Tatiana Agback

Keggin POM ions act as catalysts for the deglycosylation of RNA nucleosides, demonstrating a potential mechanism underlying the genotoxicity of metal oxide nanoparticles.

Resolution in NMR is defined as the ability to distinguish and accurately determine signal positions while mitigating overlap. In the pursuit of ultimate resolution, we introduce peak probability presentations ( P ³ ), a statistical spectral representation that assigns a probability to each spectral point, indicating the likelihood that a peak maximum occurs at that location. The mapping between the traditional spectrum and P ³ is achieved using MR-Ai, a physics-inspired and computationally efficient deep-learning neural network. P ³ is validated on 60 database proteins and showcased on the challenging Tau and MATL1 proteins. Using synthetic spectra, we show that the achieved peak-localization precision closely approaches the theoretical limits set by the Cramér-Rao lower bound and Bayesian Monte Carlo estimates. Furthermore, MR-Ai enables the coprocessing of multiple spectra, facilitating direct information exchange between datasets to enhance spectral quality, particularly in cases of highly sparse sampling.

High-dimensional nuclear magnetic resonance (NMR) spectroscopy can assist in determining protein structure, but it requires time-consuming acquisition. Deep learning enables ultrafast reconstruction but is limited to spectra of up to three dimensions and cannot provide faithful reconstruction under unseen acceleration factors. Extending deep learning to handle higher-dimensional spectra and varying acceleration factors is desirable. However, scalability requires complex networks and more data, seriously hindering applications. To address this, we designed a network to learn data in one dimension (1D). First, time-domain signals were modeled as the outer product of 1D exponentials. Then, each 1D exponential was approximated with a rank-one Hankel matrix. Last, reconstruction error was corrected with a neural network. Here, we demonstrate robust 3D NMR reconstruction across acceleration factors (2 to 33) using one trained network. In addition, we find that reconstruction of 4D NMR is possible with artificial intelligence. This work opens an avenue for accelerating arbitrarily high-dimensional NMR.

Conformational heterogeneity is essential for protein function, yet validating theoretical molecular dynamics (MD) ensembles remains a significant challenge. In this study, we present an approach that integrates free MD simulations, starting from an AlphaFold-generated structure, with refined experimental NMR-relaxation data to identify biologically relevant holistic time-resolved 4D conformational ensembles. Specifically, we select trajectory segments (RMSD plateaus) consistent with experimental observables. For the extracellular region of Streptococcus pneumoniae PsrSp, we found that only specific segments of the long MD trajectory aligned well with experimental data. The resulting ensembles revealed two regions with increased flexibility, both of which play important functional roles.

The crystal structure of the extracellular region of the second pneumococcal LCP, a polyisoprenyl-teichoic acid-peptidoglycan teichoic acid transferase Psr_Sp, was determined and refined to 2.15 Å resolution. Despite the low sequence homology with other LCP proteins, the Psr_Sp maintains the fold of the LCP domain, and the positions of the residues suggested to participate in the transferase function are conserved. The tunnel found in the Psr_Sp between the central β-sheet and three α-helices is wide enough to accommodate polyisoprenyl-teichoic acid. Comparison of the crystallographic temperature factors of LCP from distinct bacteria demonstrated that the four long loops located close to the teichoic acid and peptidoglycan binding sites have different relative mobilities. To compare the dynamics of the Psr_Sp in crystalline state and in solution, NMR spectra were recorded, and 88% of the residues were assigned in the ¹H-¹⁵N TROSY HSQC spectra. Perfect accordance in the secondary structure of the crystal structure of Psr_Sp with NMR data demonstrated correct assignment. Moreover, the relative mobility of the essential loops estimated from the crystallographic B-factor is in good agreement with order parameter S², predicted from chemical shift. We hypothesize that the dynamics of these loops are important for the substrate promiscuity of LCP proteins.

