Molecular Systems Design & Engineering最新文献

Peptides are naturally potent and selective therapeutics with massive potential; however, low cell membrane permeability limits their clinical implementation, particularly for hydrophilic, anionic peptides with intracellular targets. To overcome this limitation, esterification of anionic carboxylic acids on therapeutic peptides can simultaneously increase hydrophobicity and net charge to facilitate cell internalization, whereafter installed esters can be cleaved hydrolytically to restore activity. To date, however, most esterified therapeutics contain either a single esterification site or multiple esters randomly incorporated on multiple sites. This investigation provides molecular engineering insight into how the number and position of esters installed onto the therapeutic peptide α carboxyl terminus 11 (αCT11, RPRPDDLEI) with 4 esterification sites affect hydrophobicity and the hydrolysis process that reverts the peptide to its original form. After installing methyl esters onto αCT11 using Fischer esterification, we isolated 5 distinct products and used 2D nuclear magnetic resonance spectroscopy, reverse-phase high performance liquid chromatography, and mass spectrometry to determine which residues were esterified in each and the resulting increase in hydrophobicity. We found esterifying the C-terminal isoleucine to impart the largest increase in hydrophobicity. Monitoring ester hydrolysis showed the C-terminal isoleucine ester to be the most hydrolytically stable, followed by the glutamic acid, whereas esters on aspartic acids hydrolyze rapidly. LC-MS revealed the formation of transient intramolecular aspartimides prior to hydrolysis to carboxylic acids. In vitro proof-of-concept experiments showed esterifying αCT11 to increase cell migration into a scratch, highlighting the potential of multi-site esterification as a tunable, reversible strategy to enable the delivery of therapeutic peptides.

Metal–organic frameworks (MOFs) are promising platforms for designing photoresponsive materials due to their structural versatility and tunable properties. However, challenges remain in fine-tuning the photoresponsive behavior while maintaining the high stability of MOFs. In this study, we synthesized a MOF containing redox-active pyromellitic diimide (PMDI) groups and unsaturated Zr₆ clusters named Zr-PMDI-DMF and fine-tuned its photochromic properties by exchanging the coordination solvent molecules on the Zr sites. Unlike traditional Zr₆ clusters with bidentate carboxylate coordination, Zr-PMDI-DMF features monodentate carboxylate coordination with the exposed Zr sites occupied by solvent molecules. We post-synthetically exchanged the coordinated N,N-dimethylformamide (DMF) solvent molecules with 2-(dimethylamino)ethanol (DMAE), N-methyltetrahydropyrrole (NMP), and dimethyl sulfoxide (DMSO) and determined the structures of the coordinated solvent molecules using single-crystal X-ray diffraction. Through photochromic and bleaching cycle experiments, electron paramagnetic resonance spectroscopy, and density functional theory calculations, we found that the coordinated solvents act as electron donors. In contrast, PMDI ligands act as electron acceptors, causing intra-framework electron transfer and photochromism. The rate of the photochromic response correlated with the electron-donating ability of the solvents, following the trend of DMAE > NMP > DMSO > DMF.

Developing an accurate predictive model of solvent effects on reaction kinetics is a challenging task, yet it can play an important role in process development. While first-principles or machine learning models are often compute- or data-intensive, simple surrogate models, such as multivariate linear or quadratic regression models, are useful when computational resources and data are scarce. The judicious choice of a small set of training data, i.e., a set of solvents in which quantum mechanical (QM) calculations of liquid-phase rate constants are to be performed, is critical to obtaining a reliable model. This is, however, made especially challenging by the highly irregular shape of the discrete space of possible experiments (solvent choices). In this work, we demonstrate that when choosing a set of computer experiments to generate training data, the D-optimality criterion value of the chosen set correlates well with the likelihood of achieving good model performance. With the Menshutkin reaction of pyridine and phenacyl bromide as a case study, this finding is further verified via the evaluation of the surrogate models regressed using D-optimal solvent sets generated from four distinct selection spaces. We also find that incorporating quadratic terms in the surrogate model and choosing the D-optimal solvent set from a selection space similar to the test set can significantly improve the accuracy of reaction rate constant predictions while using a small training dataset. Our approach holds promise for the use of statistical optimality criteria for other types of computer experiments, supporting the construction of surrogate models with reduced resource and data requirements.

