Pectin aerogels, with very low density (around 0.1 g cm-3) and high specific surface area (up to 600 m2 g-1), are excellent thermal insulation materials since their thermal conductivity is below that of air at ambient conditions (0.025 W m-1 K-1). However, due to their intrinsic hydrophilicity, pectin aerogels collapse when in contact with water vapor, losing superinsulating properties. In this work, first, pectin aerogels were made, and the influence of the different process parameters on the materials\' structure and properties were studied. All neat pectin aerogels had a low density (0.04-0.11 g cm-1), high specific surface area (308-567 m2 g-1), and very low thermal conductivity (0.015-0.023 W m-1 K-1). Then, pectin aerogels were hydrophobized via the chemical vapor deposition of methyltrimethoxysilane using different reaction durations (2 to 24 h). The influence of hydrophobization on material properties, especially on thermal conductivity, was recorded by conditioning in a climate chamber (25 °C, 80% relative humidity). Hydrophobization resulted in the increase in thermal conductivity compared to that of neat pectin aerogels. MTMS deposition for 16 h was efficient for hydrophobizing pectin aerogels in moist environment (contact angle 115°) and stabilizing material properties with no fluctuation in thermal conductivity (0.030 W m-1 K-1) and density for the testing period of 8 months.

ZnO nanorod nonwoven fabrics (ZNRN) were developed through hydrothermal synthesis to facilitate the prevention of the transmission of respiratory pathogens. The superhydrophobicity and antibacterial properties of ZNRN were improved through the response surface methodology. The synthesized material exhibited significant water repellency, indicated by a water contact angle of 163.9°, and thus demonstrated antibacterial rates of 91.8% for Escherichia coli (E. coli) and 79.75% for Staphylococcus aureus (S. aureus). This indicated that E. coli with thinner peptidoglycan may be more easily killed than S. aureus. This study identified significant effects of synthesis conditions on the antibacterial effectiveness, with comprehensive multivariate analyses elucidating the underlying correlations. In addition, the ZnO nanorod structure of ZNRN was characterized through SEM and XRD analyses. It endows the properties of superhydrophobicity (thus preventing bacteria from adhering to the ZNRN surface) and antibacterial capacity (thus damaging cells through the puncturing of these nanorods). Consequently, the alignment of two such features is desired to help support the development of personal protective equipment, which assists in avoiding the spread of respiratory infections.

The biological activities of hesperidin-related compounds, such as hesperetin laurate (HTL), hesperetin (HT), hesperidin (HD), and hesperidin glucoside (HDG), were investigated in vitro. The compounds showed different hydrophobicities, and the octanol-water partition coefficient log P were 7.28 ± 0.06 for HTL, 2.59 ± 0.04 for HT, 2.13 ± 0.03 for HD, and -3.45 ± 0.06 for HDG, respectively. In the DPPH assay and β-carotene bleaching assay to determine antioxidant capacity, all compounds tested showed antioxidant activity in a concentration-dependent manner, although to varying degrees. HTL and HT showed similarly high activities compared to HD or HDG. HD and HDG did not show a significant difference despite the difference in solubility between the two. Cytotoxicity was high; in the order of hydrophobicity-HTL > HT > HD > HDL in keratinocyte HaCaT cells. All compounds tested showed reducing effects on cellular inflammatory mediators and cytokines induced by UV irradiation. However, HTL and HT effectively reduced nitric oxide (NO), tumor necrosis factor α (TNF-α), and interleukin-6 (IL-6) levels compared to HD and HDG. The inhibitory effects of hesperidin-related compounds on skin-resident microorganisms were evaluated by measuring minimum inhibitory concentration (MIC). HTL showed the highest inhibitory effects against Staphylococcus aureus, Cutibacterium acnes, Candida albicans, and Malassezia furfur, followed by HT, while HD and HDF showed little effect. In conclusion, the hydrophobicity of hesperidin-related compounds was estimated to be important for biological activity in vitro, as was the presence or absence of the sugar moiety.

Non-biodegradable plastic materials pose environmental hazards and contribute to pollution. Arabinoxylan (AX) films have been created for applications in food packaging to replace these materials. The water interaction characteristics of biodegradable AX films were assessed following the extraction of AX from dry-milled corn bran (DCB), wet-milled corn bran (WCB), and dried distiller\'s grains with solubles (DDGS). Films were prepared with laccase and sorbitol before surface modification with lipase-vinyl acetate. Water solubility of the modified DCB films was significantly reduced (p < 0.05); however, the water solubility of modified WCB films decreased insignificantly (p > 0.05) compared to unmodified films. Water vapor permeability of the modified AX films from WCB and DDGS was significantly reduced (p < 0.05), unlike their unmodified counterparts. The biodegradation rates of the modified WCB AX and DDGS films increased after 63 and 99 days, respectively, compared to the unmodified films. The hydrophilic nature of AX polymers from WCB and DDGS enhances the biodegradability of the films. This study found that the modified WCB AX film was more hydrophobic, and the modified DDGS AX film was more biodegradable than the modified DCB AX film. Overall, surface modifications have potential for improving hydrophobicity of biopolymer films.

Preparation of hydrophobic coatings is still a challenge for researchers in various fields of science. One of the easiest ways consists of the use of special modifiers. However, usually such modifiers are poorly compatible with organic polymeric matrixes, which leads to segregation of modifiers and deterioration of coating properties. In this work, we have synthesized a number of organosilicon copolymers and studied their compatibility with epoxy matrix and hydrophobic efficiency. It was shown that the increase of phenyl-containing units leads to increase of compatibility but decreases hydrophobic efficiency. Addition of small amounts of such modifiers into commercial epoxy paint material can lead to an increase of contact angle of the final coating from 63 to 87° without deterioration of other physico-mechanical properties. These results open new perspectives in preparation of organosilicon hydrophobic modifiers with directed properties for fields of application such as paints and coating materials.

