Will We Witness Enzymatic or Pd-(Oligo)Peptide Catalysis in Suzuki Cross-Coupling Reactions?

Despite the development of numerous advanced ligands for Pd-catalyzed Suzuki cross-coupling reaction, the potential of (oligo)peptides serving as ligands remains unexplored. This study demonstrates via density functional theory (DFT) modeling that (oligo)peptide ligands can drive superior activity compared to classic phosphines in these reactions. The utilization of natural amino acids such as Met, SeMet, and His leads to strong binding of the Pd center, thereby ensuring substantial stability of the system. The increasing sustainability and economic viability of (oligo)peptide synthesis open new prospects for applying Pd-(oligo)peptide systems as greener catalysts. The feasibility of de novo engineering an artificial Pd-based enzyme for Suzuki cross-coupling is discussed, laying the groundwork for future innovations in catalytic systems.

Implementation of machine learning protocols to predict the hydrolysis reaction properties of organophosphorus substrates using descriptors of electron density topology.

Prediction of catalytic reaction efficiency is one of the most intriguing and challenging applications of machine learning (ML) algorithms in chemistry. In this study, we demonstrated a strategy for utilizing ML protocols applied to Quantum Theory of Atoms In Molecules (QTAIM) parameters to predict the ability of the A17 L47K catalytic antibody to covalently capture organophosphate pesticides. We found that the novel "composite" DFT functional B97-3c could be effectively employed for fast and accurate initial geometry optimization, aligning well with the input dataset creation. QTAIM descriptors proved to be well-established in describing the examined dataset using density-based and hierarchical clustering algorithms. The obtained clusters exhibited correlations with the chemical classes of the input compounds. The precise physical interpretation of the QTAIM properties simplifies the explanation of feature impact for both supervised and unsupervised ML protocols. It also enables acceleration in the search for entries with desired properties within large databases. Furthermore, our findings indicated that Ridge Regression with Laplacian kernel and CatBoost Regressor algorithms demonstrated suitable performance in handling small datasets with non-trivial dependencies. They were able to predict the actual reaction barrier values with a high level of accuracy. Additionally, the CatBoost Classifier proved reliable in discriminating between "active" and "inactive" compounds.

Fluorescently Labeled PLGA Nanoparticles for Visualization In Vitro and In Vivo: The Importance of Dye Properties.

Fluorescently labeled nanoparticles are widely used for evaluating their distribution in the biological environment. However, dye leakage can lead to misinterpretations of the nanoparticles' biodistribution. To better understand the interactions of dyes and nanoparticles and their biological environment, we explored PLGA nanoparticles labeled with four widely used dyes encapsulated (coumarin 6, rhodamine 123, DiI) or bound covalently to the polymer (Cy5.5.). The DiI label was stable in both aqueous and lipophilic environments, whereas the quick release of coumarin 6 was observed in model media containing albumin (42%) or liposomes (62%), which could be explained by the different affinity of these dyes to the polymer and lipophilic structures and which we also confirmed by computational modeling (log PDPPC/PLGA: DiI-2.3, Cou6-0.7). The importance of these factors was demonstrated by in vivo neuroimaging (ICON) of the rat retina using double-labeled Cy5.5/Cou6-nanoparticles: encapsulated Cou6 quickly leaked into the tissue, whereas the stably bound Cy.5.5 label remained associated with the vessels. This observation is a good example of the possible misinterpretation of imaging results because the coumarin 6 distribution creates the impression that nanoparticles effectively crossed the blood-retina barrier, whereas in fact no signal from the core material was found beyond the blood vessels.

Towards Understanding Afghanistan Pea Symbiotic Phenotype Through the Molecular Modeling of the Interaction Between LykX-Sym10 Receptor Heterodimer and Nod Factors.

The difference in symbiotic specificity between peas of Afghanistan and European phenotypes was investigated using molecular modeling. Considering segregating amino acid polymorphism, we examined interactions of pea LykX-Sym10 receptor heterodimers with four forms of Nodulation factor (NF) that varied in natural decorations (acetylation and length of the glucosamine chain). First, we showed the stability of the LykX-Sym10 dimer during molecular dynamics (MD) in solvent and in the presence of a membrane. Then, four NFs were separately docked to one European and two Afghanistan dimers, and the results of these interactions were in line with corresponding pea symbiotic phenotypes. The European variant of the LykX-Sym10 dimer effectively interacts with both acetylated and non-acetylated forms of NF, while the Afghanistan variants successfully interact with the acetylated form only. We additionally demonstrated that the length of the NF glucosamine chain contributes to controlling the effectiveness of the symbiotic interaction. The obtained results support a recent hypothesis that the LykX gene is a suitable candidate for the unidentified Sym2 allele, the determinant of pea specificity toward Rhizobium leguminosarum bv. viciae strains producing NFs with or without an acetylation decoration. The developed modeling methodology demonstrated its power in multiple searches for genetic determinants, when experimental detection of such determinants has proven extremely difficult.

Sol-gel derived boehmite nanostructures is a versatile nanoplatform for biomedical applications.

Alumina is one of the most promising carriers for drug delivery due to the long history of its usage as a vaccine adjuvant. Sol-gel synthesis provides excellent conditions for entrapment of biomolecules within an inorganic cage providing stabilization of proteins under the extremal conditions. In this paper, we show in vitro investigation of monodisperse alumina xerogel nanocontainers (AXNCs) using bovine serum albumin as a model protein entrapped in sol-gel alumina building blocks. Particularly, dose and cell-type dependent cytotoxicity in HeLa and A549 cancer cell lines were employed as well as investigation of antibacterial effect and stability of AXNCs in different biological media. It was shown, that the release of entrapped protein could be provided only in low pH buffer (as in cancer cell cytoplasm). This property could be applied for anticancer drug development. We also discovered boehmite nanoparticles effect on horizontal gene transfer and observed the appearance of antibiotic resistance by means of exchanging of the corresponding plasmid between two different E. coli strains. The present work may help to understand better the influence of AXNCs on various biological systems, such as prokaryotic and eukaryotic cells, and the activity of AXNCs in different biological media.