Selective and Controlled Grafting from PVDF-Based Materials by Oxygen-Tolerant Green-Light-Mediated ATRP

Poly(vinylidene fluoride) (PVDF) shows excellent chemical and thermal resistance and displays high dielectric strength and unique piezoelectricity, which are enabling for applications in membranes, electric insulators, sensors, or power generators. However, its low polarity and lack of functional groups limit wider applications. While inert, PVDF has been modified by grafting polymer chains by atom transfer radical polymerization (ATRP), albeit via an unclear mechanism, given the strong C-F bonds. Herein, we applied eosin Y and green-light-mediated ATRP to modify PVDF-based materials. The method gave nearly quantitative (meth)acrylate monomer conversions within 2 h without deoxygenation and without the formation of unattached homopolymers, as confirmed by control experiments and DOSY NMR measurements. The gamma distribution model that accounts for broadly dispersed polymers in DOSY experiments was essential and serves as a powerful tool for the analysis of PVDF. The NMR analysis of poly(methyl acrylate) graft chain-ends on PVDF-CTFE (statistical copolymer with chlorotrifluoroethylene) was carried out successfully for the first time and showed up to 23 grafts per PVDF-CTFE chain. The grafting density was tunable depending on the solvent composition and light intensity during the grafting. The initiation proceeded either from the C-Cl sites of PVDF-CTFE or via unsaturations in the PVDF backbones. The dehydrofluorinated PVDF was 20 times more active than saturated PVDF during the grafting. The method was successfully applied to modify PVDF, PVDF-HFP, and Viton A401C. The obtained PVDF-CTFE-g-PnBMA materials were investigated in more detail. They featured slightly lower crystallinity than PVDF-CTFE (12-18 vs 24.3%) and had greatly improved mechanical performance: Young's moduli of up to 488 MPa, ductility of 316%, and toughness of 46 × 106 J/m3.

Raman Diffusion-Ordered Spectroscopy

The Stokes-Einstein relation, which relates the diffusion coefficient of a molecule to its hydrodynamic radius, is commonly used to determine molecular sizes in chemical analysis methods. Here, we combine the size sensitivity of such diffusion-based methods with the structure sensitivity of Raman spectroscopy by performing Raman diffusion-ordered spectroscopy (Raman-DOSY). The core of the Raman-DOSY setup is a flow cell with a Y-shaped channel containing two inlets: one for the sample solution and one for the pure solvent. The two liquids are injected at the same flow rate, giving rise to two parallel laminar flows in the channel. After the flow stops, the solute molecules diffuse from the solution-filled half of the channel into the solvent-filled half at a rate determined by their hydrodynamic radius. The arrival of the solute molecules in the solvent-filled half of the channel is recorded in a spectrally resolved manner by Raman microspectroscopy. From the time series of Raman spectra, a two-dimensional Raman-DOSY spectrum is obtained, which has the Raman frequency on one axis and the diffusion coefficient (or equivalently, hydrodynamic radius) on the other. In this way, Raman-DOSY spectrally resolves overlapping Raman peaks arising from molecules of different sizes. We demonstrate Raman-DOSY on samples containing up to three compounds and derive the diffusion coefficients of small molecules, proteins, and supramolecules (micelles), illustrating the versatility of Raman-DOSY. Raman-DOSY is label-free and does not require deuterated solvents and can thus be applied to samples and matrices that might be difficult to investigate with other diffusion-based spectroscopy methods.

Computational Prediction of Drug-Disease Association Based on Graph-Regularized One Bit Matrix Completion

Investigation of existing drugs is an effective alternative to the discovery of new drugs for treating diseases. This task of drug re-positioning can be assisted by various kinds of computational methods to predict the best indication for a drug given the open-source biological datasets. Owing to the fact that similar drugs tend to have common pathways and disease indications, the association matrix is assumed to be of low-rank structure. Hence, the problem of drug-disease association prediction can be modeled as a low-rank matrix completion problem. In this work, we propose a novel matrix completion framework that makes use of the side-information associated with drugs/diseases for the prediction of drug-disease indications modeled as neighborhood graph: Graph regularized 1-bit matrix completion (GR1BMC). The algorithm is specially designed for binary data and uses parallel proximal algorithm to solve the aforesaid minimization problem taking into account all the constraints including the neighborhood graph incorporation and restricting predicted scores within the specified range. The results have been validated on two standard databases by evaluating the AUC across the 10-fold cross-validation splits. The usage of the method is also evaluated through a case study where top 5 indications are predicted for novel drugs, which then are verified with the CTD database.

