Molecular Systems Design & Engineering最新文献

The local interpretable model-agnostic explanations method was applied to identify substructures that represent the mutagenicity of chemical compounds using machine learning models. Random forest and extremely randomized trees were used to build models to be explained using the Hansen and Bursi Ames mutagenicity datasets. The models were analyzed using precision, recall, F1, and accuracy metrics. The aim of this study is to address the challenge of identifying substructures that indicate the mutagenicity of chemical compounds. The goal is to provide stable and consistent explanations for the mutagenicity of chemical compounds, which is crucial for trust and acceptance of the findings, especially in the sensitive field of computational toxicology. This approach is significant as it contributes to the interpretability and explainability of machine learning models, particularly in the context of identifying substructures associated with mutagenicity, thereby advancing the field of computational toxicology. Identifying substructures that represent the mutagenicity of chemical compounds is important because it can help predict the potential toxicity of new chemical compounds. This is particularly relevant in fields such as drug development and environmental toxicology, where the potential risks of exposure to new compounds need to be carefully evaluated. Some examples of chemical compounds that have been identified as mutagenic include epoxides, N-aryl compounds, nitro compounds, aromatic amines, N-oxides, nitro-containing compounds, and polycyclic aromatic hydrocarbons with a bay-region. These examples demonstrate the importance of identifying and studying mutagenic chemical compounds to better understand their potential risks and adverse effects on human health and the environment.

To mitigate food losses and ensure a robust cold chain in transportation, sensors play a pivotal role in swiftly and visibly monitoring storage conditions. The most commonly used indicators for reporting temperature violations are based on devices capable of signaling when a threshold temperature has been reached or exceeded or on disposable colorimetric sensors. A potential alternative, which uses reusable colorimetric sensors, may come from utilizing systems capable of displaying reversible color changes upon temperature variations; in this regard, molecules exhibiting thermo- and photochromic properties such as N-salicylideneaniline derivatives (anils) have emerged as promising candidates due to the simplicity of their synthesis and their ability to respond to temperature and light stimuli. In this study we have synthesized a family of anils through mechanochemistry, focusing on H/F substituents on the bromoaniline residue. The compounds were fully characterized using XRD and thermal techniques, and their thermo- and photochromic properties were explored via infrared spectroscopy. Among the series, the most suitable compound, i.e., a photochromic one showing a neat color change (from white to red/orange) quickly naked eye-detectable and whose back reaction is slow or virtually negligible at low temperatures, was identified and incorporated into a carboxymethyl cellulose (CMC) biopolymer matrix to produce a composite film, which was further characterized via XRD, thermal analyses and mechanical tests. The selected compound maintained its photochromic behavior upon embedding, and UV irradiation induced a color change in the film from colorless to red, while reversibility was evaluated at different temperatures (−19 °C, 4 °C and RT) using UV-vis spectroscopy. The composite film maintained a deep red color at −19 °C and 4 °C for seven weeks, while rapidly reversing to white/yellowish at room temperature, making it a suitable candidate for the development of sensors for cold chain transport and scenarios requiring rapid visual inspection of storage conditions.

Similar sizes and boiling points of acetylene (C₂H₂) and carbon dioxide (CO₂) make CO₂ separation from C₂H₂/CO₂ mixtures challenging. In this work, a pillared-layer ultramicroporous Zn-mipa-datz material featuring a C₂H₂-matching cavity was successfully prepared to achieve high-efficiency C₂H₂/CO₂ separation. The separation performance of Zn-mipa-datz on C₂H₂/CO₂ mixtures was investigated through gas adsorption isotherms and dynamic breakthrough experiments. Zn-mipa-datz possessed high C₂H₂ separation efficiency for C₂H₂/CO₂ mixtures. The molecular simulation demonstrated that the strong C₂H₂–host interaction was achieved by the synergistic effect of C–N electrostatic interactions and C–H⋯N H bonds.

Post-SELEX modifications assisted by in silico modelling are powerful tools to improve the performance of aptamers, by providing a rational approach for the selection of modified-versions of aptamers. In this study, a complete in silico analysis of the three-dimensional structure of a previously selected DNA aptamer (Apt5) against staphylococcal enterotoxin A (SEA) was performed. Locked nucleic acid (LNA) modifications were introduced in key locations and their effect on the aptamer structure and docking were evaluated. Promising LNA aptamers were then synthetized and their dissociation constants (K_D), as well as stability, were evaluated. From the in silico analysis, it was possible to identify three promising LNA variations that did not affect drastically the three-dimensional structure and the molecular docking with the toxin. The K_D of the LNA aptamers were higher than the DNA aptamer (Apt5: K_D = 13 ± 2 nM, LNA13: K_D = 157 ± 39 nM, LNA14: K_D = 74 ± 24 nM, LNA15: K_D = 143 ± 28 nM), but remained in the low nanomolar range. Even so, the K_D of LNA14 was not significantly different (P < 0.05) compared to the value of the original aptamer and the introduction of LNA increased its thermal stability, increasing the range of functionality of the original aptamer. However, the introduced modifications were not enough to increase the biological stability of the aptamer, remaining susceptible to a complete degradation by endonucleases and exonucleases in 5 minutes. Altough partial modifications with LNA may not be able to overcome all the limitations of DNA aptamers, post-SELEX modifications assisted by in silico modelling have shown promising results in predicting functional modified aptamers, avoiding a time-consuming and expensive trial and error approach.

