Molecular Properties Research Articles

ZORA Basis Sets of 5 and 6 Zeta Valence Qualities for H-Ar: Application in Calculations of Atomic and Molecular Properties

ZORA Basis Sets of 5 and 6 Zeta Valence Qualities for H-Ar: Application in Calculations of Atomic and Molecular Properties

The effect of additives on the biodegradation of polyhydroxyalkanoate (PHA) in marine field trials

The persistence of conventional fossil fuel-derived plastics in marine ecosystems has raised significant environmental concerns. Biodegradable plastics are being explored as an alternative. This study investigates the biodegradation behaviour in two marine environments of melt-extruded sheets of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) bioplastic as well as blends of PHBV with a non-toxic plasticiser (triethyl citrate, TEC) and composites of PHBV with wood flour. Samples were submerged for up to 35 weeks in two subtropical marine conditions: on the sandy seabed in the sublittoral benthic zone and the sandy seabed of an open air mesocosm with pumped seawater. Rates of biodegradation, lag times and times to 95 % mass loss (T95) were determined through mass loss data and Gompertz modelling. Mechanisms of biodegradation were studied through changes in molecular weight, mechanical properties and surface features. Results reveal a rapid biodegradation rate for all PHBV samples, demonstrating a range of specific biodegradation rates relative to exposed surface area of 0.03 ± 0.01 to 0.09 ± 0.04 mg.d−1.cm−2. This rapid rate of biodegradation meant that the subtle variations in biodegradation mechanisms across different sample thicknesses and additive compositions had little effect on overall lifetimes, with the T95 for most samples being around 250–350 days, regardless of site, highlighting the robust biodegradability of PHBV in seawater. It was only the PHBV-wood flour composite that showed faster biodegradation, and that was only in the exposed ocean site. The mesocosm site was otherwise shown to be a good model for the open ocean, with very comparable biodegradation rates and changes in mechanical properties over time.

Ensemble-Based Virtual Screening Led to the Discovery of Novel Lead Molecules as Potential NMBAs.

Neuromuscular blocking agents (NMBAs) are routinely used during anesthesia to relax skeletal muscle. Nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels; NMBAs can induce muscle paralysis by preventing the neurotransmitter acetylcholine (ACh) from binding to nAChRs situated on the postsynaptic membranes. Despite widespread efforts, it is still a great challenge to find new NMBAs since the introduction of cisatracurium in 1995. In this work, an effective ensemble-based virtual screening method, including molecular property filters, 3D pharmacophore model, and molecular docking, was applied to discover potential NMBAs from the ZINC15 database. The results showed that screened hit compounds had better docking scores than the reference compound d-tubocurarine. In order to further investigate the binding modes between the hit compounds and nAChRs at simulated physiological conditions, the molecular dynamics simulation was performed. Deep analysis of the simulation results revealed that ZINC257459695 can stably bind to nAChRs' active sites and interact with the key residue Asp165. The binding free energies were also calculated for the obtained hits using the MM/GBSA method. In silico ADMET calculations were performed to assess the pharmacokinetic properties of hit compounds in the human body. Overall, the identified ZINC257459695 may be a promising lead compound for developing new NMBAs as an adjunct to general anesthesia, necessitating further investigations.

Design of a Humidity Sensor for a PPE Kit Using a Flexible Paper Substrate.

The present work reports the rapid sweat detection inside a PPE kit using a flexible humidity sensor based on hydrothermally synthesized ZnO (zinc oxide) nanoflowers (ZNFs). Physical characterization of ZNFs was done using scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transmission infrared spectroscopy (FTIR), UV-visible, particle size analysis, Raman analysis, and X-ray photoelectron spectroscopy (XPS) analysis, and the hydrophilicity was investigated by using contact angle measurement. Fabrication of a flexible sensor was done by deposition on the paper substrate using the spin coating technique. It exhibited high sensitivity and low response and recovery times in the humidity range 10-95%RH. The sensor demonstrated the highest sensitivity of 296.70 nF/%RH within the humidity range 55-95%RH, and the rapid response and recovery times were also calculated and found as 5.10/1.70 s, respectively. The selectivity of the proposed sensor was also analyzed, and it is highly sensitive to humidity. The humidity sensing characteristics were theoretically witnessed in terms of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) and electronic properties of sensing materials in ambient and humid conditions. These theoretical results are evidence of the interaction of ZnO with humidity. Overall, the present study provides a scope of architecture-enabled paper-based humidity sensors for the detection of sweat levels inside PPE kits for health workers.

