Systematic Method for the Exploration, Representation, and Classification of the Diphenylalanine Solvatomorphic Space.

An understanding of the conditions that govern the self-assembly process of peptides is a fundamental step toward the design of new nanostructures that possess interesting properties. In this work, we first synthesize and explore extensively diphenylalanine (FF) self-assembling crystals formed in different solvents (i.e., solvatomorphs) using polarized optical microscopy and transmission electron microscopy. Then, we develop a numerical method that allows an unambiguous classification of the solvatomorphs through a K-means automatic clustering method. In addition, we generate a two-dimensional (2D) representation of the solvatomorphic space together with the clustering results via a principal component analysis (PCA). The classification is based on structural similarities of solvatomorphs as revealed by the analysis of their respective infrared spectra. Among the 20 samples considered, 4 clear clusters are extracted within which the compounds show very similar crystalline structures. The information extracted allows us to assign many of the peaks that appear in the complex IR spectra of the samples considered. The implementation of the overall procedure we propose, i.e., "GAULOIS" and "REFRACT-R", is transferable to other types of spectra and paves the way for a systematic, fast, and accurate classification method applicable to various types of experimental spectroscopic data.

NMR shifts in aluminosilicate glasses via machine learning.

Machine learning (ML) approaches are investigated for the prediction of nuclear magnetic resonance (NMR) parameters in aluminosilicate glasses, for which NMR has proven to be a cutting-edge method over the last decade. DFT computations have emerged as a new dimension for complementing these NMR methods although suffering from severe limitations in terms of size, time and computational resources consumption. While previous approaches tend to use DFT-GIPAW calculations for the prediction of NMR parameters in glassy systems, we propose to employ ML methods, characterized by a speed similar to that of classical molecular dynamics while the accuracy of ab initio methods can be reached. We design ML procedures to predict the isotropic magnetic shielding (σiso) for different multicomponent relevant glass compositions. The ML predictions of σiso deviate from DFT-GIPAW calculations, when including relaxed and room-temperature structures, by 0.7 ppm for 29Si (1.0% of the total span of the calculated ) and 1.5 ppm for 17O (1.9%) in SiO2 glasses, 1.4 ppm for 23Na (1.5%) in Na2O-SiO2 and 1.5 ppm for 27Al (2.1%) in Al2O3-Na2O-SiO2 systems. We compare the performances obtained for a set of three descriptors suitable for encoding atomic local environments information (atom-centered representations) together with seven popular ML algorithms with a focus on the simple (but robust) linear ridge regression (LRR) and the popular smooth overlap of atomic positions (SOAP) descriptor.

The role of 2D/3D spin-polarization interactions in hybrid copper hydroxide acetate: new insights from first-principles molecular dynamics.

The magnetic properties response of the layered hybrid material copper hydroxide acetate Cu2(OH)3CH3COO·H2O is studied as a function of the applied pressure within first-principles molecular dynamics. We are able to elucidate the interplay between the structural properties of this material and its magnetic character, both at the local (atomic) level and at the bulk level. We performed a detailed analysis of the intralayer spin configurations occurring for each value of the imposed projection along the z-axis for the total spin and of the applied pressure. The transition from an antiferromagnetic to a ferromagnetic state at high pressure (above 3 GPa) goes along with a vanishing difference between the spin polarizations pertaining to each layer. Therefore, at high pressure, copper hydroxide acetate is a ferromagnet with no changes of spin polarization in the direction perpendicular to the inorganic layers.

Quantitative sampling of conformational heterogeneity of a DNA hairpin using molecular dynamics simulations and ultrafast fluorescence spectroscopy.

Molecular dynamics (MD) simulations and time resolved fluorescence (TRF) spectroscopy were combined to quantitatively describe the conformational landscape of the DNA primary binding sequence (PBS) of the HIV-1 genome, a short hairpin targeted by retroviral nucleocapsid proteins implicated in the viral reverse transcription. Three 2-aminopurine (2AP) labeled PBS constructs were studied. For each variant, the complete distribution of fluorescence lifetimes covering 5 orders of magnitude in timescale was measured and the populations of conformers experimentally observed to undergo static quenching were quantified. A binary quantification permitted the comparison of populations from experimental lifetime amplitudes to populations of aromatically stacked 2AP conformers obtained from simulation. Both populations agreed well, supporting the general assumption that quenching of 2AP fluorescence results from pi-stacking interactions with neighboring nucleobases and demonstrating the success of the proposed methodology for the combined analysis of TRF and MD data. Cluster analysis of the latter further identified predominant conformations that were consistent with the fluorescence decay times and amplitudes, providing a structure-based rationalization for the wide range of fluorescence lifetimes. Finally, the simulations provided evidence of local structural perturbations induced by 2AP. The approach presented is a general tool to investigate fine structural heterogeneity in nucleic acid and nucleoprotein assemblies.