Ultrafast methods for relaxation and diffusion

Relaxation and diffusion NMR measurements offer an approach to studying rotational and translational motion of molecules non-invasively, and they also provide chemical resolution complementary to NMR spectra. Multidimensional experiments enable the correlation of relaxation and diffusion parameters as well as the observation of molecular exchange phenomena through relaxation or diffusion contrast. This review describes how to accelerate multidimensional relaxation and diffusion measurements significantly through spatial encoding. This so-called ultrafast Laplace NMR approach shortens the experiment time to a fraction and makes even single-scan experiments possible. Single-scan experiments, in turn, significantly facilitate the use of nuclear spin hyperpolarization methods to boost sensitivity. The ultrafast Laplace NMR method is also applicable with low-field, mobile NMR instruments, and it can be exploited in many disciplines. For example, it has been used in studies of the dynamics of fluids in porous materials, identification of intra- and extracellular metabolites in cancer cells, and elucidation of aggregation phenomena in atmospheric surfactant solutions.

Using NMR to Test Molecular Mobility during a Chemical Reaction

We evaluate critically the use of pulsed gradient spin-echo nuclear magnetic resonance to measure molecular mobility during chemical reactions. With raw NMR spectra available in a public depository, we confirm the boosted mobility during the click chemical reaction (Wang et al. Science 2020 369, 537-541) regardless of the order of magnetic field gradient (linearly increasing, linearly decreasing, random sequence). We also confirm boosted mobility for the Diels-Alder chemical reaction. The conceptual advantage of the former chemical system is that a constant reaction rate implies a constant catalyst concentration, whereas that of the latter is the absence of a paramagnetic catalyst, precluding paramagnetism as an objection to the measurements. The data and discussion in this paper show the reliability of experiments when one avoids convection, allows decay of nuclear spin magnetization between successive pulses and recovery of its intensity between gradients, and satisfies quasi-steady state during the time window to acquire each datum. Especially important is to make comparisons on the time scale of the actual chemical reaction kinetics. We discuss possible sources of mistaken conclusions that are desirable to avoid.

Self-organization Properties of a GPCR-Binding Peptide with a Fluorinated Tail Studied by Fluorine NMR Spectroscopy

Conjugation of the bioactive apelin-17 peptide with a fluorocarbon chain results in self-organization of the peptide into micelles. Fluorine NMR spectroscopy studies show that the fluoropeptide's micelles are monodisperse, while proton NMR indicates that the peptide moiety remains largely disordered despite micellization. A very fast exchange rate is measured between the free and micellar states of the peptide which enables the number of molecules present in the micelle to be estimated as 200, in agreement with values found by dynamic light scattering measurements.

Spatial molecular-dynamically ordered NMR spectroscopy of intact bodies and heterogeneous systems

Noninvasive evaluation of the spatial distribution of chemical composition and diffusion behavior of materials is becoming possible by advanced nuclear magnetic resonance (NMR) pulse sequence editing. However, there is room for improvement in the spectral resolution and analytical method for application to heterogeneous samples. Here, we develop applications for comprehensively evaluating compounds and their dynamics in intact bodies and heterogeneous systems from NMR data, including spatial z-position, chemical shift, and diffusion or relaxation. This experiment is collectively named spatial molecular-dynamically ordered spectroscopy (SMOOSY). Pseudo-three-dimensional (3D) SMOOSY spectra of an intact shrimp and two heterogeneous systems are recorded to evaluate this methodology. Information about dynamics is mapped onto two-dimensional (2D) chemical shift imaging spectra using a pseudo-spectral imaging method with a processing tool named SMOOSY processor. Pseudo-2D SMOOSY spectral images can non-invasively assess the different dynamics of the compounds at each spatial z-position of the shrimp's body and two heterogeneous systems.

