Cross-specificity of protective human antibodies against Klebsiella pneumoniae LPS O-antigen.

Humoral immune responses to microbial polysaccharide surface antigens can prevent bacterial infection but are typically strain specific and fail to mediate broad protection against different serotypes. Here we describe a panel of affinity-matured monoclonal human antibodies from peripheral blood immunoglobulin M-positive (IgM+) and IgA+ memory B cells and clonally related intestinal plasmablasts, directed against the lipopolysaccharide (LPS) O-antigen of Klebsiella pneumoniae, an opportunistic pathogen and major cause of antibiotic-resistant nosocomial infections. The antibodies showed distinct patterns of in vivo cross-specificity and protection against different clinically relevant K. pneumoniae serotypes. However, cross-specificity was not limited to K. pneumoniae, as K. pneumoniae-specific antibodies recognized diverse intestinal microbes and neutralized not only K. pneumoniae LPS but also non-K. pneumoniae LPS. Our data suggest that the recognition of minimal glycan epitopes abundantly expressed on microbial surfaces might serve as an efficient humoral immunological mechanism to control invading pathogens and the large diversity of the human microbiota with a limited set of cross-specific antibodies.

Tracking Li atoms in real-time with ultra-fast NMR simulations.

We present for the first time a multiscale machine learning approach to jointly simulate atomic structure and dynamics with the corresponding solid state Nuclear Magnetic Resonance (ssNMR) observables. We study the use-case of spin-alignment echo (SAE) NMR for exploring Li-ion diffusion within the solid state electrolyte material Li3PS4 (LPS) by calculating quadrupolar frequencies of 7Li. SAE NMR probes long-range dynamics down to microsecond-timescale hopping processes. Therefore only a few machine learning force field schemes are able to capture the time- and length scales required for accurate comparison with experimental results. By using a new class of machine learning interatomic potentials, known as ultra-fast potentials (UFPs), we are able to efficiently access timescales beyond the microsecond regime. In tandem, we have developed a machine learning model for predicting the full 7Li electric field gradient (EFG) tensors in LPS. By combining the long timescale trajectories from the UFP with our model for 7Li EFG tensors, we are able to extract the autocorrelation function (ACF) for 7Li quadrupolar frequencies during Li diffusion. We extract the decay constants from the ACF for both crystalline ß-LPS and amorphous LPS, and find that the predicted Li hopping rates are on the same order of magnitude as those predicted from the Li dynamics. This demonstrates the potential for machine learning to finally make predictions on experimentally relevant timescales and temperatures, and opens a new avenue of NMR crystallography: using machine learning dynamical NMR simulations for accessing polycrystalline and glass ceramic materials.

The EFG Rosetta Stone: translating between DFT calculations and solid state NMR experiments.

We present a comprehensive study on the best practices for integrating first principles simulations in experimental quadrupolar solid-state nuclear magnetic resonance (SS-NMR), exploiting the synergies between theory and experiment for achieving the optimal interpretation of both. Most high performance materials (HPMs), such as battery electrodes, exhibit complex SS-NMR spectra due to dynamic effects or amorphous phases. NMR crystallography for such challenging materials requires reliable, accurate, efficient computational methods for calculating NMR observables from first principles for the transfer between theoretical material structure models and the interpretation of their experimental SS-NMR spectra. NMR-active nuclei within HPMs are routinely probed by their chemical shielding anisotropy (CSA). However, several nuclear isotopes of interest, e.g.7Li and 27Al, have a nuclear quadrupole and experience additional interactions with the surrounding electric field gradient (EFG). The quadrupolar interaction is a valuable source of information about atomistic structure, and in particular, local symmetry, complementing the CSA. As such, there is a range of different methods and codes to choose from for calculating EFGs, from all-electron to plane wave methods. We benchmark the accuracy of different simulation strategies for computing the EFG tensor of quadrupolar nuclei with plane wave density functional theory (DFT) and study the impact of the material structure as well as the details of the simulation strategy. Especially for small nuclei with few electrons, such as 7Li, we show that the choice of physical approximations and simulation parameters has a large effect on the transferability of the simulation results. To the best of our knowledge, we present the first comprehensive reference scale and literature survey for 7Li quadrupolar couplings. The results allow us to establish practical guidelines for developing the best simulation strategy for correlating DFT to experimental data extracting the maximum benefit and information from both, thereby advancing further research into HPMs.