The crystal structure of the extracellular region of the second pneumococcal LCP, a polyisoprenyl-teichoic acid-peptidoglycan teichoic acid transferase Psr _Sp , was determined and refined to 2.15Å resolution. Despite the low sequence homology with other LCP proteins, the Psr _Sp maintains the fold of the LCP domain and the positions of the 15 residues suggested to participate in the transferase function are conserved. The empty tunnel found in the Psr _Sp between the central β-sheet and three α-helices is wide enough to accommodate polyisoprenyl-teichoic acid. Comparison of the crystallographic temperature factors of LCP from distinct bacteria demonstrated that the four long loops located close to the teichoic acid and peptidoglycan binding sites have different relative mobility. To compare the dynamics of the Psr _Sp in crystalline state and in solution, NMR spectra were recorded, and 88% of the residues were assigned in the ¹ H- ¹⁵ N TROSY HSQC spectra. Comparison of the secondary structure of the crystal structure of Psr _Sp with NMR data demonstrated a perfect concordance between the results using these two methods. Moreover, the relative mobility of the essential loops estimated from the crystallographic B-factor is in good agreement with order parameter S ² , predicted from chemical shift. We hypothesize that the dynamics of these loops are important for the substrate promiscuity of LCP proteins.

Mucosa-associated lymphoid tissue lymphoma-translocation protein 1 (MALT1) is an attractive target for the development of modulatory compounds in the treatment of lymphoma and other cancers. While the three-dimensional structure of MALT1 has been previously determined through X-ray analysis, its dynamic behaviour in solution has remained unexplored. We present here dynamic analyses of the apo MALT1 form along with the E549A mutation. This investigation used NMR ¹⁵N relaxation and NOE measurements between side-chain methyl groups. Our findings confirm that MALT1 exists as a monomer in solution, and demonstrate that the domains display semi-independent movements in relation to each other. Our dynamic study, covering multiple time scales, along with the assessment of conformational populations by Molecular Dynamic simulations, Alpha Fold modelling and PCA analysis, put the side chain of residue W580 in an inward position, shedding light at potential mechanisms underlying the allosteric regulation of this enzyme.

Mucosa-associated lymphoid tissue lymphoma-translocation protein 1 (MALT1) has emerged as an attractive target for the development of modulatory compounds, particularly in the treatment of lymphoma and other cancers. While the three-dimensional structure of MALT1(PCASP-Ig3) _339–719 has been previously determined through X-ray analysis, its dynamic behaviour in solution has remained largely unexplored. We present here inaugural dynamic analyses of the apo MALT1(PCASP-Ig3) _339–719 form along with its mutated variant, E549A. This investigation harnessed an array of NMR relaxation techniques, including longitudinal and transverse ¹⁵ N auto-relaxation, heteronuclear NOE, transverse cross-correlated relaxation and NOE measurements between side-chain methyl groups. Our findings unequivocally confirm that MALT1(PCASP-Ig3) _339–719 exists solely as a monomer in solution, and demonstrate that the two domains display semi-independent movements in relation to each other. Our extensive dynamic study, covering a range of time scales, along with the assessment of diverse conformational populations for MALT1(PCASP-Ig3) _339–719 , by Molecular Dynamic simulations, Alpha Fold modelling and PCA analysis, shed light at potential mechanisms underlying the allosteric regulation of this enzyme, and the specific importance of interdomain motions.

The dengue protease NS2B/NS3pro has been reported to adopt either an ‘open’ or a ‘closed’ conformation. We have developed a conformational filter that combines NMR with MD simulations to identify conformational ensembles that dominate in solution. Experimental values derived from relaxation parameters for the backbone and methyl side chains were compared with the corresponding back-calculated relaxation parameters of different conformational ensembles obtained from free MD simulations. Our results demonstrate a high prevalence for the ‘closed’ conformational ensemble while the ‘open’ conformation is absent, indicating that the latter conformation is most probably due to crystal contacts. Conversely, conformational ensembles in which the positioning of the co-factor NS2B results in a ‘partially’ open conformation, previously described in both MD simulations and X-ray studies, were identified by our conformational filter. Altogether, we believe that our approach allows for unambiguous identification of true conformational ensembles, an essential step for reliable drug discovery.

Nuclear magnetic resonance (NMR) spectroscopy has become a formidable tool for biochemistry and medicine. Although J-coupling carries essential structural information it may also limit the spectral resolution. Homonuclear decoupling remains a challenging problem. In this work, we introduce a new approach that uses a specific coupling value as prior knowledge, and the Hankel property of the exponential NMR signal to achieve broadband heteronuclear decoupling using the low-rank method. Our results on synthetic and realistic HMQC spectra demonstrate that the proposed method not only effectively enhances resolution by decoupling, but also maintains sensitivity and suppresses spectral artefacts. The approach can be combined with non-uniform sampling, which means that the resolution can be further improved without any extra acquisition time.