Understanding the intricate interactions governing protein and peptide behavior in liquid-liquid phase separation (LLPS) is crucial for unraveling biological functions and dysfunctions. This study employs a residue-leveled coarse-grained molecular dynamics approach to simulate the phase separation of repetitive polyproline and polyarginine peptides (poly PR) with varying lengths and sequences in solution, considering different concentrations and temperatures. Our findings highlight the crucial role of sequence order in promoting LLPS in peptides with identical lengths of repetitive sequences. Interestingly, repetitive peptides containing fewer than 10 polyarginine repeats exhibit no LLPS, even at salt concentrations up to 3 M. Notably, our simulations align with experimental observations, pinpointing a salt concentration of 2.7 M for PR25-induced LLPS. Utilizing the same methodology, we predict the required salt concentrations for LLPS induction as 1.2 M, 1.5 M, and 2.7 M for PR12, PR15, and PR35, respectively. These predictions demonstrate good agreement with experimental results. Extending our investigation to include the peptide glutamine and arginine (GR15) in DNA solution, our simulations mirror experimental observations of phase separation. To unveil the molecular forces steering peptide phase separation, we introduce a dielectric constant modifier and hydrophobicity disruptor into poly PR systems. Our coarse-grained analysis includes an examination of temperature effects, leading to the inference that both hydrophobic and electrostatic interactions drive phase separation in peptide systems.

In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. Here, we present a set of molecular surface descriptors specifically designed for predicting antibody developability. We assess the performance of these descriptors by benchmarking their correlations with an extensive array of experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, and polyspecificity. Further, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant, hydrophobicity scales, structure prediction methods, and the impact of conformational sampling. Notably, we observe systematic shifts in the distribution of surface descriptors depending on the structure prediction method used, driving weak correlations of surface descriptors across structure models. Averaging the descriptor values over conformational distributions from molecular dynamics mitigates the systematic shifts and improves the consistency across different structure prediction methods, albeit with inconsistent improvements in correlations with biophysical data. Based on our benchmarking analysis, we propose six in silico developability risk flags and assess their effectiveness in predicting potential developability issues for a set of case study molecules.

Hydrophobic surfaces can improve the long-term mechanical response of polymers by delaying their degradation caused by moisture absorption over time. This improvement in long-term mechanical performance can significantly increase the lifespan of polymers used in various biomedical applications, such as total joint replacement prostheses applications. Although a number of surface modification techniques have been developed over the years, such as introduction of various textures on the surface; their specific influences on hydrophobicity enhancement as well as long-term mechanical performance are yet to be fully understood. In this study, surface textures, with variation in type and geometry, are introduced on model Ultrahigh Molecular Weight Polyethylene (UHMWPE) and High Density Polyethylene (HDPE) surfaces to study the effect of surface modification on hydrophobicity and long-term mechanical performance under environmental conditions. The results show that introduction of surface textures significantly improves the hydrophobicity of model polymers. Texture length or diameter significantly affects the improvement in hydrophobicity. However, texture spacing does not have significant influence on the improvement in hydrophobicity. How this improvement in hydrophobicity facilitates improving the long-term mechanical performance under environmental conditions is investigated. The study provides useful guidelines to improve the long-term mechanical response of polymers for various applications, including biomedical applications.

Caddisfly larvae produce silk containing heavy and light fibroins, similar to the silk of Lepidoptera, for the construction of underwater structures. We analyzed the silk of Limnephilus lunatus belonging to the case-forming suborder Integripalpia. We analyzed the transcriptome, mapped the transcripts to a reference genome and identified over 80 proteins using proteomic methods, and checked the specificity of their expression. For comparison, we also analyzed the transcriptome and silk proteome of Limnephilus flavicornis. Our results show that fibroins and adhesives are produced together in the middle and posterior parts of the silk glands, while the anterior part produces enzymes and an unknown protein AT24. The number of silk proteins of L. lunatus far exceeds that of the web-spinning Plectrocnemia conspersa, a previously described species from the suborder Annulipalpia. Our results support the idea of increasing the structural complexity of silk in rigid case builders compared to trap web builders.

Protein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge. A key to improve affinity prediction is to capture interactions between proteins and ligands effectively. Existing deep-learning-based computational approaches use 3D grids, 4D tensors, molecular graphs, or proximity-based adjacency matrices, which are either resource-intensive or do not directly represent potential interactions. In this paper, we propose atomic-level distance features and attention mechanisms to capture better specific protein-ligand interactions based on donor-acceptor relations, hydrophobicity, and π -stacking atoms. We argue that distances encompass both short-range direct and long-range indirect interaction effects while attention mechanisms capture levels of interaction effects. On the very well-known CASF-2016 dataset, our proposed method, named Distance plus Attention for Affinity Prediction (DAAP), significantly outperforms existing methods by achieving Correlation Coefficient (R) 0.909, Root Mean Squared Error (RMSE) 0.987, Mean Absolute Error (MAE) 0.745, Standard Deviation (SD) 0.988, and Concordance Index (CI) 0.876. The proposed method also shows substantial improvement, around 2% to 37%, on five other benchmark datasets. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap. Scientific Contribution StatementThis study innovatively introduces distance-based features to predict protein-ligand binding affinity, capitalizing on unique molecular interactions. Furthermore, the incorporation of protein sequence features of specific residues enhances the model\'s proficiency in capturing intricate binding patterns. The predictive capabilities are further strengthened through the use of a deep learning architecture with attention mechanisms, and an ensemble approach, averaging the outputs of five models, is implemented to ensure robust and reliable predictions.