Polypeptide fusion tags that can direct the assembly of folded proteins into supramolecular networks are attractive for creating functional biomaterials. A practical challenge is identifying polypeptide sequences that form supramolecular networks in response to specific user-controlled stimuli, which is advantageous for producing polypeptide–protein fusions using cell-based expression hosts. Here, we report an N-glycosylation tag, (GGGSGGGSGGNWTT)₁₀ or “NGT,” that assembles into a supramolecular network at reduced temperatures when fused to a folded protein. For example, NGT fused to superfolder green fluorescent protein (NGTsfGFP) formed materials that emitted green fluorescence in blue light, while NGT fused to NanoLuc luciferase (NGTnL) formed materials that emitted blue light in the presence of the chemical substrate furimazine. Oscillatory rheology established the materials as weak viscoelastic gels that can undergo shear-thinning and self-healing. Gel formation could be disrupted by mutating the asparagines in NGT to glutamines, introducing a chaotropic agent, or modifying the asparagines in NGT with glucose, suggesting a role for hydrogen bonds involving asparagine in supramolecular network formation. A mixture of soluble NGTsfGFP and NGTnL formed a multifunctional gel at reduced temperature that demonstrated bioluminescence resonance energy transfer between the nL and sfGFP domains in the presence of furimazine. Collectively, these data establish NGT as a temperature-responsive polypeptide tag that can be used to create functional biomaterials from soluble fusion proteins synthesized by cell-based hosts.

3D Voronoi scaffolds are widely applied in the field of additive manufacturing as they are known for their light weight structural resilience and share many topological similarities to various natural (bone, tumours, lymph node) and synthetic environments (foam, functionally gradient porous materials). Unfortunately, the structural design features that promote these topological similarities (such as the number of vertices) are often unpredictable and require the trial and error of varying design features to achieve the desired 3D Voronoi structure. This article provides a toolkit, consisting of equations, based on over 12 000 3D Voronoi structures. These equations allow design features, such as the number of generating points (G), to be efficiently and accurately predicted based on the desired structural parameters (within ±3G). Based on these equations we are proposing, to the best of our knowledge, two new mathematical conjectures that relate the number of vertices or edges, and the average edge length to G in Voronoi structures. These equations have been validated for a wide range of parameter values and Voronoi network sizes. A design code is provided allowing any of over 12 000 structures to be selected, easily adjusted based on user requirements, and 3D printed. Biomedical case studies relevant to T-cell culturing, bone scaffolds and kidney tumours are presented to illustrate the design code.

This study aims to elucidate the properties of aluminum nitrite nanotubes (AlNNT) encapsulated with phosphorus (P@AlNNT), sulphur (S@AlNNT), and silicon (Si@AlNNT) heteroatoms for use as biosensors for 5-hydroxyindoleacetic acid (5HIAA). It was considered an indicative biomarker for carcinoid tumors and investigated using the density functional theory (DFT) at the ωB97XD/def2svp level of theory. With adsorption energies of −0.009 eV, 0.055 eV, and 0.044 eV for 5HIAA_P@AINNT, 5HIAA_S@AINNT, and 5HIAA_Si@AINNT, respectively, the 5HIAA_P@AINNT was the only favorable system for adsorption of 5HIAA. According to the topological investigation, the hydrogen bond strength was in the order of 5HIAA_Si@AlNNT > 5HIAA_S@AlNNT > 5HIAA_P@AlNNT. This was also confirmed by NCI-RDG analysis. Regarding sensory parameters, as per the fraction of electron transfer, 5HIAA_S@AlNNT had the highest propensity to react with the sensor followed by 5HIAA_Si@AlNNT. The order of recovery time (τ) was recorded to be 5HIAA_P@AlNNT < 5HIAA_S@AlNNT < 5HIAA_Si@AlNNT. It was recorded that the systems 5HIAA_S@AlNNT and 5HIAA_Si@AlNNT had longer recovery times at 310 K when compared to their recovery times at 298 K. However, the system 5HIAA_P@AlNNT records a minute shorter recovery time at 298 K compared to its recovery time at 310 K. Results from molecular dynamic simulation reveal that 5HIAA_S@AlNNT and 5HIAA_Si@AlNNT are more thermally stable, which is necessary for reliable and accurate detection. System 5HIAA_P@AlNNT records the most favourable adsorption property and considerable sensing characteristics.

