process could be characterized and quantified. This shows the general applicablility of the novel labeling scheme for 1H RD experiments of nucleic acids.
Excited protons: Nucleotide chemistry and state of the art NMR methods are combined to probe excited states of RNA and DNA via proton relaxation dispersion experiments. The approach will be very useful for the elucidation of high‐resolution excited state structures of nucleic acids.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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