Biomineralization has garnered attention not only for its fundamental role in understanding the mechanisms of biomineral formation but also as a method for fabricating next-generation functional materials. In this study, we investigated the nucleation, crystal growth, and particle growth processes of calcium phosphates (CaPs) formed using selective mineralization at the hydrogel interface induced by the fusion of peptide hydrogels. After 1 day of mineralization, band-like white precipitates were observed at the fusion interface of the hydrogels. Notably, the nucleation and crystal growth of the mineralized CaP exhibited different behaviors owing to the differences in the properties of the reaction interface for mineralization. The selective nucleation and crystal growth of the CaPs at the hydrogel interface were attributed to (1) the local concentration of mineral sources near the peptide network, driven by electrostatic interactions between the polar functional groups and mineral source ions, and (2) selective crystal growth of the CaPs induced by the nanostructure of the surface functional groups.

Multifunctional composites with rapid self-healing performance have been widely applied in various fields. However, different types of fillers result in decreased self-healing efficiency and present agglomeration and poor compatibility especially at high filler contents. Here, based on the different surface modifications of barium titanate (BT) and silicon carbide (SiC) and the amide-bond synergistic effects between these fillers, self-healing supramolecular composites with high filler contents (up to 30%) are reported, and exhibit high strength, dielectric and thermal-conduction properties. Modification significantly improves the dispersion of these fillers, and greatly enhances the coexistence and synergy between these fillers. This three-phase amide-bonded supramolecular composite exhibits a high tensile strength of 3.22 MPa compared to other self-healing materials such as self-healing hydrogels, a high dielectric constant of 23, a high thermal conductivity of 0.36 W m⁻¹ K⁻¹ and a superior self-healing efficiency of above 94%. These performances are ascribed to the formation of amide bonds between the amino groups in 3-aminopropyltriethoxysilane (KH550)-modified silicon carbide (SiC-NH₂) and the carboxyl groups in tartaric acid (TA)-modified barium titanate (BT-TA), which can provide efficient supramolecular interactions between different fillers, as well as more reversible hydrogen bonding for the matrix. This three-phase amide-bonded supramolecular composite provides an effective strategy to improve the self-healing properties of multifunctional composites, and will bring pioneering functions to electronic packaging materials, dielectric energy storage materials, environmental energy and other fields, which can open up broad application prospects.

The estimation of polymer properties is of crucial importance in many domains such as energy, healthcare, and packaging. Recently, graph neural networks (GNNs) have shown promising results for the prediction of polymer properties based on supervised learning. However, the training of GNNs in a supervised learning task demands a huge amount of polymer property data that is time-consuming and computationally/experimentally expensive to obtain. Self-supervised learning offers great potential to reduce this data demand through pre-training the GNNs on polymer structure data only. These pre-trained GNNs can then be fine-tuned on the supervised property prediction task using a much smaller labeled dataset. We propose to leverage self-supervised learning techniques in GNNs for the prediction of polymer properties. We employ a recent polymer graph representation that includes essential features of polymers, such as monomer combinations, stochastic chain architecture, and monomer stoichiometry, and process the polymer graphs through a tailored GNN architecture. We investigate three self-supervised learning setups: (i) node- and edge-level pre-training, (ii) graph-level pre-training, and (iii) ensembled node-, edge- & graph-level pre-training. We additionally explore three different transfer strategies of fully connected layers with the GNN architecture. Our results indicate that the ensemble node-, edge- & graph-level self-supervised learning with all layers transferred depicts the best performance across dataset size. In scarce data scenarios, it decreases the root mean square errors by 28.39% and 19.09% for the prediction of electron affinity and ionization potential compared to supervised learning without the pre-training task.