Interface between graphene and liquid Cu from molecular dynamics simulations.

Controllable synthesis of defect-free graphene is crucial for applications since the properties of graphene are highly sensitive to any deviations from the crystalline lattice. We focus here on the emerging use of liquid Cu catalysts, which have high potential for fast and efficient industrial-scale production of high-quality graphene. The interface between graphene and liquid Cu is studied using force field and ab initio molecular dynamics, revealing a complete or partial embedding of finite-sized flakes. By analyzing flakes of different sizes, we find that the size-dependence of the embedding can be rationalized based on the energy cost of embedding vs bending the graphene flake. The embedding itself is driven by the formation of covalent bonds between the under-coordinated edge C atoms and the liquid Cu surface, which is accompanied by a significant charge transfer. In contrast, the central flake atoms are located around or slightly above 3 Å from the liquid Cu surface and exhibit weak van der Waals-bonding and much lower charge transfer. The structural and electronic properties of the embedded state revealed in our work provide the atomic-scale information needed to develop effective models to explain the special growth observed in experiments where various interesting phenomena such as flake self-assembly and rotational alignment, high growth speeds, and low defect densities in the final graphene product have been observed.

Ab Initio Simulation of pH-Sensitive Biomarkers in Magnetic Resonance Imaging.

An ab initio simulation scheme is introduced as a theoretical prescreening approach to facilitate and enhance the research for pH-sensitive biomarkers. The proton 1H and carbon 13C nuclear magnetic resonance (NMR) chemical shifts of the recently published marker for extracellular pH, [1,5-13C2]zymonic acid (ZA), and the as yet unpublished ( Z)-4-methyl-2-oxopent-3-enedioic acid (OMPD) were calculated with ab initio methods as a function of the pH. The influence of the aqueous solvent was taken into account either by an implicit solvent model or by explicit water molecules, where the latter improved the accuracy of the calculated chemical shifts considerably. The theoretically predicted chemical shifts allowed for a reliable NMR peak assignment. The p Ka value of the first deprotonation of ZA and OMPD was simulated successfully whereas the parametrization of the implicit solvent model does not allow for an accurate description of the second p Ka. The theoretical models reproduce the pH-induced chemical shift changes and the first p Ka with sufficient accuracy to establish the ab initio prescreening approach as a valuable support to guide the experimental search for pH-sensitive biomarkers.

Deuteration of Hyperpolarized 13 C-Labeled Zymonic Acid Enables Sensitivity-Enhanced Dynamic MRI of pH.

Aberrant pH is characteristic of many pathologies such as ischemia, inflammation or cancer. Therefore, a non-invasive and spatially resolved pH determination is valuable for disease diagnosis, characterization of response to treatment and the design of pH-sensitive drug-delivery systems. We recently introduced hyperpolarized [1,5-13 C2 ]zymonic acid (ZA) as a novel MRI probe of extracellular pH utilizing dissolution dynamic polarization (DNP) for a more than 10000-fold signal enhancement of the MRI signal. Here we present a strategy to enhance the sensitivity of this approach by deuteration of ZA yielding [1,5-13 C2 , 3,6,6,6-D4 ]zymonic acid (ZAd ), which prolongs the liquid state spin lattice relaxation time (T1 ) by up to 39 % in vitro. Measurements with ZA and ZAd on subcutaneous MAT B III adenocarcinoma in rats show that deuteration increases the signal-to-noise ratio (SNR) by up to 46 % in vivo. Furthermore, we demonstrate a proof of concept for real-time imaging of dynamic pH changes in vitro using ZAd , potentially allowing for the characterization of rapid acidification/basification processes in vivo.