Molecular Interactions Research Articles

Evolution and development of the conduction system in the vertebrate heart: a role for hemodynamics and the epicardium.

Development of the heart is a very intricate and multiplex process as it involves not only the three spatial dimensions but also the fourth or time dimension. Over time, the heart of an embryo needs to adapt its function to serve the increasing complexity of differentiation and growth towards adulthood. It becomes even more perplexing by expanding time into millions of years, allocating related species in the tree of life. As the evolution of soft tissues can hardly be studied, we have to rely on comparative embryology, supported heavily by genetic and molecular approaches. These techniques provide insight into relationships, not only between species, but also between cell populations, signaling mechanisms, molecular interactions and physical factors such as hemodynamics. Heart development depends on differentiation of a mesodermal cell population that - in more derived taxa - continues in segmentation of the first and second heart field. These fields deliver not only the cardiomyocytes, forming the three-dimensionally looping cardiac tube as a basis for the chambered heart, but also the enveloping epicardium. The synchronized beating of the heart is then organized by the conduction system. In this Review, the epicardium is introduced as an important player in cardiac differentiation, including the conduction system.

Clustering of RNA Polymerase II C-Terminal Domain Models upon Phosphorylation.

RNA polymerase II (Pol II) C-terminal domain (CTD) is known to have crucial roles in regulating transcription. CTD has also been highly recognized for undergoing phase separation, which is further associated with its regulatory functions. However, the molecular interactions that the CTD forms to induce clustering to drive phase separations and how the phosphorylation of the CTD affects clustering are not entirely known. In this work, we studied the concentrated solutions of two heptapeptide repeat (2CTD) models at different phosphorylation patterns and protein and ion concentrations using all-atom molecular dynamics simulations to investigate clustering behavior and molecular interactions driving the cluster formation. Our results show that salt concentration and phosphorylation patterns play an important role in determining the clustering pattern, specifically at low protein concentrations. The balance between inter- and intrapeptide interactions and counterion coordination together impact the clustering behavior upon phosphorylation.

Reliable deep learning in anomalous diffusion against out-of-distribution dynamics.

Anomalous diffusion plays a crucial rule in understanding molecular-level dynamics by offering valuable insights into molecular interactions, mobility states and the physical properties of systems across both biological and materials sciences. Deep-learning techniques have recently outperformed conventional statistical methods in anomalous diffusion recognition. However, deep-learning networks are typically trained by data with limited distribution, which inevitably fail to recognize unknown diffusion models and misinterpret dynamics when confronted with out-of-distribution (OOD) scenarios. In this work, we present a general framework for evaluating deep-learning-based OOD dynamics-detection methods. We further develop a baseline approach that achieves robust OOD dynamics detection as well as accurate recognition of in-distribution anomalous diffusion. We demonstrate that this method enables a reliable characterization of complex behaviors across a wide range of experimentally diverse systems, including nicotinic acetylcholine receptors in membranes, fluorescent beads in dextran solutions and silver nanoparticles undergoing active endocytosis.

Analysis of molecular interactions of an aqueous benzoates with glycolic and lactic acid: spectroscopic, acoustic, and volumetric approach

Volumetric as well as Acoustic properties for the ternary liquid combination (water + sodium methyl p-hydroxybenzoate + glycolic acid/lactic acid) are evaluated at several temperatures using a Densimeter (DSA 5000 M) over the range of concentrations (0.000, 0.015, 0.025, 0.035) mol.kg−1. Several thermo-acoustical properties were computed utilizing the experimental data. These values were then used to derive several parameters, including the apparent molar volume( V∅ ); partial molar volume (V∅0); apparent molar isentropic compression (K∅,S); transfer properties(Δ V∅0, Δ K∅,s0 ); partial molar isentropic compression (K∅,s0); apparent molar expansibility ( E∅0 ) as well as its first derivative ( ∂Eϕ0/∂T ) P; and coefficient of thermal expansivity( αp ). The obtained results are explained by analyzing the several interactions that take place inside the liquid system by implementing the theory of co-sphere overlap. Utilizing these thermodynamical parameters, the interaction coefficients are computed to evaluate the ternary mixture corresponding to the interactions between solutes and solvents. FTIR spectrum analysis revealed the presence of H-bonds along with the other molecular interactions in the analyzed systems, as shown by the shifting of the O-H stretching band.