Synthesis of Benzimidazole Based 1,2,4‐Triazole Derivatives as Potential Anticancer Agents: In Silico Techniques

AbstractCompounds with a 1,2,4–triazole scaffold serve an essential role in organic synthesis, particularly in the synthesis of bioactive organic compounds; thus, the development of new approaches for modifying this scaffold is a very interesting framework of this study. To contribute to the development of an efficient method for the conversion of anticancer triazoles, a novel series of benzimidazole based 1,2,4‐triazoles was designed and synthesized. All the newly synthesized derivatives were characterized by NMR (1H &13C), FT–IR and mass spectrometry. Among the tested compounds, hybrids 5 b, 5 g exhibited exceptional anticancer susceptibilities with IC50=8.8±0.9, 9.2±1.5 μM against the tested HCA‐7cancer cell line. Anticancer profiles showed that compounds 5 d, 5 i had better anticancer inhibitory potency against a breast cancer cell line MCF‐7with IC50values 8.3±2.1, 10.6±1.2 μM, whereas 5 d, and 5 g showed potent anticancer activity against colorectal cancer cell line HT29 with IC50 values 7.1±±0.9, 7.7±1.2 μM respectively. From docking results, 5 d demonstrated highly stable binding amino acids LeuA : 2247, GlyA : 2449, SerA : 2409, HisA : 2379, AspA : 2373, LysA : 2318, IleA : 2317, IleA : 2246, IleA : 2412, AlaA : 2410, GlnA : 2452, AsnA : 2245, TyrA : 2243, and HisA : 2448, which play a crucial role enabling optimal ligand binding in a crystal structure tyrosine phosphatase mutated in colorectal cancer (PDB: 1WCH). Furthermore, the physicochemical and absorption, distribution, metabolism, and excretion (ADME) filtration molecular properties, estimation of bioactivity, and toxicity scores of these scaffolds were evaluated.

Pathogenic Homocystinuria-Associated T236N Mutation Dramatically Alters the Biochemical Properties of Cystathionine Beta-Synthase Protein.

Cystathione beta-synthase (CBS) T236N is a novel mutation associated with pyridoxine non-responsiveness, which presents a significant difficulty in the medical treatment of homocystinuria. Reported severe phenotypes in homocystinuria patients highlight the urgent requirement to comprehend the molecular mechanisms underlying mutation pathogenicity for the advancement of the disease. In this study, we used a multidisciplinary approach to investigate the molecular properties of bacterially expressed and purified recombinant CBST236N protein, which we directly compared to those of the wild-type (CBSWT) protein. Our data revealed a profound impact of the p.T236N mutation on CBS enzymatic activity, with a dramatic reduction of ~96% compared to the CBSWT protein. Circular dichroism (CD) experiments indicated that the p.T236N mutation did not significantly alter the secondary structure of the protein. However, CD spectra unveiled distinct differences in the thermal stability of CBSWT and CBST236N mutant protein species. In addition, chemical denaturation experiments further highlighted that the CBSWT protein exhibited greater thermodynamic stability than the CBST236N mutant, suggesting a destabilizing effect of this mutation. Our findings provide an explanation of the pathogenicity of the p.T236N mutation, shedding light on its role in severe homocystinuria phenotypes. This study contributes to a deeper understanding of CBS deficiency and may improve the development of targeted therapeutic strategies for affected individuals.