Automatised pharmacophoric deconvolution of plant extracts - application to Cinchona bark crude extract

We present a development of the "Plasmodesma" dereplication method [Margueritte et al., Magn. Reson. Chem., 2018, 56, 469]. This method is based on the automatic acquisition of a standard set of NMR experiments from a medium sized set of samples differing by their bioactivity. From this raw data, an analysis pipeline is run and the data is analysed by leveraging machine learning approaches in order to extract the spectral fingerprints of the active compounds. The optimal conditions for the analysis are determined and tested on two different systems, a synthetic sample where a single active molecule is to be isolated and characterized, and a complex bioactive matrix with synergetic interactions between the components. The method allows the identification of the active compounds and performs a pharmacophoric deconvolution. The program is freely available on the Internet, with an interactive visualisation of the statistical analysis, at https://plasmodesma.igbmc.science.

A pure shift and spin echo based approach for high-resolution diffusion-ordered NMR spectroscopy

Diffusion-ordered NMR spectroscopy (DOSY) can be used for separating mixture components according to their individual diffusion behaviors, thus offering a powerful tool for the analysis of compound mixtures. However, conventional DOSY experiments generally encounter the problem of limited resolution in the spectral domain, particularly for applications to complex mixtures that contains crowed resonances in 1D NMR. In addition, chemical exchange effects, bringing about spurious component signals, pose another limitation for interpreting DOSY measurements. Here, a general DOSY method is proposed based on pure shift extraction and spin echo evolution to obtain high-resolution 2D DOSY spectra, along with the suppression on effects of chemical exchange and J coupling. Both theoretical analyses and experimental results suggest that the proposed method is useful for high-resolution DOSY measurements on complex mixtures that contains crowded or even overlapped NMR resonances and exchanging spin systems.

Regularized inversion of the Laplace transform for series of experiments

Not only in low-field nuclear magnetic resonance, Laplace inversion is a relevant and challenging topic. Considerable conceptual and technical progress has been made, especially for the inversion of data encoding two decay dimensions. Distortion of spectra by overfitting of even moderate noise is counteracted requiring a priori smooth spectra. In this contribution, we treat the case of simple and fast one-dimensional decay experiments that are repeated many times in a series in order to study the evolution of a sample or process. Incorporating the a priori knowledge that also in the series dimension evolution should be smooth, peak position can be stabilized and resolution improved in the decay dimension. It is explained how the standard one-dimensional regularized Laplace inversion can be extended quite simply in order to include regularization in the series dimension. Obvious improvements compared with series of one-dimensional inversions are presented for simulated as well as experimental data. For the latter, comparison with multiexponential fitting is performed.

Diffusion Profiling of Therapeutic Proteins by Using Solution NMR Spectroscopy

Characterizing changes to structure and behavior is an important aspect of therapeutic protein development. NMR spectroscopy is well suited to study interactions and higher-order structure that could impact biological function and safety. We used NMR diffusion methods to describe the overall behavior of proteins in solution by defining a "diffusion profile" that captures the complexities in diffusion behavior. Diffusion profiles offer a simple means to interpret protein solution behavior as a distribution of sizes and association states. As a characterization method, diffusion profiling is well suited to complement and augment traditional biophysical and NMR methods to probe the solution behavior of therapeutic proteins.

Automatic differential analysis of NMR experiments in complex samples

Liquid state nuclear magnetic resonance (NMR) is a powerful tool for the analysis of complex mixtures of unknown molecules. This capacity has been used in many analytical approaches: metabolomics, identification of active compounds in natural extracts, and characterization of species, and such studies require the acquisition of many diverse NMR measurements on series of samples. Although acquisition can easily be performed automatically, the number of NMR experiments involved in these studies increases very rapidly, and this data avalanche requires to resort to automatic processing and analysis. We present here a program that allows the autonomous, unsupervised processing of a large corpus of 1D, 2D, and diffusion-ordered spectroscopy experiments from a series of samples acquired in different conditions. The program provides all the signal processing steps, as well as peak-picking and bucketing of 1D and 2D spectra, the program and its components are fully available. In an experiment mimicking the search of a bioactive species in a natural extract, we use it for the automatic detection of small amounts of artemisinin added to a series of plant extracts and for the generation of the spectral fingerprint of this molecule. This program called Plasmodesma is a novel tool that should be useful to decipher complex mixtures, particularly in the discovery of biologically active natural products from plants extracts but can also in drug discovery or metabolomics studies.