Molecular interactions at the interface: polyoxometalates of the Anderson-Evans type and lipid membranes

Molecular interactions at the interface: polyoxometalates of the Anderson-Evans type and lipid membranes

CJE-PCHF: Chinese Joint Entity and Relation Extraction Model Based on Progressive Contrastive Learning and Heterogeneous Feature Fusion

The joint extraction of entities and relations is a critical task in information extraction, and its performance directly affects the performance of downstream tasks. However, existing joint extraction models based on deep learning exhibit weak processing capabilities for the phenomenon of multiple pronunciations of one character and multiple characters of one pronunciation when processing Chinese texts, resulting in a performance loss. To address these issues, this paper introduces part-of-speech (POS) and pinyin features to aid the model in learning semantic features that are more contextually appropriate. We propose a Chinese Joint Entity and Relation Extraction Model based on progressive contrastive learning and heterogeneous feature fusion (CJE-PCHF). During model training, an interactive fusion network based on progressive contrastive learning is employed to learn the dependencies between pinyin, POS, and semantic features. This guides the model in heterogeneous feature fusion, capturing higher-order semantic associations between heterogeneous features. On the commonly used DuIE evaluation dataset for joint extraction, our model achieved a significant improvement, with the F1 score increasing by 5.4% compared to the benchmark model CasRel.

Comparison of the Neutralization Power of Sotrovimab Against SARS-CoV-2 Variants: Development of a Rapid Computational Method.

The rapid evolution of SARS-CoV-2 imposed a huge challenge on disease control. Immune evasion caused by genetic variations of the SARS-CoV-2 spike protein's immunogenic epitopes affects the efficiency of monoclonal antibody-based therapy of COVID-19. Therefore, a rapid method is needed to evaluate the efficacy of the available monoclonal antibodies against the new emerging variants or potential novel variants. The aim of this study is to develop a rapid computational method to evaluate the neutralization power of anti-SARS-CoV-2 monoclonal antibodies against new SARS-CoV-2 variants and other potential new mutations. The amino acid sequence of the extracellular domain of the spike proteins of the severe acute respiratory syndrome coronavirus (GenBank accession number YP_009825051.1) and SARS-CoV-2 (GenBank accession number YP_009724390.1) were used to create computational 3D models for the native spike proteins. Specific mutations were introduced to the curated sequence to generate the different variant spike models. The neutralization potential of sotrovimab (S309) against these variants was evaluated based on its molecular interactions and Gibbs free energy in comparison to a reference model after molecular replacement of the reference receptor-binding domain with the variant's receptor-binding domain. Our results show a loss in the binding affinity of the neutralizing antibody S309 with both SARS-CoV and SARS-CoV-2. The binding affinity of S309 was greater to the Alpha, Beta, Gamma, and Kappa variants than to the original Wuhan strain of SARS-CoV-2. However, S309 showed a substantially decreased binding affinity to the Delta and Omicron variants. Based on the mutational profile of Omicron subvariants, our data describe the effect of the G339H and G339D mutations and their role in escaping antibody neutralization, which is in line with published clinical reports. This method is rapid, applicable, and of interest to adapt the use of therapeutic antibodies to the treatment of emerging variants. It could be applied to antibody-based treatment of other viral infections.

Computer-Aided Design of VEGFR-2 Inhibitors as Anticancer Agents: A Review.

Due to its intricate molecular and structural characteristics, vascular endothelial growth factor receptor 2 (VEGFR-2) is essential for the development of new blood vessels in various pathological processes and conditions, especially in cancers. VEGFR-2 inhibitors have demonstrated significant anticancer effects by blocking many signaling pathways linked to tumor growth, metastasis, and angiogenesis. Several small compounds, including the well-tolerated sunitinib and sorafenib, have been approved as VEGFR-2 inhibitors. However, the widespread side effects linked to these VEGFR-2 inhibitors-hypertension, epistaxis, proteinuria, and upper respiratory infection-motivate researchers to search for new VEGFR-2 inhibitors with better pharmacokinetic profiles. The key molecular interactions required for the interaction of the small molecules with the protein target to produce the desired pharmacological effects are identified using computer-aided drug design (CADD) methods such as pharmacophore and QSAR modeling, structure-based virtual screening, molecular docking, molecular dynamics (MD) simulation coupled with MM/PB(GB)SA, and other computational strategies. This review discusses the applications of these methods for VEGFR-2 inhibitor design. Future VEGFR-2 inhibitor designs may be influenced by this review, which focuses on the current trends of using multiple screening layers to design better inhibitors.