Electrowetting display (EWD) technology is among the most promising reflective display technologies due to its full-color capabilities and fast video-speed performance. The colored EWD inks are typically prepared by dissolving soluble organic dyes in non-polar solvents, which significantly influence the color performance, electro-optical behaviour, and longevity of EWD devices. In this study, density functional theory (DFT) at the PBE1PBE/6-31G* level and time-dependent density functional theory (TD-DFT) at the M06-2X/6-31G* level were utilized to calculate a series of benzobisthiadiazole-based donor–acceptor–donor (D–A–D) type near-infrared organic dyes for EWDs, providing structural and spectral data to aid in spectral assignment. The quantum chemical calculations' results align with our experimental synthesis data, showing molecular colors spanning blue, green, and cyan. Detailed investigations into the properties of these dyes, including absorption, electro-optical response, and photo-stability, were conducted. The experimental outcomes indicate that these organic dyes are excellent candidates for EWD applications.

The development of hole transport materials with desirable properties is important for the fabrication of efficient organic light-emitting diodes (OLEDs). The present work demonstrates an approach for developing a library of phenothiazine-based hole transport materials (HTMs) for OLED application with considerably good triplet energy (theoretical). Furthermore, the single-crystal structure analysis at the molecular level for some of the developed molecules reveals the possibility of poor electronic communications between the corresponding units. Theoretical studies on transition dipole orientation revealed that all the present phenothiazine-based molecules have appreciable transition dipole orientation. Hence, the objective of the current work has been to assess the impact of chemical structures on certain features of a group of phenothiazine-based functional molecular HTMs with donor–acceptor characteristics. Finally, the hole-only devices (HODs) were fabricated with the synthesized materials as HTMs, and these showed an enhancement in current density with the increase in operating voltage from ∼2–8 V. All these theoretical and experimental outcomes suggested that the present set of molecules could be used as possible efficient HTMs for OLED applications.

Porous nanostructures exhibit remarkable nanoplatforms for payload delivery to diseased cells with high loading capacity, favorable release profiles, improved hemocompatibility, biocompatibility, and safe clearance after biodegradation. Metal–organic frameworks (MOFs), periodic mesoporous organosilica (PMO), or biodegradable periodic mesoporous organosilica (BPMO) epitomize a similar category of structured and crystalline porous coordinated compounds or nanocomposites. Additionally, their elevated surface-to-volume ratio, customizable porous configurations, and convenient attachment of favorable ligands to the central metal ions enhance drug loading and release, further demonstrating their potential for drug delivery applications. This review focuses on these materials, including Fe-MOFs, Cu-MOFs, Zr-MOFs, PMO and BPMO, along with multicompartmental mesoporous nanostructures, detailing their specific engineering, chemistry, and optimal drug delivery applications.

α-Synuclein (αSYN), and its tendency to self-aggregate, plays an important role in the development of Parkinson's disease (PD). αSYN aggregates are characterized by a stacking of αSYN chains and an interaction between the stackings to form dimer-like structures. The stability of these supramolecular assemblies is ensured by the presence of numerous residues that adopt “β-strand” and then “β-sheet” conformations, implying multiple interactions within and between the chains of αSYN. Following our previous study on the ability of small organic molecules to form columnar supramolecular assemblies (organic nanotubes, ONs) [Le Bras, L.; Dory, Y. L.; Champagne, B. Computational prediction of the supramolecular self-assembling properties of organic molecules: the role of conformational flexibility of amide moieties. Phys. Chem. Chem. Phys., 2021, 23, 20453–20465], we propose here to unravel the ability of these ONs to interact with αSYN aggregates. More than an interaction, we expect the organic molecules to avoid the complete aggregation process and ideally to induce destabilization of the stacking. Both molecular dynamics simulation and quantum mechanical-based calculations are used to identify the key parameters of the interaction and the resulting (de)stabilization of the assembly.