Effects of molecular structure and charge state on the foaming and emulsifying properties of Spirulina protein isolates

Microalgae protein holds great potential for various applications in the food industry. However, the current knowledge regarding microalgae protein remains limited, with little information available on its functional properties. Furthermore, the relationship between its molecular structure and functional properties is not well defined, which limits its application in food processing. This study aims to addresses these gaps though an analysis of the emulsibility and foamability of various soluble protein isolates from two species of Spirulina (Arthospira platensis and Spirulina platensis), and the functional properties of Spirulina protein isolates in relation to its molecular structure and charge state. Results revealed that the degree of cross-linking and aggregation or folding and curling of protein tertiary structures was higher in the highly soluble Spirulina protein isolates (AP50% and SP50%) than in the low-solubility isolates (AP30% and SP30%). The foaming capacity (FC) of AP50% and SP50% was found to be lower than that of AP30% and SP30%. Spirulina protein isolates can stably adsorb at the air-water interface for at least 20 min and possessed good interfacial activity. A high pH value was found to promote cross-linking of protein particles at the oil-water interface, thereby reinforcing the internal network structure of emulsions and increasing viscosity. These findings provide preliminary insights for potential applications of Spirulina protein isolates in food production, especially towards quality improvement.

N4BP1 functions as a dimerization-dependent linear ubiquitin reader which regulates TNF signalling

Signalling through TNFR1 modulates proinflammatory gene transcription and programmed cell death, and its impairment causes autoimmune diseases and cancer. NEDD4-binding protein 1 (N4BP1) is a critical suppressor of proinflammatory cytokine production that acts as a regulator of innate immune signalling and inflammation. However, our current understanding about the molecular properties that enable N4BP1 to exert its suppressive potential remain limited. Here, we show that N4BP1 is a novel linear ubiquitin reader that negatively regulates NFκB signalling by its unique dimerization-dependent ubiquitin-binding module that we named LUBIN. Dimeric N4BP1 strategically positions two non-selective ubiquitin-binding domains to ensure preferential recognition of linear ubiquitin. Under proinflammatory conditions, N4BP1 is recruited to the nascent TNFR1 signalling complex, where it regulates duration of proinflammatory signalling in LUBIN-dependent manner. N4BP1 deficiency accelerates TNFα-induced cell death by increasing complex II assembly. Under proapoptotic conditions, caspase-8 mediates proteolytic processing of N4BP1, resulting in rapid degradation of N4BP1 by the 26 S proteasome, and acceleration of apoptosis. In summary, our findings demonstrate that N4BP1 dimerization creates a novel type of ubiquitin reader that selectively recognises linear ubiquitin which enables the timely and coordinated regulation of TNFR1-mediated inflammation and cell death.

Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation.

The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for encoding molecular features from canonical SMILES, in conjunction with a CNN-based model to predict drug-induced QT interval prolongation (DIQT), drug-induced teratogenicity (DIT), and drug-induced rhabdomyolysis (DIR). Our results demonstrate that the proposed model outperforms conventional models applied in previous studies for predicting DIQT, DIT, and DIR. Notably, an analysis of the learned linear attention maps highlights amines, alcohol, ethers, and aromatic halogen compounds as strongly associated with the three types of ADRs. These findings hold promise for enhancing drug discovery pipelines and reducing the drug attrition rate due to safety concerns.

Molecular reconstruction of crude oil and simulation of distillation processes: An approach based on predefined molecules

Utilizing molecular management is a robust method for tackling the obstacles presented during the worldwide energy shift and for streamlining the utilization of crude oil resources. A fundamental component of this management technique is molecular reconstruction technology. In this study, a molecular model of crude oil is constructed using 151 deterministic molecules and artificially constructed molecules with average structures. To establish the molecular property matrix, the group contribution method and molecular dynamics simulations are employed to compute the molecular properties. An optimization algorithm is then employed to convert true boiling point profiles, SARA (Saturate, Aromatic, Resin, and Asphaltene) contents, and API of crude oils into detailed molecular content distributions. These distributions, alongside the actual boiling point curve data, are integrated into chemical process simulation software to model the distillation process of the crude oil. Notably, the molecular weights and mass densities of the process outlet streams derived from these two calculations are generally in good agreement.