Genes related to microglia polarization and immune infiltration in Alzheimer's Disease.

Alzheimer's Disease (AD) remains a significant challenge due to its complex etiology and socio-economic burden. In this study, we investigated the roles of macrophage polarization-related hub genes in AD pathology, focusing on their impact on immune infiltration and gene regulation in distinct brain regions. Using Gene Expression Omnibus (GEO) datasets GSE110226 (choroid plexus) and GSE1297 (hippocampal CA1), we identified key genes-EDN1, HHLA2, KL, TREM2, and WWTR1-associated with AD mechanisms and immune responses. Based on these findings, we developed a diagnostic model demonstrating favorable calibration and clinical applicability. Furthermore, we explored molecular interactions within mRNA-transcription factor and mRNA-miRNA regulatory networks, providing deeper insights into AD progression and identifying potential therapeutic targets. The novel identification of WWTR1 and HHLA2 as biomarkers expands the diagnostic toolkit for AD, offering new perspectives on the disease's underlying immune dynamics. However, external dataset validation and further in vitro and in vivo studies are required to confirm these results and their clinical relevance.

Quercetin: Potential antidiabetic effects through enzyme inhibition and starch digestibility

AbstractDiabetes mellitus involves high blood sugar levels due to insufficient insulin action. Furthermore, enzymes such as α‐amylase and α‐glucosidase break down carbohydrates into glucose, leading to postprandial hyperglycemia. Flavonoids, particularly quercetin, inhibit these enzymes, slowing carbohydrate digestion and reducing glucose absorption. Quercetin has significant hypoglycemic effects with inhibitory concentration (IC50) values comparable to acarbose, a standard inhibitor, suggesting its potential as a natural alternative for diabetes management. In silico models, including molecular docking, molecular dynamics (MD) simulations, and quantitative structure‐activity relationship (QSAR) approaches, help researchers understand the molecular interactions of therapeutic agents. These techniques identify potential inhibitors, determine enzyme‐inhibitor structures, and calculate binding energies, correlating findings with in vitro or in vivo data. Molecular docking predicts molecular orientations, MD simulations offer insights into enzyme–inhibitor dynamics, and QSAR models predict inhibitory potential based on structural properties. Studies have shown that quercetin effectively inhibits α‐glucosidase and α‐amylase by forming hydrogen bonds with specific amino acid residues. Quercetin interacts with starches and reduces their digestibility, increases the formation of resistant starch, lowers the glycemic index, and inhibits digestive enzymes. Studies show that the effects of quercetin on starch digestion vary with concentration and type of starch, and its incorporation into foods such as bakery products, pasta, etc. can significantly decrease starch hydrolysis. The incorporation of quercetin into starch matrices may aid in the development of functional foods aimed at improving glycemic control.

Adipogenic Effects of Cresyl Diphenyl Phosphate (Triphenyl Phosphate Alternative) through Peroxisome Proliferator-Activated Receptor Gamma Pathway: A Comprehensive Study Integrating In Vitro, In Vivo, and In Silico from Molecule to Health Risk.

Cresyl diphenyl phosphate (CDP), a novel organophosphate ester (OPE), has been detected in various environmental and human samples. However, there is very limited knowledge regarding its toxicity, mechanisms of action, and potential health risks. Using new alternative methods (NAMs), across the molecular interactions, signaling pathways, cell functions, animal effects, and population risks, we investigated the potential adipogenic effects and associated risks of CDP and legacy OPE triphenyl phosphate (TPHP) by acting on peroxisome proliferator-activated receptor gamma (PPARγ). Among the 19 screened OPEs, CDP bound to PPARγ with the highest binding potency, followed by TPHP. CDP activated PPARγ through fitting into the binding pocket with strong hydrophobicity and hydrogen bond interactions; CDP exhibited higher potency compared to TPHP. In 3T3-L1 cells, CDP enhanced the PPARγ-mediated adipogenesis activity, exhibiting greater potency than TPHP. The intracellular concentration and receptor-bound concentrations (RBC) of CDP were also higher than those of TPHP in both HEK293 cells and 3T3-L1 cells. In mice, exposure to CDP activated the PPARγ-mediated adipogenic pathway, leading to an increased white adipose tissue weight gain. Overall, CDP could bind to and activate PPARγ, thereby promoting preadipocyte differentiation and the development of white adipose tissue. Its potential obesogenic risks should be of high concern.