In recent years, nanocellulose has emerged as a sustainable and environmentally friendly alternative to traditional petroleum-derived structural polymers. Sourced either from plants, algae, or bacteria, nanocellulose can be processed into colloid, gel, film and fiber forms. However, the required fundamental understanding of process parameters that govern the morphology and structure–property relationships of nanocellulose systems, from colloidal suspensions to bulk materials, has not been developed and generalized for all forms of cellulose. This further hinders the more widespread adoption of this biopolymer in applications. Our study investigates the dispersion of cellulose nanofibers (CNFs) produced by a bacterial–yeast co-culture, in solvents, highlighting the role of thermodynamic interactions in influencing their colloidal behavior. By adjusting Hansen solubility parameters, we controlled the thermodynamic relationship between CNFs and solvents across various concentrations, studying the dilute to semi-dilute regimes. Rheological measurements revealed that the threshold at which a concentration-based regime transition occurs is distinctly solvent-dependent. Complementing rheological analysis with small angle X-ray scattering and zeta potential measurements, our findings reveal that enhancing CNF–solvent interactions increases excluded volume in the dilute regime, emphasizing the importance of the balance between fiber–fiber and fiber–solvent interactions. Moreover, we investigated the transition from colloidal to solid state by creating films from dispersions with varying interaction parameters in semi-dilute regimes. Through mechanical testing and scanning electron microscopy imaging of the fracture surfaces, we highlight the significance of electrokinetic effects in such transitions, as dispersions with higher electrokinetic stabilization gave rise to stronger and tougher films despite having less favorable thermodynamic interaction parameters. Our work provides insights into the thermodynamic and electrokinetic interplay that governs bacterial CNF dispersion, offering a foundation for future application and a deeper understanding of nanocellulose's colloidal and structure-property relationships.

In the current research, we have unveiled an advanced technique termed the quantitative read-across structure–activity relationship (q-RASAR) framework to harness the power of machine learning (ML) for significantly enhancing the precision of predictions related to blood–brain barrier (BBB) permeability. It is important to emphasize that the central objective of this study is not to introduce an additional model for predicting BBB permeability. Instead, our focus is on highlighting the improvement in quantitatively predicting the BBB permeability of organic compounds by utilizing the q-RASAR approach. This innovative methodology strives to enhance the precision of evaluating neuropharmacological implications and streamline the drug development process. In this investigation, we developed a machine learning (ML)-based q-RASAR PLS regression model using a large dataset comprising 1012 diverse classes of heterocyclic and aromatic compounds, obtained from the freely accessible B3DB database (accessible at https://github.com/theochem/B3DB) to predict BBB permeability during the lead discovery phase for central nervous system (CNS) drugs. The model's predictive capability underwent validation using two external sets, encompassing a total of 1 130 315 compounds, including synthetic compounds and natural products (NPs) for data gap filling and other two external sets comprising 116 drug-like/drug compounds from the FDA and ChEMBL databases to assess the model's reliability against the reported BBB permeability values. This study aimed to bridge the data gap by employing a predictive regression model to estimate the BBB permeability for both synthetic compounds and natural products (NPs). To further enhance predictability, we have developed various other ML-based q-RASAR models. The insights from the developed model highlight the pivotal roles played by hydrophobicity, electronic effects, degree of ionization, and steric factors as essential features facilitating the traversal of the blood–brain barrier. This research not only advances our understanding of the molecular determinants influencing the permeability of central nervous system drugs but also establishes a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in the realms of drug development and design.

Bioink in three-dimensional (3D) bioprinting of biomimetic tissue scaffolds has emerged as a key factor for the success of tissue engineering and regenerative medicine. The bioinks used for extrusion 3D bioprinting have hydrogel matrices with different kinds of polymeric biomaterials such as proteins, peptides, polysaccharides, hydrophilic synthetic polymers, and others. Natural polysaccharides such as alginate, chitosan, and hyaluronic acid have garnered significant attention as bioink materials due to their excellent biocompatibility, extracellular matrix mimetic properties, biodegradability, injectability, bioprintablilty and structural versatility among their many advantages, even though many research groups focus on the study of protein-based bioinks to utilize their high potential of cell adhesiveness. This review encompasses recent advancements of polysaccharide-based hydrogels and bioinks for bioengineered tissue regeneration and reconstruction, especially by focusing on fabrication of multilayered complex structures for biomimetic tissue engineering applications.