Potential Diagnostic and Clinical Significance of Selected Genetic Alterations in Glioblastoma.

Glioblastoma is currently considered the most common and, unfortunately, also the most aggressive primary brain tumor, with the highest morbidity and mortality rates. The average survival of patients diagnosed with glioblastoma is 14 months, and only 2% of patients survive 3 years after surgery. Based on our clinical experience and knowledge from extensive clinical studies, survival is mainly related to the molecular biological properties of glioblastoma, which are of interest to the general medical community. Our study examined a total of 71 retrospective studies published from 2016 through 2022 and available on PubMed that deal with mutations of selected genes in the pathophysiology of GBM. In conclusion, we can find other mutations within a given gene group that have different effects on the prognosis and quality of survival of a patient with glioblastoma. These mutations, together with the associated mutations of other genes, as well as intratumoral heterogeneity itself, offer enormous potential for further clinical research and possible application in therapeutic practice.

Recent Advances of Chiral Isolated and Small Organic Molecules: Structure and Properties for Circularly Polarized Luminescence.

This paper explores recent advancements in the field of circularly polarized luminescence (CPL) exhibited by small and isolated organic molecules. The development and application of small CPL molecule are systematically reviewed through eight different chiral skeleton sections. Investigating the intricate interplay between molecular structure and CPL properties, the paper aims at providing and enlighting novel strategies for CPL-based applications.

Spacial regulation of precise aperture in metal–organic frameworks towards ultrahigh-selective mixed matrix membranes for low-energy-consumption C3H6/C3H8 separation

Membrane technology based on metal–organic framework (MOF) with molecular sieving properties is a promising alternative to energy-intensive conventional distillation for C3H6/C3H8 separation. However, precisely regulating the aperture of MOF materials for industrial separation processes with low energy consumption remains a significant challenge. Herein, we introduce a post-synthetic defect exchange, using ortho-trifluoromethylbenzoic acid (o-TBA) in defective UiO-66, resulting in a mixed matrix membrane (MMM) with a C3H6/C3H8 ideal selectivity of ∼ 104 and C3H6 permeability of ∼ 293 Barrer, and exhibiting a C3H6/C3H8 selectivity ∼ 36 with C3H6 permeability of ∼ 188 Barrer for a C3H6/C3H8 (50:50) mixture. Computational simulations confirm impact of o-TBA-UiO on reducing pore sizes and providing moderate propylene affinity. Static adsorption and solution-diffusion experimental results show that the o-TBA-UiO/6FDA-DAM membrane provides fast C3H6 diffusion rates but retards C3H8 diffusion, leading to ultrahigh C3H6/C3H8 selectivity. A large-area membrane of 2400 cm2 was prepared to promote practical applications, mainly because of the considerably enhanced affinity between o-TBA-UiO and 6FDA-DAM. At a feed pressure of 1 bar, the 1812-type membrane module exhibited a modest decline in single gas separation performance with a C3H6 permeability of ∼ 175 Barrer and a C3H6/C3H8 ideal selectivity of ∼ 96. Finally, a potential two-stage membrane process with low energy consumption was designed. This study provides a robust design for MOF-based MMMs with molecular sieving for energy-efficient gas separation.