Role of Intermolecular Interactions in Deep Eutectic Solvents for CO2 Capture: Vibrational Spectroscopy and Quantum Chemical Studies.

Recent research and reviews on CO2 capture methods, along with advancements in industry, have highlighted high costs and energy-intensive nature as the primary limitations of conventional direct air capture and storage (DACS) methods. In response to these challenges, deep eutectic solvents (DESs) have emerged as promising absorbents due to their scalability, selectivity, and lower environmental impact compared to other absorbents. However, the molecular origins of their enhanced thermal stability and selectivity for DAC applications have not been explored before. Therefore, the current study focuses on a comprehensive investigation into the molecular interactions within an alkaline DES composed of potassium hydroxide (KOH) and ethylene glycol (EG). Combining Fourier transform infrared (FT-IR) and quantum chemical calculations, the study reports structural changes and intermolecular interactions induced in EG upon addition of KOH and its implications on CO2 capture. Experimental and computational spectroscopic studies confirm the presence of noncovalent interactions (hydrogen bonds) within both EG and the KOH-EG system and point to the aggregation of ions at higher KOH concentrations. Additionally, molecular electrostatic potential (MESP) surface analysis, natural bond orbital (NBO) analysis, quantum theory of atoms-in-molecules (QTAIM) analysis, and reduced density gradient-noncovalent interaction (RDG-NCI) plot analysis elucidate changes in polarizability, charge distribution, hydrogen bond types, noncovalent interactions, and interaction strengths, respectively. Evaluation of explicit and hybrid models assesses their effectiveness in representing intermolecular interactions. This research enhances our understanding of molecular interactions in the KOH-EG system, which are essential for both the absorption and desorption of CO2. The study also aids in predicting and selecting DES components, optimizing their ratios with salts, and fine-tuning the properties of similar solvents and salts for enhanced CO2 capture efficiency.

Fritillaria steroidal alkaloids and their multi-target therapeutic mechanisms: insights from network pharmacology.

Medicinal Fritillaria herbs, known for their rich content of steroidal alkaloids, have emerged as promising candidates in the treatment of chronic diseases due to their diverse pharmacological properties. Leveraging advancements in network pharmacology and molecular docking, this study explores the multi-target mechanisms through which these alkaloids exert therapeutic effects. The integration of bioinformatics, systems biology, and pharmacology in drug discovery has provided insights into the molecular interactions and pathways influenced by Fritillaria steroidal alkaloids. This review synthesizes comprehensive literature from 1985 to 2024, revealing the potential of these compounds in addressing respiratory diseases, inflammation, and cancer. The integration of traditional Chinese medicine (TCM) with modern pharmacological techniques underscores the relevance of these compounds in next-generation drug discovery. While initial findings are promising, further empirical validation is necessary to fully harness the therapeutic potential of Fritillaria steroidal alkaloids.

Engineering interfacial molecular interactions on Ag Hollow fibre gas diffusion electrodes for high efficiency in CO2 conversion to CO.

The electrochemical CO2 reduction reaction (CO2RR) occurs at the nanoscale interface of the electrode-electrolyte. Therefore, tailoring the interfacial properties in the interface microenvironment provides a powerful strategy to optimise the activity and selectivity of electrocatalysts towards the desired products. Here, the microenvironment at the electrode-electrolyte interface of the flow-through Ag-based hollow fibre gas diffusion electrode (Ag HFGDE) is modulated by introducing surfactant cetyltrimethylammonium bromide (CTAB) as the electrolyte additive. The porous hollow fibre configuration and gas penetration mode facilitate the CO2 mass transfer and the formation of the triple-phase interface. Through the ordered arrangement of hydrophobic long-alkyl chains, CTAB molecules at the electrode/electrolyte interface promoted CO2 penetration to active sites and repelled water to reduce the activity of competitive hydrogen evolution reaction (HER). By applying CTAB-containing catholyte, Ag HFGDE achieved a high CO Faradaic efficiency (FE) of over 95 % in a wide potential range and double the partial current density of CO. The enhancement of CO selectivity and suppression of hydrogen was attributed to the improvement of charge transfer and the CO2/H2O ratio enhancement. These findings highlight the importance of adjusting the local microenvironment to enhance the reaction kinetics and product selectivity in the electrochemical CO2 reduction reaction CO2RR.

Fog Harvesting Via Multistage Edge‐Effect Condensation

AbstractThe escalating global population and rapid industrialization have precipitated a severe freshwater scarcity crisis. To address this challenge, innovative strategies are essential to exploit unconventional water sources such as atmospheric vapor. This study proposes a novel approach integrating 3D printing technology with surface chemistry principles for atmospheric fog harvesting. The approach entails leveraging 3D printing technology, chosen for its cost‐effectiveness, unparalleled design flexibility to design intricate geometries, and time efficiency to fabricate cylindrical millimeter‐scale size vertical pillars. Harnessing the intricate interplay of surface phenomena, condensation preferentially occurs on the pillar tops due to surface phenomena. The pillars are imbued with nonadecane, a hydrocarbon renowned for its low surface energy characteristics ensures the uninterrupted progression of water droplet formation, coalescence, and seamless transportation, unveiling a symphony of molecular interactions at the microscale. The design demonstrates promising results, yielding an impressive yield of ≈3.956 g of water per hour from 1 cm2 area. Geometric discontinuities associated with parahydrophobic pillars amplify water harvesting via an edge effect. This study represents a significant step toward a more sustainable and technologically advanced solution for the global water crisis.

Computational exploration of acefylline derivatives as MAO-B inhibitors for Parkinson’s disease: insights from molecular docking, DFT, ADMET, and molecular dynamics approaches

Monoamine oxidase B (MAO-B) plays a pivotal role in the deamination process of monoamines, encompassing crucial neurotransmitters like dopamine and norepinephrine. The heightened interest in MAO-B inhibitors emerged after the revelation that this enzyme could potentially catalyze the formation of neurotoxic compounds from endogenous and exogenous sources. Computational screening methodologies serve as valuable tools in the quest for novel inhibitors, enhancing the efficiency of this pursuit. In this study, 43 acefylline derivatives were docked against the MAO-B enzyme for their chemotherapeutic potential and binding affinities that yielded GOLD fitness scores ranging from 33.21 to 75.22. Among them, five acefylline derivatives, namely, MAO-B14, MAO-B15, MAO-B16, MAO-B20, and MAO-B21, displayed binding affinities comparable to the both standards istradefylline and safinamide. These derivatives exhibited hydrogen-bonding interactions with key amino acids Phe167 and Ile197/198, suggesting their strong potential as MAO-B inhibitors. Finally, molecular dynamics (MD) simulations were conducted to evaluate the stability of the examined acefylline derivatives over time. The simulations demonstrated that among the examined acefylline derivatives and standards, MAO-B21 stands out as the most stable candidate. Density functional theory (DFT) studies were also performed to optimize the geometries of the ligands, and molecular docking was conducted to predict the orientations of the ligands within the binding cavity of the protein and evaluate their molecular interactions. These results were also validated by simulation-based binding free energies via the molecular mechanics energies combined with generalized Born and surface area solvation (MM-GBSA) method. However, it is necessary to conduct in vitro and in vivo experiments to confirm and validate these findings in future studies.

A molecular dynamics simulation study of EthylChlorophyllide A molecules confined in a SiO2 nanoslit.

This paper investigates the dynamic behavior of EthylChlorophyllide A (EChlideA) molecules in a methanol solution confined within a 4nm silica nanoslit, using molecular dynamics simulations over a duration of 1ms. Three systems, containing 1, 2, and 4 solutes, were studied at 298K. The results demonstrate that EChlideA molecules predominantly adsorb onto the silica surfaces, driven by specific interactions between chlorin ring's methyl group and the hydroxyl groups of the silica. This adsorption leads to stable binding, particularly in less crowded environments, as indicated by the potential of mean force analysis. Higher molecular concentrations, such as those with four EChlideA molecules, introduce variation in binding strength due to molecular aggregation and complex interactions. The orientation analysis reveals that the chlorin ring tends to align parallel to the surface, requiring rotational adjustments during surface diffusion. In addition, solvent coordination around the Mg ion remains consistent under bulk conditions, although with some variation in higher concentrations. This study also highlights a decrease in linear diffusion and an increase in rotational relaxation times for EChlideA molecules within the confined nanoslit, reflecting the influence of molecular concentration and arrangement on their dynamics. These findings provide valuable insights into the role of surface interactions, molecular orientation, and solvent coordination in confined environments, offering implications for the design of nanoscale systems.