Ab initio spectroscopic studies of AlF and AlCl molecules

In this work, we report the results from our spectroscopic study on AlF and AlCl molecules. We carry out detailed electronic structure calculations in both the molecules, including obtaining the potential energy surfaces of the [Formula: see text] ground electronic state and some of the low-lying excited electronic states belonging to [Formula: see text] and [Formula: see text] symmetries. This is followed by evaluating the spectroscopic constants and molecular properties such as electric dipole moments and electric quadrupole moments. Throughout, we employ the multi-reference configuration interaction method and work with high-quality quadruple zeta basis sets. Further, transition dipole moments between the ground electronic state and singlet excited states are also studied. The relevant vibrational parameters are computed by solving the vibrational Schrödinger equation. Subsequently, the vibrational energy spacings and transition dipole moments between the vibrational levels belonging to the same electronic states are used to evaluate the spontaneous and black-body radiation-induced transition rates, followed by computing the lifetimes. Finally, the energy differences between rotational levels belonging to different vibrational levels and within an electronic state as well as the Einstein coefficients are reported.

Effective C4 Separation by Zeolite Metal-Organic Framework Composite Membranes.

Inorganic zeolites have excellent molecular sieving properties, but they are difficult to process into macroscopic structures. In this work, we use metal-organic framework (MOF) glass as substrates to engineer the interface with inorganic zeolites, and then assemble the discrete crystalline zeolite powders into monolithic structures. The zeolites are well dispersed and stabilized within the MOF glass matrix, and the monolith has satisfactory mechanical stabilities for membrane applications. We demonstrate the effective separation performance of the membrane for 1,3-butadiene (C4H6) from other C4 hydrocarbons, which is a crucial and challenging separation in the chemical industry. The membrane achieves a high permeance of C4H6 (693.00±21.83 GPU) and a high selectivity over n-butene, n-butane, isobutene, and isobutane (9.72, 9.94, 10.31, and 11.94, respectively). This strategy opens up new possibilities for developing advanced membrane materials for difficult hydrocarbon separations.

Synthesis and Characterization of Poly(butylene glycol adipate-co-terephthalate/diphenylsilanediol adipate-co-terephthalate) Copolyester.

The copolyester poly(butylene glycol adipate-co-terephthalate/diphenylsilanediol adipate-co-terephthalate) (PBDAT) was synthesized by the melt polycondensation method using terephthalic acid, adipic acid, 1,4-butanediol, and diphenylsilylene glycol as the raw materials. The molecular chain structure, thermal properties, thermal stability, mechanical properties, and degradation behaviors of the copolyesters were investigated. The results showed that the prepared PBDAT copolyesters exhibited good thermal stability and mechanical properties. With the increase in diphenylsilanediol (DPSD) content, the thermal stability of PBDAT and the melting temperature both increased. The tensile strength and elastic modulus of PBDAT also exhibited an increasing tend. When the DPSD content was 12.5% (PBDAT-12.5), the tensile strength, the elastic modulus, and elongation at break were 30.56 MPa, 238 MPa, and 219%, respectively. With the increase in diphenylsilanediol content, the hydrophilicity of PBDAT decreased, but PBDAT still shows good degradability and the thermal degradation T5% temperature was 355 °C. The thermal degradation of PBDAT was also improved.

Effective C4 Separation by Zeolite Metal–Organic Framework Composite Membranes

AbstractInorganic zeolites have excellent molecular sieving properties, but they are difficult to process into macroscopic structures. In this work, we use metal–organic framework (MOF) glass as substrates to engineer the interface with inorganic zeolites, and then assemble the discrete crystalline zeolite powders into monolithic structures. The zeolites are well dispersed and stabilized within the MOF glass matrix, and the monolith has satisfactory mechanical stabilities for membrane applications. We demonstrate the effective separation performance of the membrane for 1,3‐butadiene (C4H6) from other C4 hydrocarbons, which is a crucial and challenging separation in the chemical industry. The membrane achieves a high permeance of C4H6 (693.00±21.83 GPU) and a high selectivity over n‐butene, n‐butane, isobutene, and isobutane (9.72, 9.94, 10.31, and 11.94, respectively). This strategy opens up new possibilities for developing advanced membrane materials for difficult hydrocarbon separations.