LC-QTOF-MS/MS metabolic profiling and hepatoprotective effects of Litsea monopetala bark methanol extract against liver injury in rats and HepG2 cells.

Fresh stem bark decoction of Litsea monopetala has been practiced for the treatment of jaundice and other liver disorders by the tribal communities of Thakht-e-Sulaiman hills from West Pakistan. As per the folkloric claim, this study aims to identify the phytoconstituents and evaluate the hepatoprotective action of stem bark methanol extract of L. monopetala (LMME). The in-vitro hepatoprotective effect of L. monopetala was performed by H2O2-induced toxicity in the HepG2 cell line and in-vivo by cclt;sub>4-induced hepatotoxicity in Wistar albino rats taking Silymarin as standard drug. Phytoconstituents were identified using LC-QTOF-MS analysis followed by in-silico docking and validation. Molecular docking interactions between identified compounds of L. monopetala and two target proteins, namely 1VJY and 5HYK were presented. In this study, treatment with LMME at 100 µg/mL showed 67.73 % cell viability as compared to H2O2 (100 µM) treated alone i.e., 18.55 % in the HepG2 cell line. In-vivo treatment of LMME reversed the altered serum biochemical parameters and reduced the inflammatory response similar to that of the Silymarin-treated group supported by histopathological investigation. This research reveals that L. monopetala is a rich source of flavonoids and phenols which supports its hepatoprotective effects and is proposed for its usage as a promising hepatoprotective agent after controlled trials.

Mechanistic Insights into Curvature Formation in Synthetic Vesicles

AbstractThe mimicking of natural lipid bilayers with synthetic amphiphilic systems is of great interest to researchers, as insights could lead to better understanding of the complexities of cell membranes, as well as new materials and healthcare technologies. Recapitulating natural lipid asymmetry across bilayer membranes has important implications for curvature in cell, vesicle, and organelle morphologies, but has been challenging to achieve with synthetic lipid combinations or standard amphiphilic block copolymers. In a recent article, Elizebath et al. report the synthesis of a new type of synthetic amphiphile able to induce asymmetry in an artificial bilayer membrane dynamically. The molecules were designed around an extended π‐conjugated hydrophobic core with tertiary amine‐terminated oxyalkylene side chains. Protonation of the tertiary amines on the bilayer exterior leads to curvature induction, bilayer fission, and vesicle formation as monitored by time‐resolved spectroscopy techniques and microscopy. The results were further validated with density functional theory (DFT) calculations. The delicate balance between different molecular scale interactions in the supramolecular structures led to the dynamic transformation of the bilayer membranes. Insights described could be used to advance the assembly of hierarchical life‐like materials.

Solid-State Structures and Properties of Lignin Hydrogenolysis Oil Compounds: Shedding a Unique Light on Lignin Valorization

This article explores the important, and yet often overlooked, solid-state structures of selected bioaromatic compounds commonly found in lignin hydrogenolysis oil, a renewable bio-oil that holds great promise to substitute fossil-based aromatic molecules in a wide range of chemical and material industrial applications. At first, single-crystal X-ray diffraction (SCXRD) was applied to the lignin model compounds, dihydroconiferyl alcohol, propyl guaiacol, and eugenol dimers, in order to elucidate the fundamental molecular interactions present in such small lignin-derived polyols. Then, considering the potential use of these lignin-derived molecules as building blocks for polymer applications, structural analysis was also performed for two chemically modified model compounds, i.e., the methylene-bridging propyl-guaiacol dimer and propyl guaiacol and eugenol glycidyl ethers, which can be used as precursors in phenolic and epoxy resins, respectively, thus providing additional information on how the molecular packing is altered following chemical modifications. In addition to the expected H-bonding interactions, other interactions such as π–π stacking and C–H∙∙∙π were observed. This resulted in unexpected trends in the tendencies towards the crystallization of lignin compounds. This was further explored with the aid of DSC analysis and CLP intermolecular energy calculations, where the relationship between the major interactions observed in all the SCXRD solid-state structures and their physico-chemical properties were evaluated alongside other non-crystallizable lignin model compounds. Beyond lignin model compounds, our findings could also provide important insights into the solid-state structure and the molecular organization of more complex lignin fragments, paving the way to the more efficient design of lignin-based materials with improved properties for industrial applications or improving downstream processing of lignin oils in biorefining processes, such as in enhancing the separation and isolation of specific bioaromatic compounds).