A comparative study of molecular structure, rheology, and thermal characteristics of deacetylated konjac glucomannan-treated gluten during frozen storage

Konjac glucomannan is extensively employed to enhance the quality of wheat-based products. Despite this, the utilization of natural KGM in the food industry is restricted due to its inadequate mechanical attributes, heightened susceptibility to humidity, and unappealing taste profile. In this study, the influence of deacetylated konjac glucomannan (DKGM) on the structure, rheology, and thermal characteristics of gluten during frozen storage was explored. Furthermore, the mechanism outlining the action between gluten and DKGM was analyzed. The secondary structure results showed that DKGM promoted the formation of α-helices and β-sheets in gluten. Scanning electron microscopy analysis confirmed that DKGM made the gluten network more stable. Moreover, DKGM provided a significant improvement in the rheological and thermal properties of frozen gluten. This is attributes to the fact that DKGM alters the hydrophobic residues in frozen gluten. Overall, this work indicates that the molecular structure and thermal properties of gluten are optimally substantial improvements at a substitution content of 1.5% D3 (deacetylation degree of 95%) in the dough. These findings provide a reference value for the application of DKGM in frozen wheat-based foods.

Fast and effective molecular property prediction with transferability map.

Effective transfer learning for molecular property prediction has shown considerable strength in addressing insufficient labeled molecules. Many existing methods either disregard the quantitative relationship between source and target properties, risking negative transfer, or require intensive training on target tasks. To quantify transferability concerning task-relatedness, we propose Principal Gradient-based Measurement (PGM) for transferring molecular property prediction ability. First, we design an optimization-free scheme to calculate a principal gradient for approximating the direction of model optimization on a molecular property prediction dataset. We have analyzed the close connection between the principal gradient and model optimization through mathematical proof. PGM measures the transferability as the distance between the principal gradient obtained from the source dataset and that derived from the target dataset. Then, we perform PGM on various molecular property prediction datasets to build a quantitative transferability map for source dataset selection. Finally, we evaluate PGM on multiple combinations of transfer learning tasks across 12 benchmark molecular property prediction datasets and demonstrate that it can serve as fast and effective guidance to improve the performance of a target task. This work contributes to more efficient discovery of drugs, materials, and catalysts by offering a task-relatedness quantification prior to transfer learning and understanding the relationship between chemical properties.

Antimicrobial Activity Classification of Imidazolium Derivatives Predicted by Artificial Neural Networks

PurposeThis study assesses the Multilayer Perceptron (MLP) neural network, complemented by other Machine Learning techniques (CART, PCA), in predicting the antimicrobial activity of 140 newly designed imidazolium chlorides against Klebsiella pneumoniae before synthesis. Emphasis is on leveraging molecular properties for predictive analysis.MethodsClassification and regression decision trees (CART) identified the top 200 predictive molecular descriptors. Principal Component Analysis (PCA) reduced these descriptors to 5 components, retaining 99.57% of raw data information. Antimicrobial activity, categorized as high or low, was based on experimentally proven minimal inhibitory concentration (MIC), with a cut-point at MIC = 0.856 mol/L. A 12-fold cross-validation trained the MLP (architecture 5-12-2 with 5 Principal Components).ResultsThe MLP exhibited commendable performance, achieving almost 90% correct classifications across learning, validation, and test sets, outperforming models without PCA dimension reduction. Key metrics, including accuracy (0.907), sensitivity (0.905), specificity (0.909), and precision (0.891), were notably high. These results highlight the MLP model's efficacy with PCA as a high-quality classifier for determining antimicrobial activity.ConclusionsThe study concludes that the MLP neural network, along with CART and PCA, is a robust tool for predicting the antimicrobial activity class of imidazolium chlorides against Klebsiella pneumoniae. CART and PCA, used in this study, allowed input variable reduction without significant information loss. High classification accuracy and associated metrics affirm the method’s potential utility in pre-synthesis assessments, offering valuable insights for antimicrobial compound design.